From d40ec8e5132404b6b028a1c1b61b2defcf8da6f0 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Fri, 21 Jan 2022 17:01:30 -0800 Subject: [PATCH 001/197] Simulation framework and classes --- simwas/AA.dat | 2 + simwas/__main__.py | 7 ++- simwas/classes.py | 15 ++++++ simwas/sim_admixture.py | 116 ++++++++++++++++++++++++++++++++++++++++ 4 files changed, 139 insertions(+), 1 deletion(-) create mode 100644 simwas/AA.dat create mode 100644 simwas/classes.py create mode 100644 simwas/sim_admixture.py diff --git a/simwas/AA.dat b/simwas/AA.dat new file mode 100644 index 00000000..b91af499 --- /dev/null +++ b/simwas/AA.dat @@ -0,0 +1,2 @@ +40 Admixed CEU YRI +6 0 0.2 0.8 diff --git a/simwas/__main__.py b/simwas/__main__.py index 68c4f59f..b47f2be1 100644 --- a/simwas/__main__.py +++ b/simwas/__main__.py @@ -5,7 +5,7 @@ from pathlib import Path from typing import Union, Tuple -from . import data, tree +#from . import data, tree @click.group() @@ -20,6 +20,11 @@ def main(): @main.command() +@click.option('--invcf') +@click.option('--sample_info') +@click.option('--model', required=True) +@click.option('--coords', required=True) +@click.option('--out', required=True) def simulate(): """ Use the tool to simulate genotypes diff --git a/simwas/classes.py b/simwas/classes.py new file mode 100644 index 00000000..09d94b8c --- /dev/null +++ b/simwas/classes.py @@ -0,0 +1,15 @@ + +# Genetic markers that allow us to determine recombination break points +class GeneticMarkers: + # TODO fill out all required information for a SNP marker + def __init__(self): + pass + # NOTE ensure this class can work with the beagle genetic map + + + +# stored segment lengths that allow us to recover population at marker locations +class HaplotypeSegments: + # TODO figure out all necessary variables for recovering these segments + def __init__(self): + pass diff --git a/simwas/sim_admixture.py b/simwas/sim_admixture.py new file mode 100644 index 00000000..7c8cbb3e --- /dev/null +++ b/simwas/sim_admixture.py @@ -0,0 +1,116 @@ +import vcf +import numpy as np +from classes import GeneticMarkers, HaplotypeSegments + +# TODO at a certain point we are going to need to ensure populations in model file are also in invcf files +# This is only required for outputting the haplotypes in a vcf + +def simulate_gt(model_file, coords_file, seed=None): + """ + Simulate admixed genotypes based on the parameters of model_file. + Arguments + model + File with the following structure. (Must be tab delimited) + Header = # samples, Admixed, {all pop labels} + Below = generation#, frac, frac + ex: 40 Admixed CEU YRI + 1 0 0.05 0.95 + 2 0.20 0.05 0.75 + coords_file + File containing genetic map coords in cM used for recombination points + seed + Seed used for randomization. + Return + + """ + # initialize seed used for breakpoints + if seed: + np.random.seed(seed) + print(f"Using seed {seed}") + + # load population samples and labels to be simulated + mfile = open(model_file, 'r') + num_samples, not_used, *pops = mfile.readline().strip().split() + + # number of haplotypes simulated per generation + haps_per_gen = max(10000, 20 * num_samples) + + # starting generation is 0 + prev_gen = 0 + + # iterate over generations in model file + for gen in mfile: + # setup population proportions and generations to simulate + cur_gen, *pop_fracs = gen.strip.split() + cur_gen = int(cur_gen) + pop_fracs = np.array(pop_fracs).astype(np.float) + sim_gens = cur_gen - pre_gen + + assert sim_gens > 0 + assert np.sum(pop_fracs) == 1 + + # sim first generation + prev_gen_samples = _simulate(num_samples, pop_fracs, pre_gen) + + # simulate remaining generations + for generations in range(1, sim_gens): + # simulate next generations using previous generations to sample from for admixture + next_gen_samples = _simulate(num_samples, pop_fracs, pre_gen+i, prev_gen_samples) + prev_gen_samples = next_gen_samples + + prev_gen = cur_gen + + mfile.close() + return + + +def _simulate(samples, pop_fracs, pop_gen, prev_gen_samples=None) + # generate all samples + for sample in range(samples): + prev_chrom = -1 + prev_map_pos = 0.0 + + # if there is no previous generation randomly choose population based on frac + parent_pop = 0 + if not prev_gen_samples: + parent_pop = np.random.choice(np.arange(len(pop_fracs)), p=pop_fracs) + + # choose a haplotype to copy + # if previous generation of samples randomize two haplotypes + # otherwise only choose a single haplotype + haplotypes = -1*np.ones(2) + if not parent_pop: + # choose two of previous samples as haplotypes to choose from + haplotypes = np.random.choice(np.arange(samples), size=2, replace=False) + + # haplotypes only matters when pop == 0 otherwise we are sampling from a population which has a label + # choose our starting chromosome + homolog = np.random.randint(2) + + # iterate over all coords in the coords file + for coord in coords: + cur_chrom = # TODO finish + cur_map_pos = # TODO finish + if prev_chrom < 0: + prev_chrom = cur_chrom + if cur_chrom != prev_chrom: + # TODO end of chromosome save haplotype segments + + prev_chrom = cur_chrom + homolog = np.random.randint(2) + prev_map_pos = cur_map_pos + + # check if a recombination event occurs + # TODO function or logic for recombination event + dist = cur_map_pos - prev_map_pos + if recomb: + # TODO save haplotype segments since were moving to a new homolog after this breakpoint + homolog = 1-homolog + prev_map_pos = cur_map_pos + prev_chrom = cur_chrom + + # TODO record remaining segments + + # TODO output debug message for individual simulated + + From e94ea4506e5f63f50de68c8732c5190808ccd0b6 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Sun, 23 Jan 2022 16:51:52 -0800 Subject: [PATCH 002/197] added GeneticMarker class --- simwas/classes.py | 20 ++++++++++++++------ simwas/sim_admixture.py | 28 ++++++++++++++++++++++------ 2 files changed, 36 insertions(+), 12 deletions(-) diff --git a/simwas/classes.py b/simwas/classes.py index 09d94b8c..98e54594 100644 --- a/simwas/classes.py +++ b/simwas/classes.py @@ -1,15 +1,23 @@ # Genetic markers that allow us to determine recombination break points -class GeneticMarkers: - # TODO fill out all required information for a SNP marker - def __init__(self): - pass - # NOTE ensure this class can work with the beagle genetic map +class GeneticMarker: + def __init__(self, chrom, cm_map_pos, bp_map_pos) + self.chrom = chrom + self.cm_map_pos = cm_map_pos + self.bp_map_pos = bp_map_pos + + def get_chrom(self): + return self.chrom + + def get_map_pos(self): + return self.cm_map_pos + def get_bp_pos(self): + return self.bp_map_pos # stored segment lengths that allow us to recover population at marker locations -class HaplotypeSegments: +class HaplotypeSegment: # TODO figure out all necessary variables for recovering these segments def __init__(self): pass diff --git a/simwas/sim_admixture.py b/simwas/sim_admixture.py index 7c8cbb3e..d916159e 100644 --- a/simwas/sim_admixture.py +++ b/simwas/sim_admixture.py @@ -1,6 +1,6 @@ import vcf import numpy as np -from classes import GeneticMarkers, HaplotypeSegments +from classes import GeneticMarker, HaplotypeSegment # TODO at a certain point we are going to need to ensure populations in model file are also in invcf files # This is only required for outputting the haplotypes in a vcf @@ -32,6 +32,15 @@ def simulate_gt(model_file, coords_file, seed=None): mfile = open(model_file, 'r') num_samples, not_used, *pops = mfile.readline().strip().split() + # coord file structure chr variant cMcoord bpcoord + coords = [] + with open(coords_file, 'r') as cfile: + for line in cfile: + # create marker from each line and append to coords + data = line.strip().split() + gen_mark = GeneticMarker(int(data[0]), float(data[2]), int(data[3])) + coords.append(gen_mark) + # number of haplotypes simulated per generation haps_per_gen = max(10000, 20 * num_samples) @@ -50,12 +59,12 @@ def simulate_gt(model_file, coords_file, seed=None): assert np.sum(pop_fracs) == 1 # sim first generation - prev_gen_samples = _simulate(num_samples, pop_fracs, pre_gen) + prev_gen_samples = _simulate(num_samples, pop_fracs, pre_gen, coords) # simulate remaining generations for generations in range(1, sim_gens): # simulate next generations using previous generations to sample from for admixture - next_gen_samples = _simulate(num_samples, pop_fracs, pre_gen+i, prev_gen_samples) + next_gen_samples = _simulate(num_samples, pop_fracs, pre_gen+i, coords, prev_gen_samples) prev_gen_samples = next_gen_samples prev_gen = cur_gen @@ -64,7 +73,7 @@ def simulate_gt(model_file, coords_file, seed=None): return -def _simulate(samples, pop_fracs, pop_gen, prev_gen_samples=None) +def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # generate all samples for sample in range(samples): prev_chrom = -1 @@ -89,8 +98,8 @@ def _simulate(samples, pop_fracs, pop_gen, prev_gen_samples=None) # iterate over all coords in the coords file for coord in coords: - cur_chrom = # TODO finish - cur_map_pos = # TODO finish + cur_chrom = coord.get_chrom() + cur_map_pos = coord.get_map_pos() if prev_chrom < 0: prev_chrom = cur_chrom if cur_chrom != prev_chrom: @@ -113,4 +122,11 @@ def _simulate(samples, pop_fracs, pop_gen, prev_gen_samples=None) # TODO output debug message for individual simulated +# TODO remove +def test(): + model_file = './AA.dat' + coords_file = '/storage/mlamkin/data/simwas/plink.chr10.GRCh38.map' + simulate_gt(model_file, coords_file) +if __name__ == '__main__': + test() From 5a8ad8ac3219c0d352ef774132776cc940996111 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Wed, 26 Jan 2022 12:02:08 -0800 Subject: [PATCH 003/197] Completed Classes --- simwas/classes.py | 7 +++---- simwas/sim_admixture.py | 25 +++++++++++++++++++++---- 2 files changed, 24 insertions(+), 8 deletions(-) diff --git a/simwas/classes.py b/simwas/classes.py index 98e54594..cc51517d 100644 --- a/simwas/classes.py +++ b/simwas/classes.py @@ -1,4 +1,3 @@ - # Genetic markers that allow us to determine recombination break points class GeneticMarker: def __init__(self, chrom, cm_map_pos, bp_map_pos) @@ -18,6 +17,6 @@ def get_bp_pos(self): # stored segment lengths that allow us to recover population at marker locations class HaplotypeSegment: - # TODO figure out all necessary variables for recovering these segments - def __init__(self): - pass + def __init__(self, pop_num, end_mark): + self.pop_num = pop_num + self.end_mark = end_mark diff --git a/simwas/sim_admixture.py b/simwas/sim_admixture.py index d916159e..d71faa69 100644 --- a/simwas/sim_admixture.py +++ b/simwas/sim_admixture.py @@ -75,7 +75,9 @@ def simulate_gt(model_file, coords_file, seed=None): def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # generate all samples + hap_samples = [] for sample in range(samples): + segments = [] prev_chrom = -1 prev_map_pos = 0.0 @@ -97,22 +99,28 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): homolog = np.random.randint(2) # iterate over all coords in the coords file - for coord in coords: + for ind, coord in enumerate(coords): cur_chrom = coord.get_chrom() cur_map_pos = coord.get_map_pos() if prev_chrom < 0: prev_chrom = cur_chrom + + # Ended chromosome write segment if cur_chrom != prev_chrom: # TODO end of chromosome save haplotype segments + # TODO figure out what marker should be by seeing how output works + segments.append(get_segment(parent_pop, haplotypes[homolog], marker, prev_gen_samples)) + # update homolog, previous chrom, and previous position prev_chrom = cur_chrom homolog = np.random.randint(2) prev_map_pos = cur_map_pos + continue - # check if a recombination event occurs - # TODO function or logic for recombination event + # check if a recombination event occurs and if so write segment and swap homolog dist = cur_map_pos - prev_map_pos - if recomb: + recomb_prob = 1-np.exp(-dist) + if np.random.choice([0, 1], 1, p=[1-recomb_prob, recomb_prob]: # TODO save haplotype segments since were moving to a new homolog after this breakpoint homolog = 1-homolog prev_map_pos = cur_map_pos @@ -122,6 +130,15 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # TODO output debug message for individual simulated + # append segments of 1 sample + hap_samples.append(segments) + return hap_samples + +# NOTE in their code they set marker = previous # in for loop however here +# we should set to object most likely +def get_segment(pop, haplotype, marker, prev_gen_samples): + return segment + # TODO remove def test(): model_file = './AA.dat' From 41a4a76c975a7c46d85eb77727d3348f7fcd6187 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Mon, 7 Feb 2022 20:42:44 -0800 Subject: [PATCH 004/197] Restructure of repo --- simwas/AA.dat | 2 -- simwas/{classes.py => admixture-sim/admix_storage.py} | 0 simwas/{ => admixture-sim}/sim_admixture.py | 0 3 files changed, 2 deletions(-) delete mode 100644 simwas/AA.dat rename simwas/{classes.py => admixture-sim/admix_storage.py} (100%) rename simwas/{ => admixture-sim}/sim_admixture.py (100%) diff --git a/simwas/AA.dat b/simwas/AA.dat deleted file mode 100644 index b91af499..00000000 --- a/simwas/AA.dat +++ /dev/null @@ -1,2 +0,0 @@ -40 Admixed CEU YRI -6 0 0.2 0.8 diff --git a/simwas/classes.py b/simwas/admixture-sim/admix_storage.py similarity index 100% rename from simwas/classes.py rename to simwas/admixture-sim/admix_storage.py diff --git a/simwas/sim_admixture.py b/simwas/admixture-sim/sim_admixture.py similarity index 100% rename from simwas/sim_admixture.py rename to simwas/admixture-sim/sim_admixture.py From 17e724fa32b4f758a88b0ac215a8b24d00c5fed0 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Tue, 8 Feb 2022 21:07:02 -0800 Subject: [PATCH 005/197] Refactor repository --- {simwas => haptools}/__init__.py | 0 {simwas => haptools}/__main__.py | 6 +- haptools/admixture_sim/admix_storage.py | 35 +++++++ .../admixture_sim}/sim_admixture.py | 91 ++++++++++++++----- {simwas => haptools}/data/__init__.py | 0 {simwas => haptools}/data/data.py | 0 {simwas => haptools}/data/genotypes.py | 0 {simwas => haptools}/data/phenotypes.py | 0 simwas/admixture-sim/admix_storage.py | 22 ----- 9 files changed, 110 insertions(+), 44 deletions(-) rename {simwas => haptools}/__init__.py (100%) rename {simwas => haptools}/__main__.py (76%) create mode 100644 haptools/admixture_sim/admix_storage.py rename {simwas/admixture-sim => haptools/admixture_sim}/sim_admixture.py (59%) rename {simwas => haptools}/data/__init__.py (100%) rename {simwas => haptools}/data/data.py (100%) rename {simwas => haptools}/data/genotypes.py (100%) rename {simwas => haptools}/data/phenotypes.py (100%) delete mode 100644 simwas/admixture-sim/admix_storage.py diff --git a/simwas/__init__.py b/haptools/__init__.py similarity index 100% rename from simwas/__init__.py rename to haptools/__init__.py diff --git a/simwas/__main__.py b/haptools/__main__.py similarity index 76% rename from simwas/__main__.py rename to haptools/__main__.py index b47f2be1..7de1ae2b 100644 --- a/simwas/__main__.py +++ b/haptools/__main__.py @@ -1,11 +1,14 @@ #!/usr/bin/env python +import sys import click from pathlib import Path from typing import Union, Tuple #from . import data, tree +sys.path.append('./admixture_sim') +from .admixture_sim.sim_admixture import simulate_gt, write_breakpoint @click.group() @@ -29,7 +32,8 @@ def simulate(): """ Use the tool to simulate genotypes """ - pass + breakpoints = simulate_gt(model_file, coords_file, seed=None) + write_breakpoint(breakpoints, out) if __name__ == "__main__": diff --git a/haptools/admixture_sim/admix_storage.py b/haptools/admixture_sim/admix_storage.py new file mode 100644 index 00000000..5eaa8723 --- /dev/null +++ b/haptools/admixture_sim/admix_storage.py @@ -0,0 +1,35 @@ +# Genetic markers that allow us to determine recombination break points +class GeneticMarker: + def __init__(self, chrom, cm_map_pos, bp_map_pos): + self.chrom = chrom + self.cm_map_pos = cm_map_pos + self.bp_map_pos = bp_map_pos + + def get_chrom(self): + return self.chrom + + def get_map_pos(self): + return self.cm_map_pos + + def get_bp_pos(self): + return self.bp_map_pos + + +# stored segment lengths that allow us to recover population at marker locations +class HaplotypeSegment: + def __init__(self, pop_num, end_coord): + """ + Note the beginning of the segment is inferred based on previous + segments stored throughout the simulation process. + Arguments + pop_num - population label + end_coord - Ending coordinate of the haplotype segment + """ + self.pop_num = pop_num + self.end_coord = end_coord + + def get_end_coord(self): + return self.end_coord + + def get_pop(self): + return self.pop_num diff --git a/simwas/admixture-sim/sim_admixture.py b/haptools/admixture_sim/sim_admixture.py similarity index 59% rename from simwas/admixture-sim/sim_admixture.py rename to haptools/admixture_sim/sim_admixture.py index d71faa69..86284fd5 100644 --- a/simwas/admixture-sim/sim_admixture.py +++ b/haptools/admixture_sim/sim_admixture.py @@ -1,6 +1,6 @@ import vcf import numpy as np -from classes import GeneticMarker, HaplotypeSegment +from .admix_storage import GeneticMarker, HaplotypeSegment # TODO at a certain point we are going to need to ensure populations in model file are also in invcf files # This is only required for outputting the haplotypes in a vcf @@ -46,6 +46,7 @@ def simulate_gt(model_file, coords_file, seed=None): # starting generation is 0 prev_gen = 0 + next_gen_samples = [] # iterate over generations in model file for gen in mfile: @@ -53,24 +54,23 @@ def simulate_gt(model_file, coords_file, seed=None): cur_gen, *pop_fracs = gen.strip.split() cur_gen = int(cur_gen) pop_fracs = np.array(pop_fracs).astype(np.float) - sim_gens = cur_gen - pre_gen + sim_gens = cur_gen - prev_gen assert sim_gens > 0 assert np.sum(pop_fracs) == 1 - # sim first generation - prev_gen_samples = _simulate(num_samples, pop_fracs, pre_gen, coords) + # sim generation + next_gen_samples = _simulate(num_samples, pop_fracs, pre_gen, coords, next_gen_samples) # simulate remaining generations for generations in range(1, sim_gens): # simulate next generations using previous generations to sample from for admixture - next_gen_samples = _simulate(num_samples, pop_fracs, pre_gen+i, coords, prev_gen_samples) - prev_gen_samples = next_gen_samples + next_gen_samples = _simulate(num_samples, pop_fracs, pre_gen+i, coords, next_gen_samples) prev_gen = cur_gen mfile.close() - return + return next_gen_samples def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): @@ -80,6 +80,8 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): segments = [] prev_chrom = -1 prev_map_pos = 0.0 + start_bp = 0 + end_bp = 0 # if there is no previous generation randomly choose population based on frac parent_pop = 0 @@ -99,17 +101,24 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): homolog = np.random.randint(2) # iterate over all coords in the coords file - for ind, coord in enumerate(coords): + + for coord in coords: + # get current chromosome and position cur_chrom = coord.get_chrom() cur_map_pos = coord.get_map_pos() + + # start of new segment used for storing previous generation samples + if segments: + start_bp = segments[-1].get_end_coord() + 1 + if prev_chrom < 0: prev_chrom = cur_chrom # Ended chromosome write segment if cur_chrom != prev_chrom: - # TODO end of chromosome save haplotype segments - # TODO figure out what marker should be by seeing how output works - segments.append(get_segment(parent_pop, haplotypes[homolog], marker, prev_gen_samples)) + # Store haplotype + segments.extend(get_segment(parent_pop, haplotypes[homolog], + start_bp, end_bp, prev_gen_samples)) # update homolog, previous chrom, and previous position prev_chrom = cur_chrom @@ -120,28 +129,68 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # check if a recombination event occurs and if so write segment and swap homolog dist = cur_map_pos - prev_map_pos recomb_prob = 1-np.exp(-dist) - if np.random.choice([0, 1], 1, p=[1-recomb_prob, recomb_prob]: - # TODO save haplotype segments since were moving to a new homolog after this breakpoint + if np.random.choice([0, 1], 1, p=[1-recomb_prob, recomb_prob]): + # Store haplotype segments switching homologs + segments.extend(get_segment(parent_pop, haplotypes[homolog], + start_bp, end_bp, prev_gen_samples)) homolog = 1-homolog + + # update positions and chromosome prev_map_pos = cur_map_pos prev_chrom = cur_chrom + end_bp = coord.get_bp_pos() - # TODO record remaining segments - - # TODO output debug message for individual simulated + # Record remaining segments + segments.extend(get_segment(parent_pop, haplotypes[homolog], start_bp, + end_bp, prev_gen_samples)) # append segments of 1 sample hap_samples.append(segments) return hap_samples -# NOTE in their code they set marker = previous # in for loop however here -# we should set to object most likely -def get_segment(pop, haplotype, marker, prev_gen_samples): - return segment +def get_segment(pop, haplotype, start_coord, end_coord, prev_gen_samples): + """ + Create a segment or segments for an individual of the current generation + using either a population label (>0) or the previous generation's samples if + the admix pop type (0) is used. + Arguments + pop - population to generate haplotype segments + haplotype - index of range [0, len(prev_gen_samples)] to identify + the parent haplotype to copy segments from + start_coord - starting coordinate from where to begin taking segments + from previous generation samples + end_coord - ending coordinate of haplotype segment + prev_gen_samples - the previous generation simulated used as the parents + for the current generation + Returns + A list of HaplotypeSegments storing the population type and end coordinate + """ + # Take from population data not admixed data + if pop: + return [HaplotypeSegment(pop, end_coord)] + + # Take from previous admixed data + else: + segments = [] + prev_gen_segments = prev_gen_samples[haplotype] + + # iterate over haplotype segments to collect relevant ones + for prev_segment in prev_gen_segments: + # check if current segment is within boundaries + if prev_segment.get_end_coord() < start_coord: + continue + if prev_segment.get_end_coord() > end_coord: + break + + segments.append(prev_segment) + + # Append last segment using previous segments population + segments.append(HaplotypeSegment(segments[-1].get_pop(), end_coord)) + return segments # TODO remove def test(): - model_file = './AA.dat' + model_file = '/storage/mlamkin/data/simwas/AA.dat' coords_file = '/storage/mlamkin/data/simwas/plink.chr10.GRCh38.map' simulate_gt(model_file, coords_file) diff --git a/simwas/data/__init__.py b/haptools/data/__init__.py similarity index 100% rename from simwas/data/__init__.py rename to haptools/data/__init__.py diff --git a/simwas/data/data.py b/haptools/data/data.py similarity index 100% rename from simwas/data/data.py rename to haptools/data/data.py diff --git a/simwas/data/genotypes.py b/haptools/data/genotypes.py similarity index 100% rename from simwas/data/genotypes.py rename to haptools/data/genotypes.py diff --git a/simwas/data/phenotypes.py b/haptools/data/phenotypes.py similarity index 100% rename from simwas/data/phenotypes.py rename to haptools/data/phenotypes.py diff --git a/simwas/admixture-sim/admix_storage.py b/simwas/admixture-sim/admix_storage.py deleted file mode 100644 index cc51517d..00000000 --- a/simwas/admixture-sim/admix_storage.py +++ /dev/null @@ -1,22 +0,0 @@ -# Genetic markers that allow us to determine recombination break points -class GeneticMarker: - def __init__(self, chrom, cm_map_pos, bp_map_pos) - self.chrom = chrom - self.cm_map_pos = cm_map_pos - self.bp_map_pos = bp_map_pos - - def get_chrom(self): - return self.chrom - - def get_map_pos(self): - return self.cm_map_pos - - def get_bp_pos(self): - return self.bp_map_pos - - -# stored segment lengths that allow us to recover population at marker locations -class HaplotypeSegment: - def __init__(self, pop_num, end_mark): - self.pop_num = pop_num - self.end_mark = end_mark From 60472dadf66a8b5278605b2b186156584d81e3c8 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Tue, 8 Feb 2022 23:38:42 -0800 Subject: [PATCH 006/197] Completed Simulator (Needs to be tested) --- haptools/__main__.py | 13 +++--- haptools/admixture_sim/admix_storage.py | 7 ++- haptools/admixture_sim/sim_admixture.py | 59 +++++++++++++++++++------ 3 files changed, 59 insertions(+), 20 deletions(-) diff --git a/haptools/__main__.py b/haptools/__main__.py index 7de1ae2b..e307a4bf 100644 --- a/haptools/__main__.py +++ b/haptools/__main__.py @@ -8,14 +8,14 @@ #from . import data, tree sys.path.append('./admixture_sim') -from .admixture_sim.sim_admixture import simulate_gt, write_breakpoint +from .admixture_sim.sim_admixture import simulate_gt, write_breakpoints @click.group() @click.version_option() def main(): """ - simwas: Simulate phenotypes for GWAS and subsequent fine-mapping + haptools: Simulate phenotypes for GWAS and subsequent fine-mapping Use real variants to simulate real, biological LD patterns. """ @@ -28,14 +28,15 @@ def main(): @click.option('--model', required=True) @click.option('--coords', required=True) @click.option('--out', required=True) -def simulate(): +def simulate(invcf, sample_info, model, coords, out): """ Use the tool to simulate genotypes """ - breakpoints = simulate_gt(model_file, coords_file, seed=None) - write_breakpoint(breakpoints, out) + print(model, coords, out) + breakpoints = simulate_gt(model, coords, seed=None) + write_breakpoints(breakpoints, out) if __name__ == "__main__": # run the CLI if someone tries 'python -m simwas' on the command line - main(prog_name="simwas") + main(prog_name="haptools") diff --git a/haptools/admixture_sim/admix_storage.py b/haptools/admixture_sim/admix_storage.py index 5eaa8723..9d422531 100644 --- a/haptools/admixture_sim/admix_storage.py +++ b/haptools/admixture_sim/admix_storage.py @@ -17,19 +17,24 @@ def get_bp_pos(self): # stored segment lengths that allow us to recover population at marker locations class HaplotypeSegment: - def __init__(self, pop_num, end_coord): + def __init__(self, pop_num, chrom, end_coord): """ Note the beginning of the segment is inferred based on previous segments stored throughout the simulation process. Arguments pop_num - population label + chrom - chromosome the haplotype segment lies on. end_coord - Ending coordinate of the haplotype segment """ self.pop_num = pop_num + self.chrom = chrom self.end_coord = end_coord def get_end_coord(self): return self.end_coord + def get_chrom(self): + return self.chrom + def get_pop(self): return self.pop_num diff --git a/haptools/admixture_sim/sim_admixture.py b/haptools/admixture_sim/sim_admixture.py index 86284fd5..171e898d 100644 --- a/haptools/admixture_sim/sim_admixture.py +++ b/haptools/admixture_sim/sim_admixture.py @@ -31,6 +31,7 @@ def simulate_gt(model_file, coords_file, seed=None): # load population samples and labels to be simulated mfile = open(model_file, 'r') num_samples, not_used, *pops = mfile.readline().strip().split() + num_samples = int(num_samples) # coord file structure chr variant cMcoord bpcoord coords = [] @@ -51,7 +52,7 @@ def simulate_gt(model_file, coords_file, seed=None): # iterate over generations in model file for gen in mfile: # setup population proportions and generations to simulate - cur_gen, *pop_fracs = gen.strip.split() + cur_gen, *pop_fracs = gen.strip().split() cur_gen = int(cur_gen) pop_fracs = np.array(pop_fracs).astype(np.float) sim_gens = cur_gen - prev_gen @@ -60,18 +61,40 @@ def simulate_gt(model_file, coords_file, seed=None): assert np.sum(pop_fracs) == 1 # sim generation - next_gen_samples = _simulate(num_samples, pop_fracs, pre_gen, coords, next_gen_samples) + print(f"Simulating generation {prev_gen+1}") + next_gen_samples = _simulate(num_samples, pop_fracs, prev_gen, coords, next_gen_samples) # simulate remaining generations - for generations in range(1, sim_gens): + for i in range(1, sim_gens): + print(f"Simulating generation {prev_gen+i+1}") # simulate next generations using previous generations to sample from for admixture - next_gen_samples = _simulate(num_samples, pop_fracs, pre_gen+i, coords, next_gen_samples) + next_gen_samples = _simulate(num_samples, pop_fracs, prev_gen+i, coords, next_gen_samples) prev_gen = cur_gen mfile.close() return next_gen_samples +def write_breakpoints(breakpoints, out): + breakpt_file = out + '.bp' + print(f"Outputting breakpoint file {breakpt_file}") + with open(breakpt_file, 'a') as output: + for ind, sample in enumerate(breakpoints): + # Get sample number and haplotype number + haplotype = (ind)%2 + 1 + sample_num = ind//2 + 1 + + # write header for sample + output.write(f"Sample_{sample_num}_{haplotype}\n") + + # write all segments for sample + for segment in sample: + # write all segments for current sample + pop = segment.get_pop() + chrom = segment.get_chrom() + end_coord = segment.get_end_coord() + output.write(f"{pop}\t{chrom}\t{end_coord}\n") + return def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # generate all samples @@ -101,23 +124,24 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): homolog = np.random.randint(2) # iterate over all coords in the coords file - for coord in coords: # get current chromosome and position cur_chrom = coord.get_chrom() cur_map_pos = coord.get_map_pos() - + # start of new segment used for storing previous generation samples if segments: start_bp = segments[-1].get_end_coord() + 1 + # first segment so update chrom and starting map pos if prev_chrom < 0: prev_chrom = cur_chrom + prev_map_pos = cur_map_pos # Ended chromosome write segment if cur_chrom != prev_chrom: # Store haplotype - segments.extend(get_segment(parent_pop, haplotypes[homolog], + segments.extend(get_segment(parent_pop, haplotypes[homolog], prev_chrom, start_bp, end_bp, prev_gen_samples)) # update homolog, previous chrom, and previous position @@ -131,7 +155,7 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): recomb_prob = 1-np.exp(-dist) if np.random.choice([0, 1], 1, p=[1-recomb_prob, recomb_prob]): # Store haplotype segments switching homologs - segments.extend(get_segment(parent_pop, haplotypes[homolog], + segments.extend(get_segment(parent_pop, haplotypes[homolog], prev_chrom, start_bp, end_bp, prev_gen_samples)) homolog = 1-homolog @@ -141,14 +165,15 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): end_bp = coord.get_bp_pos() # Record remaining segments - segments.extend(get_segment(parent_pop, haplotypes[homolog], start_bp, - end_bp, prev_gen_samples)) + if segments: start_bp = segments[-1].get_end_coord() + 1 + segments.extend(get_segment(parent_pop, haplotypes[homolog], prev_chrom, + start_bp, end_bp, prev_gen_samples)) # append segments of 1 sample hap_samples.append(segments) return hap_samples -def get_segment(pop, haplotype, start_coord, end_coord, prev_gen_samples): +def get_segment(pop, haplotype, chrom, start_coord, end_coord, prev_gen_samples): """ Create a segment or segments for an individual of the current generation using either a population label (>0) or the previous generation's samples if @@ -157,6 +182,7 @@ def get_segment(pop, haplotype, start_coord, end_coord, prev_gen_samples): pop - population to generate haplotype segments haplotype - index of range [0, len(prev_gen_samples)] to identify the parent haplotype to copy segments from + chrom - chromosome the haplotype segment lies on start_coord - starting coordinate from where to begin taking segments from previous generation samples end_coord - ending coordinate of haplotype segment @@ -167,7 +193,7 @@ def get_segment(pop, haplotype, start_coord, end_coord, prev_gen_samples): """ # Take from population data not admixed data if pop: - return [HaplotypeSegment(pop, end_coord)] + return [HaplotypeSegment(pop, chrom, end_coord)] # Take from previous admixed data else: @@ -184,8 +210,15 @@ def get_segment(pop, haplotype, start_coord, end_coord, prev_gen_samples): segments.append(prev_segment) + # If there is not a segment within the boundary use the segment + # that spans the boundary respective population + if not segments: + out_pop = prev_segment.get_pop() + else: + out_pop = segments[-1].get_pop() + # Append last segment using previous segments population - segments.append(HaplotypeSegment(segments[-1].get_pop(), end_coord)) + segments.append(HaplotypeSegment(out_pop, chrom, end_coord)) return segments # TODO remove From fb7c4c943eba7dad927a8473559d6887203c5318 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Wed, 9 Feb 2022 00:48:44 -0800 Subject: [PATCH 007/197] Fixed formatting --- haptools/__main__.py | 2 +- .../{admixture_sim => simulate}/admix_storage.py | 0 .../{admixture_sim => simulate}/sim_admixture.py | 0 poetry.lock | 14 +++++++++++++- 4 files changed, 14 insertions(+), 2 deletions(-) rename haptools/{admixture_sim => simulate}/admix_storage.py (100%) rename haptools/{admixture_sim => simulate}/sim_admixture.py (100%) diff --git a/haptools/__main__.py b/haptools/__main__.py index 1acb4e5d..df4858cc 100644 --- a/haptools/__main__.py +++ b/haptools/__main__.py @@ -8,7 +8,7 @@ #from . import data, tree sys.path.append('./admixture_sim') -from .admixture_sim.sim_admixture import simulate_gt, write_breakpoints +from .simulate.sim_admixture import simulate_gt, write_breakpoints @click.group() diff --git a/haptools/admixture_sim/admix_storage.py b/haptools/simulate/admix_storage.py similarity index 100% rename from haptools/admixture_sim/admix_storage.py rename to haptools/simulate/admix_storage.py diff --git a/haptools/admixture_sim/sim_admixture.py b/haptools/simulate/sim_admixture.py similarity index 100% rename from haptools/admixture_sim/sim_admixture.py rename to haptools/simulate/sim_admixture.py diff --git a/poetry.lock b/poetry.lock index 099760f2..0d0d25c4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -505,6 +505,14 @@ category = "main" optional = false python-versions = "*" +[[package]] +name = "pyvcf" +version = "0.6.8" +description = "Variant Call Format (VCF) parser for Python" +category = "main" +optional = false +python-versions = "*" + [[package]] name = "requests" version = "2.26.0" @@ -759,7 +767,7 @@ test = [] [metadata] lock-version = "1.1" python-versions = "^3.7" -content-hash = "4712eb9ccb3de59f0b849ed829024e23c96a3c9f782389dbf789ad59d0371c31" +content-hash = "509f99cc8aa5ab1f60dbe59b84de15cf1b3ee8b2eae8e84cff90d7c556bc0aa9" [metadata.files] alabaster = [ @@ -1060,6 +1068,10 @@ pytz = [ {file = "pytz-2021.3-py2.py3-none-any.whl", hash = "sha256:3672058bc3453457b622aab7a1c3bfd5ab0bdae451512f6cf25f64ed37f5b87c"}, {file = "pytz-2021.3.tar.gz", hash = "sha256:acad2d8b20a1af07d4e4c9d2e9285c5ed9104354062f275f3fcd88dcef4f1326"}, ] +pyvcf = [ + {file = "PyVCF-0.6.8.linux-x86_64.tar.gz", hash = "sha256:1b3c833dcf4cca830e8255e3ac0573d2e69f588f71f7a5efb2a92b45d7b70021"}, + {file = "PyVCF-0.6.8.tar.gz", hash = "sha256:e9d872513d179d229ab61da47a33f42726e9613784d1cb2bac3f8e2642f6f9d9"}, +] requests = [ {file = "requests-2.26.0-py2.py3-none-any.whl", hash = "sha256:6c1246513ecd5ecd4528a0906f910e8f0f9c6b8ec72030dc9fd154dc1a6efd24"}, {file = "requests-2.26.0.tar.gz", hash = "sha256:b8aa58f8cf793ffd8782d3d8cb19e66ef36f7aba4353eec859e74678b01b07a7"}, From e44bacca3c2ad598e1984e50aa2ce5370e17068a Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Sat, 12 Feb 2022 18:52:41 -0800 Subject: [PATCH 008/197] Increased runtime by precalculating randomized events in the simulation process --- haptools/__main__.py | 8 +- haptools/simulate/sim_admixture.py | 135 +++++++++++++++++++++-------- 2 files changed, 105 insertions(+), 38 deletions(-) diff --git a/haptools/__main__.py b/haptools/__main__.py index df4858cc..ac7055cc 100644 --- a/haptools/__main__.py +++ b/haptools/__main__.py @@ -25,14 +25,14 @@ def main(): @click.option('--invcf') @click.option('--sample_info') @click.option('--model', required=True) -@click.option('--coords', required=True) +@click.option('--map', 'recombmap', required=True) @click.option('--out', required=True) -def simulate(invcf, sample_info, model, coords, out): +def simulate(invcf, sample_info, model, recombmap, out): """ Use the tool to simulate genotypes """ - breakpoints = simulate_gt(model, coords) - write_breakpoint(breakpoints, out) + samples, breakpoints = simulate_gt(model, recombmap) + write_breakpoints(samples, breakpoints, out) if __name__ == "__main__": diff --git a/haptools/simulate/sim_admixture.py b/haptools/simulate/sim_admixture.py index f399221b..3d9e1635 100644 --- a/haptools/simulate/sim_admixture.py +++ b/haptools/simulate/sim_admixture.py @@ -1,4 +1,5 @@ import numpy as np +import time from .admix_storage import GeneticMarker, HaplotypeSegment # TODO at a certain point we are going to need to ensure populations in model file are also in invcf files @@ -42,12 +43,15 @@ def simulate_gt(model_file, coords_file, seed=None): coords.append(gen_mark) # number of haplotypes simulated per generation - haps_per_gen = max(10000, 20 * num_samples) + haps_per_gen = max(5000, 20 * num_samples) # starting generation is 0 prev_gen = 0 next_gen_samples = [] + # Time code + start = time.time() + # iterate over generations in model file for gen in mfile: # setup population proportions and generations to simulate @@ -61,22 +65,29 @@ def simulate_gt(model_file, coords_file, seed=None): # sim generation print(f"Simulating generation {prev_gen+1}") - next_gen_samples = _simulate(num_samples, pop_fracs, prev_gen, coords, next_gen_samples) + next_gen_samples = _simulate(haps_per_gen, pop_fracs, prev_gen, coords, next_gen_samples) # simulate remaining generations for i in range(1, sim_gens): print(f"Simulating generation {prev_gen+i+1}") # simulate next generations using previous generations to sample from for admixture - next_gen_samples = _simulate(num_samples, pop_fracs, prev_gen+i, coords, next_gen_samples) + next_gen_samples = _simulate(haps_per_gen, pop_fracs, prev_gen+i, coords, next_gen_samples) prev_gen = cur_gen + end = time.time() + print(f"Time elapsed for simulation: {end - start}") + mfile.close() - return next_gen_samples + return num_samples, next_gen_samples -def write_breakpoints(breakpoints, out): +def write_breakpoints(samples, breakpoints, out): breakpt_file = out + '.bp' print(f"Outputting breakpoint file {breakpt_file}") + + # randomly sample breakpoints to get the correct amount of samples to output + breakpoints = np.random.choice(breakpoints, size=2*samples, replace=False) + with open(breakpt_file, 'a') as output: for ind, sample in enumerate(breakpoints): # Get sample number and haplotype number @@ -98,32 +109,46 @@ def write_breakpoints(breakpoints, out): def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # generate all samples hap_samples = [] + + # pre compute haplotypes and parent population + # if there is no previous generation randomly choose population based on frac + parent_pop = np.zeros(samples) + if not prev_gen_samples: + parent_pop = np.random.choice(np.arange(len(pop_fracs)), + size=samples, p=pop_fracs) + + # choose a haplotype to copy + # if previous generation of samples randomize two haplotypes + # otherwise only choose a single haplotype + haplotypes = -1*np.ones(2*samples) + if not parent_pop[0]: + # choose two of previous samples as haplotypes to choose from + haplotypes = np.random.randint(samples, size=2*samples) + + # ensure no two haplotypes are the same + for hap in range(samples): + while haplotypes[2*hap] == haplotypes[2*hap+1]: + haplotypes[2*hap+1] = np.random.randint(samples) + + # store all probabilities to compare for recombination + prob_vals = np.random.rand(samples*len(coords)) + + # generate all samples for sample in range(samples): segments = [] prev_chrom = -1 prev_map_pos = 0.0 start_bp = 0 - end_bp = 0 - - # if there is no previous generation randomly choose population based on frac - parent_pop = 0 - if not prev_gen_samples: - parent_pop = np.random.choice(np.arange(len(pop_fracs)), p=pop_fracs) - - # choose a haplotype to copy - # if previous generation of samples randomize two haplotypes - # otherwise only choose a single haplotype - haplotypes = -1*np.ones(2) - if not parent_pop: - # choose two of previous samples as haplotypes to choose from - haplotypes = np.random.choice(np.arange(samples), size=2, replace=False) - + end_bp = 0 + p_pop = parent_pop[sample] + haps = haplotypes[2*sample:2*sample+2] + # haplotypes only matters when pop == 0 otherwise we are sampling from a population which has a label # choose our starting chromosome homolog = np.random.randint(2) # iterate over all coords in the coords file - for coord in coords: + for cind, coord in enumerate(coords): # get current chromosome and position cur_chrom = coord.get_chrom() cur_map_pos = coord.get_map_pos() @@ -140,7 +165,7 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # Ended chromosome write segment if cur_chrom != prev_chrom: # Store haplotype - segments.extend(get_segment(parent_pop, haplotypes[homolog], prev_chrom, + segments.extend(get_segment(p_pop, haps[homolog], prev_chrom, start_bp, end_bp, prev_gen_samples)) # update homolog, previous chrom, and previous position @@ -152,9 +177,9 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # check if a recombination event occurs and if so write segment and swap homolog dist = cur_map_pos - prev_map_pos recomb_prob = 1-np.exp(-dist) - if np.random.choice([0, 1], 1, p=[1-recomb_prob, recomb_prob]): + if prob_vals[sample*samples+cind] < recomb_prob: # Store haplotype segments switching homologs - segments.extend(get_segment(parent_pop, haplotypes[homolog], prev_chrom, + segments.extend(get_segment(p_pop, haps[homolog], prev_chrom, start_bp, end_bp, prev_gen_samples)) homolog = 1-homolog @@ -165,7 +190,7 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # Record remaining segments if segments: start_bp = segments[-1].get_end_coord() + 1 - segments.extend(get_segment(parent_pop, haplotypes[homolog], prev_chrom, + segments.extend(get_segment(p_pop, haps[homolog], prev_chrom, start_bp, end_bp, prev_gen_samples)) # append segments of 1 sample @@ -200,10 +225,9 @@ def get_segment(pop, haplotype, chrom, start_coord, end_coord, prev_gen_samples) prev_gen_segments = prev_gen_samples[haplotype] # iterate over haplotype segments to collect relevant ones - for prev_segment in prev_gen_segments: - # check if current segment is within boundaries - if prev_segment.get_end_coord() < start_coord: - continue + # use binary search to find starting segment to collect information + start_seg = start_segment(start_coord, chrom, prev_gen_segments) + for prev_segment in prev_gen_segments[start_seg:]: if prev_segment.get_end_coord() > end_coord: break @@ -220,11 +244,54 @@ def get_segment(pop, haplotype, chrom, start_coord, end_coord, prev_gen_samples) segments.append(HaplotypeSegment(out_pop, chrom, end_coord)) return segments -# TODO remove -def test(): - model_file = '/storage/mlamkin/data/simwas/AA.dat' - coords_file = '/storage/mlamkin/data/simwas/plink.chr10.GRCh38.map' - simulate_gt(model_file, coords_file) +def start_segment(start, chrom, segments): + """ + Find first segment that is on chrom and its end coordinate is > start via binary search. + """ + low = 0 + high = len(segments)-1 + mid = 0 + + # first segment > start implies segment prior end coord < start + while low <= high: + mid = (high+low) // 2 + + # collect coordinate and chrom information + cur_coord = segments[mid].get_end_coord() + cur_chrom = segments[mid].get_chrom() + if mid == 0: + prev_coord = -1 + prev_chrom = -1 + else: + prev_coord = segments[mid-1].get_end_coord() + prev_chrom = segments[mid-1].get_chrom() + + # check if chromosomes match otherwise update + if chrom == cur_chrom: + # check for current coords loc + if cur_coord < start: + low = mid + 1 + + elif cur_coord >= start: + if prev_chrom < cur_chrom: + return mid + + if prev_chrom == cur_chrom and prev_coord < start: + return mid + else: + high = mid - 1 + + else: + return len(segments) + + elif chrom < segments[mid].get_chrom(): + high = mid - 1 + + else: + low = mid + 1 + + return len(segments) + if __name__ == '__main__': test() From bb8afaeaf9ac37c24e53d40cf39252c6ec2e4ba8 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Sun, 13 Feb 2022 14:15:08 -0800 Subject: [PATCH 009/197] Hacked together karyogram code --- "haptools/visualization/\\" | 233 ++++++++++++++++++++++++++++ haptools/visualization/karyogram.py | 232 +++++++++++++++++++++++++++ haptools/visualization/to_remove.py | 17 ++ 3 files changed, 482 insertions(+) create mode 100644 "haptools/visualization/\\" create mode 100644 haptools/visualization/karyogram.py create mode 100644 haptools/visualization/to_remove.py diff --git "a/haptools/visualization/\\" "b/haptools/visualization/\\" new file mode 100644 index 00000000..ddec124b --- /dev/null +++ "b/haptools/visualization/\\" @@ -0,0 +1,233 @@ +import argparse +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import pylab +from matplotlib.path import Path +import matplotlib.patches as patches +import matplotlib.collections as mcol +import brewer2mpl +import os + + +def splitstr(option, opt, value, parser): + return(setattr(parser.values, option.dest, value.split(','))) + +# TODO determine whether its better to plot bp or cM and about updating output so it outputs both +def plot_karyogram(sample_name, sample_file, title, chrX, centromeres, pop_order, colors, out): + """ + Arguments + sample_name - sample to plot on karyogram + sample_file - contains all samples, chrom, pop, and hap block location + title - plot title + chrX - include chromosome X? + centromeres - centromeres location file + pop_order - comma-separated list of population labels in the order of rfmix populations + colors - colors for respective populations + out - output file to save figure + """ + + #read in bed files and get individual name + samples = [] + sample = [] + plot_sample = False + + with open(sample_file,'r') as sample_file: + for line in sample_file: + line = line.strip().split('\t') + print(len(line)) + + # check if header + if len(line) == 1: + # new header write out haplotype + if sample: + samples.append(sample) + + if sample_name in line: + plot_sample = True + sample = [] + continue + else: + plot_sample = False + + # Already collected our two haplotypes + if not plot_sample and len(samples) == 2: + break + + if plot_sample: + if not sample: + start = 1 + else: + start = sample[-1]['end'] + 1 + hap_block = {'pop': line[0], 'chrom': int(line[1]), + 'start': start, 'end': int(line[2])} + sample.append(hap_block) + + #define plotting space + fig = plt.figure() + ax = fig.add_subplot(111) + ax.set_xlim(-5,10000000) + if chrX: + ax.set_ylim(24,0) + else: + ax.set_ylim(23,0) + plt.xlabel('Genetic position (bp)') + plt.ylabel('Chromosome') + plt.title(title) + if chrX: + plt.yticks(range(1,24)) + yticks = range(1,23) + yticks.append('X') + ax.set_yticklabels(yticks) + else: + plt.yticks(range(1,23)) + + #define colors + def hex_to_rgb(value): + value = value.lstrip('#') + lv = len(value) + return tuple(int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)) + + bmap = brewer2mpl.get_map('Set1', 'qualitative', 4) + if not colors: + colors=bmap.mpl_colors + colors.append((0,0,0)) + + #define centromeres + # TODO figure out what to do with centromeres + """ + centro = open(args.centromeres) + centromeres = {} + for line in centro: + line = line.strip().split() + if chrX and line[0] == 'X': + line[0] = '23' + centromeres[line[0]] = line + """ + + #plot rectangles + for info in samples[0]: + try: + plot_rects(info['pop'], info['chrom'], info['start'], + info['end'], 'A', pop_order, colors) + except ValueError: #flexibility for chrX + plot_rects(info['pop'], 23, info['start'], + info['end'], 'A', pop_order, colors) + for info in samples[1]: + try: + plot_rects(info['pop'], info['chrom'], info['start'], + info['end'], 'B', pop_order, colors) + except ValueError: #flexibility for chrX + plot_rects(info['pop'], 23, info['start'], + info['end'], 'B', pop_order, colors) + + #write a legend + p = [] + for i in range(len(pop_order)): + p.append(plt.Rectangle((0, 0), 1, 1, color=colors[i])) + p.append(plt.Rectangle((0, 0), 1, 1, color='k')) + labs = list(pop_order) + labs.append('UNK') + leg = ax.legend(p, labs, loc=4, fancybox=True) + leg.get_frame().set_alpha(0) + + #get rid of annoying plot features + spines_to_remove = ['top', 'right'] + for spine in spines_to_remove: + ax.spines[spine].set_visible(False) + ax.xaxis.set_ticks_position('none') + ax.yaxis.set_ticks_position('none') + + fig.savefig(args.out) + +def plot_rects(anc, chrom, start, stop, hap, pop_order, colors): + # TODO update so we can work with centromeres but rn dont use + """ + centro_coords = map(float, centromeres[str(chrom)]) + if len(centro_coords) == 3: #acrocentric chromosome + mask = [ + (centro_coords[1]+2,chrom-0.4), #add +/- 2 at the end of either end + (centro_coords[2]-2,chrom-0.4), + (centro_coords[2]+2,chrom), + (centro_coords[2]-2,chrom+0.4), + (centro_coords[1]+2,chrom+0.4), + (centro_coords[1]-2,chrom), + (centro_coords[1]+2,chrom-0.4) + ] + + mask_codes = [ + Path.MOVETO, + Path.LINETO, + Path.CURVE3, + Path.LINETO, + Path.LINETO, + Path.CURVE3, + Path.LINETO, + ] + clip_mask = Path(vertices=mask, codes=mask_codes) + + else: #need to write more complicated clipping mask with centromere masked out + mask = [ + (centro_coords[1]+2,chrom-0.4), #add +/- 2 at the end of either end + (centro_coords[2]-2,chrom-0.4), + (centro_coords[2]+2,chrom+0.4), + (centro_coords[3]-2,chrom+0.4), + (centro_coords[3]+2,chrom), + (centro_coords[3]-2,chrom-0.4), + (centro_coords[2]+2,chrom-0.4), + (centro_coords[2]-2,chrom+0.4), + (centro_coords[1]+2,chrom+0.4), + (centro_coords[1]-2,chrom), + (centro_coords[1]+2,chrom-0.4) + ] + + mask_codes = [ + Path.MOVETO, + Path.LINETO, + Path.LINETO, + Path.LINETO, + Path.CURVE3, + Path.LINETO, + Path.LINETO, + Path.LINETO, + Path.LINETO, + Path.CURVE3, + Path.LINETO, + ] + clip_mask = Path(vertices=mask, codes=mask_codes) + """ + if hap == 'A': #bed_a ancestry goes on top + verts = [ + (float(start), chrom), #left, bottom + (float(start), chrom + 0.4), #left, top + (float(stop), chrom + 0.4), #right, top + (float(stop), chrom), #right, bottom + (0, 0), #ignored + ] + else: #bed_b ancestry goes on bottom + verts = [ + (float(start), chrom - 0.4), #left, bottom + (float(start), chrom), #left, top + (float(stop), chrom), #right, top + (float(stop), chrom - 0.4), #right, bottom + (0, 0), #ignored + ] + + codes = [ + Path.MOVETO, + Path.LINETO, + Path.LINETO, + Path.LINETO, + Path.CLOSEPOLY, + ] + + clip_path = Path(verts, codes) + if anc in pop_order: + col=mcol.PathCollection([clip_path],facecolor=colors[pop_order.index(anc)], linewidths=0) + else: + col=mcol.PathCollection([clip_path],facecolor=colors[-1], linewidths=0) + #if 'clip_mask' in locals(): + # col.set_clip_path(clip_mask, ax.transData) + ax.add_collection(col) + + diff --git a/haptools/visualization/karyogram.py b/haptools/visualization/karyogram.py new file mode 100644 index 00000000..d503e55e --- /dev/null +++ b/haptools/visualization/karyogram.py @@ -0,0 +1,232 @@ +import argparse +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import pylab +from matplotlib.path import Path +import matplotlib.patches as patches +import matplotlib.collections as mcol +import brewer2mpl +import os + + +def splitstr(option, opt, value, parser): + return(setattr(parser.values, option.dest, value.split(','))) + +# TODO determine whether its better to plot bp or cM and about updating output so it outputs both +def plot_karyogram(sample_name, sample_file, title, chrX, centromeres, pop_order, colors, out): + """ + Arguments + sample_name - sample to plot on karyogram + sample_file - contains all samples, chrom, pop, and hap block location + title - plot title + chrX - include chromosome X? + centromeres - centromeres location file + pop_order - comma-separated list of population labels in the order of rfmix populations + colors - colors for respective populations + out - output file to save figure + """ + + #read in bed files and get individual name + samples = [] + sample = [] + plot_sample = False + + with open(sample_file,'r') as sample_file: + for line in sample_file: + line = line.strip().split('\t') + + # check if header + if len(line) == 1: + # new header write out haplotype + if sample: + samples.append(sample) + + if sample_name in line[0]: + plot_sample = True + sample = [] + continue + else: + plot_sample = False + + # Already collected our two haplotypes + if not plot_sample and len(samples) == 2: + break + + if plot_sample: + if not sample: + start = 1 + else: + start = sample[-1]['end'] + 1 + hap_block = {'pop': line[0], 'chrom': int(line[1]), + 'start': start, 'end': int(line[2])} + sample.append(hap_block) + + #define plotting space + fig = plt.figure() + ax = fig.add_subplot(111) + ax.set_xlim(-5,100000000) + if chrX: + ax.set_ylim(24,0) + else: + ax.set_ylim(23,0) + plt.xlabel('Genetic position (bp)') + plt.ylabel('Chromosome') + plt.title(title) + if chrX: + plt.yticks(range(1,24)) + yticks = range(1,23) + yticks.append('X') + ax.set_yticklabels(yticks) + else: + plt.yticks(range(1,23)) + + #define colors + def hex_to_rgb(value): + value = value.lstrip('#') + lv = len(value) + return tuple(int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)) + + bmap = brewer2mpl.get_map('Set1', 'qualitative', 4) + if not colors: + colors=bmap.mpl_colors + colors.append((0,0,0)) + + #define centromeres + # TODO figure out what to do with centromeres + """ + centro = open(args.centromeres) + centromeres = {} + for line in centro: + line = line.strip().split() + if chrX and line[0] == 'X': + line[0] = '23' + centromeres[line[0]] = line + """ + + #plot rectangles + for info in samples[0]: + try: + plot_rects(info['pop'], info['chrom'], info['start'], + info['end'], 'A', pop_order, colors, ax) + except ValueError: #flexibility for chrX + plot_rects(info['pop'], 23, info['start'], + info['end'], 'A', pop_order, colors, ax) + for info in samples[1]: + try: + plot_rects(info['pop'], info['chrom'], info['start'], + info['end'], 'B', pop_order, colors, ax) + except ValueError: #flexibility for chrX + plot_rects(info['pop'], 23, info['start'], + info['end'], 'B', pop_order, colors, ax) + + #write a legend + p = [] + for i in range(len(pop_order)): + p.append(plt.Rectangle((0, 0), 1, 1, color=colors[i])) + p.append(plt.Rectangle((0, 0), 1, 1, color='k')) + labs = list(pop_order) + labs.append('UNK') + leg = ax.legend(p, labs, loc=4, fancybox=True) + leg.get_frame().set_alpha(0) + + #get rid of annoying plot features + spines_to_remove = ['top', 'right'] + for spine in spines_to_remove: + ax.spines[spine].set_visible(False) + ax.xaxis.set_ticks_position('none') + ax.yaxis.set_ticks_position('none') + + fig.savefig(out) + +def plot_rects(anc, chrom, start, stop, hap, pop_order, colors, ax): + # TODO update so we can work with centromeres but rn dont use + """ + centro_coords = map(float, centromeres[str(chrom)]) + if len(centro_coords) == 3: #acrocentric chromosome + mask = [ + (centro_coords[1]+2,chrom-0.4), #add +/- 2 at the end of either end + (centro_coords[2]-2,chrom-0.4), + (centro_coords[2]+2,chrom), + (centro_coords[2]-2,chrom+0.4), + (centro_coords[1]+2,chrom+0.4), + (centro_coords[1]-2,chrom), + (centro_coords[1]+2,chrom-0.4) + ] + + mask_codes = [ + Path.MOVETO, + Path.LINETO, + Path.CURVE3, + Path.LINETO, + Path.LINETO, + Path.CURVE3, + Path.LINETO, + ] + clip_mask = Path(vertices=mask, codes=mask_codes) + + else: #need to write more complicated clipping mask with centromere masked out + mask = [ + (centro_coords[1]+2,chrom-0.4), #add +/- 2 at the end of either end + (centro_coords[2]-2,chrom-0.4), + (centro_coords[2]+2,chrom+0.4), + (centro_coords[3]-2,chrom+0.4), + (centro_coords[3]+2,chrom), + (centro_coords[3]-2,chrom-0.4), + (centro_coords[2]+2,chrom-0.4), + (centro_coords[2]-2,chrom+0.4), + (centro_coords[1]+2,chrom+0.4), + (centro_coords[1]-2,chrom), + (centro_coords[1]+2,chrom-0.4) + ] + + mask_codes = [ + Path.MOVETO, + Path.LINETO, + Path.LINETO, + Path.LINETO, + Path.CURVE3, + Path.LINETO, + Path.LINETO, + Path.LINETO, + Path.LINETO, + Path.CURVE3, + Path.LINETO, + ] + clip_mask = Path(vertices=mask, codes=mask_codes) + """ + if hap == 'A': #bed_a ancestry goes on top + verts = [ + (float(start), chrom), #left, bottom + (float(start), chrom + 0.4), #left, top + (float(stop), chrom + 0.4), #right, top + (float(stop), chrom), #right, bottom + (0, 0), #ignored + ] + else: #bed_b ancestry goes on bottom + verts = [ + (float(start), chrom - 0.4), #left, bottom + (float(start), chrom), #left, top + (float(stop), chrom), #right, top + (float(stop), chrom - 0.4), #right, bottom + (0, 0), #ignored + ] + + codes = [ + Path.MOVETO, + Path.LINETO, + Path.LINETO, + Path.LINETO, + Path.CLOSEPOLY, + ] + + clip_path = Path(verts, codes) + if anc in pop_order: + col=mcol.PathCollection([clip_path],facecolor=colors[pop_order.index(anc)], linewidths=0) + else: + col=mcol.PathCollection([clip_path],facecolor=colors[-1], linewidths=0) + #if 'clip_mask' in locals(): + # col.set_clip_path(clip_mask, ax.transData) + ax.add_collection(col) + + diff --git a/haptools/visualization/to_remove.py b/haptools/visualization/to_remove.py new file mode 100644 index 00000000..01758cc8 --- /dev/null +++ b/haptools/visualization/to_remove.py @@ -0,0 +1,17 @@ +from karyogram import plot_karyogram + +def main(): + sample_name = "Sample_1" + sample_file = "/storage/mlamkin/data/simwas/ten_gen.bp" + title = "Ten Generations" + chrX = False + centromeres = [] + pop_order = ['1','2'] + colors = [] + out = "/storage/mlamkin/data/simwas/ten_gen.png" + plot_karyogram(sample_name, sample_file, title, chrX, + centromeres, pop_order, colors, out) + return + +if __name__ == '__main__': + main() From 38b091aa0656360222a68ddf6ac1b6c0c75077d4 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Sun, 13 Feb 2022 14:15:41 -0800 Subject: [PATCH 010/197] Hacked together code --- "haptools/visualization/\\" | 233 ------------------------------------ 1 file changed, 233 deletions(-) delete mode 100644 "haptools/visualization/\\" diff --git "a/haptools/visualization/\\" "b/haptools/visualization/\\" deleted file mode 100644 index ddec124b..00000000 --- "a/haptools/visualization/\\" +++ /dev/null @@ -1,233 +0,0 @@ -import argparse -import matplotlib -matplotlib.use("Agg") -import matplotlib.pyplot as plt -import pylab -from matplotlib.path import Path -import matplotlib.patches as patches -import matplotlib.collections as mcol -import brewer2mpl -import os - - -def splitstr(option, opt, value, parser): - return(setattr(parser.values, option.dest, value.split(','))) - -# TODO determine whether its better to plot bp or cM and about updating output so it outputs both -def plot_karyogram(sample_name, sample_file, title, chrX, centromeres, pop_order, colors, out): - """ - Arguments - sample_name - sample to plot on karyogram - sample_file - contains all samples, chrom, pop, and hap block location - title - plot title - chrX - include chromosome X? - centromeres - centromeres location file - pop_order - comma-separated list of population labels in the order of rfmix populations - colors - colors for respective populations - out - output file to save figure - """ - - #read in bed files and get individual name - samples = [] - sample = [] - plot_sample = False - - with open(sample_file,'r') as sample_file: - for line in sample_file: - line = line.strip().split('\t') - print(len(line)) - - # check if header - if len(line) == 1: - # new header write out haplotype - if sample: - samples.append(sample) - - if sample_name in line: - plot_sample = True - sample = [] - continue - else: - plot_sample = False - - # Already collected our two haplotypes - if not plot_sample and len(samples) == 2: - break - - if plot_sample: - if not sample: - start = 1 - else: - start = sample[-1]['end'] + 1 - hap_block = {'pop': line[0], 'chrom': int(line[1]), - 'start': start, 'end': int(line[2])} - sample.append(hap_block) - - #define plotting space - fig = plt.figure() - ax = fig.add_subplot(111) - ax.set_xlim(-5,10000000) - if chrX: - ax.set_ylim(24,0) - else: - ax.set_ylim(23,0) - plt.xlabel('Genetic position (bp)') - plt.ylabel('Chromosome') - plt.title(title) - if chrX: - plt.yticks(range(1,24)) - yticks = range(1,23) - yticks.append('X') - ax.set_yticklabels(yticks) - else: - plt.yticks(range(1,23)) - - #define colors - def hex_to_rgb(value): - value = value.lstrip('#') - lv = len(value) - return tuple(int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)) - - bmap = brewer2mpl.get_map('Set1', 'qualitative', 4) - if not colors: - colors=bmap.mpl_colors - colors.append((0,0,0)) - - #define centromeres - # TODO figure out what to do with centromeres - """ - centro = open(args.centromeres) - centromeres = {} - for line in centro: - line = line.strip().split() - if chrX and line[0] == 'X': - line[0] = '23' - centromeres[line[0]] = line - """ - - #plot rectangles - for info in samples[0]: - try: - plot_rects(info['pop'], info['chrom'], info['start'], - info['end'], 'A', pop_order, colors) - except ValueError: #flexibility for chrX - plot_rects(info['pop'], 23, info['start'], - info['end'], 'A', pop_order, colors) - for info in samples[1]: - try: - plot_rects(info['pop'], info['chrom'], info['start'], - info['end'], 'B', pop_order, colors) - except ValueError: #flexibility for chrX - plot_rects(info['pop'], 23, info['start'], - info['end'], 'B', pop_order, colors) - - #write a legend - p = [] - for i in range(len(pop_order)): - p.append(plt.Rectangle((0, 0), 1, 1, color=colors[i])) - p.append(plt.Rectangle((0, 0), 1, 1, color='k')) - labs = list(pop_order) - labs.append('UNK') - leg = ax.legend(p, labs, loc=4, fancybox=True) - leg.get_frame().set_alpha(0) - - #get rid of annoying plot features - spines_to_remove = ['top', 'right'] - for spine in spines_to_remove: - ax.spines[spine].set_visible(False) - ax.xaxis.set_ticks_position('none') - ax.yaxis.set_ticks_position('none') - - fig.savefig(args.out) - -def plot_rects(anc, chrom, start, stop, hap, pop_order, colors): - # TODO update so we can work with centromeres but rn dont use - """ - centro_coords = map(float, centromeres[str(chrom)]) - if len(centro_coords) == 3: #acrocentric chromosome - mask = [ - (centro_coords[1]+2,chrom-0.4), #add +/- 2 at the end of either end - (centro_coords[2]-2,chrom-0.4), - (centro_coords[2]+2,chrom), - (centro_coords[2]-2,chrom+0.4), - (centro_coords[1]+2,chrom+0.4), - (centro_coords[1]-2,chrom), - (centro_coords[1]+2,chrom-0.4) - ] - - mask_codes = [ - Path.MOVETO, - Path.LINETO, - Path.CURVE3, - Path.LINETO, - Path.LINETO, - Path.CURVE3, - Path.LINETO, - ] - clip_mask = Path(vertices=mask, codes=mask_codes) - - else: #need to write more complicated clipping mask with centromere masked out - mask = [ - (centro_coords[1]+2,chrom-0.4), #add +/- 2 at the end of either end - (centro_coords[2]-2,chrom-0.4), - (centro_coords[2]+2,chrom+0.4), - (centro_coords[3]-2,chrom+0.4), - (centro_coords[3]+2,chrom), - (centro_coords[3]-2,chrom-0.4), - (centro_coords[2]+2,chrom-0.4), - (centro_coords[2]-2,chrom+0.4), - (centro_coords[1]+2,chrom+0.4), - (centro_coords[1]-2,chrom), - (centro_coords[1]+2,chrom-0.4) - ] - - mask_codes = [ - Path.MOVETO, - Path.LINETO, - Path.LINETO, - Path.LINETO, - Path.CURVE3, - Path.LINETO, - Path.LINETO, - Path.LINETO, - Path.LINETO, - Path.CURVE3, - Path.LINETO, - ] - clip_mask = Path(vertices=mask, codes=mask_codes) - """ - if hap == 'A': #bed_a ancestry goes on top - verts = [ - (float(start), chrom), #left, bottom - (float(start), chrom + 0.4), #left, top - (float(stop), chrom + 0.4), #right, top - (float(stop), chrom), #right, bottom - (0, 0), #ignored - ] - else: #bed_b ancestry goes on bottom - verts = [ - (float(start), chrom - 0.4), #left, bottom - (float(start), chrom), #left, top - (float(stop), chrom), #right, top - (float(stop), chrom - 0.4), #right, bottom - (0, 0), #ignored - ] - - codes = [ - Path.MOVETO, - Path.LINETO, - Path.LINETO, - Path.LINETO, - Path.CLOSEPOLY, - ] - - clip_path = Path(verts, codes) - if anc in pop_order: - col=mcol.PathCollection([clip_path],facecolor=colors[pop_order.index(anc)], linewidths=0) - else: - col=mcol.PathCollection([clip_path],facecolor=colors[-1], linewidths=0) - #if 'clip_mask' in locals(): - # col.set_clip_path(clip_mask, ax.transData) - ax.add_collection(col) - - From 9eba3b5f4ee37fc5d5cf19c78724c28a3d45a9ad Mon Sep 17 00:00:00 2001 From: Melissa Gymrek Date: Mon, 14 Feb 2022 13:44:10 -0800 Subject: [PATCH 011/197] initiating haptools readme pages --- .gitignore | 5 +++- README.md | 26 +++++++++++++++++-- haptools/karyogram/README.md | 3 +++ .../{visualization => karyogram}/karyogram.py | 0 .../{visualization => karyogram}/to_remove.py | 0 haptools/simgenotype/README.md | 3 +++ .../admix_storage.py | 0 .../sim_admixture.py | 0 haptools/simphenotype/README.md | 3 +++ 9 files changed, 37 insertions(+), 3 deletions(-) create mode 100644 haptools/karyogram/README.md rename haptools/{visualization => karyogram}/karyogram.py (100%) rename haptools/{visualization => karyogram}/to_remove.py (100%) create mode 100644 haptools/simgenotype/README.md rename haptools/{simulate => simgenotype}/admix_storage.py (100%) rename haptools/{simulate => simgenotype}/sim_admixture.py (100%) create mode 100644 haptools/simphenotype/README.md diff --git a/.gitignore b/.gitignore index 618f9c4a..11548fd4 100644 --- a/.gitignore +++ b/.gitignore @@ -9,4 +9,7 @@ __pycache__ # pytest cache .pytest_cache # poetry -dist/ \ No newline at end of file +dist/ + +# OSX +*.DS_Store* \ No newline at end of file diff --git a/README.md b/README.md index 35127436..23933681 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,29 @@ Please wait until we have published our first tagged release before using our code. # haptools -Simulate phenotypes for fine-mapping. Use real variants to simulate real, biological LD patterns. -The Snakemake pipeline in the `snakemake/` directory uses the results of the simulation to test several fine-mapping methods, including FINEMAP and SuSiE. + +Haptools is a collection of tools for simulating and analyzing genotypes and phenotypes while taking into account haplotype information. It is particularly designed for analysis of individuals with admixed ancestries, although the tools can also be used for non-admixed individuals. Homepage: https://haptools.readthedocs.io/ + +## Installation + +UNDER CONSTRUCTION + +## Haptools utilities + +Haptools consists of multiple utilities listed below. Click on a utility to see more detailed usage information. + +* [`haptools simgenome`](haptools/simgenotype/README.md): Simulate genotypes for admixed individuals under user-specified demographic histories. `haptools simgenome` takes as input a reference set of ancestry-labeled haplotypes and a demographic model and outputs a VCF file with local ancestry information annotated for each variant. It also outputs a list of local ancestry breakpoints which can be visualized using `haptools karyogram`. The output VCF file can be used as input to downstream tools such as `haptools simphenotype` to simulate phenotype information. + +* ['haptools simphenotype'](haptools/simphenotype/README.md): Simulate a complex trait, taking into account local ancestry- or haplotype- specific effects. 'haptools simphenotype' takes as input a VCF file and outputs simulated phenotypes for each sample. + +* [`haptools karyogram`](haptools/karyogram/README.md): Visualize a "chromosome painting" of local ancestry labels based on breakpoints output by `haptools simgenome`. + + +## Contributing + +If you are interested in contributing to `haptools`, please get in touch by submitting a Github issue or contacting us at mlamkin@ucsd.edu. + + + diff --git a/haptools/karyogram/README.md b/haptools/karyogram/README.md new file mode 100644 index 00000000..365abb96 --- /dev/null +++ b/haptools/karyogram/README.md @@ -0,0 +1,3 @@ +# Haptools karyogram + +UNDER CONSTRUCTION \ No newline at end of file diff --git a/haptools/visualization/karyogram.py b/haptools/karyogram/karyogram.py similarity index 100% rename from haptools/visualization/karyogram.py rename to haptools/karyogram/karyogram.py diff --git a/haptools/visualization/to_remove.py b/haptools/karyogram/to_remove.py similarity index 100% rename from haptools/visualization/to_remove.py rename to haptools/karyogram/to_remove.py diff --git a/haptools/simgenotype/README.md b/haptools/simgenotype/README.md new file mode 100644 index 00000000..48c1369c --- /dev/null +++ b/haptools/simgenotype/README.md @@ -0,0 +1,3 @@ +# Haptools simgenotype + +UNDER CONSTRUCTION \ No newline at end of file diff --git a/haptools/simulate/admix_storage.py b/haptools/simgenotype/admix_storage.py similarity index 100% rename from haptools/simulate/admix_storage.py rename to haptools/simgenotype/admix_storage.py diff --git a/haptools/simulate/sim_admixture.py b/haptools/simgenotype/sim_admixture.py similarity index 100% rename from haptools/simulate/sim_admixture.py rename to haptools/simgenotype/sim_admixture.py diff --git a/haptools/simphenotype/README.md b/haptools/simphenotype/README.md new file mode 100644 index 00000000..9c46b012 --- /dev/null +++ b/haptools/simphenotype/README.md @@ -0,0 +1,3 @@ +# Haptools simphenotype + +UNDER CONSTRUCTION \ No newline at end of file From 4d73de8338e7b783fbe00958db81b9969c189ca5 Mon Sep 17 00:00:00 2001 From: Melissa Gymrek Date: Mon, 14 Feb 2022 13:45:17 -0800 Subject: [PATCH 012/197] fixing minor readme typos --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 23933681..98234b07 100644 --- a/README.md +++ b/README.md @@ -17,9 +17,13 @@ UNDER CONSTRUCTION Haptools consists of multiple utilities listed below. Click on a utility to see more detailed usage information. -* [`haptools simgenome`](haptools/simgenotype/README.md): Simulate genotypes for admixed individuals under user-specified demographic histories. `haptools simgenome` takes as input a reference set of ancestry-labeled haplotypes and a demographic model and outputs a VCF file with local ancestry information annotated for each variant. It also outputs a list of local ancestry breakpoints which can be visualized using `haptools karyogram`. The output VCF file can be used as input to downstream tools such as `haptools simphenotype` to simulate phenotype information. +* [`haptools simgenome`](haptools/simgenotype/README.md): Simulate genotypes for admixed individuals under user-specified demographic histories. -* ['haptools simphenotype'](haptools/simphenotype/README.md): Simulate a complex trait, taking into account local ancestry- or haplotype- specific effects. 'haptools simphenotype' takes as input a VCF file and outputs simulated phenotypes for each sample. +`haptools simgenome` takes as input a reference set of ancestry-labeled haplotypes and a demographic model and outputs a VCF file with local ancestry information annotated for each variant. It also outputs a list of local ancestry breakpoints which can be visualized using `haptools karyogram`. The output VCF file can be used as input to downstream tools such as `haptools simphenotype` to simulate phenotype information. + +* [`haptools simphenotype`](haptools/simphenotype/README.md): Simulate a complex trait, taking into account local ancestry- or haplotype- specific effects. + +'haptools simphenotype' takes as input a VCF file and outputs simulated phenotypes for each sample. * [`haptools karyogram`](haptools/karyogram/README.md): Visualize a "chromosome painting" of local ancestry labels based on breakpoints output by `haptools simgenome`. From 2c9da5566f400380a6eb481ee8f3e2f422975f7d Mon Sep 17 00:00:00 2001 From: Melissa Gymrek Date: Mon, 14 Feb 2022 13:47:17 -0800 Subject: [PATCH 013/197] minor readme changes --- README.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 98234b07..4ea87402 100644 --- a/README.md +++ b/README.md @@ -19,14 +19,13 @@ Haptools consists of multiple utilities listed below. Click on a utility to see * [`haptools simgenome`](haptools/simgenotype/README.md): Simulate genotypes for admixed individuals under user-specified demographic histories. -`haptools simgenome` takes as input a reference set of ancestry-labeled haplotypes and a demographic model and outputs a VCF file with local ancestry information annotated for each variant. It also outputs a list of local ancestry breakpoints which can be visualized using `haptools karyogram`. The output VCF file can be used as input to downstream tools such as `haptools simphenotype` to simulate phenotype information. - -* [`haptools simphenotype`](haptools/simphenotype/README.md): Simulate a complex trait, taking into account local ancestry- or haplotype- specific effects. - -'haptools simphenotype' takes as input a VCF file and outputs simulated phenotypes for each sample. +* [`haptools simphenotype`](haptools/simphenotype/README.md): Simulate a complex trait, taking into account local ancestry- or haplotype- specific effects. `haptools simphenotype` takes as input a VCF file and outputs simulated phenotypes for each sample. * [`haptools karyogram`](haptools/karyogram/README.md): Visualize a "chromosome painting" of local ancestry labels based on breakpoints output by `haptools simgenome`. +Outputs produced by these utilities are compatible with each other. For example +`haptools simgenome` outputs a VCF file with local ancestry information annotated for each variant. The output VCF file can be used as input to `haptools simphenotype` to simulate phenotype information. `haptools simgenome` also outputs a list of local ancestry breakpoints which can be visualized using `haptools karyogram`. + ## Contributing From f65204b62005b2e0a42009e4591341177a9c6dd6 Mon Sep 17 00:00:00 2001 From: Melissa Gymrek Date: Mon, 14 Feb 2022 13:54:01 -0800 Subject: [PATCH 014/197] adding simgenotypes readme --- haptools/simgenotype/README.md | 21 ++++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/haptools/simgenotype/README.md b/haptools/simgenotype/README.md index 48c1369c..82631756 100644 --- a/haptools/simgenotype/README.md +++ b/haptools/simgenotype/README.md @@ -1,3 +1,22 @@ # Haptools simgenotype -UNDER CONSTRUCTION \ No newline at end of file +`haptools simgenotype` takes as input a reference set of haplotypes in VCF format and a user-specified admixture model. It outputs a VCF file with simulated genotype information for admixed genotypes, as well as a breakpoints file that can be used for visualization. + +## Basic usage + +``` +haptools simgenotype \ + --invcf REFVCF \ + --sample_info SAMPLEINFOFILE \ + --model MODELFILE \ + --map GENETICMAP \ + --out OUTPREFIX +``` + +Detailed information about each option, and example commands using publicly available files, are shown below. + +## Detailed usage + +## File formats + +## Examples \ No newline at end of file From 06fcaaaa2fdc9ae2078754803e59ebd764f027eb Mon Sep 17 00:00:00 2001 From: Michael Lamkin Date: Mon, 14 Feb 2022 22:43:57 -0800 Subject: [PATCH 015/197] Update README.md --- haptools/simgenotype/README.md | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/haptools/simgenotype/README.md b/haptools/simgenotype/README.md index 82631756..03e91667 100644 --- a/haptools/simgenotype/README.md +++ b/haptools/simgenotype/README.md @@ -19,4 +19,15 @@ Detailed information about each option, and example commands using publicly avai ## File formats -## Examples \ No newline at end of file +Model Format +``` +{num_samples} Admixed Pop_label1 Pop_label2 +{num_generations} {admixed_freq} {pop_label1_freq} {pop_label2_freq} +``` + +Map Format + +Outfile Format + + +## Examples From e41bfeedb309fef43bf8a4a0c642fce4784e2a9c Mon Sep 17 00:00:00 2001 From: Michael Lamkin Date: Mon, 14 Feb 2022 22:46:44 -0800 Subject: [PATCH 016/197] Update README.md --- haptools/simgenotype/README.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/haptools/simgenotype/README.md b/haptools/simgenotype/README.md index 03e91667..7197eaa4 100644 --- a/haptools/simgenotype/README.md +++ b/haptools/simgenotype/README.md @@ -20,9 +20,18 @@ Detailed information about each option, and example commands using publicly avai ## File formats Model Format + +Structure of model.dat file +``` +{num_samples} Admixed Pop_label1 Pop_label2 ... Pop_labeln +{num_generations} {admixed_freq} {pop_label1_freq} {pop_label2_freq} ... {pop_labeln_freq} +``` + +Example model.dat file + ``` -{num_samples} Admixed Pop_label1 Pop_label2 -{num_generations} {admixed_freq} {pop_label1_freq} {pop_label2_freq} +40 Admixed CEU YRI +6 0 0.2 0.8 ``` Map Format From 058b0e89b4ee0b362ea173870ecb5dee3288f772 Mon Sep 17 00:00:00 2001 From: Michael Lamkin Date: Mon, 14 Feb 2022 23:01:29 -0800 Subject: [PATCH 017/197] Update README.md --- haptools/simgenotype/README.md | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/haptools/simgenotype/README.md b/haptools/simgenotype/README.md index 7197eaa4..7708ba50 100644 --- a/haptools/simgenotype/README.md +++ b/haptools/simgenotype/README.md @@ -22,6 +22,11 @@ Detailed information about each option, and example commands using publicly avai Model Format Structure of model.dat file + +`num_samples` - Total number of samples to be output by the simulator (`num_samples*2` haplotypes) +`num_generations` - Number of generations to simulate admixture, must be > 0 +`*_freq` - Frequency of populations to be present in the simulated samples + ``` {num_samples} Admixed Pop_label1 Pop_label2 ... Pop_labeln {num_generations} {admixed_freq} {pop_label1_freq} {pop_label2_freq} ... {pop_labeln_freq} @@ -33,10 +38,35 @@ Example model.dat file 40 Admixed CEU YRI 6 0 0.2 0.8 ``` +Simulating 6 generations in this case implies the first generation has population freqs `Admixed=0, CEU=0.2, YRI=0.8` and the remaining 2-6 generations have population frequency `Admixed=1, CEU=0, YRI=0` Map Format +chr - chromosome of coordinate (1-22, X) +var - variant identifier +pos cM - Position in centimorgans +pos bp - Base pair coordinate + +``` +{chr}\t{var}\t{pos cM}\t{pos bp} +``` +Beagle Genetic Maps used in simulation (GRCh38): http://bochet.gcc.biostat.washington.edu/beagle/genetic_maps/ + + Outfile Format +Sample Header - Name of sample following the structure `Sample_{number}_{hap}` eg. `Sample_10_1` for sample number 10 haplotype +pop - Population label corresponding to the index of the population in the dat file so in the example above CEU = 1, YRI = 2 +chr - chromosome (1-22, X) + +``` +Sample Header +{pop}\t{chr}\t{pos bp} +... +Sample Header 2 +... +``` ## Examples + +Example Command From 2c99a25b18915a9c36b14d80b82605305e929953 Mon Sep 17 00:00:00 2001 From: Michael Lamkin Date: Mon, 14 Feb 2022 23:12:34 -0800 Subject: [PATCH 018/197] Update README.md --- haptools/simgenotype/README.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/haptools/simgenotype/README.md b/haptools/simgenotype/README.md index 7708ba50..bf46ba71 100644 --- a/haptools/simgenotype/README.md +++ b/haptools/simgenotype/README.md @@ -17,6 +17,12 @@ Detailed information about each option, and example commands using publicly avai ## Detailed usage +`--invcf` - Input VCF file used to simulate specifiic haplotypes for resulting samples +`--sample_info` - File used to map samples in `REFVCF` to populations found in `MODELFILE` +`--model` - Parameters for simulating admixture across generations +`--map` - .map file used to determine recombination events during the simulation +`--out` - Output prefix of the structure `/path/to/output` which results in the vcf file `output.vcf.gz` and breakpoints file `output.bp` + ## File formats Model Format @@ -43,7 +49,7 @@ Simulating 6 generations in this case implies the first generation has populatio Map Format chr - chromosome of coordinate (1-22, X) -var - variant identifier +var - variant identifier pos cM - Position in centimorgans pos bp - Base pair coordinate @@ -56,7 +62,7 @@ Beagle Genetic Maps used in simulation (GRCh38): http://bochet.gcc.biostat.washi Outfile Format Sample Header - Name of sample following the structure `Sample_{number}_{hap}` eg. `Sample_10_1` for sample number 10 haplotype -pop - Population label corresponding to the index of the population in the dat file so in the example above CEU = 1, YRI = 2 +pop - Population label corresponding to the index of the population in the dat file so in the example above CEU = 1, YRI = 2 chr - chromosome (1-22, X) ``` @@ -70,3 +76,11 @@ Sample Header 2 ## Examples Example Command +``` +haptools simgenotype + --invcf 1000Genomes.vcf.gz \ + --sample_info /path/to/sampleinfo.csv \ + --model /path/to/model/file.dat \ + --map /path/to/plink/file/ \ + --out /path/to/output +``` From ec872bbf5a1b77c9f696d250fcfb01247a12e25d Mon Sep 17 00:00:00 2001 From: Michael Lamkin Date: Mon, 14 Feb 2022 23:13:14 -0800 Subject: [PATCH 019/197] Update README.md --- haptools/simgenotype/README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/haptools/simgenotype/README.md b/haptools/simgenotype/README.md index bf46ba71..3117adb5 100644 --- a/haptools/simgenotype/README.md +++ b/haptools/simgenotype/README.md @@ -48,10 +48,10 @@ Simulating 6 generations in this case implies the first generation has populatio Map Format -chr - chromosome of coordinate (1-22, X) -var - variant identifier -pos cM - Position in centimorgans -pos bp - Base pair coordinate +`chr` - chromosome of coordinate (1-22, X) +`var` - variant identifier +`pos cM` - Position in centimorgans +`pos bp` - Base pair coordinate ``` {chr}\t{var}\t{pos cM}\t{pos bp} @@ -61,9 +61,9 @@ Beagle Genetic Maps used in simulation (GRCh38): http://bochet.gcc.biostat.washi Outfile Format -Sample Header - Name of sample following the structure `Sample_{number}_{hap}` eg. `Sample_10_1` for sample number 10 haplotype -pop - Population label corresponding to the index of the population in the dat file so in the example above CEU = 1, YRI = 2 -chr - chromosome (1-22, X) +`Sample Header` - Name of sample following the structure `Sample_{number}_{hap}` eg. `Sample_10_1` for sample number 10 haplotype +`pop` - Population label corresponding to the index of the population in the dat file so in the example above CEU = 1, YRI = 2 +`chr` - chromosome (1-22, X) ``` Sample Header From 94fa0fef55f28bea6325c1da66307ab21759a5b9 Mon Sep 17 00:00:00 2001 From: Michael Lamkin Date: Mon, 14 Feb 2022 23:13:57 -0800 Subject: [PATCH 020/197] Update README.md --- haptools/simgenotype/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/haptools/simgenotype/README.md b/haptools/simgenotype/README.md index 3117adb5..9f60b60d 100644 --- a/haptools/simgenotype/README.md +++ b/haptools/simgenotype/README.md @@ -61,7 +61,7 @@ Beagle Genetic Maps used in simulation (GRCh38): http://bochet.gcc.biostat.washi Outfile Format -`Sample Header` - Name of sample following the structure `Sample_{number}_{hap}` eg. `Sample_10_1` for sample number 10 haplotype +`Sample Header` - Name of sample following the structure `Sample_{number}_{hap}` eg. `Sample_10_1` for sample number 10 haplotype 1 `pop` - Population label corresponding to the index of the population in the dat file so in the example above CEU = 1, YRI = 2 `chr` - chromosome (1-22, X) From 3f8f66240f514e83cffdafe341d0993020b1d962 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Sat, 26 Feb 2022 18:11:41 -0800 Subject: [PATCH 021/197] update file locations and fixed cM -> M --- haptools/__main__.py | 18 +++++++- .../{visualization => karyogram}/karyogram.py | 27 +++++++----- .../{visualization => karyogram}/to_remove.py | 7 +-- .../admix_storage.py | 9 +++- .../sim_admixture.py | 43 ++++++++++--------- 5 files changed, 63 insertions(+), 41 deletions(-) rename haptools/{visualization => karyogram}/karyogram.py (92%) rename haptools/{visualization => karyogram}/to_remove.py (56%) rename haptools/{simulate => simgenotype}/admix_storage.py (78%) rename haptools/{simulate => simgenotype}/sim_admixture.py (87%) diff --git a/haptools/__main__.py b/haptools/__main__.py index ac7055cc..6d3a732f 100644 --- a/haptools/__main__.py +++ b/haptools/__main__.py @@ -8,7 +8,8 @@ #from . import data, tree sys.path.append('./admixture_sim') -from .simulate.sim_admixture import simulate_gt, write_breakpoints +from .simgenotype.sim_admixture import simulate_gt, write_breakpoints +from .karyogram.karyogram import plot_karyogram @click.group() @@ -27,13 +28,26 @@ def main(): @click.option('--model', required=True) @click.option('--map', 'recombmap', required=True) @click.option('--out', required=True) -def simulate(invcf, sample_info, model, recombmap, out): +def simgenotype(invcf, sample_info, model, recombmap, out): """ Use the tool to simulate genotypes """ samples, breakpoints = simulate_gt(model, recombmap) write_breakpoints(samples, breakpoints, out) +@main.command() +@click.option('--sample_name', type=str) +@click.option('--chrX', default=False, type=bool) +@click.option('--sample_file', required=True) +@click.option('--title', required=True) +@click.option('--centromeres', required=True) +@click.option('--out', required=True) +def karyogram(sample_name, chrx, sample_file, title, centromeres, out): + """ + Use the tool to visualize breakpoints. + """ + plot_karyogram(sample_file, title, centromeres, out) + #plot_karyogram(sample_file, title, centromeres, out, sample_name="Sample_1" chrX=False, colors=None) if __name__ == "__main__": # run the CLI if someone tries 'python -m haptools' on the command line diff --git a/haptools/visualization/karyogram.py b/haptools/karyogram/karyogram.py similarity index 92% rename from haptools/visualization/karyogram.py rename to haptools/karyogram/karyogram.py index d503e55e..742a6b91 100644 --- a/haptools/visualization/karyogram.py +++ b/haptools/karyogram/karyogram.py @@ -14,22 +14,24 @@ def splitstr(option, opt, value, parser): return(setattr(parser.values, option.dest, value.split(','))) # TODO determine whether its better to plot bp or cM and about updating output so it outputs both -def plot_karyogram(sample_name, sample_file, title, chrX, centromeres, pop_order, colors, out): +# TODO update so we plot in cM because plots are much cleaner +# TODO figure out centromere +def plot_karyogram(sample_file, title, centromeres, out, sample_name="Sample_1", chrX=False, colors=None): """ Arguments - sample_name - sample to plot on karyogram sample_file - contains all samples, chrom, pop, and hap block location title - plot title - chrX - include chromosome X? centromeres - centromeres location file - pop_order - comma-separated list of population labels in the order of rfmix populations - colors - colors for respective populations out - output file to save figure - """ + sample_name - sample to plot on karyogram + chrX - include chromosome X? + colors - colors for respective populations + """ #read in bed files and get individual name samples = [] sample = [] + pop_order = [] plot_sample = False with open(sample_file,'r') as sample_file: @@ -54,23 +56,26 @@ def plot_karyogram(sample_name, sample_file, title, chrX, centromeres, pop_order break if plot_sample: + if not line[0] in pop_order: + pop_order.append(line[0]) + if not sample: - start = 1 + start = 0.0001 else: - start = sample[-1]['end'] + 1 + start = sample[-1]['end'] + 0.0001 hap_block = {'pop': line[0], 'chrom': int(line[1]), - 'start': start, 'end': int(line[2])} + 'start': start, 'end': float(line[-1])} sample.append(hap_block) #define plotting space fig = plt.figure() ax = fig.add_subplot(111) - ax.set_xlim(-5,100000000) + ax.set_xlim(-5,300) if chrX: ax.set_ylim(24,0) else: ax.set_ylim(23,0) - plt.xlabel('Genetic position (bp)') + plt.xlabel('Genetic position (cM)') plt.ylabel('Chromosome') plt.title(title) if chrX: diff --git a/haptools/visualization/to_remove.py b/haptools/karyogram/to_remove.py similarity index 56% rename from haptools/visualization/to_remove.py rename to haptools/karyogram/to_remove.py index 01758cc8..73fd4ed8 100644 --- a/haptools/visualization/to_remove.py +++ b/haptools/karyogram/to_remove.py @@ -1,16 +1,11 @@ from karyogram import plot_karyogram def main(): - sample_name = "Sample_1" sample_file = "/storage/mlamkin/data/simwas/ten_gen.bp" title = "Ten Generations" - chrX = False centromeres = [] - pop_order = ['1','2'] - colors = [] out = "/storage/mlamkin/data/simwas/ten_gen.png" - plot_karyogram(sample_name, sample_file, title, chrX, - centromeres, pop_order, colors, out) + plot_karyogram(sample_file, title, centromeres, out) return if __name__ == '__main__': diff --git a/haptools/simulate/admix_storage.py b/haptools/simgenotype/admix_storage.py similarity index 78% rename from haptools/simulate/admix_storage.py rename to haptools/simgenotype/admix_storage.py index 9d422531..d13b55d0 100644 --- a/haptools/simulate/admix_storage.py +++ b/haptools/simgenotype/admix_storage.py @@ -17,18 +17,23 @@ def get_bp_pos(self): # stored segment lengths that allow us to recover population at marker locations class HaplotypeSegment: - def __init__(self, pop_num, chrom, end_coord): + def __init__(self, pop_num, chrom, end_coord, end_pos): """ Note the beginning of the segment is inferred based on previous segments stored throughout the simulation process. Arguments pop_num - population label chrom - chromosome the haplotype segment lies on. - end_coord - Ending coordinate of the haplotype segment + end_coord - Ending coordinate in bp of the haplotype segment + end_pos - Ending coordinate in centimorgans of the hap segment """ self.pop_num = pop_num self.chrom = chrom self.end_coord = end_coord + self.end_pos = end_pos + + def get_end_pos(self): + return self.end_pos def get_end_coord(self): return self.end_coord diff --git a/haptools/simulate/sim_admixture.py b/haptools/simgenotype/sim_admixture.py similarity index 87% rename from haptools/simulate/sim_admixture.py rename to haptools/simgenotype/sim_admixture.py index 3d9e1635..87ec0ac0 100644 --- a/haptools/simulate/sim_admixture.py +++ b/haptools/simgenotype/sim_admixture.py @@ -30,7 +30,7 @@ def simulate_gt(model_file, coords_file, seed=None): # load population samples and labels to be simulated mfile = open(model_file, 'r') - num_samples, not_used, *pops = mfile.readline().strip().split() + num_samples, *pops = mfile.readline().strip().split() num_samples = int(num_samples) # coord file structure chr variant cMcoord bpcoord @@ -43,7 +43,7 @@ def simulate_gt(model_file, coords_file, seed=None): coords.append(gen_mark) # number of haplotypes simulated per generation - haps_per_gen = max(5000, 20 * num_samples) + haps_per_gen = max(100, 20 * num_samples) # TODO change back to 10000 # starting generation is 0 prev_gen = 0 @@ -65,13 +65,13 @@ def simulate_gt(model_file, coords_file, seed=None): # sim generation print(f"Simulating generation {prev_gen+1}") - next_gen_samples = _simulate(haps_per_gen, pop_fracs, prev_gen, coords, next_gen_samples) + next_gen_samples = _simulate(haps_per_gen, pops, pop_fracs, prev_gen, coords, next_gen_samples) # simulate remaining generations for i in range(1, sim_gens): print(f"Simulating generation {prev_gen+i+1}") # simulate next generations using previous generations to sample from for admixture - next_gen_samples = _simulate(haps_per_gen, pop_fracs, prev_gen+i, coords, next_gen_samples) + next_gen_samples = _simulate(haps_per_gen, pops, pop_fracs, prev_gen+i, coords, next_gen_samples) prev_gen = cur_gen @@ -86,9 +86,10 @@ def write_breakpoints(samples, breakpoints, out): print(f"Outputting breakpoint file {breakpt_file}") # randomly sample breakpoints to get the correct amount of samples to output + breakpoints = np.array(breakpoints, dtype=object) breakpoints = np.random.choice(breakpoints, size=2*samples, replace=False) - with open(breakpt_file, 'a') as output: + with open(breakpt_file, 'w') as output: for ind, sample in enumerate(breakpoints): # Get sample number and haplotype number haplotype = (ind)%2 + 1 @@ -103,19 +104,19 @@ def write_breakpoints(samples, breakpoints, out): pop = segment.get_pop() chrom = segment.get_chrom() end_coord = segment.get_end_coord() - output.write(f"{pop}\t{chrom}\t{end_coord}\n") + end_pos = segment.get_end_pos() + output.write(f"{pop}\t{chrom}\t{end_coord}\t{end_pos}\n") return -def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): +def _simulate(samples, pops, pop_fracs, pop_gen, coords, prev_gen_samples=None): # generate all samples hap_samples = [] # pre compute haplotypes and parent population - # if there is no previous generation randomly choose population based on frac + # if there is no previous generation randomly choose population based on frac] parent_pop = np.zeros(samples) if not prev_gen_samples: - parent_pop = np.random.choice(np.arange(len(pop_fracs)), - size=samples, p=pop_fracs) + parent_pop = np.random.choice(pops, size=samples, p=pop_fracs) # choose a haplotype to copy # if previous generation of samples randomize two haplotypes @@ -166,7 +167,8 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): if cur_chrom != prev_chrom: # Store haplotype segments.extend(get_segment(p_pop, haps[homolog], prev_chrom, - start_bp, end_bp, prev_gen_samples)) + start_bp, end_bp, prev_map_pos, + prev_gen_samples)) # update homolog, previous chrom, and previous position prev_chrom = cur_chrom @@ -175,12 +177,14 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): continue # check if a recombination event occurs and if so write segment and swap homolog + # NOTE cur_map_pos and prev_map_pos are in cM and we want M for calculating prob dist = cur_map_pos - prev_map_pos - recomb_prob = 1-np.exp(-dist) + recomb_prob = 1-np.exp(-dist/100) if prob_vals[sample*samples+cind] < recomb_prob: # Store haplotype segments switching homologs segments.extend(get_segment(p_pop, haps[homolog], prev_chrom, - start_bp, end_bp, prev_gen_samples)) + start_bp, end_bp, prev_map_pos, + prev_gen_samples)) homolog = 1-homolog # update positions and chromosome @@ -191,13 +195,14 @@ def _simulate(samples, pop_fracs, pop_gen, coords, prev_gen_samples=None): # Record remaining segments if segments: start_bp = segments[-1].get_end_coord() + 1 segments.extend(get_segment(p_pop, haps[homolog], prev_chrom, - start_bp, end_bp, prev_gen_samples)) + start_bp, end_bp, prev_map_pos, + prev_gen_samples)) # append segments of 1 sample hap_samples.append(segments) return hap_samples -def get_segment(pop, haplotype, chrom, start_coord, end_coord, prev_gen_samples): +def get_segment(pop, haplotype, chrom, start_coord, end_coord, end_pos, prev_gen_samples): """ Create a segment or segments for an individual of the current generation using either a population label (>0) or the previous generation's samples if @@ -210,6 +215,7 @@ def get_segment(pop, haplotype, chrom, start_coord, end_coord, prev_gen_samples) start_coord - starting coordinate from where to begin taking segments from previous generation samples end_coord - ending coordinate of haplotype segment + end_pos - ending coordinate in centimorgans prev_gen_samples - the previous generation simulated used as the parents for the current generation Returns @@ -217,7 +223,7 @@ def get_segment(pop, haplotype, chrom, start_coord, end_coord, prev_gen_samples) """ # Take from population data not admixed data if pop: - return [HaplotypeSegment(pop, chrom, end_coord)] + return [HaplotypeSegment(pop, chrom, end_coord, end_pos)] # Take from previous admixed data else: @@ -241,7 +247,7 @@ def get_segment(pop, haplotype, chrom, start_coord, end_coord, prev_gen_samples) out_pop = segments[-1].get_pop() # Append last segment using previous segments population - segments.append(HaplotypeSegment(out_pop, chrom, end_coord)) + segments.append(HaplotypeSegment(out_pop, chrom, end_coord, end_pos)) return segments def start_segment(start, chrom, segments): @@ -292,6 +298,3 @@ def start_segment(start, chrom, segments): return len(segments) - -if __name__ == '__main__': - test() From 453075a60b68daaeed6d1bb721f36d8a8a6a8fb5 Mon Sep 17 00:00:00 2001 From: mlamkin7 Date: Mon, 28 Feb 2022 23:48:10 -0800 Subject: [PATCH 022/197] removed snakemake --- haptools/__main__.py | 6 +- haptools/simgenotype/admix_storage.py | 6 +- haptools/simgenotype/sim_admixture.py | 188 ++++-- snakemake/README.md | 46 -- snakemake/config/README.md | 33 - snakemake/config/config.yml | 74 --- snakemake/log | 1 - snakemake/run.bash | 71 -- snakemake/workflow/Snakefile | 224 ------- snakemake/workflow/envs/default.yml | 14 - snakemake/workflow/envs/shapeit.yml | 5 - snakemake/workflow/envs/susie.yml | 15 - .../notebooks/run_finemap-susie.ipynb | 622 ------------------ .../run_finemap-susie_vignette.ipynb | 488 -------------- snakemake/workflow/scripts/README.md | 25 - .../workflow/scripts/finemapping_methods.R | 84 --- .../workflow/scripts/generate_phenotypes.py | 104 --- snakemake/workflow/scripts/gt_matrix.py | 128 ---- snakemake/workflow/scripts/ld_heatmap.py | 56 -- snakemake/workflow/scripts/matrix.bash | 32 - snakemake/workflow/scripts/plot_phenotypes.py | 41 -- .../workflow/scripts/summarize_results.R | 112 ---- 22 files changed, 127 insertions(+), 2248 deletions(-) delete mode 100644 snakemake/README.md delete mode 100644 snakemake/config/README.md delete mode 100644 snakemake/config/config.yml delete mode 120000 snakemake/log delete mode 100755 snakemake/run.bash delete mode 100644 snakemake/workflow/Snakefile delete mode 100644 snakemake/workflow/envs/default.yml delete mode 100644 snakemake/workflow/envs/shapeit.yml delete mode 100644 snakemake/workflow/envs/susie.yml delete mode 100644 snakemake/workflow/notebooks/run_finemap-susie.ipynb delete mode 100644 snakemake/workflow/notebooks/run_finemap-susie_vignette.ipynb delete mode 100644 snakemake/workflow/scripts/README.md delete mode 100755 snakemake/workflow/scripts/finemapping_methods.R delete mode 100755 snakemake/workflow/scripts/generate_phenotypes.py delete mode 100755 snakemake/workflow/scripts/gt_matrix.py delete mode 100755 snakemake/workflow/scripts/ld_heatmap.py delete mode 100755 snakemake/workflow/scripts/matrix.bash delete mode 100755 snakemake/workflow/scripts/plot_phenotypes.py delete mode 100755 snakemake/workflow/scripts/summarize_results.R diff --git a/haptools/__main__.py b/haptools/__main__.py index 6d3a732f..695a90da 100644 --- a/haptools/__main__.py +++ b/haptools/__main__.py @@ -26,13 +26,13 @@ def main(): @click.option('--invcf') @click.option('--sample_info') @click.option('--model', required=True) -@click.option('--map', 'recombmap', required=True) +@click.option('--mapdir', required=True) @click.option('--out', required=True) -def simgenotype(invcf, sample_info, model, recombmap, out): +def simgenotype(invcf, sample_info, model, mapdir, out): """ Use the tool to simulate genotypes """ - samples, breakpoints = simulate_gt(model, recombmap) + samples, breakpoints = simulate_gt(model, mapdir) write_breakpoints(samples, breakpoints, out) @main.command() diff --git a/haptools/simgenotype/admix_storage.py b/haptools/simgenotype/admix_storage.py index d13b55d0..f1654651 100644 --- a/haptools/simgenotype/admix_storage.py +++ b/haptools/simgenotype/admix_storage.py @@ -1,9 +1,10 @@ # Genetic markers that allow us to determine recombination break points class GeneticMarker: - def __init__(self, chrom, cm_map_pos, bp_map_pos): + def __init__(self, chrom, cm_map_pos, bp_map_pos, prev_coord): self.chrom = chrom self.cm_map_pos = cm_map_pos self.bp_map_pos = bp_map_pos + self.prev_coord = prev_coord def get_chrom(self): return self.chrom @@ -14,6 +15,9 @@ def get_map_pos(self): def get_bp_pos(self): return self.bp_map_pos + def get_prev_coord(self): + return self.prev_coord + # stored segment lengths that allow us to recover population at marker locations class HaplotypeSegment: diff --git a/haptools/simgenotype/sim_admixture.py b/haptools/simgenotype/sim_admixture.py index 87ec0ac0..4622d2cc 100644 --- a/haptools/simgenotype/sim_admixture.py +++ b/haptools/simgenotype/sim_admixture.py @@ -1,3 +1,5 @@ +import glob +import re import numpy as np import time from .admix_storage import GeneticMarker, HaplotypeSegment @@ -5,7 +7,7 @@ # TODO at a certain point we are going to need to ensure populations in model file are also in invcf files # This is only required for outputting the haplotypes in a vcf -def simulate_gt(model_file, coords_file, seed=None): +def simulate_gt(model_file, coords_dir, seed=None): """ Simulate admixed genotypes based on the parameters of model_file. Arguments @@ -16,8 +18,9 @@ def simulate_gt(model_file, coords_file, seed=None): ex: 40 Admixed CEU YRI 1 0 0.05 0.95 2 0.20 0.05 0.75 - coords_file - File containing genetic map coords in cM used for recombination points + coords_dir + Directory containing files ending in .map with genetic map coords + in cM used for recombination points seed Seed used for randomization. Return @@ -34,16 +37,57 @@ def simulate_gt(model_file, coords_file, seed=None): num_samples = int(num_samples) # coord file structure chr variant cMcoord bpcoord + # NOTE coord files in directory should have chr{1-22, X} in the name coords = [] - with open(coords_file, 'r') as cfile: - for line in cfile: - # create marker from each line and append to coords - data = line.strip().split() - gen_mark = GeneticMarker(int(data[0]), float(data[2]), int(data[3])) - coords.append(gen_mark) + + def numeric_alpha(x): + chrom = re.search(r'(?<=chr)X|\d+', x).group() + if chrom == 'X': + return 23 + else: + return int(chrom) + + # sort coordinate files to ensure coords read are in sorted order + all_coord_files = glob.glob(f'{coords_dir}/*.map') + all_coord_files.sort(key=numeric_alpha) + + # coords list has form chroms x coords + for coords_file in all_coord_files: + file_coords = [] + with open(coords_file, 'r') as cfile: + prev_coord = None + for line in cfile: + # create marker from each line and append to coords + data = line.strip().split() + gen_mark = GeneticMarker(data[0], float(data[2]), + int(data[3]), prev_coord) + prev_coord = gen_mark + file_coords.append(gen_mark) + coords.append(file_coords) + + # store end coords + end_coords = [chrom_coord[-1] for chrom_coord in coords] + + # convert coords to numpy array for easy masking + max_coords = max([len(chrom_coord) for chrom_coord in coords]) + np_coords = np.zeros((len(coords), max_coords)).astype(object) + + # precalculate recombination probabilities (given map pos in cM and we want M) + recomb_probs = -1*np.ones((len(coords), max_coords)) + for chrom, chrom_coords in enumerate(coords): + prev_map_pos = chrom_coords[0].get_map_pos() + np_coords[chrom,:len(chrom_coords)] = chrom_coords[:] + for cind, coord in enumerate(chrom_coords): + # get current position + cur_map_pos = coord.get_map_pos() + dist = cur_map_pos - prev_map_pos + recomb_probs[chrom,cind] = 1-np.exp(-dist/100) + prev_map_pos = cur_map_pos + + coords = np_coords # number of haplotypes simulated per generation - haps_per_gen = max(100, 20 * num_samples) # TODO change back to 10000 + haps_per_gen = max(10000, 20 * num_samples) # starting generation is 0 prev_gen = 0 @@ -65,13 +109,15 @@ def simulate_gt(model_file, coords_file, seed=None): # sim generation print(f"Simulating generation {prev_gen+1}") - next_gen_samples = _simulate(haps_per_gen, pops, pop_fracs, prev_gen, coords, next_gen_samples) + next_gen_samples = _simulate(haps_per_gen, pops, pop_fracs, prev_gen, + coords, end_coords, recomb_probs, next_gen_samples) # simulate remaining generations for i in range(1, sim_gens): print(f"Simulating generation {prev_gen+i+1}") # simulate next generations using previous generations to sample from for admixture - next_gen_samples = _simulate(haps_per_gen, pops, pop_fracs, prev_gen+i, coords, next_gen_samples) + next_gen_samples = _simulate(haps_per_gen, pops, pop_fracs, prev_gen+i, + coords, end_coords, recomb_probs, next_gen_samples) prev_gen = cur_gen @@ -81,10 +127,16 @@ def simulate_gt(model_file, coords_file, seed=None): mfile.close() return num_samples, next_gen_samples +# TODO +def haplotype_distribution(breakpoints, out): + return + def write_breakpoints(samples, breakpoints, out): breakpt_file = out + '.bp' print(f"Outputting breakpoint file {breakpt_file}") + # TODO get distribution of breakpoint sizes + # randomly sample breakpoints to get the correct amount of samples to output breakpoints = np.array(breakpoints, dtype=object) breakpoints = np.random.choice(breakpoints, size=2*samples, replace=False) @@ -108,7 +160,7 @@ def write_breakpoints(samples, breakpoints, out): output.write(f"{pop}\t{chrom}\t{end_coord}\t{end_pos}\n") return -def _simulate(samples, pops, pop_fracs, pop_gen, coords, prev_gen_samples=None): +def _simulate(samples, pops, pop_fracs, pop_gen, coords, end_coords, recomb_probs, prev_gen_samples=None): # generate all samples hap_samples = [] @@ -131,74 +183,67 @@ def _simulate(samples, pops, pop_fracs, pop_gen, coords, prev_gen_samples=None): while haplotypes[2*hap] == haplotypes[2*hap+1]: haplotypes[2*hap+1] = np.random.randint(samples) - # store all probabilities to compare for recombination - prob_vals = np.random.rand(samples*len(coords)) - # generate all samples for sample in range(samples): segments = [] - prev_chrom = -1 - prev_map_pos = 0.0 - start_bp = 0 - end_bp = 0 p_pop = parent_pop[sample] - haps = haplotypes[2*sample:2*sample+2] - - # haplotypes only matters when pop == 0 otherwise we are sampling from a population which has a label - # choose our starting chromosome homolog = np.random.randint(2) + haps = haplotypes[2*sample:2*sample+2] - # iterate over all coords in the coords file - for cind, coord in enumerate(coords): - # get current chromosome and position + # store all probabilities to compare for recombination + prob_vals = np.random.rand(recomb_probs.shape[0], + recomb_probs.shape[1]) + recomb_events = prob_vals < recomb_probs + true_coords = coords[recomb_events] + + # TODO fix so we only fill chroms present in our coords files + prev_chrom = "1" + for coord in true_coords: + # information to generate segments + prev_coord = coord.get_prev_coord() cur_chrom = coord.get_chrom() - cur_map_pos = coord.get_map_pos() - # start of new segment used for storing previous generation samples - if segments: - start_bp = segments[-1].get_end_coord() + 1 - - # first segment so update chrom and starting map pos - if prev_chrom < 0: - prev_chrom = cur_chrom - prev_map_pos = cur_map_pos + if segments and segments[-1].get_chrom() == cur_chrom: + start_bp = segments[-1].get_end_coord()+1 + else: + start_bp = 0 - # Ended chromosome write segment + # swapping chroms so store segments for each chrom we miss in between swap if cur_chrom != prev_chrom: - # Store haplotype - segments.extend(get_segment(p_pop, haps[homolog], prev_chrom, - start_bp, end_bp, prev_map_pos, - prev_gen_samples)) - - # update homolog, previous chrom, and previous position - prev_chrom = cur_chrom - homolog = np.random.randint(2) - prev_map_pos = cur_map_pos - continue - - # check if a recombination event occurs and if so write segment and swap homolog - # NOTE cur_map_pos and prev_map_pos are in cM and we want M for calculating prob - dist = cur_map_pos - prev_map_pos - recomb_prob = 1-np.exp(-dist/100) - if prob_vals[sample*samples+cind] < recomb_prob: - # Store haplotype segments switching homologs - segments.extend(get_segment(p_pop, haps[homolog], prev_chrom, - start_bp, end_bp, prev_map_pos, - prev_gen_samples)) - homolog = 1-homolog - - # update positions and chromosome - prev_map_pos = cur_map_pos - prev_chrom = cur_chrom - end_bp = coord.get_bp_pos() + # check if we've output the end of the prev chrom + if segments and segments[-1].get_end_coord() == end_coords[int(prev_chrom)-1].get_bp_pos(): + prev_chrom = int(prev_chrom) + 1 - # Record remaining segments - if segments: start_bp = segments[-1].get_end_coord() + 1 - segments.extend(get_segment(p_pop, haps[homolog], prev_chrom, + # figure out how many chroms in between current and next chrom + if cur_chrom == 'X': + cur_chrom = 23 + + for i in range(int(cur_chrom) - int(prev_chrom)): + # end_bp = end of chromosome + end_bp = end_coords[int(prev_chrom)+i-1].get_bp_pos() + prev_map_pos = end_coords[int(prev_chrom)+i-1].get_map_pos() + + # output segments of prev_chrom+i + segments.extend(get_segment(p_pop, haps[homolog], int(prev_chrom)+i, start_bp, end_bp, prev_map_pos, prev_gen_samples)) + + # change homolog + homolog = np.random.randint(2) + start_bp = 0 + + prev_chrom = cur_chrom + + # get end bp coord and prev map pos since it updates inside swapping chrom + end_bp = prev_coord.get_bp_pos() + prev_map_pos = prev_coord.get_map_pos() + + # Store haplotype segments switching homologs + segments.extend(get_segment(p_pop, haps[homolog], cur_chrom, + start_bp, end_bp, prev_map_pos, + prev_gen_samples)) + homolog = 1-homolog - # append segments of 1 sample hap_samples.append(segments) return hap_samples @@ -254,6 +299,11 @@ def start_segment(start, chrom, segments): """ Find first segment that is on chrom and its end coordinate is > start via binary search. """ + chromtoind = {'1':0, '2':1, '3':2, '4':3, '5':4, + '6':5, '7':6, '8':7, '9':8, '10':9, + '11':10, '12':11, '13':12, '14':13, '15':14, + '16':15, '17':16, '18':17, '19':18, '20':19, + '21':20, '22':21, 'X':22} low = 0 high = len(segments)-1 mid = 0 @@ -279,7 +329,7 @@ def start_segment(start, chrom, segments): low = mid + 1 elif cur_coord >= start: - if prev_chrom < cur_chrom: + if chromtoind[prev_chrom] < chromtoind[cur_chrom]: return mid if prev_chrom == cur_chrom and prev_coord < start: @@ -290,7 +340,7 @@ def start_segment(start, chrom, segments): else: return len(segments) - elif chrom < segments[mid].get_chrom(): + elif chromtoind[chrom] < chromtoind[segments[mid].get_chrom()]: high = mid - 1 else: diff --git a/snakemake/README.md b/snakemake/README.md deleted file mode 100644 index fe285747..00000000 --- a/snakemake/README.md +++ /dev/null @@ -1,46 +0,0 @@ -# download -Execute the following command. -``` -git clone https://github.com/aryarm/simulate_gwas -``` -You can also download example data for the pipeline. See [the config file](config/config.yml) for links and instructions. - -# setup -The pipeline is written as a Snakefile which can be executed via [Snakemake](https://snakemake.readthedocs.io). For reproduciblity, we recommend installing the version that we used (6.7.0): -``` -conda create -n snakemake -c conda-forge --no-channel-priority 'bioconda::snakemake==6.7.0' -``` -`snakemake` will [automatically install all dependencies](https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#integrated-package-management) of the pipeline upon its first execution using `conda`. - -# execution -1. Activate snakemake via `conda`: - ``` - conda activate snakemake - ``` -2. Execute the pipeline on the example data - - Locally: - ``` - ./run.bash & - ``` - __or__ on a TORQUE cluster: - ``` - qsub run.bash - ``` -### Output -All output of the pipeline will be placed in a new directory (`out/`, by default). -Log files describing the output of the pipeline will also be created there. The `log` file contains a basic description of the progress of each rule, while the `qlog` file is more detailed and will contain any errors or warnings. - -### Executing the pipeline on your own data -You must modify [the config.yaml file](config/config.yml) to specify paths to your data before you perform step 2 above. Currently, the pipeline is configured to run on our example data. - -## If this is your first time using Snakemake -Snakemake is a tool for creating and running pipelines. The contents of the pipeline are specified as a set of steps (called _rules_) in a special file, called a Snakefile. Each step consists of inputs, outputs, a shell command that uses the inputs to create the outputs, and a conda environment with software dependencies for the shell command. When you execute Snakemake, it will assemble the rules into a pipeline according to each rule's inputs and outputs, along with the outputs that you request from Snakemake. - -We recommend that you run `snakemake --help` to learn about Snakemake's options. For example, to check that the pipeline will be executed correctly before you run it, you can call Snakemake with the `-n -p -r` flags. This is also a good way to familiarize yourself with the steps of the pipeline and their inputs and outputs (the latter of which are inputs to the first rule in each workflow -- ie the `all` rule). - -Note that Snakemake will not recreate output that it has already generated, unless you request it. If a job fails or is interrupted, subsequent executions of Snakemake will just pick up where it left off. This can also apply to files that *you* create and provide in place of the files it would have generated. - -By default, the pipeline will automatically delete some files it deems unnecessary (ex: unsorted copies of a VCF). You can opt to keep these files instead by providing the `--notemp` flag to Snakemake when executing the pipeline. - - diff --git a/snakemake/config/README.md b/snakemake/config/README.md deleted file mode 100644 index 59383e65..00000000 --- a/snakemake/config/README.md +++ /dev/null @@ -1,33 +0,0 @@ -# `config.yml` - config input and options for the pipeline -In order to run the pipeline on your own data, you must specify inputs and options for the pipeline within the `config.yml` file. -## Required input -1. `ref_panel` (str) - The path to a SNP-STR haplotype reference panel for 1000G (downloaded from [here](http://gymreklab.com/2018/03/05/snpstr_imputation.html)) - - If there are multiple, per-chromosome VCFs, just specify one path but replace the contig name in the file name with `{chr}`. - - The VCF(s) must be sorted and indexed (with a `.tbi` file in the same directory). In additition, the varaint IDs of each STR in the VCF must have 'STR_' prepended. -2. `samples` (str) - The path to a 1000G samples TSV file - - You can get this file by clicking "Download the list" [here](https://www.internationalgenome.org/data-portal/sample). -3. `loci` (list of dict) - A list of loci to simulate - - Each locus dictionary has the following key-value pairs for SNP loci, a chosen STR, and a chosen SNP (only if this is preferred over a random SNP): - - `locus` (str) - composed of a contig name, a colon, the start position, a dash, and the end position of the locus - - `str` (str) - composed of a contig name, a colon, and the start position of the STR - - `snp` (str - _optional_) - composed of a contig name, a colon, and the start position of the SNP -## Options -1. `mode` (str) - Whether to run the simulation framework for a SNP ("snp") or an STR ("str") - - You can also provide a list of strings intead of a single string, if you want the pipeline to do multiple modes at once. -2. `superpop` (str) - The 1000G superpopulation code that should be used - - Please provide a three letter code, corresponding to the symbol in the samples file. This will default to EUR (aka european ancestry), if not specified. -3. `min_maf` (float) - The pipeline will discard rare SNPs with an MAF below this number - - This will default to 0 if not specified, but we recommend a value of at least 0.1 -4. `beta` (float) - The strength of association between the chosen STR or SNP and the simulated phenotype - - This will default to 0.1 if not specified. You can also provide a list of floats intead of a single float, if you want the pipeline to try a number of different values. -5. `exclude_causal` (bool) - Whether to include the causal variant in the genotypes provided to the finemapping methods - - Set this to true if you're interested in seeing how finemapping methods perform when the causal variant is absent from the data. This will default to false if not specified. You can also provide a list of booleans instead of a single boolean, if you want the pipeline to try a number of different values.s diff --git a/snakemake/config/config.yml b/snakemake/config/config.yml deleted file mode 100644 index bc24892a..00000000 --- a/snakemake/config/config.yml +++ /dev/null @@ -1,74 +0,0 @@ -# This is the Snakemake configuration file that specifies paths and -# and options for the pipeline. Anybody wishing to use -# the provided snakemake pipeline should first fill out this file with paths to -# their own data, as the Snakefile requires it. -# Every config option has reasonable defaults unless it is labeled as "required." -# All paths are relative to the directory that Snakemake is executed in. -# Note: this file is written in the YAML syntax (https://learnxinyminutes.com/docs/yaml/) - - -# Paths to a SNP-STR haplotype reference panel for 1000G -# You can download this from http://gymreklab.com/2018/03/05/snpstr_imputation.html -# If the VCFs are per-chromosome, replace the contig name in the file name with "{chr}" -# The VCF(s) must be sorted and indexed (with a .tbi file in the same directory) -# The variant IDs of STRs must have 'STR_' prepended -# required! -# ref_panel: "/projects/ps-gymreklab/resources/datasets/snpstr/1kg.snp.str.chr{chr}.vcf.gz" -ref_panel: "/projects/ps-gymreklab/resources/datasets/ukbiobank/array_imputed/pfile_converted/chr{chr}.pgen" - -# Path to a 1000G samples TSV file -# You can get this file by clicking "Download the list" here: -# https://www.internationalgenome.org/data-portal/sample -# required! -samples: /projects/ps-gymreklab/resources/datasets/snpstr/igsr_samples.tsv - -# If variants are unphased, provide the path to a SHAPEIT4 map file like these: -# https://github.com/odelaneau/shapeit4/tree/master/maps -phase_map: data/genetic_maps/chr{chr}.b37.gmap.gz - -# A list of loci, specifying variants to use within the simulations -# Each locus can be specified as a dict composed of two keys: -# 1. "locus" - a string with a contig name, a colon, the start position, a dash, and the -# end position -# 2. "str" - a string with a contig name, a colon, and the start position -# 3. (optionally) "snp" - same format as "str" -# If this key is not provided, a random SNP will be chosen. -# The STR and SNP positions should be contained within the locus. -# The positions should be provided in the same coordinate system as the reference -# genome of the VCF files. -# The contig should correspond with the contig name from the {chr} wildcard in the VCF -# required! -loci: -# - locus: 1:98001984-99001984 -# str: 1:98506615 -# snp: 1:98501984 -# - locus: 10:104457618-105457618 -# str: 10:104639652 -# snp: 10:104612335 - - locus: 16:56927279-57084844 - -# Generates phenotypes for either SNP or STR ("snp" or "str") -# If you want to run multiple modes, just provide them in a list. -# defaults to "snp" -mode: [snp, str] - -# Which 1000G superpopulation should be used? -# Please provide a three letter code, corresponding to the symbol in the samples file -# This will default to EUR (aka european ancestry), if not specified -superpop: EUR - -# Discard rare variants with a MAF below this number -# Defaults to 0 if not specified -min_maf: 0.1 - -# The strength of association between the chosen STR or SNP and the simulated phenotype -# Defaults to 0.1 if not specified -# You can also provide a list of floats, if you want to test several beta values -beta: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.5] - -# Whether to include the causal variant in the set of genotypes provided to the -# finemapping methods. Set this to true if you're interested in seeing how the -# methods perform when the causal variant is absent from the data. -# Defaults to false if not specified -# You can also provide a list of booleans, if you want to test both values -exclude_causal: [true, false] diff --git a/snakemake/log b/snakemake/log deleted file mode 120000 index 22b393ad..00000000 --- a/snakemake/log +++ /dev/null @@ -1 +0,0 @@ -out/logs \ No newline at end of file diff --git a/snakemake/run.bash b/snakemake/run.bash deleted file mode 100755 index baf164cb..00000000 --- a/snakemake/run.bash +++ /dev/null @@ -1,71 +0,0 @@ -#!/usr/bin/env bash -#PBS -V -#PBS -d . -#PBS -t 1 -#PBS -q hotel -#PBS -j oe -#PBS -o /dev/null -#PBS -N run.snakemake -#PBS -l nodes=1:ppn=3 -#PBS -l walltime=1:00:00 - - -# An example bash script demonstrating how to run the entire snakemake pipeline -# This script creates two separate log files in the output dir: -# 1) log - the basic snakemake log of completed rules -# 2) qlog - a more detailed log of the progress of each rule and any errors - -# Before running this snakemake pipeline, remember to complete the config file -# with the required input info. -# Also, make sure that this script is executed from the directory that it lives in! - -out_path="out" -mkdir -p "$out_path/logs" - -# clear leftover log files -echo ""> "$out_path/logs/log" -echo ""> "$out_path/logs/qlog" - -# try to find and activate the snakemake conda env if we need it -if ! command -v 'snakemake' &>/dev/null && \ - command -v 'conda' &>/dev/null && \ - [ "$CONDA_DEFAULT_ENV" != "snakemake" ] && \ - conda info --envs | grep "$CONDA_ROOT/snakemake" &>/dev/null; then - echo "Snakemake not detected. Attempting to switch to snakemake environment." >> "out/logs/log" - eval "$(conda shell.bash hook)" - conda activate snakemake -fi - - -# check: are we being executed from within qsub? -if [ "$ENVIRONMENT" = "BATCH" ]; then - snakemake \ - --cluster "qsub -d . -V -q condo -l walltime={resources.runtime} -l nodes=1:ppn={threads} -j oe -o /dev/null" \ - --default-resources 'runtime="00:30:00"' \ - --latency-wait 60 \ - --use-conda \ - --conda-frontend mamba \ - -k \ - -j 12 \ - -c 12 \ - "$@" >>"$out_path/logs/log" 2>>"$out_path/logs/qlog" -else - snakemake \ - --latency-wait 60 \ - --use-conda \ - --conda-frontend conda \ - -k \ - -c 12 \ - "$@" >>"$out_path/logs/log" 2>>"$out_path/logs/qlog" -fi - -exit_code="$?" -if command -v 'slack' &>/dev/null; then - if [ "$exit_code" -eq 0 ]; then - slack "snakemake finished successfully" &>/dev/null - else - slack "snakemake simulate_gwas job failed" &>/dev/null - slack "$(tail -n4 "$out_path/logs/log")" &>/dev/null - fi -fi -exit "$exit_code" diff --git a/snakemake/workflow/Snakefile b/snakemake/workflow/Snakefile deleted file mode 100644 index b9ef648b..00000000 --- a/snakemake/workflow/Snakefile +++ /dev/null @@ -1,224 +0,0 @@ -import warnings -from pathlib import Path -import snakemake.io as io -from snakemake.utils import min_version - -##### set minimum snakemake version ##### -min_version("6.7.0") - - -# IMPORT CONFIG VARIABLES -configfile: "config/config.yml" - - -def check_config(value, default=False, place=config, as_set=False): - """return true if config value exists and is true""" - value = place[value] if (value in place and place[value]) else default - return (set(value) if isinstance(value, list) else {value}) if as_set else value - - -# handle defaults -config["out"] = check_config("out", "out") -config["superpop"] = check_config("superpop", "EUR") -config["min_maf"] = check_config("min_maf", 0) -config["mode"] = check_config("mode", "snp", as_set=True) -config["beta"] = check_config("beta", 0.1, as_set=True) -config["exclude_causal"] = check_config("exclude_causal", False, as_set=True) -# remove any trailing slashes in directories and set the variables -out = str(Path(config["out"])) -logs = out + "/logs" -bench = out + "/bench" -# parse loci -loci = {loc["locus"].replace(":", "_"): loc for loc in config["loci"]} -# convert the exclude_causal var into a dict for later -exclude_causal = {("in", "ex")[val]: val for val in config["exclude_causal"]} - - -def get_vcf_chrs(vcf_path): - """return a dict of VCFs split by contig""" - if io.contains_wildcard(vcf_path): - return glob_wildcards(vcf_path).chr - return [] - - -chrs = get_vcf_chrs(config["ref_panel"]) - - -rule all: - input: - expand( - out + "/{locus}/sim/{mode}/{beta}/{causal}clude/results/susie.pdf", - locus=loci.keys(), - beta=config["beta"], - mode=config["mode"], - causal=exclude_causal.keys(), - ), - - -rule plink2vcf: - """ convert a PLINK file to VCF """ - input: - pgen=config["ref_panel"], - params: - pfile=lambda wildcards, input: str(Path(input.pgen).with_suffix('')), - out=lambda wildcards, output: str(Path(output.bcf).with_suffix('')), - start=lambda wildcards: wildcards.locus.split('-')[0], - end=lambda wildcards: wildcards.locus.split('-')[1], - chrom=lambda wildcards: wildcards.chr, - log=lambda wildcards, output: Path(output.bcf).with_suffix('log').name, - output: - vcf=temp(out + "/{chr}_{locus}/unphased.vcf.gz"), - bcf=out + "/{chr}_{locus}/unphased.bcf", - bcf_idx=out + "/{chr}_{locus}/unphased.bcf.csi", - log: - logs + "/{chr}_{locus}/plink2vcf" - conda: - "envs/default.yml" - shell: - "{ plink2 --pfile {params.pfile} --out {params.out} --from-bp {params.start} " - "--to-bp {params.end} --chr {params.chrom} " - "--export vcf bgz id-paste=iid &>{log}; rm {params.log}; } && " - "bcftools view -Ob -o {output.bcf} {output.vcf} &>>{log} && " - "tabix -p bcf {output.bcf} &>>{log}" - -def phase_gt_input(wildcards): - input_files = { 'map': config['phase_map'] } - if config["ref_panel"].endswith('.pgen'): - input_files['unphased'] = rules.plink2vcf.output.bcf - else: - input_files['unphased'] = config["ref_panel"] - input_files['unphased_idx'] = input_files['unphased'] + ".csi" - return input_files - -rule phase_gt: - """ phase an unphased set of genotypes """ - input: unpack(phase_gt_input) - params: - locus=lambda wildcards: loci[wildcards.chr + "_" + wildcards.locus]["locus"], - output: - phased=out + "/{chr}_{locus}/phased.bcf", - phased_idx=out + "/{chr}_{locus}/phased.bcf.idx", - resources: - runtime: "1:30:00" - threads: 4 - log: - logs + "/{chr}_{locus}/phase_gt" - conda: - "envs/shapeit.yml" - shell: - "shapeit4 --input {input.unphased} --map {input.map} --region {params.locus} " - "--output {output.phased} --thread {threads} --log {log} &>>{log}" - -rule vcf2gt: - """ convert a VCF into a genotype matrix (cols are vars and rows are samples) """ - input: - vcf=lambda wildcards: rules.phase_gt.output.phased if check_config('phase_map') - else config["ref_panel"], - vcf_idx=lambda wildcards: rules.phase_gt.output.phased_idx if check_config('phase_map') - else config["ref_panel"] + ".tbi", - samps=config["samples"], - params: - popn=config["superpop"], - locus=lambda wildcards: loci[wildcards.chr + "_" + wildcards.locus]["locus"], - output: - matrix=out + "/{chr}_{locus}/matrix.tsv.gz", - log: - logs + "/{chr}_{locus}/vcf2gt", - conda: - "envs/default.yml" - shell: - "workflow/scripts/matrix.bash {params.popn} '{params.locus}' " - "{input.vcf} {input.samps} 2>{log} | gzip > {output.matrix}" - - -rule str2gt: - """ sum the difference of each allele length from the reference allele length """ - input: - matrix=out + "/{locus}/matrix.tsv.gz", - params: - maf=config["min_maf"], - output: - matrix=out + "/{locus}/gt_matrix.tsv.gz", - log: - logs + "/{locus}/str2gt", - conda: - "envs/default.yml" - shell: - "workflow/scripts/gt_matrix.py -o {output.matrix} -m {params.maf} {input.matrix} 2>{log}" - - -def phen_loc(wildcards): - if config["mode"] == "snp": - if "snp" in loci[wildcards.locus]: - return "--snp-loc " + loci[wildcards.locus]["snp"].split(":")[1] - return "--max-vars 1" - else: - return "--str-loc " + loci[wildcards.locus]["str"].split(":")[1] - - -rule phens: - """ generate phenotypes from the genotype matrix """ - input: - matrix=rules.str2gt.output, - params: - loc=phen_loc, - beta=lambda wildcards: wildcards.beta, - mode=lambda wildcards: wildcards.mode, - output: - phenotypes=out + "/{locus}/sim/{mode}/{beta}/phens.tsv.gz", - log: - logs + "/{locus}/sim/{mode}/{beta}/phens", - conda: - "envs/default.yml" - shell: - "workflow/scripts/generate_phenotypes.py -o {output.phenotypes} " - "--beta-{params.mode} {params.beta} {params.loc} -- {input.matrix} &>{log}" - - -run_methods_out = out + "/{locus}/sim/{mode}/{beta}/{causal}clude/method_output" - - -rule run_methods: - """ - run the methods FINEMAP and SuSie - and generate plots with results - """ - input: - gt=rules.phens.input, - phen=rules.phens.output, - params: - outdir=run_methods_out, - exclude_causal=lambda wildcards: int(exclude_causal[wildcards.causal]), - output: - sumstats=temp(run_methods_out + "/sumstats.rds"), - finemap=run_methods_out + "/finemap.rds", - susie=run_methods_out + "/susie.rds", - log: - logs + "/{locus}/sim/{mode}/{beta}/{causal}clude/methods", - conda: - "envs/susie.yml" - shell: - "workflow/scripts/finemapping_methods.R {input} {params} &>{log}" - - -plot_results_out = out + "/{locus}/sim/{mode}/{beta}/{causal}clude/results" - - -rule plot_results: - """create plots to summarize the results of the simulations""" - input: - gt=rules.phens.input, - finemap=rules.run_methods.output.finemap, - susie=rules.run_methods.output.susie, - params: - outdir=plot_results_out, - output: - finemap_pdf=plot_results_out + "/finemap.pdf", - susie_pdf=plot_results_out + "/susie.pdf", - # susie_eff_pdf=temp(plot_results_out + "/susie_eff.pdf"), - log: - logs + "/{locus}/sim/{mode}/{beta}/{causal}clude/results", - conda: - "envs/susie.yml" - script: - "scripts/summarize_results.R" diff --git a/snakemake/workflow/envs/default.yml b/snakemake/workflow/envs/default.yml deleted file mode 100644 index 477ffbe3..00000000 --- a/snakemake/workflow/envs/default.yml +++ /dev/null @@ -1,14 +0,0 @@ -channels: - - conda-forge - - nodefaults -dependencies: - - bioconda::bcftools==1.13 - - conda-forge::datamash==1.7 - - conda-forge::pandas==1.3.0 - - conda-forge::seaborn==0.11.1 - - conda-forge::coreutils==8.31 - - conda-forge::sed==4.8 - - conda-forge::bash==5.0.018 - - conda-forge::gzip==1.10 - - bioconda::plink2==2.00a2.3 - - conda-forge::ipython # for debugging diff --git a/snakemake/workflow/envs/shapeit.yml b/snakemake/workflow/envs/shapeit.yml deleted file mode 100644 index d5e10a13..00000000 --- a/snakemake/workflow/envs/shapeit.yml +++ /dev/null @@ -1,5 +0,0 @@ -channels: - - conda-forge - - nodefaults -dependencies: - - bioconda::shapeit4=4.2.2 diff --git a/snakemake/workflow/envs/susie.yml b/snakemake/workflow/envs/susie.yml deleted file mode 100644 index 072dd72f..00000000 --- a/snakemake/workflow/envs/susie.yml +++ /dev/null @@ -1,15 +0,0 @@ -channels: - - conda-forge - - nodefaults -dependencies: - - conda-forge::r-susier==0.11.42 - - conda-forge::r-abind==1.4_5 - - conda-forge::r-dplyr==1.0.7 - - conda-forge::r-readr==1.4.0 - - aryarm::r-dscrutils==0.4.3 - - conda-forge::r-irkernel==1.2 - - conda-forge::sos-notebook==0.22.3 - - conda-forge::coreutils==8.31 - - conda-forge::sed==4.8 - - conda-forge::bash==5.0.018 - - conda-forge::gzip==1.10 diff --git a/snakemake/workflow/notebooks/run_finemap-susie.ipynb b/snakemake/workflow/notebooks/run_finemap-susie.ipynb deleted file mode 100644 index 8ac37661..00000000 --- a/snakemake/workflow/notebooks/run_finemap-susie.ipynb +++ /dev/null @@ -1,622 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "9b46eb3b-b4a2-42f1-b496-4fbd8c049fad", - "metadata": {}, - "outputs": [], - "source": [ - "library(repr)\n", - "library(abind)\n", - "library(scales)\n", - "library(ggplot2)\n", - "library(IRdisplay)\n", - "library(RColorBrewer)\n", - "options(repr.plot.width=10, repr.plot.height=5)\n", - "if (getwd() != \"/projects/ps-gymreklab/amassara/simulate_gwas\") {\n", - " setwd(\"/projects/ps-gymreklab/amassara/simulate_gwas\")\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fa7938d8-3477-49c3-889f-d72bb40503b2", - "metadata": {}, - "outputs": [], - "source": [ - "gt = \"out/1_98001984-99001984/gt_matrix.tsv.gz\"\n", - "phen = \"out/1_98001984-99001984/sim/str/0.5/phens.tsv.gz\"\n", - "out = \"out/1_98001984-99001984/susieR-notebook-output\"\n", - "exclude_causal = FALSE\n", - "dir.create(out, showWarnings = FALSE)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "dfa461f2-7a25-4396-9579-ee0880c10484", - "metadata": {}, - "outputs": [], - "source": [ - "# import genotype matrices as proper matrices\n", - "gt = read.csv(gt, sep=\"\\t\", header=T)\n", - "phen = read.csv(phen, sep=\"\\t\", header=T)\n", - "# create matrices without unecessary columns\n", - "X = as.matrix(gt[,-1])\n", - "storage.mode(X) = 'double'\n", - "y = as.matrix(phen[,ncol(phen)])\n", - "# what is the column name of the causal variant?\n", - "causal_variant = colnames(phen)[2]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1c262323-9d7e-4f0e-aba5-a6edf094b0a6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# remove the causal variant if requested\n", - "if (exclude_causal) {\n", - " X = X[,!(colnames(X) %in% c(causal_variant))]\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "911d2f58-c486-48d8-9564-e52209886ca3", - "metadata": {}, - "outputs": [], - "source": [ - "# the number of samples and the number of variants:\n", - "n = nrow(X)\n", - "p = ncol(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d0117bff-b879-4b5c-8297-16afe17aeb62", - "metadata": {}, - "outputs": [], - "source": [ - "# create vector with causal status\n", - "# this vector indicates which variant is truly causal\n", - "b = rep(0,p)\n", - "names(b) = colnames(X)\n", - "# causal_variant = 'X98501984.0' # SNP\n", - "# causal_variant = 'X98506615.1' # STR\n", - "if (exclude_causal) {\n", - " b[causal_variant] = 1\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b5ea1b04-a730-40f5-a6a8-b3bdd4e719b5", - "metadata": {}, - "outputs": [], - "source": [ - "mm_regression = function(X, Y, Z=NULL) {\n", - " if (!is.null(Z)) {\n", - " Z = as.matrix(Z)\n", - " }\n", - " reg = lapply(seq_len(ncol(Y)), function (i) simplify2array(susieR:::univariate_regression(X, Y[,i], Z)))\n", - " reg = do.call(abind, c(reg, list(along=0)))\n", - " # return array:\n", - " # out[1,,] is beta hat (the least-squares estimates of the coefficients)\n", - " # out[2,,] is se betahat (the standard errors of the beta hats)\n", - " return(aperm(reg, c(3,2,1)))\n", - "}\n", - "sumstats = mm_regression(as.matrix(X), as.matrix(y))\n", - "dat = list(X=X,Y=as.matrix(y))\n", - "input = paste0(out,'/sumstats.rds')\n", - "saveRDS(list(data=dat, sumstats=sumstats), input)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9012f8d3-4021-40b9-91cc-1ad8c63e5265", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Attaching package: 'dplyr'\n", - "\n", - "\n", - "The following objects are masked from 'package:stats':\n", - "\n", - " filter, lag\n", - "\n", - "\n", - "The following objects are masked from 'package:base':\n", - "\n", - " intersect, setdiff, setequal, union\n", - "\n", - "\n", - "\n", - "Attaching package: 'readr'\n", - "\n", - "\n", - "The following object is masked from 'package:scales':\n", - "\n", - " col_factor\n", - "\n", - "\n" - ] - } - ], - "source": [ - "output = paste0(out, \"/N2finemapping.FINEMAP\")\n", - "args = \"--n-causal-snps 2\"\n", - "commandArgs = function(...) 1\n", - "\n", - "# source(paste0(.libPaths(), '/susieR/code/finemap.R'))\n", - "source(\"/projects/ps-gymreklab/amassara/simulate_gwas/temp/finemap.R\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "cff6c030-1813-4592-8620-c28d26d80a80", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 6 × 6
ranksnpprobprob_cumsumlog10bfsnp_prob
<int><chr><dbl><dbl><dbl><dbl>
114900.999938000.62432917.1990300.99993800
2240 0.015509500.63401281.1892800.01550950
3339 0.006605720.63813720.8146890.00660572
4468 0.005757680.64173210.7546450.00575768
5589 0.005757680.64532700.7546450.00575768
6673 0.005757560.64892180.7546360.00575756
\n" - ], - "text/latex": [ - "A data.frame: 6 × 6\n", - "\\begin{tabular}{r|llllll}\n", - " & rank & snp & prob & prob\\_cumsum & log10bf & snp\\_prob\\\\\n", - " & & & & & & \\\\\n", - "\\hline\n", - "\t1 & 1 & 490 & 0.99993800 & 0.6243291 & 7.199030 & 0.99993800\\\\\n", - "\t2 & 2 & 40 & 0.01550950 & 0.6340128 & 1.189280 & 0.01550950\\\\\n", - "\t3 & 3 & 39 & 0.00660572 & 0.6381372 & 0.814689 & 0.00660572\\\\\n", - "\t4 & 4 & 68 & 0.00575768 & 0.6417321 & 0.754645 & 0.00575768\\\\\n", - "\t5 & 5 & 89 & 0.00575768 & 0.6453270 & 0.754645 & 0.00575768\\\\\n", - "\t6 & 6 & 73 & 0.00575756 & 0.6489218 & 0.754636 & 0.00575756\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 6\n", - "\n", - "| | rank <int> | snp <chr> | prob <dbl> | prob_cumsum <dbl> | log10bf <dbl> | snp_prob <dbl> |\n", - "|---|---|---|---|---|---|---|\n", - "| 1 | 1 | 490 | 0.99993800 | 0.6243291 | 7.199030 | 0.99993800 |\n", - "| 2 | 2 | 40 | 0.01550950 | 0.6340128 | 1.189280 | 0.01550950 |\n", - "| 3 | 3 | 39 | 0.00660572 | 0.6381372 | 0.814689 | 0.00660572 |\n", - "| 4 | 4 | 68 | 0.00575768 | 0.6417321 | 0.754645 | 0.00575768 |\n", - "| 5 | 5 | 89 | 0.00575768 | 0.6453270 | 0.754645 | 0.00575768 |\n", - "| 6 | 6 | 73 | 0.00575756 | 0.6489218 | 0.754636 | 0.00575756 |\n", - "\n" - ], - "text/plain": [ - " rank snp prob prob_cumsum log10bf snp_prob \n", - "1 1 490 0.99993800 0.6243291 7.199030 0.99993800\n", - "2 2 40 0.01550950 0.6340128 1.189280 0.01550950\n", - "3 3 39 0.00660572 0.6381372 0.814689 0.00660572\n", - "4 4 68 0.00575768 0.6417321 0.754645 0.00575768\n", - "5 5 89 0.00575768 0.6453270 0.754645 0.00575768\n", - "6 6 73 0.00575756 0.6489218 0.754636 0.00575756" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "finemap = readRDS(paste0(out,\"/N2finemapping.FINEMAP.rds\"))[[1]]\n", - "head(finemap$snp)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "356cfb7f-9c14-488b-9552-24c869daf700", - "metadata": {}, - "outputs": [], - "source": [ - "snp = finemap$snp\n", - "snp$snp = as.numeric(snp$snp)\n", - "snp = snp[order(snp$snp),]\n", - "# snp$b = as.character(b)\n", - "# snp$pos = as.integer(sub(\"X(\\\\S+)\\\\.\", \"\\\\1\", colnames(X)))\n", - "pip = snp$snp_prob\n", - "# b_colors = c(`0`='black', `1`='red')\n", - "# snp$ld_causal = (as.vector(cor(X[,causal_variant], X))^2)^(2.5)\n", - "# plot(snp$pos, snp$ld_causal)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "502b4294-8621-44a0-bd62-7aa6d79c8da5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pdf(paste0(out,'/finemap.pdf'), width =5, height = 5, pointsize=16)\n", - "susieR::susie_plot(pip, y='PIP', b=b, main = 'Bayesian sparse regression')\n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c4272aa4-1ce4-458f-8a04-59a76be11507", - "metadata": {}, - "outputs": [ - { - "data": { - "application/pdf": 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viLGjYvw2UPvZ1lzsgyp7JYDM9YYNWcRc5rqe6Tr7FYS1gq3k8yFjlvJf6yhrBY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\nd56LLOy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14X6MNcvkIfRqOwLtCHka2t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\ncnR4cmVmCjY0NDQ2CiUlRU9GCg==" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_pdf(file=paste0(out,'/finemap.pdf'))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6c60680f-ad0a-4c1f-89e2-95d411196949", - "metadata": {}, - "outputs": [], - "source": [ - "fitted = susieR::susie(X, y, L=5,\n", - " estimate_residual_variance=TRUE, \n", - " scaled_prior_variance=0.2,\n", - " tol=1e-3, track_fit=TRUE, min_abs_corr=0.02)\n", - "susie_pip = fitted$pip[colnames(X)]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "64d6fdde-db3e-4523-a9dd-acf9f6745efd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pdf(paste0(out,'/susie.pdf'), width =5, height = 5, pointsize=16)\n", - "susieR::susie_plot(fitted, y='PIP', b=b, max_cs=0.4, main = paste('SuSiE, ', length(fitted$sets$cs), 'CS identified'))\n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "baf46cad-fb38-412f-9490-9ce119c09ed1", - "metadata": {}, - "outputs": [ - { - "data": { - "application/pdf": 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yyWi21Lv/CLGv5JCOmXIW2DZc7IMqeymA/PWMyqCYus11I9K19jsZKwVLw+Zyyy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\nIDAgUiAvUm9vdCAyIDAgUiA+PgpzdGFydHhyZWYKNjU3MTEKJSVFT0YK" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_pdf(file=paste0(out,'/susie.pdf'))" - ] - }, - { - "cell_type": "markdown", - "id": "49b1e6b7-1164-4b30-a5b7-e105b546e41c", - "metadata": {}, - "source": [ - "## SuSiE Effect Size Estimate" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ed977234-5cb3-45d5-b6b4-bda1cf1b00ce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bhat = coef(fitted)[-1]\n", - "pdf(paste0(out,'/susie_eff.pdf'), width =5, height = 5, pointsize=16)\n", - "susieR::susie_plot(bhat, y='bhat', b=b, main = 'SuSiE, effect size estimate') \n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7b47dce6-50eb-4975-a80e-632c93a9cb35", - "metadata": {}, - "outputs": [ - { - "data": { - "application/pdf": 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8NPI/YWETnpcUUJrMaz8Wc05zg7f3S1kEVJzsZZg0Ve4bFVj7b1bNw/ogFL9JA9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u9K1+7JBpoYde7sfWp8YWWGxmLDYVS5U9faVXCy9bh2JZU7GsmbEsOYt1eq7o+4ul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\nc9Qufald+sl2GVXto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sZqyh7UnvwJL4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\n7bD0xmOstzjGNLelYwyey+99jzH9Xhl7OkiW9XaWnqNdOuW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FO459gyPidHC2OF9cPE6IK8RV4FpwJ3BXcBO4GbwS3hDvjA/F8/DL8WX4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\nRgo=" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_pdf(file=paste0(out,'/susie_eff.pdf'))" - ] - }, - { - "cell_type": "markdown", - "id": "9c480116-e278-4d7a-b5c4-0633921ecd95", - "metadata": {}, - "source": [ - "# Testing a New PIP Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "a55d0e2c-f7a6-43fa-9897-1c30d2453ee8", - "metadata": {}, - "outputs": [], - "source": [ - "finemap_results = finemap\n", - "X = as.matrix(gt[,-1])\n", - "storage.mode(X) = 'double'" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "01c1eabd-01e6-45cb-8ac2-627b591b5413", - "metadata": {}, - "outputs": [], - "source": [ - "b = rep(0, ncol(X))\n", - "names(b) = colnames(X)\n", - "if (exclude_causal) {\n", - " b = b[!(names(b) %in% c(causal_variant))]\n", - "} else {\n", - " b[causal_variant] = 1\n", - "}\n", - "\n", - "# parse the finemap data\n", - "snp = finemap_results$snp\n", - "finemap_pip = snp[order(as.numeric(snp$snp)),]$snp_prob\n", - "susie_pip = fitted$pip\n", - "\n", - "# define a function for PIP plotting\n", - "pip_plot = function(pips, X, b, susie_cs=NULL) {\n", - " # create a ggplot of the PIPs\n", - " # note that each of pips, X, and b must be ordered by variant POS\n", - " # first, initialize the values we need\n", - " b_colors = c(`0`='black', `1`='red')\n", - " causal_var = names(b[b == 1])\n", - " if (length(causal_var) == 0) {\n", - " causal_var = X[,causal_variant]\n", - " X = X[,!(colnames(X) %in% c(causal_variant))]\n", - " } else {\n", - " causal_var = X[,causal_var[1]]\n", - " }\n", - " data = data.frame(\n", - " pip = pips,\n", - " b = as.character(b),\n", - " pos = as.integer(sub(\"X(\\\\S+)\\\\.\", \"\\\\1\", names(b))),\n", - " ld_causal = as.vector(cor(causal_var, X))^2\n", - " )\n", - " if (!is.null(susie_cs)) {\n", - " data$cs = as.integer((names(b) %in% susie_cs))*2\n", - " } else {\n", - " data$cs = 0\n", - " }\n", - " # extract the causal variants to another data frame\n", - " data_causal = data[data$b == '1',]\n", - " # make the plot\n", - " ggplot(data, aes(x=pos, y=pip)) +\n", - " geom_point(aes(fill=ld_causal, stroke=cs, color=factor(cs)), size=7, shape=21) +\n", - " scale_fill_gradient(name='LD with Causal Variant', low='#FBBA72', high='#691E06') +\n", - " scale_color_manual(name='Credible Sets', values=c('transparent', '#7C9299'), guide=\"none\") +\n", - " geom_point(data=data_causal, aes(stroke=cs, color=factor(cs)), fill='red', size=7, shape=21) +\n", - " xlab('Chromosomal Position') +\n", - " ylab('Posterior Inclusion Probability (PIP)') + \n", - " ylim(0,1) +\n", - " theme_grey(base_size=16)\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "36f55461-59d8-4bb6-bde6-19351f118a87", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "null device: 1" - ], - "text/latex": [ - "\\textbf{null device:} 1" - ], - "text/markdown": [ - "**null device:** 1" - ], - "text/plain": [ - "null device \n", - " 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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y9ffvDBBwsLC7t27Tpx4sSPPvqotLT03XffLS4uzszM/PHHH+vq6rp37z516lSb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\nv7e3tyVtSt91CwoKampqrHkcVhs6dOhzzz0nhDh69OiIESPMKV9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kEjf4QzUX1l+AFAAAAK0WCSHQWsjZmB4AAADuzB4zPMkHAaewLiFM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oCpWVNTfdOmzajPH/8+zHfAdLqok/VqZB1fftpn/qUPQAACAASURBVNuG/+bSfLut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\nTiev1qj27jghTlbUt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I1Goxf+/KFSqbztGVer1XJC6O5PNBJCAPXkgoSwqKjo0KFDOTk5paWlgYGBMTEx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\nzyqVSrbJK6+8MmrUqNAfEsBlIFpdR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pGQbvwqDPNCjrHep+nr0Kg2LkY2lQr9e1WhgU08lOx8sAACAASURBVGBVZa3eoGOF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\nptfWmyz1JktEVJhSK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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 300, - "width": 600 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "pip_plot(susie_pip, X, b, susie_cs=names(fitted$pip[fitted$sets$cs[['L1']]]))\n", - "dev.off()\n", - "ggsave(paste0(out,'/susie_improved.pdf'), width=10, height=5, device='pdf')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R [conda env:simulate]", - "language": "R", - "name": "conda-env-simulate-r" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.0.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/snakemake/workflow/notebooks/run_finemap-susie_vignette.ipynb b/snakemake/workflow/notebooks/run_finemap-susie_vignette.ipynb deleted file mode 100644 index b4b9ad83..00000000 --- a/snakemake/workflow/notebooks/run_finemap-susie_vignette.ipynb +++ /dev/null @@ -1,488 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "baf46cad-fb38-412f-9490-9ce119c09ed1", - "metadata": {}, - "outputs": [], - "source": [ - "library(abind)\n", - "library(IRdisplay)\n", - "set.seed(1)\n", - "if (getwd() != \"/projects/ps-gymreklab/amassara/simulate_gwas\") {\n", - " setwd(\"/projects/ps-gymreklab/amassara/simulate_gwas\")\n", - "}\n", - "out = \"temp/susieR\"\n", - "dir.create(out, showWarnings = FALSE)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9b46eb3b-b4a2-42f1-b496-4fbd8c049fad", - "metadata": {}, - "outputs": [], - "source": [ - "n = 500\n", - "p = 1000\n", - "b = rep(0,p)\n", - "b[200] = 1\n", - "b[800] = 1\n", - "X = matrix(rnorm(n*p),nrow=n,ncol=p)\n", - "X[,200] = X[,400]\n", - "X[,600] = X[,800]\n", - "y = X %*% b + rnorm(n)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fa7938d8-3477-49c3-889f-d72bb40503b2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pdf('truth.pdf', width =5, height = 5, pointsize=16)\n", - "plot(b, col=\"black\", pch=16, main = 'True effect size')\n", - "pos = 1:length(b)\n", - "points(pos[b!=0],b[b!=0],col=2,pch=16)\n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "be5b33c0-123c-425d-92cd-d62e7cc65f78", - "metadata": {}, - "outputs": [ - { - "data": { - "application/pdf": 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WRzjaXnyNKxX766jmN37Re2h/7jE++g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\nxrJqZEEPV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Iuaqd95m7Vug4jRwL9JTm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7zNUreZ5YCQt5j6gNFsuExN7BksTzvDIh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\neWJFLIO9R9QGi2VCYu+jb8TzvDIhx+M5V0Pmo/cTrDKL5VyNx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889Oy8PzUe/n2CVWXp2lv4wS87MkrOz5Mws/WGWnp2l03l5aC5b+z4s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\nNDUzMjMKJSVFT0YK" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_pdf(file = 'truth.pdf')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7713e12c-f57e-4cbb-b488-ad470514f11d", - "metadata": {}, - "outputs": [], - "source": [ - "# alpha = 1\n", - "# y.fit = glmnet::glmnet(X,y,alpha = alpha,intercept = FALSE)\n", - "# y.cv = glmnet::cv.glmnet(X,y,alpha = alpha,intercept = FALSE,\n", - "# lambda = y.fit$lambda)\n", - "# bhat = glmnet::predict.glmnet(y.fit,type =\"coefficients\",\n", - "# s = y.cv$lambda.min)[-1,1]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b7f1f561-aa84-4fed-9f67-e43d0fc016b6", - "metadata": {}, - "outputs": [], - "source": [ - "# pdf('lasso.pdf', width =5, height = 5, pointsize=16)\n", - "# plot(bhat, col=\"black\", pch=16, main = 'Lasso')\n", - "# pos = 1:length(bhat)\n", - "# points(pos[b!=0],bhat[b!=0],col=2,pch=16) \n", - "# dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "acbcc9cb-ab6a-467b-b868-26ba45676eba", - "metadata": {}, - "outputs": [], - "source": [ - "# display_pdf('lasso.pdf')" - ] - }, - { - "cell_type": "markdown", - "id": "c3a137d6-e278-4a93-8b54-fb054e3c5668", - "metadata": { - "tags": [] - }, - "source": [ - "# Existing Bayesian methods for sparse regression" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b5ea1b04-a730-40f5-a6a8-b3bdd4e719b5", - "metadata": {}, - "outputs": [], - "source": [ - "mm_regression = function(X, Y, Z=NULL) {\n", - " if (!is.null(Z)) {\n", - " Z = as.matrix(Z)\n", - " }\n", - " reg = lapply(seq_len(ncol(Y)), function (i) simplify2array(susieR:::univariate_regression(X, Y[,i], Z)))\n", - " reg = do.call(abind, c(reg, list(along=0)))\n", - " # return array: out[1,,] is betahat, out[2,,] is shat\n", - " return(aperm(reg, c(3,2,1)))\n", - "}\n", - "sumstats = mm_regression(as.matrix(X), as.matrix(y))\n", - "dat = list(X=X,Y=as.matrix(y))\n", - "input = paste0(out,'/Toy.sumstats.rds')\n", - "saveRDS(list(data=dat, sumstats=sumstats), input)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "da206da5-978a-4b48-9079-e5aa4cab859b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Attaching package: ‘dplyr’\n", - "\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " filter, lag\n", - "\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " intersect, setdiff, setequal, union\n", - "\n", - "\n" - ] - } - ], - "source": [ - "output = paste0(out, \"/Toy.N2finemapping.FINEMAP\")\n", - "args = \"--n-causal-snps 2\"\n", - "commandArgs = function(...) 1\n", - "\n", - "# source(paste0(.libPaths(), '/susieR/code/finemap.R'))\n", - "source(\"/projects/ps-gymreklab/amassara/simulate_gwas/temp/finemap.R\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1c50e734-14ef-49b6-9492-fa018ef363fc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 6 × 11
rankconfigproblog10bfoddskprob_norm_kh2h2_0.95CImeansd
<int><chr><dbl><dbl><dbl><int><dbl><dbl><chr><chr><chr>
11200,6002.5000e-01122.1380 122.5000e-010.6029460.532836,0.677494NA,NANA,NA
22200,8002.5000e-01122.1380 122.5000e-010.6029460.532079,0.677991NA,NANA,NA
33400,6002.5000e-01122.1380 122.5000e-010.6029460.531776,0.683898NA,NANA,NA
44400,8002.5000e-01122.1380 122.5000e-010.6029460.53088,0.676159 NA,NANA,NA
55200,7682.2188e-66 57.0865Inf22.2188e-660.3465590.276517,0.419864NA,NANA,NA
66400,7682.2188e-66 57.0865Inf22.2188e-660.3465590.271193,0.424564NA,NANA,NA
\n" - ], - "text/latex": [ - "A data.frame: 6 × 11\n", - "\\begin{tabular}{r|lllllllllll}\n", - " & rank & config & prob & log10bf & odds & k & prob\\_norm\\_k & h2 & h2\\_0.95CI & mean & sd\\\\\n", - " & & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & 1 & 200,600 & 2.5000e-01 & 122.1380 & 1 & 2 & 2.5000e-01 & 0.602946 & 0.532836,0.677494 & NA,NA & NA,NA\\\\\n", - "\t2 & 2 & 200,800 & 2.5000e-01 & 122.1380 & 1 & 2 & 2.5000e-01 & 0.602946 & 0.532079,0.677991 & NA,NA & NA,NA\\\\\n", - "\t3 & 3 & 400,600 & 2.5000e-01 & 122.1380 & 1 & 2 & 2.5000e-01 & 0.602946 & 0.531776,0.683898 & NA,NA & NA,NA\\\\\n", - "\t4 & 4 & 400,800 & 2.5000e-01 & 122.1380 & 1 & 2 & 2.5000e-01 & 0.602946 & 0.53088,0.676159 & NA,NA & NA,NA\\\\\n", - "\t5 & 5 & 200,768 & 2.2188e-66 & 57.0865 & Inf & 2 & 2.2188e-66 & 0.346559 & 0.276517,0.419864 & NA,NA & NA,NA\\\\\n", - "\t6 & 6 & 400,768 & 2.2188e-66 & 57.0865 & Inf & 2 & 2.2188e-66 & 0.346559 & 0.271193,0.424564 & NA,NA & NA,NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 11\n", - "\n", - "| | rank <int> | config <chr> | prob <dbl> | log10bf <dbl> | odds <dbl> | k <int> | prob_norm_k <dbl> | h2 <dbl> | h2_0.95CI <chr> | mean <chr> | sd <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | 1 | 200,600 | 2.5000e-01 | 122.1380 | 1 | 2 | 2.5000e-01 | 0.602946 | 0.532836,0.677494 | NA,NA | NA,NA |\n", - "| 2 | 2 | 200,800 | 2.5000e-01 | 122.1380 | 1 | 2 | 2.5000e-01 | 0.602946 | 0.532079,0.677991 | NA,NA | NA,NA |\n", - "| 3 | 3 | 400,600 | 2.5000e-01 | 122.1380 | 1 | 2 | 2.5000e-01 | 0.602946 | 0.531776,0.683898 | NA,NA | NA,NA |\n", - "| 4 | 4 | 400,800 | 2.5000e-01 | 122.1380 | 1 | 2 | 2.5000e-01 | 0.602946 | 0.53088,0.676159 | NA,NA | NA,NA |\n", - "| 5 | 5 | 200,768 | 2.2188e-66 | 57.0865 | Inf | 2 | 2.2188e-66 | 0.346559 | 0.276517,0.419864 | NA,NA | NA,NA |\n", - "| 6 | 6 | 400,768 | 2.2188e-66 | 57.0865 | Inf | 2 | 2.2188e-66 | 0.346559 | 0.271193,0.424564 | NA,NA | NA,NA |\n", - "\n" - ], - "text/plain": [ - " rank config prob log10bf odds k prob_norm_k h2 \n", - "1 1 200,600 2.5000e-01 122.1380 1 2 2.5000e-01 0.602946\n", - "2 2 200,800 2.5000e-01 122.1380 1 2 2.5000e-01 0.602946\n", - "3 3 400,600 2.5000e-01 122.1380 1 2 2.5000e-01 0.602946\n", - "4 4 400,800 2.5000e-01 122.1380 1 2 2.5000e-01 0.602946\n", - "5 5 200,768 2.2188e-66 57.0865 Inf 2 2.2188e-66 0.346559\n", - "6 6 400,768 2.2188e-66 57.0865 Inf 2 2.2188e-66 0.346559\n", - " h2_0.95CI mean sd \n", - "1 0.532836,0.677494 NA,NA NA,NA\n", - "2 0.532079,0.677991 NA,NA NA,NA\n", - "3 0.531776,0.683898 NA,NA NA,NA\n", - "4 0.53088,0.676159 NA,NA NA,NA\n", - "5 0.276517,0.419864 NA,NA NA,NA\n", - "6 0.271193,0.424564 NA,NA NA,NA" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "finemap = readRDS(paste0(out,\"/Toy.N2finemapping.FINEMAP.rds\"))[[1]]\n", - "head(finemap$set)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9012f8d3-4021-40b9-91cc-1ad8c63e5265", - "metadata": {}, - "outputs": [], - "source": [ - "snp = finemap$snp\n", - "pip = snp[order(as.numeric(snp$snp)),]$snp_prob" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "502b4294-8621-44a0-bd62-7aa6d79c8da5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pdf(paste0(out,'/Toy.finemap.pdf'), width =5, height = 5, pointsize=16)\n", - "susieR::susie_plot(pip, y='PIP', b=b, main = 'Bayesian sparse regression')\n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "43dc9947-559e-4a9c-a27b-46e6470ab799", - "metadata": {}, - "outputs": [ - { - "data": { - "application/pdf": 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CiyucbSc2Tp2C9fXcexu/YL20P/8Yl3\n0I7cqJFlZrLUSpZaI0utZJmZLLNGltnJkitZcr1Zao8sFafAgn6Xn3daOEEOSMb3f/ROlt4j\nS8cJckAS2meDpaBnxuryDl5g6Suy9EWWksiSV2TJgyxzkWWuyDIXWfIgS16RpSSy9EWW/t/s\nl9NF+o9PvLN2BEiKLBXfcwsKzUZgWRBLNtdYjjZ0Ht4RMIks7H79L5prYPmeeIPtqJASWUYi\nSylkoRP45zwxlpHIchSis/KOisFc1n1YSo8sxe2X5fbLuvbLqmShWltOF/05T4xluf2yrv2y\nJllKJ0u59gubTf/xiTfb7NSYwqlyQk++tVKxLaeN/pwnWf+8Cd9THVrR2XrG0iZZ2rxZ5hNZ\npjMOayZLfSLL0ZQpPfArbXRY9MRYyiRLfSJLzWSZzrCc19mSbaj/+OQYb40svUSWoyvfWqHc\nNHIsuZClJ7L0Elk69gv7Y33dhyW3yJIbWeYgy2yR5WjLt1aoN40cS21kmYUss0WWOciSG1ny\ndbYsyFX4xyfehLPz1hPOlgv68q31OJg2ciwd58MF/WifDZaGRh2ry5t/gWU8kWU8ZCmZLOWJ\nLNntl+X2y7r2y3rIkidZyhNZSibLeMgy/pv9snJkWZksrZKl5chydGZKiTpOI7Akp6cWdKR9\ntmNplSwrk2VdZ8uK+IV/fOJduaNPamQ5SvOt9XiaNnIspZJlZLKMGlkGvrMrciF83YeljMhS\n3H5Zbr+sa7+sRhboOI0cS3P7Zbn9sq79shZZyiBLufYLO1z/8Yl35+zseFSmdgAuIHet0HEa\nORboKc39ap1Qkc4HNJbTeuvrPiwnpeEfn3iH7uiTFFlwDZkyHU6NwJJhNdpcYzkq0nmBR98k\nsjBd4r/0vRf2C1tjf/5HJ4vPAPr5ls1kgY7TyLFAT2musfQaWTr2C5tyfd2HJffIkjtZ5iTL\n7JEFV5K7Vug4jRwL9JTmGsvskWVOsuROlnydLRvCHP7xiXfr7Oz4+LPl3gG4mNy1wu3UyLEc\n29HmfrUuqEjnCxpLdW5nvc6WbJH9xyfHrytkKSmyZLdfltsv69ov0FOaaywlRZZSyDISWcZ/\ns19Ov+w/PvF+3dEnJbLgkjIV6jiNwFKopzTXWI6KdM7g0TeFLOzz/S96dWDpiIQwd+7okxZZ\ncFW5a4XnqZFjgfeoucYyWmQZ+M7uCKPwdR+WMiNLwX6Z7gfVKyQKvfXgwnLXCh2nkWNpbr8s\nt19W2C8Tv8i2uo5PeO2Xjrbaf3zi/bqjT/zZcu8A3FruWqHjNHIs0FOa+1urPtux5EyW/pCl\nX2fLjmiIf3zi/bqjT3Jkwc1lqvQ8NQJLpfeoucZSc2SplSzTeZ7zOluOkDpkT45fN8jSW2TB\n7eWuFTpOI8cCPaW5xtJbZOnYL+wE9nUfljwiSx5kmYssPjzvrQc3mLtW6DiNHAv0lOYayxyR\nZS6y5EGWmKJnfptjOQET5s7Z2dEn6b07ALeYu1Z4nho5FniPmvuxpJCnZ2sby4m28HUflpEj\ny8hkwS+xX/2RI0tx+2W5/bKu/QI9pbnGUnJkKZUsI5MlBhWZ3+ZYViULGo3et1uNLLjNTI06\nTiOwNOopzTWWViNL62RZlSwxbM/8NrBM5FCYO3f0SY8suM/ctcLz1MixwHvUXGMZPbIMnC0n\nEjB83YelrMhS8Df/8Bfa79vt2i+4z9y1Qsdp5Fia2y/L7RcfZmRrG0tZZIl5Rua3OZbT6mvu\n3NEn4WxZeZ+5a4WO08ixQE9p7seSQyCfrX30TSJLzOQzv82xnLgKc+eOPimRBfeZqdPz1Ags\nnd6j5hqLT+eztY++cZ5nDOj7UQ1hv7AJ+OcNNsnSe2TBfeauFTpOI8cCPaW5xuIzj2zto2+w\nX9YVe2R+m2PJkyxoUnrfbjOy4D5z1wodp5FjgZ7SXGPxuX22trHkSZYY3Wd+m2NpOB8mdiu9\n+iOcLRvvM3et8Dw1cizwHjX3YykhxM/WPvrGeZ4xx+/ne69EllHIgp9zv2+3ElmK2y/L7Zd1\n7RfoKc01llIiC38Ozi5lX/dhWS2yrEYWdC+9b7cWWXCfmQZ1nEZgGdRTmmssPt7P1j76ppEl\nJvyZ33ZY9MT7dcYyRmTBfeauFZ6nRo4F3qPmGovP+rO17eyZMPe54v5+vveeyIJ24tedw36p\nITvprQf3mbtW6DiNHEt3+wX3kfpssGT+QDyxjdnXfVh8+J898X7d0SfhbNl5n7lrhY7TyLFA\nT2musfgUQFv76JtMlhgEaH6bY5nOe0R30/t2q5EF95lp0vPUCCyT3qPmGouPBLS1j75xnmdM\nBfxRDWG/uGbj9w22yNJHZMF95q4VOk4jxwI9pbnG0kdkwU/GE5ucfd2HxWcE2hPv1xmLjwl8\n68F95q4VOk4jxwI9pbnG4sMCbW1jyYssMS/Q/DbHcqI0zJ07+iScLQfvM3et8Dw1cizwHjX3\nq7VBRTpn8OibRJYYHvjzvVcjCzqRU+EvwN+3W40sxe2X5fbLuvYL9JTmGkupkQW/IE+uAzql\nK5LJ/DbHgo5kuXNHn/TIgvvMtKjjNALLop7SXGNpPbKggSq5TuiUrlhB89vAkhm4IXfOWMaM\nLLjP3LXC89TIscB71FxjGTOyoI1KdXmfMLDUFFnQofy+wbBfeghseuvBfeaulTpuhcwme2Is\nuI/UZ4Ol8tfkyXVGp3wlN5nf5ljQqSx37uiTcLacvM/ctVLHrZA7aE++eibvI/XZjgUtVcl1\nSKd8pQ+a3+ZYpvMe0Rj1vt1aZMF9Zn7oeWp0WPTEWHAfqc92LGisUl3eJ3QsJYQ62RNjwe/C\n37fbjCy4z9y1Qsdp5FigpzTXWPqMLPhdeXId06lcAU/mtzkWdDDLnTv65IksuM/ctULHaeRY\noKc011jWE1nQZJVc53QqV0ih+W2OBWEdcueOPglny8X7zF0rPE+NHAu8R839ah0hrtDWPvrG\neZ4xsfBHNbTIgobm9w02yFJaZCluvyy3X9a1X6CnNNdYSoss+F15co3UqVw5UOa3ORa0Ncud\nM5Y2IgvuM3OijtMILMnpqcX7SH22Y0HHVXLt1KlcWYbmt4GlMsVD7pyx+DzDtx7cZ+5a4Xlq\n5FjgPWqusfhUQ1vbzp5ID3F1H5aaIwtanN83GPbLDPlQP9+yD1mg4zRyLNBTmvvVOkNElK19\n9E0mS0yJMr/NsaDRWe7c0Sc1suA+c9cKHaeRY4Ge0lxjyTWyoPsquQbrVK/IQ/PbHMt03iM6\nqN63W48suM/MmZ6nRmDJ9B4111hqjyzV+QHTeZ4x//DHbwv7xTU9v+5cIktfkQX3mbtW6DiN\nHAv0lOYaS1+RZWC/uGbr1K4MKfPbHEtJZEEP1as/UmTBfeauFTpOI8cCPaW5xuJjEW3to28S\nWUq6WXw0oj3xft3RJ/5smc/lyC8LPE+NHAu8R839al0hI9HWPvrGeZ4xJvFHNfTIgi7o9w02\nyRKSpXLifeaudbn9sq79Aj2lucZSemTB78qT675O7UqXMr/NsaATWu6csfjcxLce3GfmQh2n\nEVgK9ZTmGkubkQU9WMl1YKd2pSea3waWziAQuXNHnzyRBfeZu1Z4nho5FniPmmss84ks6MFS\nXd4nDCy1RBZ0RL9vMJxRnpg4lTPvM3et0HEaORboKc39rVWfDZbJ35Un14md+pU6ZX6bY0FX\ntNy5o09aZMF95q4VOk4jxwI9pbnGkltkQQ+W6vI+YWCZI7JM5z2ih+p9u43IgvvMXOl5agSW\nSu9Rc42ljsiCHizV5X1CxzJCDJU9OX5dJktIosqZ95m7Vug4jRwL9JTmGst4Igt+V55cV3Ya\nVxqV+W2OBf3RcueOPsmRBfeZu1boOI0cC/SU5hrLypEFPVjJ9WWncSUvmt/mWBAWInfu6JNw\ntiy8z9y1wvPUyLHAe9TcjyWF+EVb++gb53nGBMYf1TAiC/qj3zfYIkuIp8qF95m71uX2y7r2\nC/SU5hpLGZEFvytPri87jSuhyvw2x4L+aLlzxhLSGHc9uM/MjTpOI7A06inNNRYfx2hrGwv6\nstO4EhnNbwPLZHKI3LmjT1JkwX3mrhWep0aOBd6j5hqLj2W0tY++wdlyXsmMP997NbKgP/p9\ng2G/5BhYlSvvM3et0HEaORboKc39WHIIrLK1j76pZImZVea3ORb0R8udO/qkRxbcZ+5aoeM0\ncizQU5prLD6m0dY++qaTJSY1mt/mWKbzHtFD9b7dZmTBfWbu9Dw1Akun96i5xuLjGm1tY5nO\n84yJjT/fe2G/uP7o9w1WyDJSZMF95q4VOk4jxwI9pbnGMlJkwe/Kk+vLTr5vWyw+utGeeL/u\n6JMSWXCfuWuFjtPIsUBPaa6x+PhGW/vom0KWmOBofptjQZaI3LmjT8LZsvE+c9cKz1MjxwLv\nUXM/lhJCHG3to2+c5xlzHH/8thlZ0B+9Pw+/C3/fbjOyFLdfltsv69ov0FOaaywh0UprGwv6\nstO6Mq3Mbzss2fVHy507+uSJLLjPzIM6TiOwDOopzTUWH+hoax99g7nPlelofptjyc57RA/V\nqz9yZMF95q4VnqdGjgXeo+Yai491tLWPvnGeZ0x2/FENLbKgP/p9g2G/1JhulTvvM3et0HEa\nORboKc39aq0x3UprH33TyBLzrcxvcyzoj5Y7Zywh4nHXg/vMPKjjNHIs0FOaayw+4tHWNhb0\nZefnSnk0v82xIEtE7pyxhKDHXQ/uM/Ok56kRWCa9R801Fh/0aGsbCzJMXN1iSTHnSk+OX1fJ\nEnKucud95q4VOk4jxwI9pbnGEnKutPbRN5ib7pyrnELkoz3xft3RJzWy4D5z1wodp5FjgZ7S\nXGNZNbKgByu7vuycrtxH89scC7JE5M4dfRLOloP3mbtWeJ4aORZ4j5r71dpi8qPWPvrGeZ4x\n+/HHb1uRBf3RrzuXyBJyrvLgfeaudbn9sq79Aj2lucYScq60trGgLzunO+cq5xAAaU+8X3f0\nSYosuM/MizpOI7As6inNNZaQAKm1j77B3HxlQJrf5liQJSJ37uiTEllwn7lrheepkWOB96i5\nxhJSILX20TfO84w5kD+qoUcW9Ee/bzDslx5zrvLkfeaulTpuxZwrPfnqmbyP1GeDpfJ35dn1\nZed851zlHMIg7Yn364wlpEHuenCfuWuljlsxDVJPjAX3kfpsx4IerOz6snO+8iDNb3Msy3mP\n6KF69ccTWXCfWR56nhodFj0xFtxH6rMdS3N+wHKe55UJmUvMucquP/p9gzWyjBJZcJ+5a4WO\n08ixQE9prrGMElnwu/Ls+rJzuXOucomZkNn1R8udO/qkRRbcZ+5aoeM0cizQU5prLCETUmsf\nfdPIcmVCym9zLMgSkTtnZ8eQCbl3AO4zd63wPDVyLPAeNferdcRMSK1tLM15nlcmZC4x5yq7\n/ujXnctkCTlXefE+syTqOI3AkpyeWryP1Gc7FvyuPLu+7FzunKtcYyZkdv3RcueOPsmRBfeZ\nu1boOI0cC/XU4n2kPtuxoAcru77sXO9MSPltjiU77xE9VO/brUYW3GfuWuF5auRY4D1qrrGE\nTEitffSN8zyvTMhcY85Vdv3R7xsM+2XGnKvy8D5z1wodp5FjgZ7S3K/WGXOutPbRN4MsV85V\nZv/zPz7xfp2xhEzIXQ/uM3et0HEaORboKc01lpAJqbWNpS+y9HWzhExIPfF+3dEnKbLgPrNk\nep4agSXTe9RcYwmZkFr76JtElisTMreYc5Vdf/T7ButkCTlX5eF95q4VOk4jxwI9pbnGEnKu\ntPbRN9gv7c65yi1mQmbXHy137uiTHllwn7lrhY7TyLFAT2musYRMSK199E0ny5UJKb/NsSBL\nRO6cnR1DJuTeAbjP3LXC89TIscB71Nyv1hUzIbW2sTTneV6ZkLnFnKvs+qPfN1ghS8i5Kon3\nmaVQx2kElkI9pbnGEnKutPbRN4ksV85V7jETMrv+aLlzR5+UyIL7zF0rdJxGjgV6SnONJWRC\nau2jb3C27HcmpPw2x5Kd94geqvft1iIL7jN3rfA8NXIs8B4111hCJqTWPvrGeZ5XJmTuMecq\nu/7ovSPxu/D37Rb2S+Z95q4VOk4jxwI9pbm/teqzwTL5u/Ls+rJzv3Ouco+ZkNn1R8udO/rk\niSy4z9y1Qsdp5FigpzTXWEImpNY++uYhy5UJKb/NsSznPaKH6tUfObLgPrNUep4agaXSe9Rc\nYwmZkFr76BvneV6ZkHnEnKvs+qPfN9ggS8i5Kpn3mbtW6DiNHAv0lOYaS8i50tpH32C/jDvn\nKo+YCZldf7TcOWMJmZC7Htxn7lqh4zRyLNBTmmssIRNSaxsL+rLzuDMh5bc5FmSJyJ2zs2PI\nhNw7APeZu1Z4nho5FniPmvuxpJgJqbWNBRkmru7DEnKusuuPft9glSwh56oU3meWRh2nEVga\n9ZTmGkvIudLaR99kslw5V3nGTMjs+qPlzh19UiML7jN3rdBxGjkW6CnNNZaQCam1j77B2XLe\nmZDy2xxLdt7jdHo+ZELuenCfuWuF56mRY4H3qLnGEjIhtfbRN87zvDIh889vGvP4uZT6xye/\nl1avO5f26PdC6327LY20A7YatP/eXgib+41+PlssP09+L7w09/eKS5/9MzJnMNl/V12/c13d\nh2Wk+O8ycD582EP16o9wtqy8z9y1Qsdp5P5doKc09/t3yVCRzhk8+ibx32Wk+98lZELqiffr\njj4pkQX3maXT89QILJ3eo+YaS8iE1NpH3zjP88qEzCvmXGXXH/2+wSZZQs7V+y1byQIdp5Fj\ngZ7SXGMJOVda++gb7Jd151zlFTMhs+uPljtnLCETcteD+8xdK3ScRo4FekpzjSVkQmptY0Ff\ndl53JqT8NsfSnfeIHqpXf4SzZeN95q4VnqdGjgXeo+Z+LCVmQmrto2+c53llQuYVc66y649+\n32CNLCHnqjTeZ5ZBHacRWAb1lOYaS8i50tpH3xSyXDlX5YmZkMX1R8udO/qkRRbcZ+5aoeM0\ncizQU5prLCETUmsffYO5z50JKb/NsWTnPaKH6n27jciC+8xdKzxPjRwLvEfNNZaQCam1jSU7\nz/PKhCxPzLkqrj/6deewX2rMuSqd95m7Vug4jRwL9JTmfrXWmHOltY++echy5VyVJ2ZCFtcf\nLXfu6JMcWXCfuWuFjtPIsUBPaa6xhExIrX30TSbLlQkpv82xLOc9oofqfbvVyIL7zDLpeWoE\nlknvUXONJWRCau2jb5zneWVCvqoh7JfE/uj3DbbIEnKuSud95q4VOk4jxwI9pbnGEnKutPbR\nN5ib7pyrkmImZHH90XLnjCVkQu56cJ+5a4WO08ixQE9prrGETEitbSzoyy7pzoSU3+ZYkCUi\nd+7ok3C2HLzP3LXC89TIscB71Nyv1hYzIbX20TeJLFcmZEkx56q4/uj3DdbJEnKuyuB9ZlnU\ncRqBZVFPaa6xhJwrrX30TSXLlXNVcsyELK4/Wu6csYRMyF0P7jN3rdBxGjkW6CnNNZaQCam1\n7byGvuyS70xI+W2OJTvvcTk9HzIhdz24z9y1wvPUyLHAe9RcYwmZkFrbWLLzPK9MyJJjzlVx\n/dHvGwz7pf/ea4Fl8j5z10odt5xe+nOefPVM3kfqs8FS+bvy4vqyS75zruS3OZZRyIIeqvft\nViIL7jN3rdRxK2ZC6omx4D5Sn+1Y0IOlurxPGFhCJqSeeL/u6JMWWXCfWR96nhodFj0xFtxH\n6rMdC3qwVJf3CR1LiTlXxfVH78/D78Lft9uMLLjP3LVCx2nkWKCnNNdYQs6V1razJ/qyS7lz\nrkqJmZDF9UfLnTv65IksuM/ctULHaeRYoKc096t1xExIrX30zUOWKxNSfptj6c57RA/Vqz/C\n2XLxPnPXCs9TI8cC71FzjSVkQmrto2+c53llQpYSc66K649+32CDLCHnqizeZ9ZEHacRWJLT\nU4v3kfpsx4LflRfXl13KnXNVasyELK4/+tedA0vIhNz14D5z1wodp5FjoZ5avI/UZzuWju9s\n15dd6p0JKb/NsSBLRO6csYRMyF0P7jN3rciE1MixIBNSc40lZEJqbWNBhomr+7CEnKvi+qPf\nNxj2y4w5V/XhfeauFTpOI8cCPaW5X60z5lxp7aNvMlmunCv5bY4F/dFy544+qZEF95m7Vug4\njRwL9JTmGkvIhNTaR99UslyZkPLbHMty3iN6qN63W48suM+smZ6nRmDJ9B4111hCJqTWPvrG\neZ5XJmRpMeequP7o151LZAk5V/XhfeauFTpOI8cCPaW5xhJyrrS2nT3Rl13anXNVWsyELK4/\nWu7c0ScpsuA+c9cKHaeRY4Ge0tyv1hUzIbX20TeJLFcmpPw2x4IsEblzR5/4s+X7LZvIAs9T\nI8cC71FzjSVkQmrto2+c53llQpYWc66K649+32CTLCHnqibeZ9ZCHacRWAr1lOYaS8i50tpH\n33SyXDlXpcdMyOL6o+XOGUvIhNz14D5z1wodp5FjgZ7SXGMJmZBa286e6Msu/c6ElN/mWIrz\nHpfT8yETcteD+8xdKzxPjRwLvEfNNZaQCam1j75xnueVCVl6zLkqrj/6fYPhjPLEnKuaoTTf\nWqHjNHIs0FOa+1urPhssk78rL64vu/Q756r0mAlZXH+03LmjT1pkwX3mrhU6TiPHAj2lucYS\nMiG19tE3jSxXJqT8NseynPeIHqr37TYiC+4za6XnqRFYKr1HzTWWkAmptY1lOc/zyoQsI+Zc\nFdcf/bpzmSwh56pm3mfuWqHjNHIs0FOaaywh50prH32D/TLunKsyYiZkcf3RcueOPglny8L7\nzF0rdJxGjgV6SnM/lhQzIbX20TeZLFcmpPw2x9Kd94geqvftViML7jN3rfA8NXIs8B4111hC\nJqTWPvrGeZ5XJmQZMeequP7o9w22yBJyrmrhfWZt1HEagaVRT2musYScK6199M0gy5VzVWbM\nhCyuP1runLEclSkW3GfuWqHjNHIs0FOaayxHRTpn0M6e6Msu886ElN/mWEoiC3qoXv2RIgvu\nM3et8Dw1cizwHjXXWEImpNY++iaRpaSbJeRcFdcf/b7BsF9yzLmqlfeZu1boOI0cC/SU5n4s\nOeZcae2jbypZrpyrMmMmZHH90XLnjj7pkQX3mbtW6DiNHAv0lOYaS8iE1NpH33SyXJmQ8tsc\ny3LeI3qo3rfbjCy4z6ydnqdGYOn0HjXXWEImpNY2luU8zysTsqyYc1Vcf/T7BitkCTlXux7c\nZ+5aoeM0cizQU5prLCHnSmsffYP9su6cq7JiJmRx/dFy544+CWfLxvvMXSt0nEaOBXpKcz+W\nEjMhtfbRN4UsVyak/DbH0p33iB6q9+3WIgvuM3et8Dw1cizwHjXXWEImpNY++sZ5nlcmZFkx\n56q4/uj9efhd+Pt2m5EF95l1UMdpBJZBPaW5xhJyrrS2saAvu6w756o+MROyuv5ouXNHnzyR\nBfeZu1boOI0cC/SU5hpLyITU2kffYO5zZ0LKb3MsxXmPy+n5kAn5fss+ZIHnqZFjgfeoucYS\nMiG19tE3zvO8MiHrE3OuquuPft9g2C815lzVzvvMXSt0nEaOBXpKc79aa8y50tpH3zSyXDlX\n9YmZkNX1R8udM5aQCbnrwX3mrhU6TiPHAj2lucYSMiG1trGgL7s+dyak/DbHgiwRuXPGEjIh\ndz24z6yTnqdGYJn0HjXXWEImpNY2FmSYuLrFkmLOVXX90e8brJIl5FzVzvvMXSt0nEaOBXpK\nc40l5Fxp7aNvMDfdOVc1xUzI6vqj5c4dfRLOloP3mbtW6DiNHAv0lOZ+tbaYCam1j76pZLky\nIeW3OZbuvEf0UL1vtx5ZcJ+5a4XnqZFjgfeoucYSMiG19tE3zvO8MiFrijlX1fVHv+5cIkvI\nuaqD95l1UcdpBJZFPaW5xhJyrrS2saAvu6Y756qy//kfn3i/7uiTFFlwn7lrhY7TyLFAT2mu\nsRwV6ZzBo28w1/dtiyVkQuqJ9+uOPimRBfeZu1Z4nho5FniPmmssIRNSax994zzPKxOy5phz\nVV1/9PsGw37pMeeqTt5n7lqp41bMudKTr57J+0h9Nlgqf1deXV92zXfOlfw2x4L+aLlzxhIy\nIXc9uM/ctVLHrZgJqSfGgvtIfbZjQQ+W6vI+oWMpMRNST7xfd/TJE1lwn9keep4aHRY9MRbc\nR+qzHQt6sFSX9wkDS8i5qq4/+n2DNbKEnKs6eZ+5a4WO08ixQE9prrGEnCutffRNIcuVc1VL\nzISsrj9a7tzRJ+FsuXifuWuFjtPIsUBPae5X64iZkFr76JtGlisTUn6bY+nOe0QP1ft2G5EF\n95m7VnieGjkWeI+aaywhE1JrG0t3nueVCVlLzLmqrj/6decyWULOVV28z2yJOk4jsCSnpxbv\nI/XZjqW5/bLcfrlyrmqNmZDV9UfLnTv6JEcW3GfuWqHjNHIs1FOL95H6bMeCHqzq+rJrvTMh\n5bc5luK8x+X0fMiEfL9lM1ngeWrkWOA9aq6xhExIrX30jfM8r0zIWmPOVXX90e8bDPtlxpyr\n9vA+c9cKHaeRY4Ge0tyv1hlzrrT20TeDLFfOlfw2x4L+aLlzxhIyIXc9uM/ctULHaeRYoKc0\n11hCJqTWNhb0Zbu6xdJiJqSeeL/u6JMUWXCf2TI9T43Akuk9aq6xhExIrX30Dc6W7c6ErC3m\nXFXXH/2+wTpZQs5Ve3ifuWuFjtPIsUBPaa6xhJwrrX30TSXLlXNVW8yErK4/Wu7c0Sf+bLn/\nanCfuWuFjtPIsUBPae5X64qZkFr76JtOlisTUn6bY+nOe0QP1ft2m5EF95m7VnieGjkWeI+a\naywhE1JrG0t3nueVCVlbzLmqrj/6fYMVsoScq5Z4n9kKdZxGYCnUU5prLCHnSmsffeP2y5Vz\nVXvMhKyuP1ru3NEnJbLgPnPXCh2nkWOBntJcYzkq0jmDR9/gbNnvTEj5bY6lOO9xOT0fMiHf\nb9lCFnieGjkWeI+aaywhE1JrH33jPM8rE7L2mHNVXX/0/nbB78Lft1vYL5n3mbtW6DiNHAv0\nlOb+1qrPBsvk78qr68uu/c65qj1mQlbXHy137uiTJ7LgPrNV6jiNwFKppzTXWEImpNY++uYh\ny5UJKb8NLINZInLnjj7JkQX3mbtWeJ4aORZ4j5prLCETUmsffYOz5bgzIeuIOVfV9Ue/b7BB\nlpBz1TLvM3et0HEaORboKc01lpBzpbWPvmlkuXKu6oiZkNX1R8uds/NWyITcOwD3mbtW6DiN\nHAv0lOZ+LClmQmptY0Ffdh13JqT8NsfSnfdYEllCJuSuB/eZu1Z4nho5FniPmmssIRNSaxtL\nd55nXzfLuvYL+qPfN1glS8i5aoX3ma1Rx2kElkY9pbnGEnKutPbRN26/XDlXdcZMyOr6o+XO\nHX1SIwvuM3et0HEaORboKc01lpAJqbWPvsHZct6ZkPLbHEtx3uNyej5kQu56cJ+5a4XnqZFj\ngfeoucYSMiG19tE3zvO8MiHrjDlX1fVHv+4c9kuOOVet8j5z1wodp5FjgZ7S3I8lx5wrrW0s\n6Muu8865qjNmQlbXHy137uiTFFlwn9k6dZxGYOnUU5prLCETUmsffZPIcmVCym8Dy2KWiNy5\no09KZMF95q4VnqdGjgXeo+YaS8iE1NpH3+Bsue5MyLpizlV1/dHvG2ySJeRctcr7zF0rdJxG\njgV6SnONJeRcae2jbzpZrpyrumImZHX90XLn7OwYMiH3DsB95q4VOk4jxwI9pbkfS4mZkFrb\nWNCXXdedCSm/zbEM5z2ih+rVH09kwX3mrhWep0aOBd6j5hpLyITU2kffOM/zyoSsK+ZcVdcf\n/b7BGllCzlVrvM9sgzpOI7AM6inNNZaQc6W1j75x++XKuWpPzIRsrj9a7tzRJy2y4D5z1wod\np5FjgZ7SXGMJmZBa++gbzH3uTEj5bY6lOO8RPVTv221EFtxn7lrheWrkWOA9aq6xhExIrW0s\nxXmeVyZke2LOVXP90a87h/1SY85V67zP3LVCx2nkWKCnNPertcacK6199M1Dlivnqj0xE7K5\n/mi5c0ef5MiC+8w2qeM0AsukntJcYwmZkFr76JtMlisTUn4bWBKzROTOHX1SIwvuM3et8Dw1\ncizwHjXXWEImpNY++gZz050J2VLMuWquP/p9gy2yhJyr1nmfuWuFjtPIsUBPaa6xhJwrrX30\nzSDLlXPV2P/8j0+8X2dnx6MytQNwn7lrhY7TyLFAT2nuV2uDinTOoLGgL7v5vm2xjBRZRiIL\neqhe/ZEiC+4zd63wPDVyLPAeNddYjop0zuDRN4ksI90s69ov6I9+32CdLCHnqg3eZ7ZFHacR\nWBb1lOYaS8i50tpH37j9cuVctRwzIZvrj5Y7d/RJjyy4z9y1Qsdp5FigpzTXWEImpNY++gZz\n850JKb/NsSBLRO6csYRMyF0P7jN3rfA8NXIs8B4111hCJqTWNpbiPM8rE7LlmHPVXH/0+wbD\nfukx56pN3mfuWqnjVsy50pOvnsn7SH22Y8Hvypvry275zrlqOWZCNtcfLXfu6JMSWXCf2R/q\nOI0Oi54YC+4j9dmOBT1YzfVlt3xnQspvA0thlojcuaNPWmTBfeauFZ6nRo4F3qPmGkvIhNTa\nR99gbrkzIVuJOVfN9Ufvz8Pvwt+324wsuM/ctULHaeRYoKc011jmtV/wu/Lm+rJbuXOuWomZ\nkM31R8udO/oknC0X7zN3rdBxGjkW6CnN/WodMRNSax9985DlyoSU3+ZYhvMe0UP16o8cWXCf\nuWuF56mRY4H3qLnGEjIhtfbRN87zvDIhW4k5V831R79vsEGWkHPVFu8ze6KO0wgsyempxftI\nfbZjaW6/LLdfrpyrVmMmZHP90XLnjCVkQu56cJ+5a4WO08ixUE8t3kfqsx0LerCa68tu9c6E\nlN/mWJAlInfOWEIm5K4H95m7VnieGjkWeI+aaywhE1JrGwsyTFzdhyXkXDXXH/2+wbBfZsy5\n6g/vM3et0HEaORboKc39ap0x50prH32TyXLlXLUaMyGb64+WO3f0SY0suM/smTpOI7Bk6inN\nNZaQCam1j76pZLkyIeW3gaUxS0Tu3NEnPbLgPnPXCs9TI8cC71FzjSVkQmrto29wtmx3JmRr\nMeequf7o151LZAk5V/3hfeauFTpOI8cCPaW5xjKv/YLflTfXl93anXPVWsyEbK4/Wu7c0Sf+\nbLl3AO4zd63QcRo5Fugpzf1qXVCRzhk8+iaR5cqElN/mWIbzHtFD9b7dSmTBfeauFZ6nRo4F\n3qPmGkvIhNTaR984z3OUm2Vd+wX90e8bbJIl5Fz1xPvMXqjjNAJLoZ7SXGMJOVda++gbt1+u\nnKvWYyZkc/3RcueMJWRC7npwn7lrhY7TyLFAT2musYRMSK1tZ0/0Zbd+Z0LKb3MsyBKRO3f0\nyRNZcJ+5a4XnqZFjgfeoub+16rPBMtmDpbq8TxhYQs6Vnhy/rpEl5Fz1zPvMXSt0nEaOBXpK\nc40l5Fxp7aNvClmunKvWYyZkc/3RcueOPmmRBfeZvVLHaQSWSj2lucYSMiG19tE3jSxXJqT8\nNrAMZonInTOWkAm568F95q4VnqdGjgXeo+YaS8iE1Np2XkOGiav7sIScq+b6o193zu2XkHPV\nM+8zd63QcRo5FugpzTWWde0X/K68ub7sNu6cqzZiJmRz/dFy544+CWfLwvvMXSt0nEaOBXpK\ncz+WFDMhtfbRN5ksVyak/DbHMpz3iB6q9+1WIwvuM3et8Dw1cizwHjXXWEImpNY++sZ5nlcm\nZBsx56q5/uj3DbbIEnKueuF9Zm/UcRqBpVFPaa6xhJwrrX30jdsvV85VmzETsrn+aLlzxhIy\nIXc9uM/ctULHaeRYoKc011hCJqTWtrMn+rLbvDMh5bc5FmSJyJ07+iRFFtxn7lrheWrkWOA9\nau7HkmMmpNY++iaR5cqEbDPmXDXXH/2+wTpZQs5Vr7zP3LVCx2nkWKCnNNdYQs6V1j76ppLl\nyrlqM2ZCNtcfLXfu6JMeWXCf2Tt1nEZg6dRTmmssIRNSax9908lyZULKbwPLYpaI3DljCZmQ\nux7cZ+5a4Xlq5FjgPWqusYRMSK1tZ09kmLi6D0tJkaW4/bLcfgk5V73yPnPXCh2nkWOBntJc\nY1nXfsHvypvry26+b1ssIROyuf5ouXNHn4SzZeN95q4VOk4jxwI9pbkfS4mZkFr76JtClisT\nUn6bYxnOe0QP1ft2a5EF95m7VnieGjkWeI+aaywhE1JrH33jPM8rE7KtmHPVXH/0/jz8Lvx9\nu83IgvvMPqjjNALLoJ7SXGMJOVda21iW2y9XzlV/YiZkd/3RcueOPnkiC+4zd63QcRo5Fugp\nzTWWkAmptY++wdznzoSU3+ZYkCUid+7ok3C27LzP3LXC89TIscB71Nyv1hozIbX20TfO87wy\nIfsTc666649+32CDLCHnqnfeZ+5aoeM0cizQU5prLCHnSmsffdPIcuVc9SdmQnbXHy13zlhC\nJuSuB/eZfVLHaQSWST2lucYSMiG1trGgL7s/dyak/DawJGaJyJ0zlqMyxYL7zF0rPE+NHAu8\nR801lqMinTNoZ09kmLi6D0vIuequP/p9g7n9sq79gvvMXSt0nEaOpbn9stx+Wdd+we/Ku+vL\n7unOueopZkJ21x8td+7ok3C2HLzP3LVCx2nkWKCnNPertcVMSK199E0ly5UJKb/NsQznPaKH\n6n279ciC+8xdKzxPjRwLvEfNNZaQCam1j75xnueVCdlTzLnqrj/6decSWULOVR+8z+yLOk4j\nsCzqKc01lpBzpbWNZbn9cuVc9RwzIbvrj5Y7d/RJiiy4z9y1UsetmAmpJ8aC+0h9tmNBD1Z3\nfdk935mQ8tscC7JE5M4dfRLOlpP3mbtWeJ4aORZ4j5r71dpjJqTWPvrGeZ5XJmTPMeequ/7o\n9w02yRJyrvrkfeaulTpuxZwrPTEW3Efqsx0LflfeXV92z3fOVc8xE7K7/mi5c8YSMiF3PbjP\nHA91nEaHRU+MBfeR+mzHgh6s7vqye74zIeW3gaUwS0Tu3NEnT2TBfeauFZ6nRo4F3qPmGkvI\nhNTaR99gbrkzIXuJOVfd9Ue/bzC3X9a1X3CfuWuFjtPIsTS3X5bbL+vaL/hdeXd92b3cOVe9\nxEzI7vqj5c4dfRLOlov3mbtW6DiNHAv0lOZ+tY6YCam1j75pZLkyIeW3OZbhvEf0UL1vtxFZ\ncJ+5a4XnqZFjgfeoucYSMiG1trEM53lemZC9xpyr7vqjX3cukyXkXPXF+8yRqOM0Aktyemrx\nPlKf7Vjwu/Lu+rJ7vXOueo2ZkN31R8udO/okRxbcZ+5aoeM0cizUU4v3kfpsx4IerO76snu9\nMyHltzkWZInInTv6xJ8t32/ZTBZ4nho5FniPmvvVOmMmpNY++sZ5nlcmZK8x56q7/uj3DbbI\nEnKuxsP7zF0rdJxGjgV6SnONJeRcae2jbwZZrpyrXmMmZHf90XLnjCVkQu56cJ85MnWcRmDJ\n1FOaaywhE1JrGwv6snu9MyHlt4Hle+L9uqNPUmTBfeauFZ6nRo4F3qPmGstRkc4ZPPoGZ0vW\nfVhCzlV3/dHvG8ztl3XtF9xn7lqh4zRyLM3tl+X2y7r2C35X3l1fdm93zlVvMROyu/5ouXNH\nn/iz5d4BuM/ctULHaeRYoKc096t1xUxIrX30TSfLlQkpv82xDOc9oofqfbvNyIL7zF0rPE+N\nHAu8R801lpAJqbWNZTjP88qE7D3mXHXXH/2+wQpZQs7VSLzPHIU6TiOwFOopzTWWkHOltY++\nwX7pd85V7zETsrv+aLlzR5+UyIL7zF0rdJxGjgV6SnONJWRCau2jbwpZrkxI+W2OBVkicueO\nPvFny/dbtpAFnqdGjgXeo+b+1qrPBstkD5bq8j5hYAk5V3piLPhd+Pt2m5EF95m7Vug4jRwL\n9JTmGkvIudLaxoK+bFf3YQmZkN31R8udO/rkiSy4zxyVOk4jsFTqKc01lpAJqbWPvnnIcmVC\nym8Dy2CWiNy5o09yZMF95q4VnqdGjgXeo+YaS8iE1NpH3+BsOe5MyD5izlV3/dHvG8ztl3Xt\nF9xn7lqh4zRyLM3tl+X2y7r2C35X3l1fdh93zlUfMROyu/5ouXN2dgyZkHsH4D5z1wodp5Fj\ngZ7S3I8lxUxIrW0s6Mvu486ElN/mWJAlInfOWEIm5K4H95m7VnieGjkWeI+aaywhE1JrGwsy\nTFzdYpkx56q7/uj3DVbJEnKuRuF95mjUcRqBpVFPaa6xhJwrrX30DfbLvHOu+oyZkN31R8ud\nO/qkRhbcZ+5aoeM0cizQU5prLCETUmsffVPJcmVCym9zLMgSkTt39Ek4W1beZ+5a4Xlq5Fjg\nPWrux5JjJqTWPvrGeZ5XJmRn//M/Pjl+XSJLyLkalfeZu1boOI0cC/SU5hpLyLnS2sbSF1n6\nf7NfQiZkd/3RcueOPkmRBfeZo1PHaQSWTj2lucYSMiG19tE3iSxXJqT8NrAsZonInTv6pEQW\n3GfuWuF5auRY4D1qrrEcFemcwaNvcLZcdyZkXzHnqrv+6PcN5vbLuvYL7jN3rdBxGjmW5vbL\ncvtlXfuFvyt3fdl93TlXfcVMyO76o+XO2dkxZELuHYD7zF0rdJxGjgV6SnM/lhIzIbW2saAv\nu687E1J+m2OZzntED9WrP57IgvvMMeh5agSWQe9Rc40lZEJq7aNvnOd5ZUKOJ+ZcDdcf/b7B\nGllCztVovM/ctULHaeRYoKc011hCzpXWPvoGc58752o8MRNyuP5ouXNHn7TIgvvMXSt0nEaO\nBXpKc40lZEJq7aNvGlmuTEj5bY4FWSJy5+y8FTIh9w7AfeauFZ6nRo4F3qPmfrXWmAmptY2l\nOs/zyoQcT8y5Gq4/+nXnMllCztXovM/ctS63X9a1X6CnNNdYQs6V1j765iHLlXM1npgJOVx/\ntNy5o09yZMF95pjUcRqBZVJPaa6xhExIrX30TSbLlQkpvw0siVkicueOPqmRBfeZu1Z4nho5\nFniPmmssIRNSax99g7npzoQcKeZcDdcf/b7B3H5Z137BfeauFTpOI8fS3H5Zbr+sa7/gd+XD\n9WWPdOdcjRQzIYfrj5Y7Z2fHkAm5dwDuM3et0HEaORboKc39am0xE1JrGwv6ske6MyHltzkW\nZInInTv6JEUW3GeORc9TI7Aseo+aaywhE1JrH32TyHJlQo4cc66G649+32CdLCHnagzeZ+5a\noeM0cizQU5prLCHnSmsffYO5+c65GjlmQg7XHy137uiTHllwn7lrpY5bMRNST4wF95H6bMeC\nHqzh+rJHvjMh5bc5FmSJyJ2zs2PIhNw7APeZu1Z4nho5FniPmvvV2mMmpNY2luo8zysTcny/\naXQsI5EFvwt/9UeKLNntl+X2y7r2C/SU5hpLSZEFvytXXccn/G/2S8iEHK4/Wu7c0SclsuA+\ncz7UcRodFj0xFtxH6rMdC3qwhuvLHvnOhPz12/KvXP3HJ/3nwPPrzuXx+3+K/H27NY3sW7bY\nf9+1ljNXo5/PFsvPk185rLnPjwmgz/4ZwRm0//7V9c1l3Ycl5FwN1x+9d+Tj9su69gvuM3et\n0HEauX+X5vbLcvsl5FxpbTtHoy97lDvnapSYCTlcf7TcuaNPwtly8T5z1wodp5FjgZ7S3K/W\nETMhtfbRNw9ZrkxI+W2OZTrvET1Ur/7IkQX3mTPR89QILIneo+YaS8iE1NpH3zjP88qEHDXm\nXA3XH/2+wQZZQs7VWLzP3LVCx2nkWKinFu8j9dmOBb8rH64ve9Q752rUmAk5XH+03DljCZmQ\nux7cZ+5aoeM0cizUU4v3kfpsx4IerOH6ske9MyHltzkWZInInbOzY8iE3DsA95m7VnieGjkW\neI+a+9U6Yyak1jYWZJi4ug9LyLnSk+PXVbKEnKv58D5z17rcflnXfoGe0lxjCTlXWvvom0yW\nK+dq1JgJOVx/tNy5o09qZMF95szUcRqBJVNPaa6xhExIrX30TSXLlQkpvw0sjVkicueOPumR\nBfeZu1Z4nho5FniPmmssIRNSax99g7NluzMhR4s5V8P1R7/unNsv69ovuM/ctULHaeRYmtsv\ny+2XkHOltY0Ffdmj3TlXo8VMyOH6o+XOHX3iz5Z7B+A+c9cKHaeRY4Ge0tyv1hUzIbX20TeJ\nLFcmpPw2x4IsEblzR5+UyIL7zFnoeWoElkLvUXONJWRCau2jb5zneWVCjh5zrobrj37fYJMs\nIedqJt5n7lqh4zRyLNBTmmssIedKax99g/3S75yr0WMm5HD90XLnjCVkQu56cJ+5a4WO08ix\nQE9prrGETEitbSzoyx79zoSU3+ZYkCUid+7oE3+23DsA95m7VnieGjkWeI+a+1urPhsskz1Y\nqsv7hIFllMgyClnwu/D37VYiS3H7Zbn9sq79Aj2lucYScq60trGMQpbx3+yXkAk5XH+03Lmj\nT1pkwX3mrMyE1AgslZmQmmssIRNSax9908hyZULKbwPLYJaI3DljCZmQux7cZ+5a4Xlq5Fjg\nPWqusYRMSK1tZ09kmLi6D0vIuRquP/p157BfUsy5mpn3mbtW6DiNHEt3+wX3kfpssCz+rny4\nvuwx7pyrMWIm5HD90XLnjj4JZ8vC+8xdK3ScRo4FekpzjSVkQmrto28yWa5MSPltjmU67xE9\nVO/brUYW3GfORs9TI7A0eo+aaywhE1JrH33jPM8rE3LMmHM1XH/0+wZbZAk5V7PwPnPXCh2n\nkWOBntJcYwk5V1r76Bvsl3nnXI0ZMyGH64+WO2csIRNy14P7zF0rdJxGjgV6SnONJWRCam1j\nQV/2mHcmpPw2x4IsEblzR5+Es2XlfeauFZ6nRo4F3qPmfiwZKtI5g0ffJLJcmZBjxpyr4fqj\n3zdYJ0vIuZqV95m71uX2y7r2C/SU5hpLyLnS2kffVLJcOVdjxkzI4fqj5c4dfdIjC+4zZ6eO\n0wgsnXpKc40lZEJq7aNvOlmuTEj5bWBZzBKRO2csIRNy14P7zF0rPE+NHAu8R801lpAJqbXt\n7IkME1f3YQk5V8P1R79vMOyXEnOuZuV95q4VOk4jx9LdfsF9pD4bLMn9rtz1ZY9151yNFTMh\nh+uPljt39Ek4WzbeZ+5aoeM0cizQU5prLCETUmsffVPIcmVCym9zLNN5j+ihet9uLbLgPnMO\nep4agWXQe9RcYwmZkFr76BvneV6ZkPOJOVfT9Ufvz8Pvwt+324wsuM/ctULHaeRYoKc011hC\nzpXWtrMn+rLnc+dczSdmQk7XHy137uiTJ7LgPnPXCh2nkWOBntJcYwmZkFr76JuHLFcmpPw2\nx4IsEblzR5+Es2XnfeauFZ6nRo4F3qPmfrXWmAmptY++cZ7nlQk5n5hzNV1/9PsGG2QJOVez\n8z5z17rcflnXfoGe0lxjCTlXWvvom0aWK+dqPjETcrr+aLlzxhIyIXc9uM+ck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HPnjKWPyNIbWfIgSx6RJQ+y9EYWnwlpa9t57cHcdGVC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ez4QsMh8Nd+7ok+osdJ+5eiUdh5WwkJ5C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\nUm9vdCAyIDAgUiA+PgpzdGFydHhyZWYKNDU0MDEKJSVFT0YK" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_pdf(file=paste0(out,'/Toy.finemap.pdf'))" - ] - }, - { - "cell_type": "markdown", - "id": "8739754f-3543-4f82-b2b6-02c0100ccf8e", - "metadata": {}, - "source": [ - "# SuSiE" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "6c60680f-ad0a-4c1f-89e2-95d411196949", - "metadata": {}, - "outputs": [], - "source": [ - "fitted = susieR::susie(X, y, L=5,\n", - " estimate_residual_variance=TRUE, \n", - " scaled_prior_variance=0.2,\n", - " tol=1e-3, track_fit=TRUE, min_abs_corr=0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "64d6fdde-db3e-4523-a9dd-acf9f6745efd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pdf(paste0(out,'/Toy.susie.pdf'), width =5, height = 5, pointsize=16)\n", - "susieR::susie_plot(fitted, y='PIP', b=b, max_cs=0.4, main = paste('SuSiE, ', length(fitted$sets$cs), 'CS identified'))\n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "ad585714-acd3-48a9-aacd-6cce997c59bf", - "metadata": {}, - "outputs": [ - { - "data": { - "application/pdf": 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CiyucbSc2Tp2C9fXcexu/YL20P/8Yl3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HPnjKWPyNIbWfIgSx6RJQ+y9EYWnwlpa9t57cHcdGVC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5V/n2TMgs89Fw57Y+Kc5C55mzV9JxWAkL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\nT0YK" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_pdf(file=paste0(out,'/Toy.susie.pdf'))" - ] - }, - { - "cell_type": "markdown", - "id": "850c55d2-3ab6-40bb-9b19-753b1b46138c", - "metadata": {}, - "source": [ - "## SuSiE Effect Size Estimate" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "d9359e4c-48fc-44ee-a683-cbe6b762acd0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bhat = coef(fitted)[-1]\n", - "pdf(paste0(out,'/Toy.susie_eff.pdf'), width =5, height = 5, pointsize=16)\n", - "susieR::susie_plot(bhat, y='bhat', b=b, main = 'SuSiE, effect size estimate') \n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "eaef4cf9-3429-477d-aa85-d14d1d4cef2e", - "metadata": {}, - "outputs": [ - { - "data": { - "application/pdf": 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CiyucbSc2Tp2C9fXcexu/YL20P/8Yl3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HPnjKWPyNIbWfIgSx6RJQ+y9EYWnwlpa9t57cHcdGVC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\nxixjMMsYzjIGs6TGL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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_pdf(file=paste0(out,'/Toy.susie_eff.pdf'))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R [conda env:simulate]", - "language": "R", - "name": "conda-env-simulate-r" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "4.0.5" - }, - "toc-autonumbering": false, - "toc-showcode": true, - "toc-showmarkdowntxt": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/snakemake/workflow/scripts/README.md b/snakemake/workflow/scripts/README.md deleted file mode 100644 index 5a9e855e..00000000 --- a/snakemake/workflow/scripts/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# scripts -This directory contains various scripts used by the pipeline. However, you can use most of these scripts on their own, too. Some may even be helpful in day-to-day use. - -All python scripts implement the --help argument. For bash and R scripts, you can run `head