BBP-me-type-to-mol-ID

Description:

This is a repository containing configuration code that use me-types-mapper to reproduce results from Roussel et al., 2021 (https://www.biorxiv.org/content/10.1101/2021.11.24.469815v1)

Installation:

To install this tool, run the following steps in your shell:

git clone https://bbpgitlab.epfl.ch/molsys/bbp_me_type_to_mol_id.git
pip install -r bbp_me_type_to_mol_id/requirements.txt
pip install git+https://bbpgitlab.epfl.ch/molsys/me_types_mapper.git
pip install git+https://github.com/BlueBrain/BluePyEfe@da783256a4212b14d4f152687238754a99d1a78b

How to use:

Step 1 and 2 are optional as we made the extracted features available in the feature_extraction folder.

Step 1: Download the data (optional)

Go into downloader directory

Morphologies and electrophysiological recording from Gouwens et al., 2019:

Due to open source license issues, the python script for downloading AIBS data from Gouwens et al. (2019) could not be included. Please refer to allensdk from Allen Institute for Brain Science to download data from the Gouwens et al paper. Clear instructions on how to download morphological and electrophysiological data are provided in the allensdk documentation: Electrophysiology: https://allensdk.readthedocs.io/en/latest/_static/examples/nb/cell_types.html#Cell-Types-Database Morphologies: https://allensdk.readthedocs.io/en/latest/_static/examples/nb/cell_types.html#Cell-Morphology-Reconstructions The cell ids used in the manuscript are provided in the downloader/41593_2019_417_MOESM5_ESM.xlsx file. Data should be stored in the downloader directory in a new folder named after the dataset name (i.e. Gouwens_2019) and organized into two sub-directories: one named as ephys_traces containing the raw traces in nwb format and another named morphologies containing the morphology reconstruction in swc format. Downloading of AIBS data should be done in a dedicated virtual environment.

Morphologies and electrophysiological recording from Blue Brain Project:

Create a dedicated virtual environment for downloading BBP data. Install dependencies using pip install -r requirements_BBP_dl.txt. Finally, run python BBP_downloader.py.

Step 2: Extract features (optional)

Go into the feature_extraction directory and run first python efeatures_extractor.py and then python mfeatures_extractor.py.

Step 3: Select common features

Go into the feature_selection directory and run first python feature_selector.py. It should create two directories named figures and filtered_datasets. Then run python compute_dataset_labels.py.

Step 4: Mapping

Go into the mapping directory and run python clustering.py to reproduce the main results of the paper (https://www.biorxiv.org/content/10.1101/2021.11.24.469815v1). To reproduce the other figures run python dataset_overlap_validation.py for figure 2, python alpha_optimization_validation.py for figure 3 and python pipeline_validation.py for Table 1.

Funding & Acknowledgment:

The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.

Copyright (c) 2022-2022 Blue Brain Project/EPFL