perturbvi 0.1.1

Infer regulatory modules through informative latent component model in the single-cell Perturb-seq data

perturbVI

perturbvi is a scalable approach to infer regulatory modules through informative latent component model in the single-cell Perturb-seq data.

Installation | Example | Notes | Version | Support | Other Software

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Installation

Users can download the latest repository and then use pip:

git clone https://github.com/mancusolab/perturbvi.git
cd perturbvi
pip install .

Get Started with perturbvi

1. infer

Perform inference using SuSiE PCA to find the regulatory modules from CRISPR perturbation data

perturbvi infer <exp_csv> <guide_csv> <gene_symbol_csv> -o=output --verbose

Arguments

• exp_csv: Path to the experiment CSV file.
• guide_csv: Path to the guide CSV file.
• -o=output: Specifies the output directory name or path.
• --verbose: For logging (Optional).

Example Usage

perturbvi infer data/exp.csv data/guide.csv -o=data/out --verbose

This will save the all the output files (including the parameter file params.pkl) into the data/out folder, which can be used for the downstream tasks outlined below.

Notes

• perturbvi uses JAX with Just In Time compilation to achieve high-speed computation. However, there are some issues for JAX with Mac M1 chip. To solve this, users need to initiate conda using miniforge, and then install perturbvi using pip in the desired environment.

Version History

TBD

Support

Please report any bugs or feature requests in the Issue Tracker. If users have any questions or comments, please contact Dong Yuan ([e-mail]) and Nicholas Mancuso ([e-mail]).

Other Software

Feel free to use other software developed by Mancuso Lab:

• SuShiE: a Bayesian fine-mapping framework for molecular QTL data across multiple ancestries.
• MA-FOCUS: a Bayesian fine-mapping framework using TWAS statistics across multiple ancestries to identify the causal genes for complex traits.
• SuSiE-PCA: a scalable Bayesian variable selection technique for sparse principal component analysis
• twas_sim: a Python software to simulate TWAS statistics.
• FactorGo: a scalable variational factor analysis model that learns pleiotropic factors from GWAS summary statistics.
• HAMSTA: a Python software to estimate heritability explained by local ancestry data from admixture mapping summary statistics.

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perturbvi is distributed under the terms of the MIT license.

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This project has been set up using Hatch. For details and usage information on Hatch see https://github.com/pypa/hatch.