scverse/pertpy
Single-cell perturbation analysis
What it solves
It simplifies the analysis of large-scale single-cell perturbation experiments, allowing researchers to understand how cells respond to various stimuli such as drug treatments, genetic modifications, and environmental changes.
How it works
It provides an end-to-end framework that includes tools for:
- Harmonizing perturbation datasets.
- Automating metadata annotation.
- Calculating perturbation distances to measure the similarity between different stimuli.
- Performing differential gene expression analysis.
Who it’s for
Biologists and data scientists working with single-cell data to study the effects of cellular perturbations.
Highlights
- Part of the scverse ecosystem.
- Supports multiple solvers for differential gene expression, including pydeseq2 and edgeR.
- Integrates with tascCODA and milo for advanced analysis.
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