scvi-tools and scanpy
scvi-tools builds probabilistic models for single-cell omics data analysis and integrates seamlessly with scanpy as its primary interface and data structure (AnnData), making them complements rather than competitors.
About scvi-tools
scverse/scvi-tools
Deep probabilistic analysis of single-cell and spatial omics data
This project helps single-cell biologists and researchers analyze complex single-cell and spatial omics data. It takes raw omics data and provides outputs like integrated datasets, reduced dimensionality representations, cell type annotations, and spatial insights. This is ideal for scientists working with high-throughput biological data to understand cellular states and tissue organization.
About scanpy
scverse/scanpy
Single-cell analysis in Python. Scales to >100M cells.
This tool helps biologists and researchers analyze single-cell gene expression data to understand cell types and states. It takes raw gene expression measurements from individual cells and helps visualize, cluster, and identify differences between cell populations. It's used by scientists working with large-scale single-cell omics data.
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