scverse/scanpy

What it solves

Scanpy provides a scalable way to analyze single-cell gene expression data, enabling researchers to process and understand complex biological datasets that can exceed one million cells.

How it works

Built jointly with anndata, the toolkit implements a Python-based pipeline for biological data analysis. It handles large-scale datasets efficiently and offers experimental compatibility with dask for datasets that are too large to fit into system memory.

Who it’s for

It is designed for scientists and researchers working with single-cell omics data who need tools for preprocessing, clustering, and trajectory inference.

Highlights

• Scalable processing for datasets with over one million cells.
• Comprehensive analysis suite including preprocessing, visualization, clustering, trajectory inference, and differential expression testing.
• Integration with the anndata format for data handling.
• Experimental support for dask to handle out-of-memory datasets.