Single Cell Rna Seq Python Packages

scvi-tools

Deep probabilistic analysis of single-cell and spatial omics data

squidpy

Spatial Single Cell Analysis in Python

liana

LIANA+: an all-in-one framework for cell-cell communication

divik

Divisive Intelligent K-Means algorithm (DiviK) for joint feature selection and clustering of heavily multidimensional data.

cellrank

CellRank: dynamics from multi-view single-cell data

kb-python

A wrapper for the kallisto | bustools workflow for single-cell RNA-seq pre-processing

scdataloader

a dataloader to work with large single cell datasets from lamindb

scvi

Deep probabilistic analysis of single-cell and spatial omics data

scarf

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

toolchest-client

Python client for Toolchest

scdef

Deep exponential families for single-cell data.

scxpand

scXpand: detection of T-cell clonal expansion from single-cell RNA sequencing

sccoda

A Bayesian model for compositional single-cell data analysis

scxpand-cuda

scXpand: detection of T-cell clonal expansion from single-cell RNA sequencing

piaso-tools

Precise Integrative Analysis of Single-cell Omics.

irescue

Uncertainty-aware quantification of Transposable Elements expression in scRNA-seq

cell-blast

A BLAST-like toolkit for large-scale scRNA-seq data querying and annotation.

cxg-census-mcp

MCP server for CZ CELLxGENE Discover Census. Single-cell RNA-seq queries, ontology-aware (CL, UBERON, EFO), provenance-tracked. For Claude, Cursor, MCP clients.

scaden

Deep Learning based cell composition analysis with Scaden.

cello-classify

CellO: Gene expression-based hierarchical cell type classification using the Cell Ontology

transmorph

A unifying data integration framework.

scmalignantfinder

scMalignantFinder is a Python package specially designed for analyzing cancer single-cell RNA-seq datasets to distinguish malignant cells from their normal counterparts.

scope-bio

scOPE learns mutation-linked expression programs from bulk RNA-seq and projects them into single-cell RNA-seq to infer per-cell mutation probabilities, revealing subclonal evolutionary structure that complements CNV analyses.

patpy

Toolbox for single-cell data analysis on sample level