scanpy

Gene expression analyzer

A Python toolkit for analyzing large-scale single-cell gene expression data

Single-cell analysis in Python. Scales to >1M cells.

GitHub

2k stars
51 watching
602 forks
Language: Python
last commit: 6 days ago
Linked from 1 awesome list

anndatabioinformaticsdata-sciencemachine-learningpythonscanpyscversetranscriptomicsvisualize-data

Backlinks from these awesome lists:

Related projects:

Repository Description Stars
scverse/squidpy A Python library for analyzing and visualizing spatial molecular data from single-cell genomics and tissue images 440
scverse/scirpy Analyzes single-cell TCR and BCR data from scRNA-seq using Python 220
scverse/scvi-tools Probabilistic analysis tools for single-cell omics data 1,251
jamesjcai/scgeatoolbox A toolset for analyzing single-cell gene expression data from RNA sequencing experiments. 24
zji90/sepa A tool for analyzing single-cell RNA-seq data to identify gene expression patterns and perform GO enrichment analysis. 4
lanagarmire/ssrge A Python package that performs sparse linear model fitting on gene expression data and SNV matrices. 33
zqfang/gseapy A software package for performing gene set enrichment analysis in various types of biological data. 566
scverse/muon A Python framework for multimodal omics analysis of high-throughput biological data 218
jhu99/scbean Analyzes single-cell multi-omics data from various modalities like RNA-seq and ATAC-seq 16
lingfeiwang/normalisr A software framework for analyzing single-cell RNA sequencing data 18
nghiavtr/bpsc A package for analyzing single-cell RNA-seq data using a beta-Poisson model. 16
rabadanlab/sctda An object-oriented Python library for analyzing single-cell RNA-seq data using topological representations 7
cellgeni/scrna.seq.course An educational resource teaching computational analysis of single-cell RNA-seq data using R and Bioconductor tools 123
euxhenh/cellar An interactive software tool for analyzing single-cell omics data 31
icbi-lab/infercnvpy A Python library that infers copy number variation events from single-cell transcriptomics data. 137