# satijalab/seurat

R toolkit for single cell genomics

Repository: https://github.com/satijalab/seurat
Canonical: https://ross.abutalabs.com/products/seurat
Homepage: http://www.satijalab.org/seurat
Language: R
License: NOASSERTION
License Family: other
Topics: human-cell-atlas, single-cell-rna-seq, single-cell-genomics, cran
Last push: 2026-08-25T16:40:12+00:00

## Health v2 (maintenance only)
Score: 92/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 78, longevity 100
- inputs: {"age_days": 4123, "days_push": 8, "days_rel": 68, "gap_med": 76.5, "n_releases_24m": 7}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2790, forks 995 (observed 2026-08-28T04:07:22.058130+00:00)

## What it is
Seurat is an R toolkit for single-cell genomics developed by the Satija Lab, providing a complete pipeline for analyzing single-cell RNA-seq, multimodal, and spatially-resolved transcriptomics data. Version 5 adds scalable analysis methods, cross-modality integration via bridge integration, sketch-based workflows for millions of cells, and spatial dataset support.

## Use cases
- analyze single-cell rna-seq data in r
- cluster and visualize single cell gene expression
- integrate scrna-seq and scatac-seq datasets
- analyze spatial transcriptomics data like visium
- run differential expression on single cell clusters
- map query cells onto a multimodal reference atlas
- scale single cell analysis to millions of cells
- demultiplex hashed samples with hashtag oligos

## When to choose
- you work in R and need a mature, well-documented single-cell analysis pipeline
- you need multimodal or cross-modality integration (RNA + ATAC, protein, spatial)
- you need to analyze very large datasets with sketch-based or on-disk (BPCells) workflows
- you want extensive tutorials, vignettes, and an active community for scRNA-seq analysis

## When to avoid
- you need a Python-based single-cell workflow (consider Scanpy instead)
- you need raw sequencing processing or alignment rather than downstream analysis
- you need a non-genomics data analysis toolkit
- you cannot install R (version 4.0+) or its compiled dependencies

## Facets
- artifact type: library
- maturity: stable
- function: data-science, machine-learning, data-visualization, nlp
- domain: bioinformatics, data-science, machine-learning
- platform: windows
- tags: single-cell-genomics, scrna-seq, spatial-transcriptomics, multimodal-analysis, data-integration, clustering, dimensionality-reduction, differential-expression, r-package, cran, bioconductor-adjacent, human-cell-atlas, scatac-seq, reference-mapping, sketch-based-analysis, macos, linux, r

## Member repositories
- satijalab/seurat (main) score 92

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:22.058130+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:15:29.906498+00:00, confidence not recorded.
  - readme: https://github.com/satijalab/seurat (fetched 2026-08-28T04:07:22.058130+00:00, sha dc58ba5855b9)
  - homepage: http://www.satijalab.org/seurat (fetched 2026-08-29T09:55:11.866412+00:00, sha e01e7635fa55)
  - site_page: https://satijalab.org/seurat/articles/install_v5 (fetched 2026-08-29T09:55:11.875650+00:00, sha ee1f46d98a0a)
  - site_page: https://satijalab.org/seurat/news (fetched 2026-08-29T09:55:11.877799+00:00, sha a64dc7e4256d)
  - site_page: https://satijalab.org/seurat/authors (fetched 2026-08-29T09:55:11.880551+00:00, sha f19f7aa40c97)
- Data as of 2026-08-30T08:39:29.467469+00:00.