# seandavi/awesome-single-cell

Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

Repository: https://github.com/seandavi/awesome-single-cell
Canonical: https://ross.abutalabs.com/products/awesome-single-cell
License: MIT
License Family: permissive
Topics: rna-seq-data, gene-expression, scrna-seq-data, bioinformatics, awesome-list, dimensionality-reduction, atac-seq, cell-differentiation, python, clustering, cell-populations, gene-expression-profiles, rna-seq-experiments, cell-clusters, analysis-pipeline, data-integration, data-visualization, single-cell, spatial-transcriptomics
Last push: 2026-08-21T19:10:24+00:00

## Health v2 (maintenance only)
Score: 97/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 95, longevity 100
- inputs: {"age_days": 3717, "days_push": 12, "days_rel": 33, "gap_med": 30.0, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3840, forks 1087 (observed 2026-08-28T04:08:23.968100+00:00)

## What it is
A community-curated awesome list of software packages, tutorials, databases, and data resources for single-cell omics analysis, covering RNA-seq, ATAC-seq, and spatial transcriptomics. It organizes tools by analysis task such as clustering, trajectory inference, batch correction, and cell type annotation.

## Use cases
- find software for single-cell RNA-seq analysis
- discover tools for cell clustering and dimensionality reduction
- look up batch-effect removal methods for scRNA-seq
- find trajectory and pseudotime inference packages
- locate spatial transcriptomics analysis tools
- find tutorials and workflows for single-cell data analysis
- discover single-cell databases and web portals

## When to choose
- starting a single-cell analysis project and surveying the tool landscape
- comparing available packages for a specific single-cell task like doublet detection or data integration
- building a bioinformatics curriculum or reading list

## When to avoid
- you need a ready-to-run analysis pipeline rather than a catalog of options
- you need software itself rather than links to it

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-visualization, developer-tools
- domain: bioinformatics, data-science, awesome-lists
- platform: cross-platform
- tags: single-cell, rna-seq, atac-seq, spatial-transcriptomics, genomics, curated-list

## Member repositories
- seandavi/awesome-single-cell (main) score 97

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:23.968100+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 2026-08-29T18:26:14.209189+00:00, confidence not recorded.
  - readme: https://github.com/seandavi/awesome-single-cell (fetched 2026-08-28T04:08:23.968100+00:00, sha 2369feb60b85)
- Data as of 2026-08-30T08:39:29.467469+00:00.