# theislab/single-cell-tutorial

Single cell current best practices tutorial case study for the paper:Luecken and Theis, "Current best practices in single-cell RNA-seq analysis: a tutorial"

Repository: https://github.com/theislab/single-cell-tutorial
Canonical: https://ross.abutalabs.com/products/single-cell-tutorial
Language: Jupyter Notebook
License Family: other
Last push: 2022-12-11T23:19:01+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2851, "days_push": 1361, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1589, forks 489 (observed 2026-08-28T04:05:08.107668+00:00)

## What it is
A Jupyter Notebook-based tutorial and case study accompanying the paper 'Current best practices in single-cell RNA-seq analysis: a tutorial' by Luecken and Theis. It demonstrates best-practice workflows for single-cell RNA-seq analysis applied to a mouse intestinal epithelium dataset.

## Use cases
- learn single-cell RNA-seq analysis best practices
- reproduce figures from the single-cell RNA-seq tutorial paper
- follow a case study analyzing mouse intestinal epithelium scRNA-seq data
- set up a conda environment for single-cell analysis with Scanpy
- study marker gene detection workflows
- learn preprocessing, clustering, and annotation of scRNA-seq data

## When to choose
- you want a guided, paper-backed walkthrough of classic scRNA-seq analysis steps
- you need reproducible notebooks for a published single-cell case study
- you are teaching or learning single-cell transcriptomics fundamentals

## When to avoid
- you need the latest single-cell best practices - use the updated sc-best-practices book instead
- you need production-ready software or a maintained analysis pipeline
- you need a tool with an active license or ongoing support

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, nlp, machine-learning, data-visualization
- domain: bioinformatics, tutorials
- platform: python, cross-platform
- tags: single-cell, rna-seq, scanpy, bioinformatics, tutorial, jupyter-notebook, case-study, single-cell-rna-seq, jupyter

## Member repositories
- theislab/single-cell-tutorial (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:08.107668+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 2026-08-30T03:54:58.621317+00:00, confidence not recorded.
  - readme: https://github.com/theislab/single-cell-tutorial (fetched 2026-08-28T04:05:08.107668+00:00, sha 3c3ea07289ac)
- Data as of 2026-08-30T08:39:29.467469+00:00.