COLLECTION 02 / Explore cell states

Single-cell Analysis.

Find public single-cell data and the R or Python tools that can take an expression matrix further.

Where to start

If you are exploring a published atlas, start with a browser. If you are analyzing your own counts, choose a tool that matches your data format and environment, and keep biological replicates separate in statistical comparisons.

Tools in this collection

8 EDITORIAL LISTINGS

Open source

Scanpy

Move from a single-cell expression matrix to quality checks, clusters, and exploratory plots in Python.

Single-cell AnalysisPython package

Open source

Seurat

Explore single-cell expression data in R, from initial filtering and clustering to comparisons across samples.

Single-cell AnalysisR package

Free

CZ CELLxGENE Discover

Browse public single-cell datasets, inspect gene expression and cell annotations, or download data for follow-up analysis.

Single-cell AnalysisPublic data portal

Open source

scater

Inspect single-cell expression data with quality-control summaries, embeddings, and plots built around Bioconductor objects.

Single-cell AnalysisR package

Open source

SingleCellExperiment

Store expression matrices, gene and cell metadata, and embedding coordinates together in a standard R object.

Single-cell AnalysisR package

Open source

iSEE

Explore a prepared experiment in a browser with linked scatter plots, metadata tables, and expression views.

Single-cell AnalysisR / Shiny app

Open source

scDblFinder

Flag likely doublets, where a capture event contains more than one cell, before downstream interpretation.

Single-cell AnalysisR package

Open source

CellRank

Estimate candidate cell-state transitions and fate probabilities from processed single-cell observations.

Single-cell AnalysisPython package