Scanpy
Move from a single-cell expression matrix to quality checks, clusters, and exploratory plots in Python.
Find public single-cell data and the R or Python tools that can take an expression matrix further.
Search this collectionIf 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.
Move from a single-cell expression matrix to quality checks, clusters, and exploratory plots in Python.
Explore single-cell expression data in R, from initial filtering and clustering to comparisons across samples.
Browse public single-cell datasets, inspect gene expression and cell annotations, or download data for follow-up analysis.
Inspect single-cell expression data with quality-control summaries, embeddings, and plots built around Bioconductor objects.
Store expression matrices, gene and cell metadata, and embedding coordinates together in a standard R object.
Explore a prepared experiment in a browser with linked scatter plots, metadata tables, and expression views.
Flag likely doublets, where a capture event contains more than one cell, before downstream interpretation.
Estimate candidate cell-state transitions and fate probabilities from processed single-cell observations.
Keep a cell-by-feature matrix, annotations, and embeddings aligned in one object.
Organize modality-specific matrices and shared annotations together in a single container.
Fit probabilistic models for tasks such as learning shared expression embeddings or transferring labels.
Summarize the arrangement of cells or spots in tissue with spatial neighbor graphs and coordinate-aware plots.
Keep expression tables, images, labels, and points in a common spatial data model.
Process measurements from separate modalities and combine their neighborhood information for exploratory analysis.
Assign provisional cell-type labels to RNA expression profiles using a selected pretrained classifier.
Run supported single-cell preprocessing and analysis steps on a GPU.
Browse large expression datasets with linked embeddings, expression plots, and cluster composition views.
Estimate RNA velocity to examine candidate directions of change among observed cell states.
Read common droplet data formats and distinguish candidate cells from background droplets.
Reduce batch differences between expression datasets with methods including mutual nearest-neighbor correction.
Compare single-cell expression profiles with labeled references to suggest cell identities.
Retrieve labeled expression references for human and mouse cell annotation.
Exchange H5AD expression data between Python's AnnData format and Bioconductor's SingleCellExperiment class.
Test expression differences within annotated cell populations across samples, including pseudobulk analysis.
Test for differences in cell abundance across graph neighborhoods without relying on a single partition into clusters.
Fit candidate branching trajectories through clusters and order cells along those trajectories.
Fit smooth gene expression trends along inferred trajectories and compare patterns between branches.
Compare expression by cell type across cohorts, including designs with repeated samples.
Create configurable plots for expression, composition, and embeddings with colorblind-friendly palette options.
Overlay gene-weighted density on an embedding to show where expression from one or more features is concentrated.
Score individual cells against predefined gene sets using expression ranks.
Keep spatial coordinates and image metadata aligned with expression assays in a Bioconductor object.
Load selected published single-cell count datasets and annotations into ready-to-analyze R objects.
Normalize UMI expression with a model that accounts for sequencing depth and stabilizes variation across genes.
Align single-cell datasets in a low-dimensional space while accounting for selected batch variables.
Use R interfaces to compiled routines for single-cell quality control, clustering, and marker summaries.
Find Human Cell Atlas projects and inspect their sample, tissue, and file metadata.
Explore public studies through interactive plots and retrieve the files their owners have made available.
Search single-cell expression studies across species and inspect gene expression patterns within individual experiments.
Open hosted datasets and color cell embeddings by gene expression or sample metadata.
Browse reference brain cell expression and spatial data to compare annotated neural populations.