For getting started, we recommend Scanpy’s reimplementation → tutorial: pbmc3k of Seurat’s [^cite_satija15] clustering tutorial for 3k PBMCs from 10x Genomics, containing preprocessing, clustering and the identification of cell types via known marker genes.

_images/filter_genes_dispersion.png _images/louvain.png _images/NKG7.png _images/violin.png _images/cell_types.png


This tutorial shows how to visually explore genes using scanpy. → tutorial: plotting/core


Trajectory inference

Get started with the following example for hematopoiesis for data of [^cite_paul15]: → tutorial: paga-paul15


More examples for trajectory inference on complex datasets can be found in the PAGA repository [^cite_wolf19], for instance, multi-resolution analyses of whole animals, such as for planaria for data of [^cite_plass18].


As a reference for simple pseudotime analyses, we provide the diffusion pseudotime (DPT) analyses of [^cite_haghverdi16] for two hematopoiesis datasets: DPT example 1 [^cite_paul15] and DPT example 2 [^cite_moignard15].

Integrating datasets

Map labels and embeddings of reference data to new data: → tutorial: integrating-data-using-ingest


Spatial data


Further Tutorials

Conversion: AnnData, SingleCellExperiment, and Seurat objects


Regressing out cell cycle

See the cell cycle notebook.


Normalization with Pearson Residuals

Normalization of scRNA-seq data with Pearson Residuals, from [^cite_lause21]: → tutorial: tutorial_pearson_residuals

Scaling Computations


Simulating single cells using literature-curated gene regulatory networks [^cite_wittmann09].



See pseudotime-time inference on deep-learning based features for cell cycle reconstruction from image data [^cite_eulenberg17].