For getting started, we recommend Scanpy’s reimplementation Preprocessing and clustering 3k PBMCs of Seurat’s [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


Learn how to visually explore genes using scanpy: Core plotting functions

For advanced customization of your plots, see Customizing Scanpy plots


Trajectory inference#

Get started with the following example for hematopoiesis for data of [Paul15]: Trajectory inference for hematopoiesis in mouse


More examples for trajectory inference on complex datasets can be found in the PAGA repository [Wolf19], for instance, multi-resolution analyses of whole animals, such as for planaria for data of [Plass18].


As a reference for simple pseudotime analyses, we provide the diffusion pseudotime (DPT) analyses of [Haghverdi16] for two hematopoiesis datasets: DPT example 1 [Paul15] and DPT example 2 [Moignard15].

Integrating datasets#

Map labels and embeddings of reference data to new data: Integrating data using ingest and BBKNN


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 [Lause21]: How to preprocess UMI count data with analytic Pearson residuals

Scaling Computations#


Simulating single cells using literature-curated gene regulatory networks [Wittmann09].



See pseudotime-time inference on deep-learning based features for cell cycle reconstruction from image data [Eulenberg17].