For getting started, we recommend Scanpy’s reimplementation 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.

For more possibilities on visualizing marker genes, see this plotting gallery.

http://falexwolf.de/img/scanpy_usage/170505_seurat/filter_genes_dispersion.png http://falexwolf.de/img/scanpy_usage/170505_seurat/louvain.png http://falexwolf.de/img/scanpy_usage/170505_seurat/NKG7.png http://falexwolf.de/img/scanpy_usage/170505_seurat/violin.png http://falexwolf.de/img/scanpy_usage/170505_seurat/cell_types.png

Trajectory Inference

We offer several examples for trajectory inference on complex datasets. Get started with the Paul PAGA example for the following result on hematopoiesis:


You can extend this to multi-resolution analyses of whole animals, such as the Planaria PAGA example:


The PAGA method behind this is described in [Wolf19]. As a reference for simple pseudotime analyses, we provide the diffusion pseudotime analyses of [Haghverdi16] for two hematopoiesis datasets: The Paul DPT example [Paul15] and the Moignard DPT example [Moignard15].

Further Tutorials

Conversion: AnnData, SingleCellExperiment, and Seurat objects

See the Seurat to AnnData notebook for a tutorial on anndata2ri.

Regressing out cell cycle

See the cell cycle notebook.

Scaling Computations


Visualize and cluster 1.3M neurons from 10x Genomics.


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

http://falexwolf.de/img/scanpy_usage/170430_krumsiek11/timeseries.png http://falexwolf.de/img/scanpy_usage/170430_krumsiek11/draw_graph.png


See a pseudotime-based vs. deep-learning based cell cycle reconstruction from image data [Eulenberg17].