Tutorials

Clustering

For getting started, we recommend Scanpy’s reimplementation → tutorial: pbmc3k 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: → tutorial: visualizing-marker-genes

Trajectory inference

Get started with the following example for hematopoiesis for data of [Paul15]: → tutorial: paga-paul15

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: → tutorial: integrating-data-using-ingest

Spatial data

• Basic analysis of spatial data: → tutorial: spatial/basic-analysis

• Integrating spatial data with scRNA-seq using scanorama: → tutorial: spatial/integration-scanorama

Further Tutorials

Conversion: AnnData, SingleCellExperiment, and Seurat objects

• See Seurat to AnnData for a tutorial on anndata2ri.

• See the Scanpy in R guide for a tutorial on interacting with Scanpy from R.

Regressing out cell cycle

See the cell cycle notebook.

Scaling Computations

• Visualize and cluster 1.3M neurons from 10x Genomics.

Simulations

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

• Notebook for myeloid differentiation

• Notebook for simple toggleswitch

Images

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