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.

Visualization

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 [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].