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.
This tutorial shows how to visually explore genes using scanpy. → tutorial: plotting/core
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].
Map labels and embeddings of reference data to new data: → tutorial: integrating-data-using-ingest
Basic analysis of spatial data: → tutorial: spatial/basic-analysis
Integrating spatial data with scRNA-seq using scanorama: → tutorial: spatial/integration-scanorama
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
Visualize and cluster 1.3M neurons from 10x Genomics.
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].