Tutorials — Scanpy 1.9.1 documentation

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

Tutorials # See also For more tutorials featuring scanpy and other scverse ecosystem tools, check out the curated set of tutorials at scverse.org/learn Basic workflows # Basics Preprocessing and clustering Preprocessing and clustering 3k PBMCs (legacy workflow) Integrating data using ingest and BBKNN Visualization # Plotting Core plotting functions Customizing Scanpy plots Trajectory inference # See also For more powerful tools for analysing single cell dynamics, check out the scverse ecosystem packages: CellRank Dynamo Trajectories Trajectory inference for hematopoiesis in mouse Experimental

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