If you are new to the scverse, get started with this set of tutorials covering basic analysis and functionality of the core pacakges. -For more tutorials as well as API documentation and user guides, see the sites of individual packages.

You can also find recordings of past talks and workshops on our YouTube channel.

Tutorials

Data structures

These tutorials teach you how to work with scverse data structures. +For more tutorials as well as API documentation and user guides, see the sites of individual packages.

You can also find recordings of past talks and workshops on our YouTube channel.

Tutorials

Data structures

These tutorials teach you how to work with scverse data structures. If you are new to Python and/or scverse, we recommend you read the "getting started" and "axes" tutorials first.

Axes in AnnData and MuData

In this tutorial we showcase operations on independent AnnData objects (scRNAseq matrix + metadata), demonstrating how varied processing -workflows can be stored in one MuData object.

Working with scverse objects in backed mode

In this tutorial we show how to work with scverse data objects without loading the full -datasets in memory. AnnData and MuData objects are saved to disk as .h5ad and .h5mu files. -These formats save HDF5 stores (via h5py) that can be accessed without loading heavy data -matrices in memory (i.e. in backed mode), as a “read-only” object.

Getting started with AnnData

Tutorial helps you to explore the structure and content of single-cell +workflows can be stored in one MuData object.

Working with scverse objects in backed mode

In this tutorial, we demonstrate working with scverse data objects +(AnnData and MuData are saved as .h5ad and .h5mu files) without loading full datasets.

Getting started with AnnData

This tutorial helps you to explore the structure and content of single-cell data analysis results in a *.h5ad file using AnnData, Scanpy, and Python.

Concatenation

In this notebook we showcase how to perform concatenation, meaning to keep all sub elements of each object, and stack these elements in an ordered way.

Concatenation of multimodal data

This tutorial shows how you can concatenate 2 MuData objects that may represent complementary @@ -22,9 +20,12 @@ Single Cell Expression Atlas.

Pseudotemporal ordering

This tutorial show how a pseudotime can be constructed and compares different pseudotimes.

Perturbation modeling

This tutorial covers 3 approaches using single-cell perturbation data: Augur - identify affected cell types scGen - predict transcriptional response -Mixscape - quantify CRISPR sensitivity.

Preprocessing, clustering and cell-type annotation

This fundamental single-cell tutorial guides you through the most common analysis steps, including quality control, -normalization, feature selection, dimensionality reduction, clustering and cell-type annotation.

RNA velocity

This tutorial guides you through how RNA velocity can be inferred from single cell RNA-seq data -using scVelo.

Adaptive immune cell receptor

Tutorials for analyzing single-cell B-cell and T-cell receptor sequencing data

Single-cell T-cell receptor analysis with scirpy

In this tutorial, we show how to perfrom QC on scTCR-seq data, +Mixscape - quantify CRISPR sensitivity.

Preprocessing, clustering and cell-type annotation

This fundamental tutorial covers common analysis steps: quality control, +normalization, feature selection, dimensionality reduction, clustering, +and cell-type annotation.

RNA velocity

This tutorial guides you through how RNA velocity can be inferred from single cell RNA-seq data +using scVelo.

Spatial

Analyze spatial data generated with different technologies

Spatial analysis with squidpy

This tutorial demonstrate how to use squidpy to analyse transcriptomics +data with spatial resolution.

Spatial analysis at subcellular resolution

This tutorial shows how to use bentotools to study +gene expression at subcellular resolution.

Adaptive immune cell receptor

Tutorials for analyzing single-cell B-cell and T-cell receptor sequencing data

Single-cell T-cell receptor analysis with scirpy

In this tutorial, we show how to perfrom QC on scTCR-seq data, define clonotype, cluster receptors by their sequence similarity and compute repertoire overlaps between patients.

Surface proteins

CITE-seq analyses

CITE-seq integration

These notebooks showcase CITE-seq analysis of PBMCs with dsb normalization, MOFA+ data integration, and weighted nearest neighbors