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 For more tutorials as well as API documentation and user guides, see the sites of <a href=/packages/>individual packages</a>.</p><p>You can also find recordings of past talks and workshops on our <a href=https://www.youtube.com/channel/UCpsvsIAW3R5OdftJKKuLNMA>YouTube channel</a>.</p><h2 id=tutorials>Tutorials</h2><div id=ecosystem-tutorials><input type=text class=form-control id=tutorial-filter onkeyup=filterTutorials() placeholder="Search through 21 tutorials" title="Type in your search query"><h3>Data structures</h3><p>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.</p><div class=tutorials-list><div class="card tutorial-item border-0"><a href=https://scverse-tutorials.readthedocs.io/en/latest/notebooks/tutorial_axes_anndata_mudata.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/anndata-mudata-axes/icon.svg><div class=card-body><h5 class=card-title>Axes in AnnData and MuData</h5><p>In this tutorial we showcase operations on independent AnnData objects
-(scRNAseq matrix + metadata), demonstrating how varied processing
+(scRNAseq matrix + metadata), demonstrating how various processing
 workflows can be stored in one MuData object.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://scverse-tutorials.readthedocs.io/en/latest/notebooks/scverse_data_backed.html#working-with-scverse-objects-in-backed-mode target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/scverse-objects-in-backed-mode/icon.png><div class=card-body><h5 class=card-title>Working with scverse objects in backed mode</h5><p>In this tutorial, we demonstrate working with scverse data objects
-(AnnData and MuData are saved as .h5ad and .h5mu files) without loading full datasets.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://scverse-tutorials.readthedocs.io/en/latest/notebooks/anndata_getting_started.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/anndata-getting-started/icon.svg><div class=card-body><h5 class=card-title>Getting started with AnnData</h5><p>This tutorial helps you to explore the structure and content of single-cell
+without loading full datasets. (AnnData and MuData are saved as .h5ad and .h5mu files)</p></div></a></div><div class="card tutorial-item border-0"><a href=https://scverse-tutorials.readthedocs.io/en/latest/notebooks/anndata_getting_started.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/anndata-getting-started/icon.svg><div class=card-body><h5 class=card-title>Getting started with AnnData</h5><p>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.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://anndata.readthedocs.io/en/latest/concatenation.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/concatenation-of-unimodal-data/icon.png><div class=card-body><h5 class=card-title>Concatenation</h5><p>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.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://scverse-tutorials.readthedocs.io/en/latest/notebooks/tutorial_concatenation_anndata_mudata.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/concatenation-of-multimodal-data/icon.png><div class=card-body><h5 class=card-title>Concatenation of multimodal data</h5><p>This tutorial shows how you can concatenate 2 MuData objects that may represent complementary
 slices of the same dataset or 2 modalities into one AnnData.</p></div></a></div></div><h3>scRNA-seq</h3><p>The following tutorials show show to analyze single-cell gene expression data.</p><div class=tutorials-list><div class="card tutorial-item border-0"><a href=https://www.sc-best-practices.org/conditions/compositional.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/compositional-analysis/icon.png><div class=card-body><h5 class=card-title>Compositional analysis</h5><p>This tutorial introduces compositional analysis at cell identity
 cluster level, based on known cell types or states affected by
-perturbations using Haber dataset.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://docs.scvi-tools.org/en/stable/tutorials/notebooks/api_overview.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/scvi-batch-effect-removal/icon.svg><div class=card-body><h5 class=card-title>Batch-effect removal with scvi-tools</h5><p>In this tutorial, we demonstrate how to use scvi-tools to fit a model to single-cell count data,
+perturbations.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://docs.scvi-tools.org/en/stable/tutorials/notebooks/api_overview.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/scvi-batch-effect-removal/icon.svg><div class=card-body><h5 class=card-title>Batch-effect removal with scvi-tools</h5><p>In this tutorial, we demonstrate how to use scvi-tools to fit a model to single-cell count data,
 correct batch effects, and perform differential gene expression analysis.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://decoupler-py.readthedocs.io/en/latest/notebooks/pseudobulk.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/decoupler-pseudobulk-de/icon.png><div class=card-body><h5 class=card-title>Pseudo-bulk differential expression and functional analysis</h5><p>This notebook showcases decoupler for pathway and TF enrichment on ~5k
-Blood myeloid cells from healthy and COVID-19 infected patients in the
-Single Cell Expression Atlas.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://www.sc-best-practices.org/trajectories/pseudotemporal.html# target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/pseudotemporal-ordering/icon.png><div class=card-body><h5 class=card-title>Pseudotemporal ordering</h5><p>This tutorial show how a pseudotime can be constructed and compares different pseudotimes.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://www.sc-best-practices.org/conditions/perturbation_modeling.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/perturbation-modeling/icon.png><div class=card-body><h5 class=card-title>Perturbation modeling</h5><p>This tutorial covers 3 approaches using single-cell perturbation data:
-Augur - identify affected cell types
-scGen - predict transcriptional response
-Mixscape - quantify CRISPR sensitivity.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://scverse-tutorials.readthedocs.io/en/latest/notebooks/basic-scrna-tutorial.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/preprocessing-and-clustering/icon.webp><div class=card-body><h5 class=card-title>Preprocessing, clustering and cell-type annotation</h5><p>This fundamental tutorial covers common analysis steps: quality control,
+Blood myeloid cells from healthy and COVID-19 infected patients.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://www.sc-best-practices.org/trajectories/pseudotemporal.html# target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/pseudotemporal-ordering/icon.png><div class=card-body><h5 class=card-title>Pseudotemporal ordering</h5><p>This tutorial show how a pseudotime can be constructed and compares different pseudotimes.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://www.sc-best-practices.org/conditions/perturbation_modeling.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/perturbation-modeling/icon.png><div class=card-body><h5 class=card-title>Perturbation modeling</h5><p>This tutorial covers 3 approaches using single-cell perturbation data:
+Augur (identify affected cell types), scGen (predict transcriptional response),
+Mixscape (quantify CRISPR sensitivity).</p></div></a></div><div class="card tutorial-item border-0"><a href=https://scverse-tutorials.readthedocs.io/en/latest/notebooks/basic-scrna-tutorial.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/preprocessing-and-clustering/icon.webp><div class=card-body><h5 class=card-title>Preprocessing, clustering and cell-type annotation</h5><p>This fundamental tutorial covers common analysis steps: quality control,
 normalization, feature selection, dimensionality reduction, clustering,
 and cell-type annotation.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://scvelo.readthedocs.io/en/stable/getting_started/ target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/RNA-velocity/icon.png><div class=card-body><h5 class=card-title>RNA velocity</h5><p>This tutorial guides you through how RNA velocity can be inferred from single cell RNA-seq data
 using scVelo.</p></div></a></div></div><h3>Spatial</h3><p>Analyze spatial data generated with different technologies</p><div class=tutorials-list><div class="card tutorial-item border-0"><a href=https://squidpy.readthedocs.io/en/latest/notebooks/tutorials/tutorial_vizgen_mouse_liver.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/bentotools-subcellular-resolution/icon.png><div class=card-body><h5 class=card-title>Spatial analysis with squidpy</h5><p>This tutorial demonstrate how to use squidpy to analyse transcriptomics