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<script data-goatcounter=https://scverse.goatcounter.com/count async src=//gc.zgo.at/count.js></script></head><body><header><div class=container style=max-width:850px><nav class="navbar navbar-expand-lg navbar-light"><div class=container-fluid><a class="navbar-brand row align-items-center m-0" href=/><div id=scverse-logo class=col></div></a><button class=navbar-toggler type=button data-bs-toggle=collapse data-bs-target=#navbarSupportedContent aria-controls=navbarSupportedContent aria-expanded=false aria-label="Toggle navigation">
<span class=navbar-toggler-icon></span></button><div class="collapse navbar-collapse justify-content-end" id=navbarSupportedContent><ul class="navbar-nav mb-2 mb-lg-0 nav-buttons"><li class="nav-item dropdown"><a class="nav-link dropdown-toggle" href=/packages id=packages-dropdown role=button aria-expanded=false>Packages</a><ul class=dropdown-menu aria-labelledby=packages-dropdown><li><a class=dropdown-item href=/packages#core-packages>Core</a></li><li><a class=dropdown-item href=/packages/#ecosystem>Ecosystem</a></li></ul></li><li class=nav-item><a class="nav-link
active" href=/learn>Learn</a></li><li class=nav-item><a class=nav-link href=/people>People</a></li><li class=nav-item><a class=nav-link href=/blog>Blog</a></li><li class=nav-item><a class=nav-link href=/events>Events</a></li><li class="nav-item dropdown"><a class="nav-link dropdown-toggle" href=/about id=about-dropdown role=button aria-expanded=false>About</a><ul class=dropdown-menu aria-labelledby=about-dropdown><li><a class=dropdown-item href=/about>About scverse</a></li><li><a class=dropdown-item href=/about/mission>Mission statement</a></li><li><a class=dropdown-item href=/about/roles>Roles</a></li><li><a class=dropdown-item href=/about/code_of_conduct>Code of Conduct</a></li></ul></li><li class=nav-item><a id=join-button class="nav-link nav-button-hl" href=/join>Join</a></li></ul></div></div></nav></div></header><div id=wrapper><div id=content><div id=page-content><h1>Getting Started</h1><article class=post><div class=post-content id=tutorials-content><p>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 <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 19 tutorials" title="Type in your search query"><h3>Data structures</h3><p>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 <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
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 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.</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>Tutorial helps you to explore the structure and content of single-cell
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
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
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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 single-cell tutorial guides you through the most common analysis steps, including 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>Adaptive immune cell receptor</h3><p>Tutorials for analyzing single-cell B-cell and T-cell receptor sequencing data</p><div class=tutorials-list><div class="card tutorial-item border-0"><a href=https://scirpy.scverse.org/en/latest/tutorials/tutorial_3k_tcr.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/scirpy-tcr/icon.svg><div class=card-body><h5 class=card-title>Single-cell T-cell receptor analysis with scirpy</h5><p>In this tutorial, we show how to perfrom QC on scTCR-seq data,
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
data with spatial resolution.</p></div></a></div><div class="card tutorial-item border-0"><a href=https://bento-tools.readthedocs.io/en/latest/index.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/squidpy-spatial/icon.png><div class=card-body><h5 class=card-title>Spatial analysis at subcellular resolution</h5><p>This tutorial shows how to use bentotools to study
gene expression at subcellular resolution.</p></div></a></div></div><h3>Adaptive immune cell receptor</h3><p>Tutorials for analyzing single-cell B-cell and T-cell receptor sequencing data</p><div class=tutorials-list><div class="card tutorial-item border-0"><a href=https://scirpy.scverse.org/en/latest/tutorials/tutorial_3k_tcr.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/scirpy-tcr/icon.svg><div class=card-body><h5 class=card-title>Single-cell T-cell receptor analysis with scirpy</h5><p>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.</p></div></a></div></div><h3>Surface proteins</h3><p>CITE-seq analyses</p><div class=tutorials-list><div class="card tutorial-item border-0"><a href=https://muon-tutorials.readthedocs.io/en/latest/cite-seq/1-CITE-seq-PBMC-5k.html target=_blank><img class=card-img-top src=https://scverse.org/scverse-tutorials/CITEseq-integration/icon.png><div class=card-body><h5 class=card-title>CITE-seq integration</h5><p>These notebooks showcase CITE-seq analysis of PBMCs with dsb
normalization, MOFA+ data integration, and weighted nearest neighbors
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