scverse · Zethson · Sep 9, 2026 · Sep 9, 2026 · Sep 9, 2026 · Sep 9, 2026
diff --git a/tutorial-registry/categories.yml b/tutorial-registry/categories.yml
@@ -6,7 +6,7 @@
 
 - scRNA-seq:
     description: |
-      The following tutorials show show to analyze single-cell gene expression data.
+      The following tutorials show how to analyze single-cell gene expression data.
 
 - Spatial:
     description: |

diff --git a/tutorial-registry/tutorials/ATAC-preprocessing/meta.yaml b/tutorial-registry/tutorials/ATAC-preprocessing/meta.yaml
@@ -1,8 +1,6 @@
 name: Processing chromatin accessibility
 description: |
-  This chapter shows multimodal single-cell gene expression and
-  chromatin accessibility analysis. In this notebook, scATAC-seq
-  data processing is described.
+  Chromatin accessibility processing in a joint RNA and ATAC analysis of 10k PBMCs.
 link: https://muon-tutorials.readthedocs.io/en/latest/single-cell-rna-atac/pbmc10k/2-Chromatin-Accessibility-Processing.html
 image: icon.png
 primary_category: ATAC-seq

diff --git a/tutorial-registry/tutorials/CITEseq-integration/meta.yaml b/tutorial-registry/tutorials/CITEseq-integration/meta.yaml
@@ -1,8 +1,6 @@
 name: CITE-seq integration
 description: |
-  These notebooks showcase CITE-seq analysis of PBMCs with dsb
-  normalization, MOFA+ data integration, and weighted nearest neighbors
-  handling multimodal embeddings.
+  CITE-seq analysis of PBMCs with dsb normalization, MOFA+ integration, and weighted nearest neighbors.
 link: https://muon-tutorials.readthedocs.io/en/latest/cite-seq/1-CITE-seq-PBMC-5k.html
 image: icon.png
 primary_category: Surface proteins

diff --git a/tutorial-registry/tutorials/Joint-analysis-of-multiomic-data-with-MultiVI/meta.yaml b/tutorial-registry/tutorials/Joint-analysis-of-multiomic-data-with-MultiVI/meta.yaml
@@ -1,8 +1,6 @@
 name: Joint analysis of paired and unpaired multiomic data with MultiVI
 description: |
-  This tutorial shows how to read multiomic data, create a joint object with
-  paired/unpaired data, train MultiVI model, visualize latent space,
-  and run differential analyses.
+  Read multiomic data, create a joint object from paired and unpaired data, train MultiVI, and run differential analyses.
 link: https://docs.scvi-tools.org/en/stable/tutorials/notebooks/multimodal/MultiVI_tutorial.html
 image: icon.png
 primary_category: ATAC-seq

diff --git a/tutorial-registry/tutorials/RNA-velocity/meta.yaml b/tutorial-registry/tutorials/RNA-velocity/meta.yaml
@@ -1,7 +1,6 @@
 name: RNA velocity
 description: |
-  This tutorial guides you through how RNA velocity can be inferred from single cell RNA-seq data
-  using scVelo.
+  Infer RNA velocity from single-cell RNA-seq data with scVelo.
 link: https://scvelo.readthedocs.io/en/stable/getting_started.html
 image: icon.png
 primary_category: scRNA-seq

diff --git a/tutorial-registry/tutorials/advanced-plotting/meta.yaml b/tutorial-registry/tutorials/advanced-plotting/meta.yaml
@@ -1,7 +1,6 @@
 name: Advanced plotting
 description: |
-  This tutorial explains how to customize matplotlib plots generated
-  by scanpy or other scverse libraries.
+  Customize matplotlib plots generated by scanpy or other scverse libraries.
 link: https://scanpy.readthedocs.io/en/stable/tutorials/plotting/advanced.html
 image: icon.png
 primary_category: Tips & Tricks

diff --git a/tutorial-registry/tutorials/anndata-getting-started/meta.yaml b/tutorial-registry/tutorials/anndata-getting-started/meta.yaml
@@ -1,7 +1,6 @@
 name: Getting started with AnnData
 description: |
-  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.
+  Explore the structure and content of single-cell analysis results stored in an .h5ad file.
 link: https://scverse-tutorials.readthedocs.io/en/latest/notebooks/anndata_getting_started.html
 image: icon.svg
 primary_category: Data structures

diff --git a/tutorial-registry/tutorials/anndata-mudata-axes/meta.yaml b/tutorial-registry/tutorials/anndata-mudata-axes/meta.yaml
@@ -1,8 +1,6 @@
 name: Axes in AnnData and MuData
 description: |
-  In this tutorial we showcase operations on independent AnnData objects
-  (scRNAseq matrix + metadata), demonstrating how various processing
-  workflows can be stored in one MuData object.
+  Operations on independent AnnData objects, and how several processing workflows can live in one MuData object.
 link: https://scverse-tutorials.readthedocs.io/en/latest/notebooks/tutorial_axes_anndata_mudata.html
 primary_category: Data structures
 order: 20

diff --git a/tutorial-registry/tutorials/bentotools-subcellular-resolution/meta.yaml b/tutorial-registry/tutorials/bentotools-subcellular-resolution/meta.yaml
@@ -1,7 +1,6 @@
 name: Spatial analysis with squidpy
 description: |
-  This tutorial demonstrate how to use squidpy to analyse transcriptomics
-  data with spatial resolution.
+  Analyse transcriptomics data with spatial resolution using squidpy.
 link: https://squidpy.readthedocs.io/en/latest/notebooks/tutorials/tutorial_vizgen_mouse_liver.html
 image: icon.png
 primary_category: Spatial

diff --git a/tutorial-registry/tutorials/cellcharter-spatial-transcriptomics/meta.yaml b/tutorial-registry/tutorials/cellcharter-spatial-transcriptomics/meta.yaml
@@ -1,7 +1,6 @@
 name: Spatial clustering of spacial transcriptomics data with CellCharter
 description: |
-  This tutorial demonstrate how to use CellCharter to cluster spatial transcriptomics data, obtained with the CosMx technology from Nanostring.
-  This method can also be used for spatial proteomics data.
+  Cluster spatial transcriptomics data from Nanostring CosMx with CellCharter. Also works for spatial proteomics.
 link: https://cellcharter.readthedocs.io/en/latest/notebooks/cosmx_human_nsclc.html
 image: icon.png
 primary_category: Spatial

diff --git a/tutorial-registry/tutorials/compositional-analysis/meta.yaml b/tutorial-registry/tutorials/compositional-analysis/meta.yaml
@@ -1,8 +1,6 @@
 name: Compositional analysis
 description: |
-  This tutorial introduces compositional analysis at cell identity
-  cluster level, based on known cell types or states affected by
-  perturbations.
+  Compositional analysis at cell identity cluster level, for known cell types or states affected by perturbations.
 link: https://www.sc-best-practices.org/conditions/compositional.html
 image: icon.png
 primary_category: scRNA-seq

diff --git a/tutorial-registry/tutorials/concatenation-of-multimodal-data/meta.yaml b/tutorial-registry/tutorials/concatenation-of-multimodal-data/meta.yaml
@@ -1,7 +1,6 @@
 name: Concatenation of multimodal data
 description: |
-  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.
+  Concatenate two MuData objects representing complementary slices of a dataset, or two modalities, into one AnnData.
 link: https://scverse-tutorials.readthedocs.io/en/latest/notebooks/tutorial_concatenation_anndata_mudata.html
 image: icon.png
 primary_category: Data structures

diff --git a/tutorial-registry/tutorials/concatenation-of-unimodal-data/meta.yaml b/tutorial-registry/tutorials/concatenation-of-unimodal-data/meta.yaml
@@ -1,8 +1,6 @@
 name: Concatenation
 description: |
-  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.
+  Concatenate objects while keeping all sub-elements of each, stacked in an ordered way.
 link: https://anndata.readthedocs.io/en/stable/tutorials/concatenation.html
 image: icon.png
 primary_category: Data structures

diff --git a/tutorial-registry/tutorials/decoupler-pseudobulk-de/meta.yaml b/tutorial-registry/tutorials/decoupler-pseudobulk-de/meta.yaml
@@ -1,7 +1,6 @@
 name: Pseudo-bulk differential expression and functional analysis
 description: |
-  This notebook showcases decoupler for pathway and TF enrichment on ~5k
-  Blood myeloid cells from healthy and COVID-19 infected patients.
+  Pathway and TF enrichment on ~5k blood myeloid cells from healthy and COVID-19 patients.
 link: https://decoupler.readthedocs.io/en/latest/notebooks/scell/rna_psbk.html
 image: icon.png
 primary_category: scRNA-seq

diff --git a/tutorial-registry/tutorials/interoperability/meta.yaml b/tutorial-registry/tutorials/interoperability/meta.yaml
@@ -1,7 +1,6 @@
 name: Interoperability
 description: |
-  This document lists resources for conversion to other data formats and programming
-  languages, e.g. R, Julia, ...
+  Resources for converting scverse data to other formats and languages, such as R and Julia.
 link: https://scverse-tutorials.readthedocs.io/en/latest/notebooks/scverse_data_interoperability.html
 image: icon.png
 primary_category: Tips & Tricks

diff --git a/tutorial-registry/tutorials/perturbation-modeling/meta.yaml b/tutorial-registry/tutorials/perturbation-modeling/meta.yaml
@@ -1,8 +1,6 @@
 name: Perturbation modeling
 description: |
-  This tutorial covers 3 approaches using single-cell perturbation data:
-  Augur (identify affected cell types), scGen (predict transcriptional response),
-  Mixscape (quantify CRISPR sensitivity).
+  Augur to identify affected cell types, scGen to predict transcriptional response, Mixscape for CRISPR sensitivity.
 link: https://www.sc-best-practices.org/conditions/perturbation_modeling.html
 image: icon.png
 primary_category: scRNA-seq

diff --git a/tutorial-registry/tutorials/plotting-in-scanpy/meta.yaml b/tutorial-registry/tutorials/plotting-in-scanpy/meta.yaml
@@ -1,7 +1,6 @@
 name: Plotting in scanpy
 description: |
-  This tutorial explores the visualization possibilities of scanpy, including
-  embeddings and the visualization of marker genes and differentially expressed genes.
+  Visualization with scanpy, covering embeddings, marker genes and differentially expressed genes.
 link: https://scanpy.readthedocs.io/en/stable/tutorials/plotting/core.html
 image: icon.png
 primary_category: Tips & Tricks

diff --git a/tutorial-registry/tutorials/preprocessing-and-clustering/meta.yaml b/tutorial-registry/tutorials/preprocessing-and-clustering/meta.yaml
@@ -1,8 +1,6 @@
 name: Preprocessing, clustering and cell-type annotation
 description: |
-  This fundamental tutorial covers common analysis steps: quality control,
-  normalization, feature selection, dimensionality reduction, clustering,
-  and cell-type annotation.
+  Quality control, normalization, feature selection, dimensionality reduction, clustering and cell-type annotation.
 link: https://scverse-tutorials.readthedocs.io/en/latest/notebooks/basic-scrna-tutorial.html
 image: icon.webp
 primary_category: scRNA-seq

diff --git a/tutorial-registry/tutorials/pseudotemporal-ordering/meta.yaml b/tutorial-registry/tutorials/pseudotemporal-ordering/meta.yaml
@@ -1,6 +1,6 @@
 name: Pseudotemporal ordering
 description: |
-  This tutorial show how a pseudotime can be constructed and compares different pseudotimes.
+  Construct a pseudotime and compare different pseudotimes.
 link: https://www.sc-best-practices.org/trajectories/pseudotemporal.html#
 image: icon.png
 primary_category: scRNA-seq

diff --git a/tutorial-registry/tutorials/rapids-singlecell-gpu/meta.yaml b/tutorial-registry/tutorials/rapids-singlecell-gpu/meta.yaml
@@ -1,8 +1,6 @@
 name: GPU-accelerated single-cell analysis with rapids-singlecell
 description: |
-  This tutorial demonstrates a complete single-cell RNA-seq workflow on the GPU
-  using rapids-singlecell as a drop-in replacement for scanpy, covering
-  preprocessing, clustering, batch correction, and differential expression.
+  A complete scRNA-seq workflow on the GPU, using rapids-singlecell as a drop-in replacement for scanpy.
 link: https://rapids-singlecell.readthedocs.io/en/latest/notebooks/01_basic_workflow.html
 image: icon.svg
 primary_category: scRNA-seq

diff --git a/tutorial-registry/tutorials/scirpy-tcr/meta.yaml b/tutorial-registry/tutorials/scirpy-tcr/meta.yaml
@@ -1,8 +1,6 @@
 name: Single-cell T-cell receptor analysis with scirpy
 description: |
-  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.
+  QC scTCR-seq data, define clonotypes, cluster receptors by sequence similarity and compare repertoires.
 link: https://scirpy.scverse.org/en/latest/tutorials/tutorial_3k_tcr.html
 primary_category: Adaptive immune cell receptor
 order: 10

diff --git a/tutorial-registry/tutorials/scverse-objects-in-backed-mode/meta.yaml b/tutorial-registry/tutorials/scverse-objects-in-backed-mode/meta.yaml
@@ -1,7 +1,6 @@
 name: Working with scverse objects in backed mode
 description: |
-  In this tutorial, we demonstrate working with scverse data objects
-  without loading full datasets. (AnnData and MuData are saved as .h5ad and .h5mu files)
+  Work with .h5ad and .h5mu files without loading the full dataset into memory.
 link: https://scverse-tutorials.readthedocs.io/en/latest/notebooks/scverse_data_backed.html#working-with-scverse-objects-in-backed-mode
 primary_category: Data structures
 order: 50

diff --git a/tutorial-registry/tutorials/scvi-batch-effect-removal/meta.yaml b/tutorial-registry/tutorials/scvi-batch-effect-removal/meta.yaml
@@ -1,7 +1,6 @@
 name: Batch-effect removal with scvi-tools
 description: |
-  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.
+  Fit a scvi-tools model to single-cell counts, correct batch effects, and run differential expression.
 link: https://docs.scvi-tools.org/en/stable/tutorials/notebooks/quick_start/api_overview.html
 primary_category: scRNA-seq
 order: 20

diff --git a/tutorial-registry/tutorials/squidpy-spatial/meta.yaml b/tutorial-registry/tutorials/squidpy-spatial/meta.yaml
@@ -1,7 +1,6 @@
 name: Spatial analysis at subcellular resolution
 description: |
-  This tutorial shows how to use bentotools to study
-  gene expression at subcellular resolution.
+  Study gene expression at subcellular resolution with bento-tools.
 link: https://bento-tools.readthedocs.io/en/latest/index.html
 image: icon.png
 primary_category: Spatial