diff --git a/tutorial-registry/categories.yml b/tutorial-registry/categories.yml index ffb7b994..dc517e55 100644 --- 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 index a66f4057..22b5980e 100644 --- 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 index 49205829..33452152 100644 --- 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 index 169b9459..1c57ada4 100644 --- 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 index 9c990e65..bd56cc9c 100644 --- 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 index c688a36c..5417e0c0 100644 --- 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 index 9a62ac70..3152927c 100644 --- 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 index 706e0dcc..101d2717 100644 --- 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 index 476a82f0..b6e3fa9f 100644 --- 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 index 6430a438..17193f1d 100644 --- 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 index 32900d34..a3515dac 100644 --- 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 index ccf35093..b42fa207 100644 --- 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 index 7eb2d9a6..7d869615 100644 --- 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 index b787a309..cd294cdb 100644 --- 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 index 99880580..2c7abbde 100644 --- 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 index f9adf2f2..3ae426fa 100644 --- 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 index 60bc3e6f..ae523098 100644 --- 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 index 2c2d35a1..323cc8ae 100644 --- 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 index 7cf7d618..a90362c9 100644 --- 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 index 5a436735..ec5339da 100644 --- 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 index b0092648..382f275d 100644 --- 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 index 4171301a..b27f964a 100644 --- 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 index 83c16d3a..0b5d3656 100644 --- 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 index b920c926..817fef33 100644 --- 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