The Single Cell Notebooks for inclusive and accessible data analysis education
An open computational framework of hands-on Jupyter notebooks for training in bioinformatics — from raw reads to spatial and multimodal single-cell analysis.
Built to remove the barriers to single-cell training
Every module is self-contained, reproducible and ready to run in the cloud — no local installation, no licence fees.
Accessible education
Learning materials that work for researchers and students at every level of experience, from a first notebook to advanced analysis.
Global inclusion
Reducing geographical and economic barriers to learning single-cell RNA sequencing, with materials in four languages.
Hands-on training
Interactive modules covering everything from raw data processing to advanced analyses, including TCR sequence integration.
Reproducible by design
Run everything in Google Colab or in the provided Docker image, with pinned environments and public datasets.
Thirteen modules, from a first notebook to spatial omics
Open a module to read what it covers, then launch the notebook in your preferred language.
The interface is in Hindi; notebook content is currently available in English.
Get comfortable with Jupyter and Google Colab, then explore the main public repositories for single-cell and gene expression data through guided exercises.
Learn the R environment, its core data structures and the grammar of graphics with ggplot2 — the foundation for every analysis that follows.
Essential Linux command-line skills plus the Cell Ranger workflow that turns raw sequencing reads into count matrices.
A complete Seurat workflow: quality control, normalisation, clustering, differential expression, cell type annotation and functional enrichment.
Correct batch effects and integrate multiple datasets with Seurat and Harmony, including benchmarking to choose the best strategy.
Reconstruct differentiation trajectories and order cells in pseudotime with Monocle3.
Infer ligand-receptor interactions and cell-cell communication from expression data using LIANA.
Combine transcriptome and surface protein measurements (CITE-seq) in a single Seurat analysis of 8,617 cord blood mononuclear cells.
Explore T cell receptor repertoires and combine them with CITE-seq to characterise adaptive immune responses at single-cell resolution.
Analyse spatially resolved transcriptomes with Seurat, from region-level expression to cell type deconvolution.
Study chromatin accessibility at single-cell resolution and link open regions to transcriptional regulation.
Detect and quantify alternative polyadenylation in single-cell data with SCAPE-APA and interpret APA dynamics across cell types.
Prepare metadata and submit single-cell, bulk and spatial data to NCBI, the Single Cell Expression Atlas and the HCA Data Portal following FAIR principles.
Cite us
If these notebooks helped your work, please cite the paper.
Rojas-Hidalgo, A., Arias-Carrasco, R., Silva, J.K. et al. The Single Cell Notebooks for inclusive and accessible training in single-cell and spatial omics. Nature Genetics, 2026, Volume 58, Issue 5, Pages 789–795. ISSN 1061-4036. doi: 10.1038/s41588-026-02584
@article{scNotebooks2026,
author = {Rojas-Hidalgo, A. and Arias-Carrasco, R. and Silva, J. K. and others},
title = {The Single Cell Notebooks for inclusive and accessible training in single-cell and spatial omics},
journal = {Nature Genetics},
year = {2026},
volume = {58},
number = {5},
pages = {789--795},
issn = {1061-4036},
doi = {10.1038/s41588-026-02584}
}
The team
Listed alphabetically.