Loading repository data…
Loading repository data…
integrativebioinformatics / repository
This repository contains scNotebooks, a collection of interactive Jupyter and Google Colab notebooks designed to teach and practice single‑cell and spatial transcriptomics. The notebooks guide learners through the complete workflow from introductory steps and single‑cell pipelines to diverse analytical approaches, and FAIR and sharing data
Single-cell sequencing technologies are powerful tools used to assess genomic, transcriptomic and proteomics information at the single-cell level. In recent years, the application of techniques that use single-cell sequencing have become increasingly common in several areas of research: including medicine, agriculture, and other life sciences disciplines. Single-cell sequencing may be used to study many aspects of an organism’s biology, both in health and disease, and the results of these studies contribute immensely to advancing the understanding of organisms as a whole.
The establishment of collaborative scientific endeavors like the Human Cell Atlas or the LatinCells Project is a testament to the surging enthusiasm and curiosity in this domain. Yet, when we look towards Latin America, we find a gap in the necessary infrastructure, financial support, and subject matter expertise required to harness these cutting-edge technologies. Recognizing this, our workshop is designed to bridge this gap. We provide participants with hands-on experience in the laboratory and in-depth bioinformatics training, ensuring that the region advances in its capabilities with single-cell methodologies.
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
https://doi.org/10.1038/s41588-026-02584-0
If you used the scNotebooks for training, courses, or studies, please let us know!
Send us a message in the GitHub Discussions forum or through GitHub, we would love to hear from you.
If you would like to help translate the notebooks into another language, feel free to reach out so we can expand the project together.
Se você utilizou os scNotebooks para treinamento, cursos ou estudos, nos deixe saber!
Mande uma mensagem no fórum do GitHub Discussions ou pelo GitHub, adoraríamos saber.
Se quiser ajudar a traduzir os notebooks para outra língua, entre em contato para que possamos expandir o projeto juntos.
Si utilizaste los scNotebooks para capacitación, cursos o estudios, ¡déjanos saber!
Envíanos un mensaje en el foro de GitHub Discussions o por GitHub, nos encantaría saberlo.
Si deseas ayudar a traducir los notebooks a otro idioma, ponte en contacto para que podamos ampliar el proyecto juntos.
Our notebooks are available in multilingual versions and can be accessed in three simple ways:
You can run the notebooks directly in your browser using Google Colab, with no need to install anything locally.
Just follow our step-by-step multilingual tutorial to learn how to:
.ipynb filesIf you prefer to work offline or want a fully configured environment, you can run the notebooks using Docker.
Check out our Docker tutorial for detailed instructions on:
If you prefer a simpler way to browse the notebooks without installing anything or creating an account, you can access them directly through our official website. On the site you will find:
Please note: scNotebooks cannot be executed directly on the website; they are provided for browsing and copying code only.
If you prefer to see the executed results directly, you can access the notebooks in PDF format. On the PDFs you will find:
Please note: PDFs are currently available only in English, but they can be easily translated using any online translation tool. They are read‑only and cannot be edited or re‑executed, serving as a resource for reviewing outputs and understanding the workflow without running the code yourself.
We value continuous improvement and collaboration. To support learners and researchers, we maintain a dedicated space in GitHub Discussions, where you can engage with us directly:
Our GitHub forum is linked from the official site, providing an open channel for communication and collective learning.
Jupyter Notebooks and Google Colaboratory provide interactive environments that combine code and explanatory text, supporting reproducible analysis. In this module, learners will explore their structure, including code and text cells, and gain familiarity with key public databases for single‑cell and gene expression data across humans and other organisms. Hands‑on exercises guide users through accessing, exploring, and analyzing these resources, building essential skills in biological data manipulation.
This notebook has many embedded images may not render properly on GitHub. We recommend opening them directly in Colab for full functionality or web site.
Module:
Site:
This module introduces the R programming language, widely used in data science and bioinformatics for statistical analysis and data manipulation. Learners will explore the R environment, basic syntax, and core data structures such as vectors and data frames. The module also presents the ggplot2 package, a powerful tool for creating elegant and customizable visualizations using the grammar of graphics. Through hands-on exercises, users will practice writing R code, creating plots, and interpreting biological data, building a strong foundation for future analytical tasks.
This notebook introduces essential command-line operations in Linux, covering fundamental commands that are broadly applicable across programming languages with minimal adaptations. These foundational skills will support efficient data management and analysis in computational biology. Additionally, we will explore the key steps in processing raw sequencing reads into count matrices using Cell Ranger, discussing its main outputs and role in single-cell transcriptomics.
Single‑cell RNA‑seq analysis requires a structured workflow to ensure data quality and biological interpretability. In this module, learners will use Seurat to import raw data, apply filtering, and perform preliminary visualization as part of quality control. Key steps include evaluating quality metrics, normalizing to reduce technical variability, and clustering cells by gene expression profiles to reveal underlying heterogeneity. Building on this foundation, users will conduct differential expression and abundance analysis, annotate cell types, and perform functional enrichment to uncover regulatory mechanisms and pathways involved in development and disease. The module also introduces practical strategies for identifying marker genes and removing ambient mRNA contamination, ensuring cleaner datasets and more reliable downstream results. Through these exercises, participants gain both conceptual understanding and hands‑on skills for comprehensive scRNA‑seq analysis.
Data integration and batch correction are essential for reliable single‑cell analysis, ensuring that biological signals are not obscured by technical or donor‑specific variation. In this module, learners will investigate how differences in protocols, platforms, or sample origin generate batch effects, and how defining batch covariates influences integration outcomes. Practical exercises with Seurat and Harmony provide hands‑on experience in applying correction methods, tuning parameters, and evaluating integration quality. Benchmarking activities allow users to compare strategies, highlighting trade‑offs between reducing unwanted variation and preserving meaningful biological information. By combining theoretical concepts with applied workflows, participants gain the skills needed to select and implement effective integration approaches in diverse single‑cell studies.
This module explores how single‑cell RNA‑seq can be used to reconstruct cell‑state trajectories. Learners will study how gene expression changes dynam