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SAPTICoN, a robust no-code pipeline to analyze single cell transcriptomics data sets

Pichot, C.; Verdenaud, M.; Sandri, A.; Adam, G.; Delannoy, E.; Hilson, P.

2026-03-27 bioinformatics
10.64898/2026.03.25.714156 bioRxiv
Show abstract

Single-cell transcriptomic (SCT) analysis is essential for resolving cellular heterogeneity and uncovering the molecular foundations of development, physiology, and environmental responses. Despite its increasing importance, robust, reproducible, and broadly applicable SCT analytical tools remain largely restricted to well-annotated animal systems. This limitation poses significant challenges for biologists working on non-model species and poorly characterized tissues, where gene annotation is sparse and computational expertise is often limited. We developed a stable, end-to-end SCT analysis pipeline designed to be accessible to biologists with little training in bioinformatics and applicable to species and tissues with limited genomic annotation. Built on the Seurat framework and complemented with additional tools, the pipeline supports any organism by automatically generating R-compatible annotation packages from basic genome files. It integrates standardized workflows for data preprocessing, quality control, clustering, biomarker identification, and gene set enrichment analysis within a fixed Snakemake framework, ensuring high reproducibility. By minimizing coding requirements, the pipeline enables rigorous, biologically informed SCT analyses across diverse experimental systems.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.