References & Data Sources

References & Data Sources#

Tutorial dataset#

The worked-example tutorials use EC.h5ad, a subset of endothelial cells (EC) extracted from a public single-nucleus RNA-seq dataset of adult mouse spinal cord, comparing uninjured controls (UN, n=3 replicates) against spinal cord injury (SCI, n=3 replicates):

Squair, J.W., Gautier, M., Kathe, C., Anderson, M.A., James, N.D., Hutson, T.H., Hudelle, R., Qaiser, T., Matson, K.J.E., Barraud, Q., Levine, A.J., La Manno, G., Skinnider, M.A., Courtine, G. (2021). Confronting false discoveries in single-cell differential expression. Nature Communications 12, 5692. DOI: 10.1038/s41467-021-25960-2

  • Raw data: GEO accession GSE165003 (6 samples: 3× UN, 3× SCI).

This paper is a particularly fitting choice for scATrans tutorials: it is itself about the statistical pitfalls of treating cells as independent replicates in single-cell differential expression — the same pseudoreplication concern that motivates scATrans’s pseudobulk / mixed-model / permutation options and the Statistical Guidance & Reporting Checklist page.

Methods and libraries scATrans builds on#

Component

Reference

scanpy (preprocessing, rank_genes_groups DE backends)

Wolf, F.A., Angerer, P., Theis, F.J. (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome Biology 19, 15. DOI: 10.1186/s13059-017-1382-0. github.com/scverse/scanpy

PyDESeq2 (pseudobulk_de_backend="pydeseq2")

Muzellec, B., Teleńczuk, M., Cabeli, V., Andreux, M. (2023). PyDESeq2: a python package for bulk RNA-seq differential expression analysis. Bioinformatics 39(9), btad547. DOI: 10.1093/bioinformatics/btad547. github.com/scverse/PyDESeq2

scVelo (mode="advanced" moments smoothing)

Bergen, V., Lange, M., Peidli, S., Wolf, F.A., Theis, F.J. (2020). Generalizing RNA velocity to transient cell states through dynamical modeling. Nature Biotechnology 38, 1408–1414. DOI: 10.1038/s41587-020-0591-3. github.com/theislab/scvelo

Memento (use_memento_de=True)

Kim, M.C., Gate, R., Lee, D.S., Tolopko, A., Lu, A., Gordon, E., Shifrut, E., Garcia-Nieto, P.E., Marson, A., Ntranos, V., Ye, C.J. (2024). Method of moments framework for differential expression analysis of single-cell RNA sequencing data. Cell 187(22), 6393–6410.e16. DOI: 10.1016/j.cell.2024.09.044. github.com/yelabucsf/scrna-parameter-estimation

GSEApy (Enrichr access, run_gsea prerank engine)

github.com/zqfang/GSEApy, docs at gseapy.readthedocs.io

ggVolcano-style volcano plots (volcano_plot(style="ggvolcano"/"gradual"))

github.com/BioSenior/ggVolcano

PathwayDenester (simplify_enrichment(method="pathway_denester"))

github.com/Helmy-Lab/PathwayDenester

Bundled gene set data provenance#

GO and KEGG gene sets bundled with scATrans have their own provenance and licensing terms (GO is CC BY 4.0-derived; KEGG requires a commercial license for non-academic redistribution). See src/scatrans/data/DATA_LICENSES.md and License for details.

Citing scATrans#

If you use scATrans in your research, please cite it using the metadata in CITATION.cff (also exposed as GitHub’s “Cite this repository” button).