scMaSigPro: Differential Expression Analysis Along Single-Cell Trajectories

scMaSigPro finds genes whose expression changes along pseudotime and differs between the branching paths of a single-cell trajectory. The cells of each path are pseudo-bulked into pseudotime bins, and the polynomial generalised linear model of maSigPro is fitted to the binned counts, by default with a negative binomial family and size-factor offsets (Srivastava et al. 2024).

Workflow

1. as_scmp() or create_scmp(): a ScMaSigPro object from the counts and one pseudotime value and one path label per cell.

2. sc.squeeze(): pseudotime bins and pseudo-bulk counts for each path; sc.restruct() adds the bins of a shared root to every path.

3. sc.set.poly(): the polynomial design.

4. sc.p.vector(): the test of each gene's full model, which selects the genes with a non-flat profile.

5. sc.t.fit(): stepwise selection of the significant terms of each of these genes.

6. sc.filter(): lists of significant genes; sc.cluster.trend() clusters them.

7. plotTrend(), plotTrendCluster(), plotIntersect(): plots; scmpResults(): the results as a table with one row per gene.

Input

The workflow needs one pseudotime value and one path label per cell. assign_paths() and paths_from_graph() build them from the lineage weights or the graph of a trajectory tool; the "Input contract" section of assign_paths() describes the format.

Documentation

browseVignettes("scMaSigPro") lists the vignettes. They and these help pages are also on the package website, https://spriyansh.github.io/scMaSigPro/:

• Quick start: https://spriyansh.github.io/scMaSigPro/articles/scMaSigPro.html

• Trajectory input: https://spriyansh.github.io/scMaSigPro/articles/trajectory-input.html

• Shared root: https://spriyansh.github.io/scMaSigPro/articles/shared-root.html

• Model and tuning: https://spriyansh.github.io/scMaSigPro/articles/model-and-tuning.html

• maSigPro and the ScMaSigPro class: https://spriyansh.github.io/scMaSigPro/articles/masigpro-and-class.html

• Frequently asked questions: https://spriyansh.github.io/scMaSigPro/articles/faq.html

References

Srivastava P, Benegas Coll M, Götz S, Nueda MJ, Conesa A (2024). scMaSigPro: differential expression analysis along single-cell trajectories. Bioinformatics 40(7), btae443. doi:10.1093/bioinformatics/btae443

Conesa A, Nueda MJ, Ferrer A, Talón M (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 22(9), 1096-1102. doi:10.1093/bioinformatics/btl056

Nueda MJ, Tarazona S, Conesa A (2014). Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics 30(18), 2598-2602. doi:10.1093/bioinformatics/btu333

See also

Useful links:

• https://github.com/BioBam/scMaSigPro/

• https://spriyansh.github.io/scMaSigPro/

• Report bugs at https://github.com/BioBam/scMaSigPro/issues

Author

Maintainer: Priyansh Srivastava spriyansh29@gmail.com (ORCID)

Authors:

• Priyansh Srivastava spriyansh29@gmail.com (ORCID)

• Ana Conesa ana.conesa@csic.es (ORCID)

• María José Nueda mj.nueda@ua.es (ORCID)

• Marta Benegas Coll martabc22@gmail.com (ORCID)

Other contributors:

• European Union (Horizon 2020 Marie Skłodowska-Curie Grant Agreement No 953407) [funder]