scMaSigPro

scMaSigPro finds genes whose expression changes along pseudotime and differs between the branching paths of a single-cell trajectory. It pools the cells of each path into bins along pseudotime, adds up their counts, and fits the polynomial generalised linear models of maSigPro to these pseudo-bulk counts, with a negative binomial distribution and an offset for the sequencing depth of each bin. The genes whose expression changes, and the paths in which it changes, come out as lists, plots and clusters.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("BioBam/scMaSigPro")

Example

The bundled data set splat.sim holds a simulated trajectory with two paths, with the pseudotime of each cell in its Step column and its path in Group:

library(scMaSigPro)
data("splat.sim", package = "scMaSigPro")
scmp_ob <- as_scmp(splat.sim, additional_params = list(
    labels_exist = TRUE, exist_ptime_col = "Step", exist_path_col = "Group"
))
scmp_ob <- sc.squeeze(scmp_ob)
scmp_ob <- sc.set.poly(scmp_ob, poly_degree = 1)
scmp_ob <- sc.p.vector(scmp_ob)
scmp_ob <- sc.t.fit(scmp_ob)
scmp_ob <- sc.filter(scmp_ob, rsq = 0.7, vars = "groups")
plotTrend(scmp_ob, "Gene10")
head(scmpResults(scmp_ob))

The Quick start vignette explains each step.

Documentation

The website, https://spriyansh.github.io/scMaSigPro/, holds the help pages, the news and the vignettes, which are also in the package (browseVignettes("scMaSigPro")):

• Quick start: the workflow from a SingleCellExperiment to gene lists, plots and clusters.
• Trajectory input: pseudotime and path labels from slingshot, Monocle3 or a trajectory graph.
• Shared root: trajectories whose paths share a root and links.
• Model and tuning: the model, its settings and their effect.
• maSigPro and the ScMaSigPro class: the object and the equivalence with maSigPro.
• Frequently asked questions.

Citation

If you use scMaSigPro, please cite the article that describes it, as citation("scMaSigPro") prints it:

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

Contributing

Bug reports, questions and suggestions are welcome on the issue tracker. The contributing guide explains how to propose a change, and everyone taking part follows the code of conduct.

Licence

scMaSigPro is free software under the GNU General Public License, version 2 or later; see LICENSE.md.

Funding

This project has received funding from the European Union’s Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Skłodowska-Curie Grant Agreement No 953407.