A complete analysis of splat.sim

A ScMaSigPro object with every step of the workflow run on splat.sim, for the examples and the vignettes. The Quick start vignette runs the same steps.

Usage

data("scmp.ob", package = "scMaSigPro")

Format

A ScMaSigPro object:

Sparse: The 200 cells and 100 genes of splat.sim, with the pseudotime in Pseudotime (Step of splat.sim) and the path in Path (Group).
Dense: The 16 bins of sc.squeeze() with its defaults, 8 per path, and their pseudo-bulk counts.
Design: The design of sc.set.poly() with poly_degree = 1 and the offsets of sc.p.vector().
Profile: The test of the full model of every gene by sc.p.vector(): negative binomial family with theta 10, log link and size-factor offsets.
Estimate: The models selected by sc.t.fit() (backward selection).
Significant: The gene lists Path1 and Path2vsPath1 of sc.filter() with rsq = 0.7 and vars = "groups", and four clusters of their union from sc.cluster.trend().
Parameters: The settings of these steps.

Source

Made by system.file("scripts", "make-scmp-ob.R", package = "scMaSigPro") from splat.sim.

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

Author

Priyansh Srivastava spriyansh29@gmail.com

Examples

data("scmp.ob", package = "scMaSigPro")
scmp.ob
#> Class: ScMaSigProClass
#> nCells: 200
#> nFeatures: 100
#> Pseudotime Range: 1 100
#> Branching Paths: Path2, Path1
#> Binned Pseudotime: 1-8(Range), 4.5(Mean), 
#> Number of bins-> Path1: 8 Path2: 8
#> Average bin Size-> Path1: 12 Path2: 13
#> Polynomial Order: 1
#> No. of Significant Profiles: 50
#> No. of Influential Features: 8