Simulated bifurcating trajectory

A SingleCellExperiment with the counts of 100 genes in 200 cells, simulated with splatter (Zappia et al. 2017) along two paths that start from the same point. The vignettes Quick start and Model and tuning analyse it.

Usage

data("splat.sim", package = "scMaSigPro")

Format

A SingleCellExperiment with 100 genes (rows) and 200 cells (columns):

counts assay

The simulated counts.

colData

Cell, the cell name; Batch, the batch (one, "Batch1"); Group, the path of the cell, a factor with the levels "Path1" and "Path2"; ExpLibSize, its expected library size; Step, how far along its path the cell is (1 to 100), used as the pseudotime; sizeFactor, the size factor of scuttle::logNormCounts().

rowData

Gene, the gene name; BaseGeneMean, the base mean of the gene; OutlierFactor, its outlier factor; GeneMean, its mean after the outlier factor; LocalDEFacPath1 and LocalDEFacPath2, its differential expression factor along each path; DEFacPath1 and DEFacPath2, its factor at the end of each path; SigmaFacPath1 and SigmaFacPath2, the factor of its non-linear change along each path (0 for a linear change).

reducedDims

PCA, the first two principal components of the log-normalised counts.

Source

Simulated with splatter::splatSimulatePaths() (splatter 1.26.0, set.seed(123), 200 cells, 100 genes, two paths of 100 steps from the origin, de.prob = 0.3, de.facLoc = 0.2), normalised with scuttle::logNormCounts() and reduced with scater::runPCA(). The script is system.file("scripts", "make-splat-sim.R", package = "scMaSigPro").

References

Zappia L, Phipson B, Oshlack A (2017). Splatter: simulation of single-cell RNA sequencing data. Genome Biology 18, 174. doi:10.1186/s13059-017-1305-0

See also

Other datasets: multi.lin.sce, scmp.ob

Author

Priyansh Srivastava spriyansh29@gmail.com

Examples

data("splat.sim", package = "scMaSigPro")
table(SummarizedExperiment::colData(splat.sim)$Group)
#> 
#> Path1 Path2 
#>    97   103