Simulated multifurcating trajectory

A SingleCellExperiment with the counts of 501 genes in 1977 cells of a simulated trajectory with three end points and a shared root, with the pseudotime and the path labels of each cell. The vignettes Trajectory input and Shared root analyse it.

Usage

data("multi.lin.sce", package = "scMaSigPro")

Format

A SingleCellExperiment with 501 genes (rows) and 1977 cells (columns):

counts assay

The simulated counts.

colData

cell_id, the cell name; group, the path label: "root" for the cells on all three lineages, "path_Y_15", "path_Y_18" or "path_Y_52" for the cells on one, and "path_Y_18|path_Y_15" for the cells on these two; Monocle3_Pseudotime, the pseudotime of Monocle 3.

reducedDims

TSNE, the two dimensions of the t-SNE embedding.

The rowData has no columns.

Source

The multifurcating simulation of Van den Berge et al. (2020), made with dyntoy (Saelens et al. 2019): multifurcating_4.rds of https://github.com/statOmics/tradeSeqPaper. Monocle 3 (Cao et al. 2019) normalised the counts, embedded the cells with PCA and t-SNE, learnt the principal graph and gave the pseudotime from the root node Y_51. Each cell is on the lineages whose shortest path from the root, on that graph, holds its nearest node, as in the Monocle 3 steps of the tradeSeq vignette; three lineages are kept and the cells on none of them are left out. The script is system.file("scripts", "make-multi-lin-sce.R", package = "scMaSigPro").

References

Van den Berge K, Roux de Bézieux H, Street K, Saelens W, Cannoodt R, Saeys Y, Dudoit S, Clement L (2020). Trajectory-based differential expression analysis for single-cell sequencing data. Nature Communications 11, 1201. doi:10.1038/s41467-020-14766-3

Saelens W, Cannoodt R, Todorov H, Saeys Y (2019). A comparison of single-cell trajectory inference methods. Nature Biotechnology 37(5), 547-554. doi:10.1038/s41587-019-0071-9

Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ, Trapnell C, Shendure J (2019). The single-cell transcriptional landscape of mammalian organogenesis. Nature 566(7745), 496-502. doi:10.1038/s41586-019-0969-x

See also

Other datasets: scmp.ob, splat.sim

Author

Priyansh Srivastava spriyansh29@gmail.com

Examples

data("multi.lin.sce", package = "scMaSigPro")
table(SummarizedExperiment::colData(multi.lin.sce)$group)
#> 
#>           path_Y_15           path_Y_18 path_Y_18|path_Y_15           path_Y_52 
#>                 307                 676                  43                 498 
#>                root 
#>                 453