Run runTSCANDEG function to obtain changes along a trajectory

Source: R/runTSCAN.R

Wrapper for identifying genes with significant changes with respect to one of the TSCAN pseudotimes

runTSCANDEG(
  inSCE,
  pathIndex,
  useAssay = "logcounts",
  discardCluster = NULL,
  log2fcThreshold = 0
)

Arguments

inSCE

Input SingleCellExperiment object.

pathIndex

Path number for which the pseudotime values should be used. PathIndex corresponds to one path from the root node to one of the terminal nodes.

useAssay

Character. The name of the assay to use. This assay should contain log normalized counts.

discardCluster

Optional. Clusters which are not of use or masks other interesting effects can be discarded.

log2fcThreshold

Only output DEGs with the absolute values of log2FC larger than this value. Default 0

Value

A SingleCellExperiment object with genes that decrease and increase in expression with increasing pseudotime along the path in the MST.

Author

Nida Pervaiz

Examples

data("scExample", package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
rowData(sce)$Symbol <- rowData(sce)$feature_name
rownames(sce) <- rowData(sce)$Symbol
sce <- scaterlogNormCounts(sce, assayName = "logcounts")
sce <- runDimReduce(inSCE = sce, method = "scaterPCA", 
                    useAssay = "logcounts", reducedDimName = "PCA")
#> Thu Apr 28 11:29:06 2022 ... Computing Scater PCA.
sce <- runDimReduce(inSCE = sce, method = "rTSNE", useReducedDim = "PCA", 
                    reducedDimName = "TSNE")
#> Thu Apr 28 11:29:06 2022 ... Computing RtSNE.
#> Warning: using `useReducedDim`, `run_pca` and `ntop` forced to be FALSE/NULL
sce <- runTSCAN (inSCE = sce, useReducedDim = "PCA", seed = NULL)
#> Thu Apr 28 11:29:07 2022 ... Running 'scran SNN clustering'
#> Cluster involved in path 4 are: 1:5
#> Number of estimated paths is 1
sce <- runTSCANDEG(inSCE = sce, pathIndex = 4)