Wrapper for identifying genes with significant changes with respect to one of the TSCAN pseudotimes
runTSCANDEG(
inSCE,
pathIndex,
useAssay = "logcounts",
discardCluster = NULL,
log2fcThreshold = 0
)Input SingleCellExperiment object.
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.
Character. The name of the assay to use. This assay should contain log normalized counts.
Optional. Clusters which are not of use or masks other interesting effects can be discarded.
Only output DEGs with the absolute values of log2FC
larger than this value. Default 0
A SingleCellExperiment object with genes that decrease and increase in expression with increasing pseudotime along the path in the MST.
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)