Run runTSCANClusterDEAnalysis function to observe changes between paths and to obtain DE genes

Source: R/runTSCAN.R

Wrapper for looking for differences in expression between paths of a branched trajectory. The differential expression analysis may highlight genes which are responsible for the branching event

runTSCANClusterDEAnalysis(
  inSCE,
  useClusters,
  useAssay = "logcounts",
  fdrThreshold = 0.05
)

Arguments

inSCE

Input SingleCellExperiment object.

useClusters

Choose the cluster containing the branch point in the data in order to recompute the pseudotimes so that the root lies at the cluster center, allowing us to detect genes that are associated with the divergence of the branches.

useAssay

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

fdrThreshold

Only out put DEGs with FDR value smaller than this value. Default 0.05.

Value

A SingleCellExperiment object with DE genes that are significant in our path of interest and are not significant and/or changing in the opposite direction in the other paths.

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 11:29:03 2022 ... Computing Scater PCA.
sce <- runDimReduce(inSCE = sce, method = "rTSNE", useReducedDim = "PCA", 
                    reducedDimName = "TSNE")
#> Thu Apr 28 11:29:04 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 11:29:04 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)
sce <- runTSCANClusterDEAnalysis(inSCE = sce, useClusters = 5)
#> Clusters involved in path 4 are: c(1, 4, 5)
#> Clusters involved in path 3 are: c(2, 3, 5)
#> Number of estimated paths of cluster 5 is 2. Following are the terminal nodes for each path respectively: c("4", "3")