Run plotTSCANPseudotimeHeatmap function to plot heatmap for top genes — plotTSCANPseudotimeHeatmap • singleCellTK

A wrapper function which visualizes outputs from the runTSCANDEG function. Plots the top genes that increase in expression with increasing pseudotime along the path in the MST

plotTSCANPseudotimeHeatmap(inSCE, pathIndex, topN = 50)

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.
topN: An integer. Only to plot this number of top genes along the path in the MST, in terms of log2FC value. Use NULL to cancel the top N subscription. Default 50.

Value

A plot with the top genes that 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 11:27:13 2022 ... Computing Scater PCA.
sce <- runDimReduce(inSCE = sce, method = "rTSNE", useReducedDim = "PCA", 
                    reducedDimName = "TSNE")
#> Thu Apr 28 11:27:13 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 11:27:14 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)
plotTSCANPseudotimeHeatmap(inSCE = sce, pathIndex = 4,topN = 5)