Test gene expression changes along a TSCAN trajectory path

Source: R/runTSCAN.R

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

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

Arguments

inSCE

Input SingleCellExperiment object.

pathIndex

Path index for which the pseudotime values should be used. This corresponds to the terminal node of specific path from the root node to the terminal node. Run listTSCANTerminalNodes(inSCE) for available options.

useAssay

Character. The name of the assay to use for testing the expression change. Should be log-normalized. Default "logcounts"

discardCluster

Cluster(s) which are not of use or masks other interesting effects can be discarded. Default NULL.

Value

The input inSCE with results updated in metadata.

Author

Nida Pervaiz

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runTSCAN(inSCE = mouseBrainSubsetSCE,
                                useReducedDim = "PCA_logcounts")
#> Mon Dec 19 18:10:43 2022 ... Running 'scran SNN clustering' with 'louvain' algorithm
#> Mon Dec 19 18:10:44 2022 ...   Identified 2 clusters
#> Mon Dec 19 18:10:44 2022 ... Running TSCAN to estimate pseudotime
#> Mon Dec 19 18:10:45 2022 ...   Clusters involved in path index 2 are: 1, 2
#> Mon Dec 19 18:10:45 2022 ...   Number of estimated paths is 1
terminalNodes <- listTSCANTerminalNodes(mouseBrainSubsetSCE)
mouseBrainSubsetSCE <- runTSCANDEG(inSCE = mouseBrainSubsetSCE,
                                   pathIndex = terminalNodes[1])