Find DE genes between all TSCAN paths rooted from given cluster

Source: R/runTSCAN.R

This function finds all paths that root from a given cluster useCluster, and performs tests to identify significant features for each path, and are not significant and/or changing in the opposite direction in the other paths. Using a branching cluster (i.e. a node with degree > 2) may highlight features which are responsible for the branching event. MST has to be pre-calculated with runTSCAN.

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

Arguments

inSCE

Input SingleCellExperiment object.

useCluster

The cluster to be regarded as the root, has to existing in colData(inSCE)$TSCAN_clusters.

useAssay

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

fdrThreshold

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

Value

The input inSCE with results updated in metadata.

Author

Nida Pervaiz

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runTSCAN(inSCE = mouseBrainSubsetSCE,
                                useReducedDim = "PCA_logcounts")
#> Wed Jul 26 13:03:13 2023 ... Running 'scran SNN clustering' with 'louvain' algorithm
#> Wed Jul 26 13:03:13 2023 ...   Identified 2 clusters
#> Wed Jul 26 13:03:13 2023 ... Running TSCAN to estimate pseudotime
#> Wed Jul 26 13:03:14 2023 ...   Clusters involved in path index 2 are: 1, 2
#> Wed Jul 26 13:03:14 2023 ...   Number of estimated paths is 1
mouseBrainSubsetSCE <- runTSCANClusterDEAnalysis(inSCE = mouseBrainSubsetSCE,
                                         useCluster = 1)
#> Wed Jul 26 13:03:14 2023 ... Finding DEG between TSCAN branches
#> Wed Jul 26 13:03:14 2023 ...   Clusters involved in path index 2 are: 1, 2