Find DE genes between all TSCAN paths rooted from given cluster — runTSCANClusterDEAnalysis • singleCellTK

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")
#> Tue Jun 28 22:07:34 2022 ... Running 'scran SNN clustering' with 'louvain' algorithm
#> Tue Jun 28 22:07:35 2022 ...   Identified 2 clusters
#> Tue Jun 28 22:07:35 2022 ... Running TSCAN to estimate pseudotime
#> Tue Jun 28 22:07:35 2022 ...   Clusters involved in path index 2 are: 1, 2
#> Tue Jun 28 22:07:35 2022 ...   Number of estimated paths is 1
mouseBrainSubsetSCE <- runTSCANClusterDEAnalysis(inSCE = mouseBrainSubsetSCE,
                                         useCluster = 1)
#> Tue Jun 28 22:07:35 2022 ... Finding DEG between TSCAN branches
#> Tue Jun 28 22:07:36 2022 ...   Clusters involved in path index 2 are: 1, 2