Wrapper for obtaining a pseudotime ordering of the cells by projecting them onto the minimum spanning tree (MST)

runTSCAN(inSCE, useReducedDim = "PCA", cluster = NULL, seed = 12345)

Arguments

inSCE

Input SingleCellExperiment object.

useReducedDim

Character. A low-dimension representation in reducedDims, will be used for both clustering if cluster not specified and MST construction. Default "PCA".

cluster

Grouping for each cell in inSCE. A vector with equal length to the number of the cells in inSCE, or a single character for retriving colData variable. Default NULL, will run runScranSNN to obtain.

seed

An integer. Random seed for clustering if cluster is not specified. Default 12345.

Value

The input inSCE object with pseudotime ordering of the cells along the paths and the cluster label stored in colData, and other unstructured information in metadata.

Author

Nida Pervaiz

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runTSCAN(inSCE = mouseBrainSubsetSCE,
                                useReducedDim = "PCA_logcounts")
#> Sat Mar 18 10:31:31 2023 ... Running 'scran SNN clustering' with 'louvain' algorithm
#> Sat Mar 18 10:31:32 2023 ...   Identified 2 clusters
#> Sat Mar 18 10:31:32 2023 ... Running TSCAN to estimate pseudotime
#> Sat Mar 18 10:31:32 2023 ...   Clusters involved in path index 2 are: 1, 2
#> Sat Mar 18 10:31:32 2023 ...   Number of estimated paths is 1