quickPseudotime: Quick MST-based pseudotime
In scran: Methods for Single-Cell RNA-Seq Data Analysis

Description Usage Arguments Details Value Author(s) See Also Examples

A convenience wrapper to quickly compute a minimum spanning tree (MST) on the cluster centroids to obtain a pseudotime ordering of the cells. This function is now deprecated as it has been moved to the TSCAN package itself.

quickPseudotime(
  x,
  clusters,
  use = 1,
  outgroup = FALSE,
  outscale = 3,
  start = NULL
)

`x`	A named list of numeric matrices containing dimensionality reduction results. All matrices should have the same number of cells, i.e., rows. Alternatively, a SingleCellExperiment containing such results in its `reducedDims`.
`clusters`	A factor of length equal to the number of cells in `x`, specifying the cluster assignment for each cell.
`use`	Integer scalar or string specifying the entry of `x` to use for MST construction and pseudotime calculations.
`outgroup, outscale`	Arguments passed to `createClusterMST`.
`start`	Arguments passed to `orderClusterMST`.

This function simply calls, in order:

rowsum, to compute the average low-dimensional coordinates for each cluster.
createClusterMST on the average coordinates specified by use.
orderClusterMST on the average and per-cell coordinates specified by use.
connectClusterMST on the average coordinates for all entries of x.

A List containing:

centered, a list of numeric matrices containing the averaged coordinates for each cluster. Each matrix corresponds to a dimensionality reduction result in x.
mst, a graph object containing the cluster-level MST computed on the coordinates from use.
ordering, a numeric matrix of pseudotimes for various paths through the MST computed from use.
connected, a list of data.frames containing the edge coordinates between centers. Each data.frame corresponds to a dimensionality reduction result in x.

Aaron Lun

createClusterMST and friends, for the functions that do the actual work.

# Mocking up some data:
library(scuttle)
sce <- mockSCE(ncells=500)
sce <- logNormCounts(sce)
sce <- scater::runPCA(sce)
clusters <- clusterSNNGraph(sce, use.dimred="PCA")

# Quickly computing the pseudotime:
out <- quickPseudotime(sce, clusters, use="PCA")
out$mst
head(out$ordering)