perCellEntropy: Compute the per-cell entropy
In TSCAN: Tools for Single-Cell Analysis

Description Usage Arguments Details Value Author(s) References Examples

Compute the entropy of each cell, using this as a proxy for the differentiation status.

perCellEntropy(x, ...)

## S4 method for signature 'ANY'
perCellEntropy(x, BPPARAM = NULL)

## S4 method for signature 'SummarizedExperiment'
perCellEntropy(x, ..., assay.type = "counts")

`x`	A numeric matrix-like object containing counts for each cell (column) and feature (row). Alternatively, a SummarizedExperiment object containing such a matrix.
`...`	For the generic, further arguments to pass to specific methods. For the SummarizedExperiment method, further arguments to pass to the ANY method.
`BPPARAM`	A BiocParallelParam object from BiocParallel, specifying how calculations should be parallelized.
`assay.type`	An integer or string specifying the assay to use from a SummarizedExperiment `x`.

Entropy values are computed from the proportion of counts assigned to each feature within a given cell. The central idea is that undifferentiated cells have higher entropies because they are not yet committed to a single lineage, and thus have low but persistent activity of the transcriptional programs for all lineages. The cluster with the highest entropy values can be used to determine the start cluster in orderCells.

A numeric vector of entropies for all cells in x. Cells with all-zero values in x will be assigned NA entropies.

Aaron Lun

Grun D et al. (2016). De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19, 266-77

Gulati GS et al. (2020). Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405-11

Guo M et al. (2017) SLICE: determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Res. 45, e54

sce <- scuttle::mockSCE()
ent <- perCellEntropy(sce)
summary(ent)

# Compute average entropy over mock clusters.
clusters <- sample(ncol(sce), 5)
by.cluster <- split(ent, clusters)
mean.cluster.ent <- vapply(by.cluster, mean, 0)