findMalignant: Find Malignant Subset of Cells

In jlaffy/infercna: Infer Copy Number Alterations And Clonality In (Single-Cell) RNA-seq Data

Find Malignant Subset of Cells

Description

Find the malignant and non-malignant subsets of cells from a gene-by-cell matrix of CNA values.

Usage

findMalignant(
  cna,
  refCells = NULL,
  samples = scalop::unique_sample_names(colnames(cna), max.nchar = 6),
  gene.quantile = 0.9,
  gene.quantile.for.corr = 0.5,
  gene.quantile.for.signal = gene.quantile,
  use.bootstraps = TRUE,
  n.bootstraps = 10000,
  prob = 0.95,
  coverage = 0.8,
  verbose = TRUE,
  plot = TRUE,
  ...
)

Arguments

cna	a matrix of gene rows by cell columns containing CNA values.
refCells	a character vector or list of character vectors denoting the cell IDs of the known non-malignant 'Reference' cells (usually those that were used in the call to infercna::infercna to correct the CNA values). This is relevant only if these cells are in the CNA matrix. Default: NULL
samples	a character vector of unique sample names to group the cells by. This is relevant only if multiple tumours are represented in the cna matrix, as it allows tumour-specific average CNA profiles to be computed in the call to infercna::cnaCor. See Details section for more information. Default: scalop::unique_sample_names(colnames(cna))
gene.quantile	calculate CNA measures including only top / "hotspot" genes according to their squared CNA values across all cells. Value between 0 and 1 denoting the quantile of genes to include. Default: 0.9
gene.quantile.for.corr	as above but for CNA correlations specifically. Default: gene.quantile
gene.quantile.for.signal	as above but for CNA signal specifically. Default: gene.quantile
use.bootstraps	logical; if TRUE, the function uses a bootstrapping method to subsample values and identify the malignant and non-malignant groups iteratively. This method is more sensitive to differing group sizes, so will be useful if you believe one group to be much smaller than the other. Default: TRUE
n.bootstraps	number of bootstrap replicates. Relevant only if <use.bootstraps> is TRUE. Default: 10000
prob	a numeric value >= 0 and <= 1; the minimum posterior probability required for an cell to be assigned to a mode. Default: 0.8
coverage	the fraction of cells that must have a posterior probability higher than <prob> to one of two modes in order for the distribution to qualify as bimodal. Default: 0.8
verbose	print progress messages. Default: TRUE
plot	logical; scatter plot of cells' CNA correlations against CNA signal. This uses infercna::cnaScatterPlot. In order for the infercna::findMalignant function to be meaningful, expect two distinct groups of cells in the plot. Default: TRUE
...	other arguments passed to infercna::fitBimodal.

Details

This function attempts to fit gaussian bimodal distributions to each of two parameters that describe the extent of CNAs per cell. The first of these is CNA correlation (infercna::cnaCor), which measures the pearson correlations between individual cells' CNA profiles and the average profile of their tumours of origin. The second measure is CNA signal (infercna::cnaSignal), which computes the cell averages of squared CNA values. The function then assigns cells residing in the 2nd (higher-value) mode by both measures as malignant, cells residing in the 1st (low-value) mode by both measures as non-malignant, and any remaining cells with conflicting assignments to modes as unassigned.

Value

if bimodality was not found, returns FALSE. If bimodality was found, returns a list containing the malignant and non-malignant cell IDs.

See Also

unique_sample_names,hms_span,comply

jlaffy/infercna documentation built on Jan. 26, 2024, 11:24 p.m.