estimate.exp.prob.param: Estimate gene expression probability based on experimental...

In heiniglab/scPower: Experimental design framework for scRNAseq population studies (eQTL and DE)

Estimate gene expression probability based on experimental parameters

Description

This function estimates the expression probability of each gene in pseudobulk with a certain cutoff of more than min.counts UMI counts, based on experimental parameters which lead to certain mean and dispersion values for each gene

Usage

estimate.exp.prob.param(
  nSamples,
  readDepth,
  nCellsCt,
  read.umi.fit,
  gamma.mixed.fits,
  ct,
  disp.fun.param,
  min.counts = 3,
  perc.indiv.expr = 0.5,
  cutoffVersion = "absolute",
  nGenes = 21000,
  samplingMethod = "quantiles"
)

Arguments

nSamples	Sample size
readDepth	Target read depth per cell
nCellsCt	Mean number of cells per individual and cell type
read.umi.fit	Data frame for fitting the mean UMI counts per cell depending on the mean readds per cell (required columns: intercept, reads (slope))
gamma.mixed.fits	Data frame with gamma mixed fit parameters for each cell type (required columns: parameter, ct (cell type), intercept, meanUMI (slope))
ct	Cell type of interest (name from the gamma mixed models)
disp.fun.param	Function to fit the dispersion parameter dependent on the mean (required columns: ct (cell type), asymptDisp, extraPois (both from taken from DEseq))
min.counts	Expression cutoff in one individual: if cutoffVersion=absolute, more than this number of UMI counts for each gene per individual and cell type is required; if cutoffVersion=percentage, more than this percentage of cells need to have a count value large than 0
perc.indiv.expr	Expression cutoff on the population level: if number < 1, percentage of individuals that need to have this gene expressed to define it as globally expressed; if number >=1 absolute number of individuals that need to have this gene expressed
cutoffVersion	Either "absolute" or "percentage" leading to different interpretations of min.counts (see description above)
nGenes	Number of genes to simulate (should match the number of genes used for the fitting)
samplingMethod	Approach to sample the gene mean values (either taking quantiles or random sampling)

Value

Vector with expression probabilities for each gene

heiniglab/scPower documentation built on Jan. 9, 2025, 12:13 p.m.