estimate.exp.prob.values: Estimate gene expression probability based on mean and...

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

Estimate gene expression probability based on mean and dispersion values

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 the mean and the disperion value of each gene

Usage

estimate.exp.prob.values(
  mu,
  size,
  nCellsCt,
  nSamples,
  min.counts = 3,
  perc.indiv.expr = 0.5,
  cutoffVersion = "absolute"
)

Arguments

mu	Estimated mean value in sc data per gene (vector)
size	Estimated size parameter in sc data from the negative binomial fit (1/dispersion parameter), also per gene (vector)
nCellsCt	Mean number of cells per individual and cell type
nSamples	Total sample size
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)

Value

Vector with expression probabilities for each gene

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