estimate.exp.prob.count.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 - variant for Smart-seq or when calculating directly based on UMI counts (without read UMI fit)

Description

This is an adaption of function estimate.exp.prob.param were directly the mean UMI parameter can be set in the variable meanCellCount (no read.umi.fit and read depths required). This version can also be used to model read based single cell methods, such as Smart-seq, by setting the parameter meanCellCounts to their read depth

Usage

estimate.exp.prob.count.param(
  nSamples,
  nCellsCt,
  meanCellCounts,
  gamma.mixed.fits,
  ct,
  disp.fun.param,
  min.counts = 3,
  perc.indiv.expr = 0.5,
  cutoffVersion = "absolute",
  nGenes = 21000,
  samplingMethod = "quantiles",
  countMethod = "UMI",
  gene_length_prior = NULL
)

Arguments

nSamples	Sample size
nCellsCt	Mean number of cells per individual and cell type
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	Data frame with parameters 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)
countMethod	Specify if it is a UMI or read based method (by "UMI" or "read"). For a read based method, the dispersion function is fitted linear, for a UMI based method constant.
gene_length_prior	Vector with gene length, ordered by expression rank (the first entry for the highest expressed gene); only required for Smart-seq (countMethod="read")
meanCellCount	Either mean UMI counts per cell for UMI-based methods or mean read counts per cell for read-based methods

Value

Vector with expression probabilities for each gene

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