select.cutoffs: Identifying the expression threshold combination that...

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

Identifying the expression threshold combination that maximizes the detection power

Description

Identifying the expression threshold combination that maximizes the detection power

Usage

select.cutoffs(
  umi_range,
  pop_range,
  nSamples,
  nCells,
  readDepth,
  ct.freq,
  type,
  ref.study,
  ref.study.name,
  cellsPerLane,
  read.umi.fit,
  gamma.mixed.fits,
  ct,
  disp.fun.param,
  ...
)

Arguments

umi_range	Vector with UMI counts to test
pop_range	Vector with population thresholds to test
nSamples	Sample size
nCells	Number of cells per individual
readDepth	Target read depth per cell
ct.freq	Frequency of the cell type of interest
type	(eqtl/de) study
ref.study	Data frame with reference studies to be used for expression ranks and effect sizes (required columns: name (study name), rank (expression rank), FoldChange (DE study) /Rsq (eQTL study))
ref.study.name	Name of the reference study. Will be checked in the ref.study data frame for it (as column name).
cellsPerLane	Maximal number of cells per 10X lane
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))
...	additional arguments that can be passed to power.general.restrictedDoublets() (excluding min.UMI.counts and perc.indiv.expr that will be evaluated in the function)

Value

Results for threshold combination with the maximal detection power

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