power.sameReadDepth.withDoublets: Power calculation for a DE/eQTL study with same read depth as...

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

Power calculation for a DE/eQTL study with same read depth as the fitted gamma distribution

Description

This is a simplified version of the function power.general.withDoublets to be used on a gamma fit not parameterized for UMI/read counts. It evaluates the effect of different samples sizes and cells per person, keeping the same read depth as in the experiment used for fitting.

Usage

power.sameReadDepth.withDoublets(
  nSamples,
  nCells,
  ct.freq,
  type,
  ref.study,
  ref.study.name,
  samplesPerLane,
  gamma.parameters,
  ct,
  disp.fun.param,
  mappingEfficiency = 0.8,
  multipletRate = 7.67e-06,
  multipletFactor = 1.82,
  min.UMI.counts = 3,
  perc.indiv.expr = 0.5,
  cutoffVersion = "absolute",
  nGenes = 21000,
  samplingMethod = "quantiles",
  multipletRateGrowth = "linear",
  sign.threshold = 0.05,
  MTmethod = "Bonferroni",
  useSimulatedPower = TRUE,
  simThreshold = 4,
  speedPowerCalc = FALSE,
  indepSNPs = 10,
  ssize.ratio.de = 1,
  returnResultsDetailed = FALSE
)

Arguments

nSamples	Sample size
nCells	Number of cells per individual
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).
samplesPerLane	Maximal number of individuals per 10X lane
gamma.parameters	Data frame with gamma parameters for each cell type (required columns: ct (cell type), s1, r1, s2, r2, p1, p2/p3 (gamma parameters for both components))
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))
mappingEfficiency	Fraction of reads successfully mapped to the transcriptome in the end (need to be between 1-0)
multipletRate	Expected increase in multiplets for additional cell in the lane
multipletFactor	Expected read proportion of multiplet cells vs singlet cells
min.UMI.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)
multipletRateGrowth	Development of multiplet rate with increasing number of cells per lane, "linear" if overloading should be modeled explicitly, otherwise "constant". The default value for the parameter multipletRate is matching the option "linear".
sign.threshold	Significance threshold
MTmethod	Multiple testing correction method (possible options: "Bonferroni","FDR","none")
useSimulatedPower	Option to simulate eQTL power for small mean values to increase accuracy (only possible for eQTL analysis)
simThreshold	Threshold until which the simulated power is taken instead of the analytic (only for the eQTL analysis)
speedPowerCalc	Option to speed power calculation by skipping all genes with an expression probability less than 0.01 (as overall power is anyway close to 0)
indepSNPs	Number of independent SNPs assumed for each loci (for eQTL Bonferroni multiple testing correction the number of tests are estimated as number expressed genes * indepSNPs)
ssize.ratio.de	In the DE case, ratio between sample size of group 0 (control group) and group 1 (1=balanced design)
returnResultsDetailed	If true, return not only summary data frame, but additional list with exact probability vectors

Value

Power to detect the DE/eQTL genes from the reference study in a single cell experiment with these parameters

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