power.smartseq: Power calculation for a DE/eQTL study with Smart-seq design

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

Power calculation for a DE/eQTL study with Smart-seq design

Description

This function to calculate the detection power for a DE or eQTL study, given DE/eQTL genes from a reference study, in a single cell Smart-seq RNAseq study. The power depends on the cost determining parameter of sample size, number of cells per individual and read depth.

Usage

power.smartseq(
  nSamples,
  nCells,
  readDepth,
  ct.freq,
  type,
  ref.study,
  ref.study.name,
  gamma.mixed.fits,
  ct,
  disp.linear.fit,
  mappingEfficiency = 0.8,
  multipletFraction = 0,
  multipletFactor = 1.82,
  min.norm.count = 3,
  perc.indiv.expr = 0.5,
  cutoffVersion = "absolute",
  nGenes = 21000,
  samplingMethod = "quantiles",
  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
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).
gamma.mixed.fits	Data frame with gamma mixed fit parameters for each cell type (required columns: parameter, ct (cell type), intercept, meanReads (slope))
ct	Cell type of interest (name from the gamma mixed models)
disp.linear.fit	Function to fit the dispersion parameter dependent on the mean (parameter linear dependent on read depth) (required columns: parameter, ct (cell type), intercept, meanReads (slope))
mappingEfficiency	Fraction of reads successfully mapped to the transcriptome in the end (need to be between 1-0)
multipletFraction	Multiplet fraction in the experiment as a constant factor
multipletFactor	Expected read proportion of multiplet cells vs singlet cells
min.norm.count	Expression cutoff in one individual: if cutoffVersion=absolute, more than this number of counts per kilobase transcript 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)
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
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.