analyzeComparative: Run a comparative analysis between conditions
In YosefLab/MPRAnalyze: Statistical Analysis of MPRA data

Description Usage Arguments Value Examples

Run a comparative analysis between conditions

analyzeComparative(
  obj,
  rnaDesign,
  dnaDesign = NULL,
  fit.se = FALSE,
  reducedDesign = NULL,
  correctControls = TRUE,
  verbose = TRUE,
  mode = "classic",
  BPPARAM = NULL
)

`obj`	the MpraObject
`rnaDesign`	the design for the RNA model.
`dnaDesign`	the design for the DNA model. Only terms that are matched with the RNA design should be included.
`fit.se`	logical, if TRUE the standard errors of the coefficients are extracted from the model. These are necessary for computing coefficient- based testing, but make the model fitting slower. Deafult: FALSE
`reducedDesign`	the design for the reduced RNA model, for a likelihood- ratio testing scheme. The Reduced design must be nested within the full design (i.e all terms in the reduced must be included in the full).
`correctControls`	if TRUE (default), use the negative controls to establish the null hypothesis, correcting for systemic bias in the data
`verbose`	print progress reports (default: TRUE)
`mode`	whether to run in classic mode ("classic") or in scalable mode ("scale"). Scale mode is only available in situations when each RNA observation has a single corresponding DNA observation.
`BPPARAM`	a parallelization object created by BiocParallel. This overwrites the BPPARAM object set in the object creation.

the MpraObject with fitted models for the input enhancers

data <- simulateMPRA(tr = rep(2,5), da=c(rep(0,2), rep(1,3)), 
                     nbatch=2, nbc=15)
obj <- MpraObject(dnaCounts = data$obs.dna, 
                  rnaCounts = data$obs.rna, 
                  colAnnot = data$annot)
obj <- estimateDepthFactors(obj, lib.factor = "batch", which.lib = "both")
## run an LRT-based analysis, as recommnded:
obj <- analyzeComparative(obj, dnaDesign = ~ batch + barcode + condition, 
                              rnaDesign = ~ condition, reducedDesign = ~ 1)
                              
## alternatively, run a coefficient-based analysis:
obj <- analyzeComparative(obj, dnaDesign = ~ batch + barcode + condition, 
                              rnaDesign = ~ condition, fit.se = TRUE)