R/processFit.R

In OscarBrock/gCrisprTools: Suite of Functions for Pooled Crispr Screen QC and Analysis

##' @title Preprocess a 'MArrayLM' model fit object to include only one contrast. 
##' @description This function preprocesses a fit object returned from eBayes to include only the values relevant to the
##' \code{modelTerm} specified.  
##' @param fit An object of class MArrayLM to be processed. 
##' @param modelTerm The model coefficient to be isolated for downstream analyses. 
##' @return A \code{MArrayLM} object for downstream processing. 
##' @author Russell Bainer
##' @import limma
##' @keywords internal
##' @examples 
##' 
##' #Load and preprocess data
##' data('es')
##' library(Biobase)
##' library(limma)
##' 
##' #Make a multi-level contrast
##' design <- model.matrix(~ 0 + TREATMENT_NAME, pData(es))
##' colnames(design) <- gsub('TREATMENT_NAME', '', colnames(design))
##' contrasts <- makeContrasts((ControlExpansion - ControlReference), (DeathExpansion - ControlExpansion), levels = design)
##' 
##' #Make a multi-level fit object
##' vm <- voom(exprs(es), design)
##' fit <- lmFit(vm, design)
##' fit <- contrasts.fit(fit, contrasts)
##' fit <- eBayes(fit)  
##' 
##' #And trim it
##' fit2  <- ct.preprocessFit(fit, modelTerm = '(DeathExpansion - ControlExpansion)')
##' 
##' ncol(fit)
##' ncol(fit2)
##' @export 

ct.preprocessFit <- function(fit, modelTerm) {
    if (!methods::is(fit, "MArrayLM")) {
        stop(deparse(substitute(fit)), " is not an MArrayLM object.")
    }
    if (!(modelTerm %in% colnames(fit$coefficients))) {
        stop("Specified coefficient is not present in the fit object.")
    }
    if (!("p.value" %in% names(fit))) {
        warning(deparse(substitute(fit)), " does not contain p-values quantifying the evidence for differential gRNA abundance. Eventually, you will need to process it with eBayes(), treat(), or a similar function.")
    }

    fit$coefficients <- as.matrix(fit$coefficients[, modelTerm])
    colnames(fit$coefficients) <- modelTerm
    fit$stdev.unscaled <- as.matrix(fit$stdev.unscaled[, modelTerm])
    colnames(fit$stdev.unscaled) <- modelTerm
    fit$t <- as.matrix(fit$t[, modelTerm])
    colnames(fit$t) <- modelTerm
    fit$p.value <- as.matrix(fit$p.value[, modelTerm])
    colnames(fit$p.value) <- modelTerm
    if ("lods" %in% names(fit)) {
        fit$lods <- as.matrix(fit$lods[, modelTerm])
        colnames(fit$lods) <- modelTerm
    }

    return(fit)
}