R/GCbias.R

Defines functions ct.GCbias

Documented in ct.GCbias

##' @title Visualization of gRNA GC Content Trends
##' @description This function visualizes relationships between gRNA GC content and their measured abundance or 
##' various differential expression model estimates. 
##' @param data.obj An \code{ExpressionSet} or fit (\code{MArrayLM}) object to be analyzed for the presence of GC 
##' content bias. 
##' @param ann An annotation \code{data.frame}, used to estimate GC content for each guide. Guides are annotated by 
##' row, and the object must minimally contain a \code{target} column containing a character 
##' vector that indicates the corresponding nucleotide sequences.
##' @param sampleKey An optional sample key, supplied as a factor linking the samples to experimental 
##' variables. The \code{names} attribute should exactly match those present in \code{eset}, and the 
##' control set is assumed to be the first \code{level}. Ignored in the analysis of model fit objects. 
##' @param lib.size An optional vector of voom-appropriate library size adjustment factors, usually calculated with \code{\link[edgeR]{calcNormFactors}} 
##' and transformed to reflect the appropriate library size. These adjustment factors are interpreted as the total library sizes for each sample, 
##' and if absent will be extrapolated from the columnwise count sums of the \code{exprs} slot of the \code{eset}.
##' @return An image relating GC content to experimental observations on the default device. If the 
##' provided \code{data.obj} is an \code{ExpressionSet}, this takes the form of a scatter plot where the 
##' GC% and abundance estimates of each guide are represented as points overlaid
##' with a smoothed trendline within each sample. If \code{data.obj} is a fit object describing a linear model 
##' contrast, then four panels are returned describing the GC content distribution and its relationship 
##' to guide-level fold change, standard deviation, and P-value estimates.  
##' @author Russell Bainer
##' @examples 
##' data('es')
##' data('ann')
##' data('fit')
##' 
##' ct.GCbias(es, ann)
##' ct.GCbias(fit, ann)
##' @export

ct.GCbias <- function(data.obj, ann, sampleKey = NULL, lib.size = NULL){

  #check inputs
  if(class(data.obj) == 'ExpressionSet'){
      is.fit <- FALSE
      d <- exprs(data.obj)
      
      if (is.null(lib.size)){
        lib.size <- colSums(d)
      }else if(!is.numeric(lib.size) | (length(lib.size) != ncol(d))){
        stop('If specified, lib.size must be a numeric vector of the same 
             length as the number of samples in the eset.')
      } 
    
      d <- t(log2(t(d + 0.5)/(lib.size + 1) * 1e+06))

      if(is.null(sampleKey)){
        sampleKey <- factor(colnames(d))
        names(sampleKey) <- colnames(d)
      }

    invisible(ct.inputCheck(sampleKey, data.obj))
    ann <- ct.prepareAnnotation(ann, object = data.obj, throw.error = FALSE)
        
    } else if(class(data.obj) == 'MArrayLM'){

      is.fit <- TRUE
      ann <- ct.prepareAnnotation(ann, object = data.obj, throw.error = FALSE)

      } else {
        stop('Please provide an Expressionset or MArrayLM object for GC analysis.')
        }

    if(!('target' %in% names(ann))){
      stop('The provided annotation object does not contain a "target" column annotating the gRNA sequences.')
    } else if(!is.character(ann$target)){
      message('The "target" column of the provided annotation object is not a character vector. Coercing.')
    }

  #Calculate GC
  seq <- toupper(ann$target)
  seqlen <- vapply(seq, nchar, numeric(1))
  gc <- vapply(gregexpr("[CG]", seq, perl = TRUE), length, integer(1))/seqlen

  if(any(seqlen == 0)){
      gc[seqlen == 0] <- NA
      warning(paste('Could not calculate GC% for', sum(seqlen == 0), 'guides. Omitting.'))
  }
  
  #if a fit object, plot relationship between GC and P, coefficients, variance
  if(is.fit){
    ltp <- -log10(data.obj$p.value)
    ylims <- range(c(ltp, data.obj$coefficients[,1]))
    
    par(mfrow = c(2,2))
    hist(gc, 
         xlim = c(0,1),
         xlab = 'gRNA %GC', 
         main = 'GC Content by gRNA', 
         breaks = max(seqlen))    
    
    plot(gc, data.obj$coefficients[,1], xlim = c(0,1), 
         xlab = 'gRNA %GC', ylab = '',
         main = 'Log2 Fold Change', 
         pch = 16, col = rgb(0,0,1, 0.2))
    lines(spline(gc, data.obj$coefficients[,1]), col = 'black', lwd = 2)
    
    plot(gc, data.obj$stdev.unscaled[,1], xlim = c(0,1), 
         xlab = 'gRNA %GC', ylab = '',
         main = 'Standard Deviation', 
         pch = 16, col = rgb(1,0,0, 0.2))
    lines(spline(gc, data.obj$stdev.unscaled[,1]), col = 'black', lwd = 2)

    plot(gc, ltp, xlim = c(0,1), 
         xlab = 'gRNA %GC', ylab = '',
         main = '-Log10 P-value', 
         pch = 16, col = rgb(0,1,0, 0.2))
    lines(spline(gc, ltp), col = 'black', lwd = 2)
    return(invisible())
  } else {
    colors <- colorRampPalette(c(rgb(1, 0, 0, 0.2), rgb(0, 0, 1, 0.2)), alpha = TRUE)(ncol(d))
    spline.color <- substr(colors, 1, 7)
    plot(NA, NA, 
         xlab = 'gRNA %GC',
         ylab = 'Log2 Counts Per Million',
         main = 'GC Content vs. Measured Abundance',
         ylim = range(d), 
         xlim = c(0,1)
         )
    invisible(lapply(1:ncol(d),  
          function(x){
            points(gc, d[,x], pch = 16, col = colors[x])
            lines(spline(gc, d[,x]), col = spline.color[x], lwd = 4)
            }))
    legend('topleft', names(sampleKey), fill = spline.color)
    }
  }  

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gCrisprTools documentation built on Nov. 17, 2017, 1:37 p.m.