R/volcanoPlot.R

In DAPAR: Tools for the Differential Analysis of Proteins Abundance with R

#' Plots a volcanoplot after the differential analysis.
#' Typically, the log of Fold Change is represented on the X-axis and the
#' log10 of the p-value is drawn on the Y-axis. When the \code{threshold_pVal}
#' and the \code{threshold_logFC} are set, two lines are drawn respectively on
#' the y-axis and the X-axis to visually distinguish between differential and
#' non differential data.
#' 
#' @title Volcanoplot of the differential analysis
#' @param logFC A vector of the log(fold change) values of the differential
#' analysis.
#' @param pVal A vector of the p-value values returned by the differential
#' analysis.
#' @param threshold_pVal A floating number which represents the p-value that
#' separates differential and non-differential data.
#' @param threshold_logFC A floating number which represents the log of the
#' Fold Change that separates differential and non-differential data.
#' @param conditions A list of the names of condition 1 and 2 used for the
#' differential analysis.
#' @param colors xxx
#' @return A volcanoplot
#' @author Florence Combes, Samuel Wieczorek
#' @examples
#' utils::data(Exp1_R25_pept, package='DAPARdata')
#' obj <- Exp1_R25_pept[1:1000]
#' keepThat <- mvFilterGetIndices(obj, condition='WholeMatrix', threshold=ncol(obj))
#' obj <- mvFilterFromIndices(obj, keepThat)
#' qData <- Biobase::exprs(obj)
#' sTab <- Biobase::pData(obj)
#' limma <- limmaCompleteTest(qData,sTab)
#' diffAnaVolcanoplot(limma$logFC[,1], limma$P_Value[,1])
#' 
#' @export
#'
#' @importFrom Biobase pData exprs fData
#' @import graphics
#' 
diffAnaVolcanoplot <- function(logFC=NULL, 
                                pVal=NULL, 
                                threshold_pVal=1e-60, 
                                threshold_logFC=0, 
                                conditions=NULL, colors=NULL){

xtitle <- paste("log2 ( mean(",conditions[2],") / mean(",conditions[1],") )",
                sep="")

if(is.null(colors)){ colors <- list(In = "orange", Out = "grey")}

if (is.null(logFC)||is.null(pVal)) {

    p <- plot(-1,-1
            , xlab = xtitle
            , ylab="- log10 ( p-value )"
            ,xlim = range(0,1)
            , xaxt='n'
            ,yaxt='n'
    )
    return (NULL)
}

x <- logFC
y <- -log10(pVal)

colorCode <- c(colors$Out, colors$In)
color <- rep(colorCode[1], length(y))

for (i in 1:length(y)){
    if ( (y[i] >= threshold_pVal) && (abs(x[i]) >= threshold_logFC) ){
    color[i] <- colorCode[2]
    }
}

p <- plot(x
            , y
            , xlab = xtitle
            , ylab=  "- log10 ( p-value )"
            , xlim = c(-max(abs(x)), max(abs(x)))
            , ylim = c(0, max(y))
            , col = color
            , pch = 16
            , las = 1
            , cex = 1
            , cex.lab = 1.5
            , cex.axis = 1.5
            , cex.main = 3
)

abline(h = threshold_pVal, col = "gray")
abline(v = threshold_logFC, col = "gray")
abline(v = -threshold_logFC, col = "gray")

return(p)
}


#' Plots an interactive volcanoplot after the differential analysis.
#' Typically, the log of Fold Change is represented on the X-axis and the
#' log10 of the p-value is drawn on the Y-axis. When the \code{threshold_pVal}
#' and the \code{threshold_logFC} are set, two lines are drawn respectively on
#' the y-axis and the X-axis to visually distinguish between differential and
#' non differential data. With the use of the package Highcharter, a 
#' customizable tooltip appears when the user put the mouse's pointer over 
#' a point of the scatter plot.
#' 
#' @title Volcanoplot of the differential analysis
#' @param df A dataframe which contains the following slots :
#' x : a vector of the log(fold change) values of the differential analysis,
#' y : a vector of the p-value values returned by the differential analysis.
#' index : a vector of the rowanmes of the data.
#' This dataframe must has been built with the option stringsAsFactors set 
#' to FALSE. There may be additional slots which will be used to show 
#' informations in the tooltip. The name of these slots must begin with the 
#' prefix "tooltip_". It will be automatically removed in the plot.
#' @param threshold_pVal A floating number which represents the p-value that
#' separates differential and non-differential data.
#' @param threshold_logFC A floating number which represents the log of the
#' Fold Change that separates differential and non-differential data.
#' @param conditions A list of the names of condition 1 and 2 used for the
#' differential analysis.
#' @param clickFunction A string that contains a JavaScript function used to 
#' show info from slots in df. The variable this.index refers to the slot 
#' named index and allows to retrieve the right row to show in the tooltip.
#' @param palette xxx
#' @param swap A boolean that indicates if the conditions have been swaped 
#' @return An interactive volcanoplot
#' @author Samuel Wieczorek
#' @examples
#' \donttest{
#' library(highcharter) 
#' utils::data(Exp1_R25_pept, package='DAPARdata')
#' obj <- Exp1_R25_pept[1:1000]
#' keepThat <- mvFilterGetIndices(obj, condition='WholeMatrix', threshold=ncol(obj))
#' obj <- mvFilterFromIndices(obj, keepThat)
#' qData <- Biobase::exprs(obj)
#' sTab <- Biobase::pData(obj)
#' data <- limmaCompleteTest(qData,sTab)
#' df <- data.frame(x=data$logFC, y = -log10(data$P_Value),index = as.character(rownames(obj)))
#' colnames(df) <- c("x", "y", "index")
#' tooltipSlot <- c("Sequence", "Score")
#' df <- cbind(df,Biobase::fData(obj)[tooltipSlot])
#' colnames(df) <- gsub(".", "_", colnames(df), fixed=TRUE)
#' if (ncol(df) > 3){
#'     colnames(df)[4:ncol(df)] <- 
#'     paste("tooltip_", colnames(df)[4:ncol(df)], sep="")}
#' hc_clickFunction <- JS("function(event) {Shiny.onInputChange('eventPointClicked', [this.index]+'_'+ [this.series.name]);}")
#' cond <- c("25fmol", "10fmol")
#' diffAnaVolcanoplot_rCharts(df, 2.5, 1, cond,hc_clickFunction) 
#' }
#' 
#' @export
#'
#' @importFrom Biobase pData exprs fData
#'
diffAnaVolcanoplot_rCharts <- function(df, 
                                        threshold_pVal=1e-60, 
                                        threshold_logFC=0, 
                                        conditions=NULL, 
                                        clickFunction=NULL,
                                       palette=NULL,
                                       swap = FALSE){
    
  
    xtitle <- paste("log2 ( mean(",conditions[2],") / mean(",conditions[1],") )",sep="")
    
    if (is.null(clickFunction)){
        clickFunction <- 
            JS("function(event) {Shiny.onInputChange('eventPointClicked', [this.index]+'_'+ [this.series.name]);}")
    }
    
    if(is.null(palette)){
      palette <- list(In='orange', Out='gray')
    } 
    
    df <- cbind(df, 
                g=ifelse(df$y >= threshold_pVal & abs(df$x) >= threshold_logFC, "g1", "g2")
    )
    
    
    i_tooltip <- which(startsWith(colnames(df),"tooltip"))
    txt_tooltip <- NULL
    for (i in i_tooltip){
        t <- txt_tooltip <- paste(txt_tooltip,"<b>",gsub("tooltip_", "", 
                                                        colnames(df)[i], 
                                                        fixed=TRUE), 
                                 " </b>: {point.", colnames(df)[i],"} <br> ", 
                                 sep="")
    }
    
    leftBorder <- data.frame(x=c(min(df$x), -threshold_logFC,-threshold_logFC),
                             y = c(threshold_pVal,threshold_pVal,max(df$y)))
    rightBorder <- data.frame(x=c(max(df$x), threshold_logFC,threshold_logFC),
                             y = c(threshold_pVal,threshold_pVal,max(df$y)))
    
    title <- NULL
    #title <- paste0(cond[1], '_vs_', cond[2])
    if (swap == TRUE){
      title <- paste0(conditions[2], '_vs_', conditions[1])
    } else {
      title <- paste0(conditions[1], '_vs_', conditions[2])
    }
    
    h1 <-  highchart() %>%
        hc_add_series(data = df, type = "scatter", hcaes(x,y,group=g)) %>%
        hc_colors(c(palette$In, palette$Out)) %>%
        my_hc_chart(zoomType = "xy",chartType="scatter") %>%
        hc_legend(enabled = FALSE) %>%
       hc_title(text = title,
               margin = 20, align = "center",
               style = list(size = 20, color = "black", useHTML = TRUE)) %>%
        hc_yAxis(title = list(text="-log10(pValue)")) %>%
        hc_xAxis(title = list(text = "logFC"),
                 plotLines=list(list(color= "grey" , width = 1, value = 0, zIndex = 5))) %>%
        hc_tooltip(headerFormat= '',pointFormat = txt_tooltip) %>%
        hc_plotOptions( line = list(marker=list(enabled=FALSE),
                                    dashStyle="Dash"),
                        series = list( animation=list(duration = 100),
                                       cursor = "pointer", 
                                       point = list( events = list( 
                                           click = clickFunction ) ) ) ) %>%
        my_hc_ExportMenu(filename = "volcanoplot") %>%
      hc_add_series(data = leftBorder, type = "line", color='grey') %>%
      hc_add_series(data = rightBorder, type = "line", color='grey')
    
    return(h1)
}