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DESCRIPTION

@@ -1,5 +1,5 @@
 Package: DIAgui
-Version: 1.4.0
+Version: 1.4.1
 Type: Package
 Title: A shiny app for processing DIAnn output in proteomics
 Author: Marc-Antoine GERAULT
@@ -31,6 +31,7 @@ Imports:
     data.table,
     tidyr,
     mice,
+    imputeLCMD,
     openxlsx,
     plotly,
     ggplot2 (>= 3.3.0),
@@ -43,7 +44,8 @@ Imports:
     DT
 Suggests: 
     knitr,
-    rmarkdown
+    rmarkdown,
+    impute
 Enhances:
     diann
 LinkingTo: 

R/DIAgui.R

@@ -0,0 +1,22 @@
+#' DIAgui package
+#'
+#' Contains a shiny app to process data from DIAnn
+#'
+#' @docType package
+#'
+#' @author Marc-Antoine GERAULT \email{[email protected]}
+#'
+#' @name DIAgui
+#'
+#' @import dplyr
+#' @import tidyr
+#' @import stringr
+#' @import Rcpp
+#' @import RcppEigen
+#' @import iq
+#' @import seqinr
+#' @import ggplot2
+#'
+#'
+#' @useDynLib DIAgui
+NULL

R/MDS_DIA.R

@@ -0,0 +1,134 @@
+#' MDS_DIA
+#'
+#' Print MDS of processed data from DIAnn
+#'
+#' @param data Processed data from DIAnn (from iq processing or diann_matrix for example)
+#' @param transformation Which transformation do you want to apply (log2 or none)
+#' @param tit Title of your plot
+#' @param data_type The type of data you want to visualize; either 'intensity', 'Top3', 'iBAQ' or 'all'.
+#' @param colorspace A vector of colors of same length as the number of condition you have to plot.
+#'  If NULL, will plot default ggplot2 colors.
+#'
+#' @return ggplot2 MDS graph
+#'
+#' @export
+
+MDS_DIA <- function(data, transformation = c("log2", "none"),
+                    tit = "", data_type = c("intensity", "Top3", "iBAQ", "all"),
+                    colorspace = NULL){
+  m <- as.data.frame(data)
+  cl <- lapply(m, class)
+  cl <- cl == "numeric"
+  if(sum(cl) == 0){
+    message("No numeric data !")
+    return(NULL)
+  }
+  else if(sum(cl) == 1){
+    frac <- names(m)[cl]
+    rw <- rownames(m)
+    m <- as.data.frame(m[,cl])
+    names(m) <- frac
+    rownames(m) <- rw
+  }
+  else{
+    m <- m[,cl]
+    to_rm <- stringr::str_which(colnames(m), "nbTrypticPeptides|peptides_counts_all|^pep_count_")
+    if(length(to_rm) > 0){
+      if(length(to_rm) == ncol(m)){
+        message("No numeric data !")
+        return(NULL)
+      }
+      else{
+        m <- m[,-to_rm]
+      }
+    }
+    data_type <- match.arg(data_type)
+    if(data_type == "Top3"){
+      m <- m[,stringr::str_which(colnames(m), "^Top3_")]
+    }
+    else if(data_type == "iBAQ"){
+      m <- m[,stringr::str_which(colnames(m), "^iBAQ_")]
+    }
+    else if(data_type == "intensity"){
+      idx <- stringr::str_which(colnames(m), "^iBAQ_|^Top3_")
+      if(length(idx) > 0){
+        m <- m[,-idx]
+      }
+    }
+    else if(data_type == "all"){
+      message("You chose to keep all numeric data, they may differ completly.")
+    }
+    else{
+      stop("data_type can only be 'intensity', 'Top3', 'iBAQ' or 'all' .")
+    }
+  }
+  tit2 <- ""
+  transformation <- match.arg(transformation)
+  if(transformation == "log2"){
+    if(stringr::str_length(tit) == 0){
+      tit2 <- ", Log2 transformed"
+    }
+    m <- log2(m)
+  }
+  else if(transformation != "none"){
+    transformation <- "none"
+  }
+  if(stringr::str_length(tit) == 0){
+    tit <- paste0(deparse(substitute(data)), tit2)
+  }
+
+  m <- as.matrix(m)
+  bad <- rowSums(is.finite(m)) < ncol(m)
+  if(any(bad)){
+    m <- m[!bad, , drop = FALSE]
+  }
+  if(nrow(m) == 0){
+    p <- ggplot2::ggplot(data.frame(x = c(0,1), y = c(0,1)), ggplot2::aes(x,y, label = "s")) +
+      ggplot2::geom_text(x=0.5, y=0.5, label = "No rows with only finite value", size = 10) +
+      ggplot2::theme(axis.text.x = ggplot2::element_blank(),
+            axis.title.x = ggplot2::element_blank(),
+            axis.ticks.x = ggplot2::element_blank(),
+            axis.text.y = ggplot2::element_blank(),
+            axis.title.y = ggplot2::element_blank(),
+            axis.ticks.y = ggplot2::element_blank())
+  }
+  else{
+    nbsamples <- ncol(m)
+    if(length(colorspace) > 0 & length(colorspace) != nbsamples){
+      stop(paste("Your data contains", nbsamples, "conditions and you put",
+                 length(colorspace), "colors. Please provide the right number of colors."))
+    }
+
+    namesample <- colnames(m)
+    dd <- matrix(0,nrow=nbsamples,ncol=nbsamples,dimnames=list(namesample,namesample))
+    for (i in 2:(nbsamples)){
+      for (j in 1:(i-1)){
+        dd[i,j]=sqrt(mean((m[,i]-m[,j])^2))
+      }
+    }
+
+    m <- cmdscale(as.dist(dd), k=2)
+    m <- as.data.frame(m)
+    colnames(m) <- c("x", "y")
+    m$name <- rownames(m)
+
+    p <- ggplot2::ggplot(m, ggplot2::aes(x, y, color = name)) +
+      ggplot2::geom_point(size = 1.8) +
+      ggrepel::geom_label_repel(ggplot2::aes(label = name), max.overlaps = Inf, min.segment.length = 0) +
+      ggplot2::labs(title = tit,
+           subtitle = "MDS plot",
+           x = "Dimension 1", y = "Dimension 2") +
+      ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5),
+            legend.position = "none") +
+      ggplot2::xlim(-max(abs(m$x)), max(abs(m$x))) +
+      ggplot2::ylim(-max(abs(m$y)), max(abs(m$y)))
+
+
+    if(length(colorspace)){
+      p <- p +
+        scale_color_manual(values = colorspace)
+    }
+  }
+
+  return(p)
+}

R/RcppExports.R

@@ -0,0 +1,11 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+col_max <- function(quantities, m, n) {
+    .Call('_DIAgui_col_max', PACKAGE = 'DIAgui', quantities, m, n)
+}
+
+maxlfq_solve <- function(quantities, peptides, samples, margin = -10.0001) {
+    .Call('_DIAgui_maxlfq_solve', PACKAGE = 'DIAgui', quantities, peptides, samples, margin)
+}
+

R/corrDIA.R

@@ -0,0 +1,171 @@
+#' corrDIA
+#'
+#' Plot correlation plot from each fraction from processed data from DIAnn
+#'
+#' @param data Processed data from DIAnn (from iq processing or diann_matrix for example)
+#' @param transformation Which transformation do you want to apply (log2 or none)
+#' @param tit Title of your plot
+#' @param data_type The type of data you want to visualize; either 'intensity', 'Top3', 'iBAQ' or 'all'.
+#' @param gradient_color Three colors for the printed gradient color on the correlation plot
+#' @param plot_pairs Plot pairwise correlation plot. If FALSE, will plot correlation matrix plot.
+#'
+#' @return ggplot2 correlation plot
+#'
+#' @export
+
+corrDIA <- function(data, transformation = c("none", "log2"),
+                    tit = "", data_type = c("intensity", "Top3", "iBAQ", "all"),
+                    gradient_color = c("#09009D", "#ffffff", "#BE0010"),
+                    plot_pairs = FALSE){
+  if(length(gradient_color) != 3){
+    stop("Please provide exactly three colors for the gradient color.")
+  }
+  d <- as.data.frame(data)
+  cl <- lapply(d, class)
+  cl <- cl == "numeric"
+  if(sum(cl) == 0){
+    message("No numeric data !")
+    return(NULL)
+  }
+  else if(sum(cl) == 1){
+    frac <- names(d)[cl]
+    rw <- rownames(d)
+    d <- as.data.frame(d[,cl])
+    names(d) <- frac
+    rownames(d) <- rw
+  }
+  else{
+    d <- d[,cl]
+    to_rm <- stringr::str_which(colnames(d), "nbTrypticPeptides|peptides_counts_all|^pep_count_")
+    if(length(to_rm) > 0){
+      if(length(to_rm) == ncol(d)){
+        message("No numeric data !")
+        return(NULL)
+      }
+      else{
+        d <- d[,-to_rm]
+      }
+    }
+
+    data_type <- match.arg(data_type)
+    if(data_type == "Top3"){
+      d <- d[,stringr::str_which(colnames(d), "^Top3_")]
+    }
+    else if(data_type == "iBAQ"){
+      d <- d[,stringr::str_which(colnames(d), "^iBAQ_")]
+    }
+    else if(data_type == "intensity"){
+      idx <- stringr::str_which(colnames(d), "^iBAQ_|^Top3_")
+      if(length(idx) > 0){
+        d <- d[,-idx]
+      }
+    }
+    else if(data_type == "all"){
+      message("You chose to keep all numeric data, they may differ completly.")
+    }
+    else{
+      stop("data_type can only be 'intensity', 'Top3', 'iBAQ' or 'all' .")
+    }
+  }
+
+  tit2 <- ""
+  transformation <- match.arg(transformation)
+  if(transformation == "log2"){
+    if(stringr::str_length(tit) == 0){
+      tit2 <- ", Log2 transformed"
+    }
+    d <- log2(d)
+  }
+  else if(transformation != "none"){
+    transformation <- "none"
+  }
+
+  if(stringr::str_length(tit) == 0){
+    tit <- paste0("Correlation plot", tit2)
+  }
+
+  if(plot_pairs){
+    gn <- unname(unlist(mapply(function(x, y){
+      n <- paste(x, "vs", names(d)[y:ncol(d)]);
+      n
+    },
+    names(d), 1:ncol(d), SIMPLIFY = FALSE)))
+
+    g <- lapply(gn,
+                function(x){
+                  x <- strsplit(x, " vs ")[[1]]
+                  if(x[1] == x[2]){
+                    g <- ggplot2::ggplot(d) +
+                      ggplot2::geom_density(ggplot2::aes(.data[[x[1]]])) +
+                      ggplot2::theme_bw()
+
+                    if(x[1] != names(d)[ncol(d)]){
+                      g <- g +
+                        ggplot2::theme(axis.title.x = ggplot2::element_blank())
+                    }
+                    if(x[1] != names(d)[1]){
+                      g <- g +
+                        ggplot2::theme(axis.title.y = ggplot2::element_blank())
+                    }
+                    else{
+                      g <- g + ggplot2::labs(y = names(d)[1])
+                    }
+                  }
+                  else{
+                    cf <- coef(lm(d[[x[2]]] ~ d[[x[1]]]))
+                    g <- ggplot2::ggplot(d) +
+                      ggplot2::geom_point(ggplot2::aes(.data[[x[1]]], .data[[x[2]]])) +
+                      ggplot2::geom_abline(intercept = cf[[1]],
+                                           slope = cf[[2]],
+                                           color = "red") +
+                      ggplot2::theme_bw()
+
+                    if(x[2] != names(d)[ncol(d)]){
+                      g <- g +
+                        ggplot2::theme(axis.title.x = ggplot2::element_blank())
+                    }
+                    if(x[1] != names(d)[1]){
+                      g <- g +
+                        ggplot2::theme(axis.title.y = ggplot2::element_blank())
+                    }
+                  };
+                  g
+                })
+    names(g) <- gn
+
+    gn <- expand.grid(names(d), names(d))
+    gn <- paste(gn[[1]], "vs", gn[[2]])
+    g <- lapply(gn, function(x) g[[x]])
+
+    g <- cowplot::plot_grid(plotlist = g, nrow = ncol(d), ncol = ncol(d),
+                            labels = tit, hjust = -2)
+  }
+  else{
+    corr <- d %>%
+      cor(use = "pairwise.complete.obs") %>%
+      as.data.frame() %>%
+      tibble::rownames_to_column("with") %>%
+      tidyr::gather("fraction", "corr", -with)
+
+    g <- ggplot2::ggplot(corr, ggplot2::aes(with, fraction, fill = corr)) +
+      ggplot2::geom_tile(color = "white") +
+      ggplot2::geom_text(ggplot2::aes(label = round(corr, 3)),
+                         color = "black", size = 6) +
+      ggplot2::scale_fill_gradientn(colors = gradient_color,
+                                    values = c(0,0.5,1),
+                                    breaks = c(-1,0,1),
+                                    limits = c(-1,1)) +
+      ggplot2::scale_y_discrete(limits = rev) +
+      ggplot2::labs(title = tit) +
+      ggplot2::theme_bw() +
+      ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1),
+                     axis.text = ggplot2::element_text(size = 12),
+                     axis.title = ggplot2::element_blank(),
+                     plot.title = ggplot2::element_text(hjust = 0.5),
+                     legend.title = ggplot2::element_blank())
+  }
+
+  return(g)
+}
+
+