Merge pull request #14 from LUMC/develop

Merge develop

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: dgeAnalysis
 Type: Package
 Title: dgeAnalysis
-Version: 1.3.1
+Version: 1.3.2
 Author@R:
   person("Tom", "Kuipers", email = "[email protected]", role = c("aut", "cre"))
 Description:

R/de.R

@@ -8,12 +8,12 @@
 #' @param data_samples Dataframe, Containing all sample data
 #'
 #' @return se, (SummerizedExperiment) With counts
-#' 
+#'
 #' @export
 
 readCountsFromTable <- function(data_counts, data_samples) {
   out <- as.matrix(data_counts[, colnames(data_counts) %in% rownames(data_samples)])
-  se <- SummarizedExperiment(assays=list(counts=out))
+  se <- SummarizedExperiment(assays = list(counts = out))
   se
 }
 
@@ -24,14 +24,14 @@ readCountsFromTable <- function(data_counts, data_samples) {
 #' @param data_samples Dataframe, Containing all sample data
 #'
 #' @return se, (SummerizedExperiment) With samples and counts
-#' 
+#'
 #' @export
 
-addSamplesFromTableToSE <- function(se, data_samples){
+addSamplesFromTableToSE <- function(se, data_samples) {
   data_samples <- droplevels(data_samples)
   samples <- intersect(colnames(se), rownames(data_samples))
-  se <- se[,samples]
-  colData(se) <- DataFrame(data_samples[samples,, drop = FALSE])
+  se <- se[, samples]
+  colData(se) <- DataFrame(data_samples[samples, , drop = FALSE])
   se
 }
 
@@ -42,13 +42,13 @@ addSamplesFromTableToSE <- function(se, data_samples){
 #' @param data_annotation Dataframe, Containing all annotation data
 #'
 #' @return se, (SummerizedExperiment) With samples, counts and annotation
-#' 
+#'
 #' @export
 
-addAnnotationsFromTableToSE <- function(se, data_annotation){
+addAnnotationsFromTableToSE <- function(se, data_annotation) {
   features <- intersect(rownames(se), rownames(data_annotation))
-  se <- se[features,]
-  rowData(se) <- DataFrame(data_annotation[features,])
+  se <- se[features, ]
+  rowData(se) <- DataFrame(data_annotation[features, ])
   se
 }
 
@@ -63,14 +63,14 @@ addAnnotationsFromTableToSE <- function(se, data_annotation){
 #' @param raw Dataframe, Containing count data
 #'
 #' @return Integer, With total counted reads
-#' 
+#'
 #' @export
 
-getCount <- function(x, raw){
+getCount <- function(x, raw) {
   if (x["feature"] %in% rownames(raw)) {
     return(raw[x["feature"], x["sample"]])
   } else {
-    features <- rownames(raw)[! grepl("^__", rownames(raw))]
+    features <- rownames(raw)[!grepl("^__", rownames(raw))]
     return(sum(raw[features, x["sample"]]))
   }
 }
@@ -81,12 +81,13 @@ getCount <- function(x, raw){
 #' @param se SummerizedExperiment, With samples, counts (and annotation)
 #'
 #' @return out, (Dataframe) With total counts per available mapping feature
-#' 
+#'
 #' @export
 
-alignmentSummary <- function(se){
-  specialFeatures <- rownames(se)[ grepl( "^__", rownames(se) ) ]
-  out <- expand.grid(feature=c("aligned", specialFeatures), sample=colnames(se))
+alignmentSummary <- function(se) {
+  specialFeatures <- rownames(se)[grepl("^__", rownames(se))]
+  out <-  expand.grid(feature = c("aligned", specialFeatures),
+                sample = colnames(se))
   out$count <- apply(out, 1, getCount, assays(se)$counts)
   out
 }
@@ -98,19 +99,20 @@ alignmentSummary <- function(se){
 #' @param max Integer, The maximum rank that is used
 #'
 #' @return out, (Dataframe) With total read counts per gene
-#' 
+#'
 #' @export
 
-complexityData <- function(se, max){
-  features <- rownames(se)[ ! grepl( "^__", rownames(se) ) ]
+complexityData <- function(se, max) {
+  features <- rownames(se)[!grepl("^__", rownames(se))]
   ranks <- c(1:max)
-  out <- expand.grid(rank=ranks, sample=colnames(se))
-  for (x in colnames(se)){
-    values <- as.vector(assay(se)[features,x])
+  out <- expand.grid(rank = ranks, sample = colnames(se))
+  for (x in colnames(se)) {
+    values <- as.vector(assay(se)[features, x])
     sorted <- sort(values, T)
     total <- sum(sorted)
     out[out$sample == x, "value"] <- cumsum(sorted)[1:max]
-    out[out$sample == x, "fraction"] <- out[out$sample == x, "value"] /total
+    out[out$sample == x, "fraction"] <-
+      out[out$sample == x, "value"] / total
   }
   out
 }
@@ -122,10 +124,10 @@ complexityData <- function(se, max){
 #' @param max Integer, The maximum rank that is used
 #'
 #' @return count, (Dataframe) With mapping feature, sample and LogCPM
-#' 
+#'
 #' @export
 
-stackDge <- function(dge){
+stackDge <- function(dge) {
   count <- stack(dge$counts)
   names(count) <- c("feature", "sample", "logCPM")
   count
@@ -139,27 +141,29 @@ stackDge <- function(dge){
 #' @param biasColumn String, Value on which to calculate confidence
 #'
 #' @return prediction, (Vector) With coordinates of prediction locations (line plot)
-#' 
+#'
 #' @export
 
 gamConfidenceFit <- function(deTab, biasColumn) {
   method.args = list()
   method.args$method <- "REML"
 
   formula <- avgLog2FC ~ s(columnHere, bs = "cs")
-  formula <- paste(gsub("columnHere", parse(text=biasColumn), formula))
-  formula <- eval(parse(text = gsub("\\", "", paste(formula[2], formula[3], sep=" ~ "), fixed=TRUE)))
+  formula <- paste(gsub("columnHere", parse(text = biasColumn), formula))
+  formula <- eval(parse(text = gsub(
+    "\\", "", paste(formula[2], formula[3], sep = " ~ "), fixed = TRUE
+  )))
 
   base.args <- list(quote(formula), data = quote(deTab))
   gamModel <- do.call(mgcv::gam, c(base.args, method.args))
 
   prediction <- deTab[, c(biasColumn, "avgLog2FC")]
-  prediction <- cbind(prediction, predict(gamModel, se.fit=TRUE))
-  prediction <- prediction[order(prediction[[biasColumn]]), ]
+  prediction <- cbind(prediction, predict(gamModel, se.fit = TRUE))
+  prediction <- prediction[order(prediction[[biasColumn]]),]
 
   setRange <- range(1, nrow(prediction))
   result <- round(seq(setRange[1], setRange[2], length.out = 500))
-  prediction <- prediction[c(result),]
+  prediction <- prediction[c(result), ]
 }
 
 ## --------------------------------------------------------------------------