Merge pull request #20 from LUMC/develop

Develop

CHANGELOG.md

@@ -4,6 +4,19 @@ Changelog
 <!--
 Newest changes should be on top.
 -->
+version 1.4.4
+---------------------------
++ Add file names and md5 to markdown
++ Add new CPM filter for number of samples (percentage)
++ Add dendrogram to dimension reduction
++ Change export file name and remove quotes from exported dataframe
++ Small infobox update
++ Add tSNE plot to dimension reduction
++ Move normalized MDS to Dimension reduction
++ Change PCA tab to dimension reduction
++ Remove MDS 3D and PCA 3D
++ Fix false warning: Contrast are the same error
+
 version 1.4.3
 ---------------------------
 + Add prior count to CPM calculation

DESCRIPTION

@@ -1,9 +1,9 @@
 Package: dgeAnalysis
 Type: Package
 Title: dgeAnalysis
-Version: 1.4.3
+Version: 1.4.4
 Author@R:
-  person("Tom", "Kuipers", email = "t.b.kuipers@outlook.com", role = c("aut", "cre"))
+  person("Tom", "Kuipers", email = "t.b.kuipers@lumc.nl", role = c("aut", "cre"))
 Description:
     R shiny tool to perform differential gene expression and view the results interactively.
 Depends: R (>= 3.6.1)
@@ -27,12 +27,12 @@ Imports:
     scales (>= 1.1.1),
     broom (>= 0.7.1),
     plotly (>= 4.9.2),
+    Rtsne (>= 0.15),
     rmarkdown (>= 2.4),
     gprofiler2 (>= 0.2.0),
-    igraph (>= 1.2.5),
-    WGCNA (>= 1.69)
+    igraph (>= 1.2.5)
 biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG,
     MultipleComparison, Pathways, Reactome, Visualization,
     Sequencing, RNASeq, GeneExpression, Transcription,
     Normalization, DifferentialExpression, Bayesian, Regression,
-    PrincipalComponent, WGCNA
+    PrincipalComponent, gProfiler, DGE, dgeAnalysis

NAMESPACE

@@ -28,24 +28,17 @@ export(informationBox)
 export(list2df)
 export(list2graph)
 export(ma_plot)
-export(multidimensionalScaling2dPlot)
-export(multidimensionalScaling3dPlot)
+export(multidimensionalScalingPlot)
 export(pValuePlot)
-export(plot_module_trait_relation_heat)
-export(plot_modules)
-export(plot_power)
-export(plot_soft)
-export(plot_trait_heat)
+export(pcaPlot)
 export(plotlyGraph)
-export(plotly_dendro_heat)
 export(plotly_dendrogram)
 export(readCountsFromTable)
 export(relevelSamples)
-export(samplePca2dPlot)
-export(samplePca3dPlot)
 export(stackDge)
 export(startApp)
 export(topDgeHeatmapPlot)
+export(tsnePlot)
 export(variableHeatmapPlot)
 export(variancePcaPlot)
 export(volcanoPlot)

R/de.R

@@ -167,3 +167,115 @@ gamConfidenceFit <- function(deTab, biasColumn) {
 }
 
 ## --------------------------------------------------------------------------
+
+## ----- TREE PLOTS -----
+
+
+#' Get heigth information from tree object.
+#' Get labels/names from tree ends.
+#' Calculate start and end positions of tree branches
+#' Save line coordinates in dataframe
+#'
+#' @param tree Hclust object, tree object
+#'
+#' @return new, (Dataframe) dataframe with all dendrogram line coordinates
+#'
+#' @export
+
+get_dendrogram_data <- function(tree,
+                                labels = NULL,
+                                horiz = FALSE,
+                                reverseDirection = FALSE,
+                                hang = 0.1,
+                                xlab = "",
+                                ylab = "",
+                                axes = TRUE,
+                                cex.labels = 1,
+                                ...,
+                                adjustRange = FALSE) {
+  hang.gr = hang
+  if (hang < 0)
+    hang.gr = 0.1
+  n = length(tree$order)
+  heights = tree$height
+  range = range(heights)
+  hang.scaled = hang.gr * (max(heights) - min(heights))
+  range[1] = range[1] - hang.scaled
+
+  indexLim = c(0.5, n + 0.5)
+  if (adjustRange)
+  {
+    ctr = mean(indexLim)
+    indexLim = ctr + (indexLim - ctr) / 1.08
+  }
+
+  nMerge = n - 1
+  if (is.null(labels))
+    labels = tree$labels
+  if (is.null(labels))
+    labels = rep("", n)
+  if (is.na(labels[1]))
+    labels = rep("", n)
+  if (is.logical(labels) && labels[1] == "FALSE")
+    labels = rep("", n)
+
+  singleton.x = rep(NA, n)
+  singleton.x[tree$order] = c(1:n)
+  cluster.x = rep(NA, n)
+
+  new <- data.frame(matrix(
+    ncol = 5,
+    nrow = 0,
+    dimnames = list(NULL, c("label", "x", "y", "xend", "yend"))
+  ))
+  for (m in 1:nMerge) {
+    o1 = tree$merge[m, 1]
+    o2 = tree$merge[m, 2]
+    h = heights[m]
+    hh = if (hang > 0)
+      h - hang.scaled
+    else
+      range[1]
+    h1 = if (o1 < 0)
+      hh
+    else
+      heights[o1]
+    h2 = if (o2 < 0)
+      hh
+    else
+      heights[o2]
+
+    x1 = if (o1 < 0)
+      singleton.x[-o1]
+    else
+      cluster.x[o1]
+    x2 = if (o2 < 0)
+      singleton.x[-o2]
+    else
+      cluster.x[o2]
+
+    cluster.x[m] = mean(c(x1, x2))
+
+    label1 <- labels[-o1]
+    label2 <- labels[-o2]
+    if (length(label1) > 1) {
+      label1 <- ""
+    }
+    if (length(label2) > 1) {
+      label2 <- ""
+    }
+
+    if (!is.na(x1)) {
+      new[nrow(new) + 1, ] <- c(label1, x1, h1, x1, h)
+      if (!is.na(x2)) {
+        new[nrow(new) + 1, ] <- c("", x1, h, x2, h)
+        new[nrow(new) + 1, ] <- c(label2, x2, h2, x2, h)
+      }
+    }
+  }
+  new[2:5] <- sapply(new[2:5], as.double)
+  new <- new[order(new$x), ]
+  return(new)
+}
+
+## --------------------------------------------------------------------------