Major POMA Update 2023

.github/workflows/R-CMD-check.yml

@@ -1,10 +1,10 @@
 on:
   push:
     branches:
-      - master
+      - main
   pull_request:
     branches:
-      - master
+      - main
 
 name: R-CMD-check
 
@@ -20,7 +20,7 @@ jobs:
         config:
           - {os: windows-latest, r: 'release'}
           - {os: macOS-latest, r: 'release'}
-          #- {os: ubuntu-18.04, r: 'release'}
+          - {os: ubuntu-18.04, r: 'release'}
 
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: POMA
 Title: Tools for Omics Data Analysis   
-Version: 1.7.19
+Version: 1.8.47
 Authors@R: 
     c(person(given = "Pol",
              family = "Castellano-Escuder",
@@ -13,7 +13,7 @@ Description:
     and statistical analysis of omics datasets. The main aim of POMA is to enable a flexible data cleaning and 
     statistical analysis processes in one comprehensible and user-friendly R package. This package has a Shiny app 
     version called POMAShiny that implements all POMA functions. See https://github.com/pcastellanoescuder/POMAShiny. 
-    See Castellano-Escuder P, González-Domínguez R, Carmona-Pontaque F, et al. (2021) <doi:10.1371/journal.pcbi.1009148> 
+    See Castellano-Escuder et al. (2021) <doi:10.1371/journal.pcbi.1009148> 
     for more details.
 License: GPL-3
 Encoding: UTF-8
@@ -40,16 +40,22 @@ Imports:
     dbscan,
     dplyr,
     DESeq2,
+    FSA,
     ggplot2,
     ggrepel,
     glasso (>= 1.11),
     glmnet,
     impute,
+    janitor,
     limma,
+    lme4,
     magrittr,
+    MASS,
+    Matrix,
     mixOmics,
     randomForest,
     RankProd (>= 3.14),
+    rlang,
     rmarkdown,
     SummarizedExperiment,
     tibble,
@@ -66,7 +72,7 @@ Suggests:
     tidyverse,
     testthat (>= 2.3.2)
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.3
 Depends: R (>= 4.0)
 VignetteBuilder: knitr
 URL: https://github.com/pcastellanoescuder/POMA

NAMESPACE

@@ -4,23 +4,32 @@ export("%>%")
 export(PomaBoxplots)
 export(PomaClust)
 export(PomaCorr)
+export(PomaCreateObject)
 export(PomaDESeq)
 export(PomaDensity)
-export(PomaEDA)
 export(PomaHeatmap)
 export(PomaImpute)
+export(PomaLM)
+export(PomaLMM)
 export(PomaLasso)
 export(PomaLimma)
-export(PomaMultivariate)
 export(PomaNorm)
 export(PomaOddsRatio)
 export(PomaOutliers)
+export(PomaPCA)
 export(PomaPCR)
+export(PomaPLS)
 export(PomaRandForest)
 export(PomaRankProd)
-export(PomaSummarizedExperiment)
 export(PomaUMAP)
 export(PomaUnivariate)
 export(PomaVolcano)
+export(poma_pal_c)
+export(poma_pal_d)
+export(scale_color_poma_c)
+export(scale_color_poma_d)
+export(scale_fill_poma_c)
+export(scale_fill_poma_d)
+export(theme_poma)
 importFrom(magrittr,"%<>%")
 importFrom(magrittr,"%>%")

NEWS.md

@@ -1,3 +1,18 @@
+# POMA 1.8.47
+
+* New POMA theme and colorblind-friendly palette
+* Available sample normalization (sum and quantile)
+* New feature normalization methods
+* Extensive review and improvement of all `POMA` functions
+* Major documentation updates
+* Rename `PomaSummarizedExperiment` to `PomaCreateObject`
+* Auto-recognition of variable types and automatic variable re-labeling in `PomaCreateObject`
+* Available violin plots with `PomaBoxplots`
+* New functions `PomaPCA` and `PomaPLS` as stand-alone functions from the old `PomaMultivariate` (deprecated)
+* Other new functions: `PomaLM`, `PomaLMM`
+* Post-hoc pairwise comparisons in `PomaUnivariate`
+* Update tests and vignettes
+
 # POMA 1.7.19
 
 * New `biocViews` and `Description`
@@ -13,7 +28,7 @@
 
 * `MSnbase::MSnSet` class has been replaced by the `SummarizedExperiment` class
 * Color scale for all plots set to `viridis` (without yellow)
-* All output tables provided as `tibble` istead of `matrix` or `data.frame`
+* All output tables provided as `tibble` instead of `matrix` or `data.frame`
 * Allow users to select specific covariates and their position (importance) in the model for `PomaLimma`, `PomaUnivariate(method = "anova")`, and `PomaOddsRatio`
 * Add SD to `PomaUnivariate` output tables
 * Reduce dependencies

R/PomaBoxplots.R

@@ -1,107 +1,138 @@
 
-#' Classical Boxplots
+#' Boxplots and Violin Plots
 #'
-#' @description PomaBoxplots() generates a boxplot for subjects or features. This boxplot can help in the comparison between pre and post normalized data and in the "validation" of the normalization process.
+#' @description `PomaBoxplots` generates boxplots and violin plots for samples and features. This function can be used for data exploration (e.g., comparison between pre and post normalized datasets).
 #'
-#' @param data A SummarizedExperiment object.
-#' @param group Groupping factor for the plot. Options are "samples" and "features". Option "samples" (default) will create a boxplot for each sample and option "features" will create a boxplot of each variable.
-#' @param jitter Logical. If it's TRUE (default), the boxplot will show all points.
-#' @param feature_name A vector with the name/s of feature/s to plot. If it's NULL (default) a boxplot of all features will be created.
-#' @param label_size Numeric indicating the size of x-axis labels.
-#' @param legend_position Character indicating the legend position. Options are "none", "top", "bottom", "left", and "right".
+#' @param data A `SummarizedExperiment` object.
+#' @param x Character. Options are "samples" (to visualize sample boxplots) and "features" (to visualize feature boxplots). Default is "samples".
+#' @param violin Logical. Indicates if violin plots should be displayed instead of boxplots. Default is FALSE.
+#' @param feature_name Character vector. Indicates the feature/s to display. Default is NULL (all features will be displayed).
+#' @param theme_params List. Indicates `theme_poma` parameters.
 #'
 #' @export
 #'
-#' @return A ggplot2 object.
+#' @return A `ggplot` object.
 #' @author Pol Castellano-Escuder
 #'
 #' @importFrom magrittr %>%
 #' 
 #' @examples 
 #' data("st000284")
 #' 
-#' # samples
-#' PomaBoxplots(st000284)
+#' # Sample boxplots
+#' st000284 %>%
+#' PomaNorm() %>% 
+#' PomaBoxplots(theme_params = list(axistext = "y"))
 #' 
-#' # features
-#' PomaBoxplots(st000284, group = "features")
+#' # Sample violin plots
+#' st000284 %>%
+#' PomaNorm() %>% 
+#' PomaBoxplots(violin = TRUE, theme_params = list(axistext = "y"))
+#' 
+#' # All feature boxplots
+#' st000284 %>% 
+#' PomaNorm() %>% 
+#' PomaBoxplots(x = "features", theme_params = list(axis_x_rotate = TRUE))
+#'              
+#' # Specific feature boxplots
+#' st000284 %>% 
+#' PomaNorm() %>% 
+#' PomaBoxplots(x = "features", 
+#'              feature_name = c("ornithine", "orotate"))
 #'              
-#' # concrete features
-#' PomaBoxplots(st000284, group = "features", 
+#' # Specific feature violin plots
+#' st000284 %>% 
+#' PomaNorm() %>% 
+#' PomaBoxplots(x = "features", 
+#'              violin = TRUE,
 #'              feature_name = c("ornithine", "orotate"))
 PomaBoxplots <- function(data,
-                         group = "samples",
-                         jitter = FALSE,
+                         x = "samples",
+                         violin = FALSE,
                          feature_name = NULL,
-                         label_size = 10,
-                         legend_position = "bottom"){
-
-  if (missing(data)) {
-    stop("data argument is empty!")
-  }
+                         theme_params = list(legend_title = FALSE, axis_x_rotate = TRUE),
+                         ...) {
+
   if(!is(data, "SummarizedExperiment")){
-    stop("data is not a SummarizedExperiment object. \nSee POMA::PomaSummarizedExperiment or SummarizedExperiment::SummarizedExperiment")
+    stop("data is not a SummarizedExperiment object. \nSee POMA::PomaCreateObject or SummarizedExperiment::SummarizedExperiment")
   }
-  if (!(group %in% c("samples", "features"))) {
-    stop("Incorrect value for group argument!")
+  if (!(x %in% c("samples", "features"))) {
+    stop("Incorrect value for x argument")
   }
   if (!is.null(feature_name)) {
     if(!any(feature_name %in% rownames(SummarizedExperiment::assay(data)))) {
-      stop("None of the specified features found")
+      stop("Features not found")
     }
-    if(!all(feature_name %in% rownames(SummarizedExperiment::assay(data)))){
-      warning(paste0("Feature/s ",
-                     paste0(feature_name[!feature_name %in% rownames(SummarizedExperiment::assay(data))], collapse = ", "),
+    if(!all(feature_name %in% rownames(SummarizedExperiment::assay(data)))) {
+      message(paste0(paste0(feature_name[!feature_name %in% rownames(SummarizedExperiment::assay(data))], collapse = ", "),
                      " not found"))
     }
   }
-  if(!(legend_position %in% c("none", "top", "bottom", "left", "right"))) {
-    stop("Incorrect value for legend_position argument!")
-  }
 
-  e <- t(SummarizedExperiment::assay(data))
-  target <- SummarizedExperiment::colData(data) %>%
-    as.data.frame() %>% 
-    tibble::rownames_to_column("ID") %>%
-    dplyr::rename(Group = 2) %>%
-    dplyr::select(ID, Group)
+  plot_data <- t(SummarizedExperiment::assay(data))
+
+  grouping_factor <- ifelse(ncol(SummarizedExperiment::colData(data)) > 0, 
+                            is.factor(SummarizedExperiment::colData(data)[,1]), FALSE)
 
-  data <- cbind(target, e)
+  if (grouping_factor) {
+    plot_data <- data.frame(sample_id = rownames(SummarizedExperiment::colData(data)),
+                            group_factor = SummarizedExperiment::colData(data)[,1], 
+                            plot_data)
+  } else {
+    if (ncol(SummarizedExperiment::colData(data)) > 0) {
+      sample_names <- rownames(SummarizedExperiment::colData(data))
+    } else {
+      sample_names <- colnames(SummarizedExperiment::assay(data))
+    }
+
+    if (is.null(sample_names)) {
+      sample_names <- paste0("sample_", 1:ncol(SummarizedExperiment::assay(data)))
+    }
+
+    plot_data <- data.frame(sample_id = sample_names,
+                            group_factor = "no_groups",
+                            plot_data)
+  }
 
-  if(group == "samples"){
-    plot_data <- data %>%
-      tidyr::pivot_longer(cols = -c(ID, Group)) %>%
-      ggplot2::ggplot(ggplot2::aes(ID, value, color = Group))
+  if (x == "samples") {
+    plot_data <- plot_data %>%
+      tidyr::pivot_longer(cols = -c(sample_id, group_factor)) %>%
+      dplyr::group_by(sample_id) %>% 
+      dplyr::mutate(median_rank = median(value, na.rm = TRUE)) %>% 
+      dplyr::ungroup() %>% 
+      ggplot2::ggplot(ggplot2::aes(reorder(sample_id, median_rank), value))
   }
 
-  else {
-    if(is.null(feature_name)){
-      plot_data <- data %>%
-        dplyr::select(-ID) %>%
-        tidyr::pivot_longer(cols = -Group) %>%
-        ggplot2::ggplot(ggplot2::aes(name, value, color = Group))
-
-    } else {
-      plot_data <- data %>%
-        dplyr::select(-ID) %>%
-        tidyr::pivot_longer(cols = -Group) %>%
-        dplyr::filter(name %in% feature_name) %>%
-        ggplot2::ggplot(ggplot2::aes(name, value, color = Group))
+  else if (x == "features") {
+    plot_data <- plot_data %>%
+      dplyr::select(-sample_id) %>%
+      tidyr::pivot_longer(cols = -group_factor)
+
+    if (!is.null(feature_name)){
+      plot_data <- plot_data %>% 
+        dplyr::filter(name %in% feature_name)
     }
+
+    plot_data <- plot_data %>% 
+      ggplot2::ggplot(ggplot2::aes(name, value))
   }
 
   plot_complete <- plot_data +
-    ggplot2::geom_boxplot() +
-    {if(jitter)ggplot2::geom_jitter(alpha = 0.5, position = ggplot2::position_jitterdodge())} +
-    ggplot2::theme_bw() +
-    ggplot2::labs(x = "", 
-                  y = "Value") +
-    ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1, size = label_size),
-                   legend.title = ggplot2::element_blank(),
-                   legend.position = legend_position) +
-    ggplot2::scale_colour_viridis_d(begin = 0, end = 0.8)
+    # boxplots
+    {if(grouping_factor & !violin)ggplot2::geom_boxplot(ggplot2::aes(color = group_factor, fill = group_factor), alpha = 0.5)} +
+    {if(!grouping_factor & !violin)ggplot2::geom_boxplot(alpha = 0.5)} +
+    # violin plots
+    {if(grouping_factor & violin)ggplot2::geom_violin(ggplot2::aes(color = group_factor, fill = group_factor), alpha = 0.5)} +
+    {if(!grouping_factor & violin)ggplot2::geom_violin(alpha = 0.5)} +
+    # aesthetics
+    ggplot2::labs(x = NULL, 
+                  y = "Value",
+                  fill = NULL,
+                  color = NULL) +
+    do.call(theme_poma, theme_params) +
+    scale_fill_poma_d() +
+    scale_color_poma_d()
 
   return(plot_complete)
-
 }