warning fixed

eleonore-schneeg · Apr 4, 2023 · ffaaa9c · ffaaa9c
1 parent 117daeb
commit ffaaa9c
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Showing 41 changed files with 56 additions and 835 deletions.
diff --git a/NAMESPACE b/NAMESPACE
@@ -20,9 +20,7 @@ export(integrate_with_DIABLO)
 export(integrate_with_MBPLS)
 export(integrate_with_MEIFESTO)
 export(integrate_with_MOFA)
-export(integrate_with_iCluster)
 export(integrate_with_sMBPLS)
-export(integrative_results_clustering)
 export(integrative_results_supervised)
 export(integrative_results_unsupervised)
 export(interactive_network)
@@ -50,14 +48,6 @@ export(volcano_plot_limma)
 import(ggplot2)
 import(igraph)
 importFrom(AnnotationDbi,mapIds)
-importFrom(ClassDiscovery,distanceMatrix)
-importFrom(ComplexHeatmap,"%v%")
-importFrom(ComplexHeatmap,Heatmap)
-importFrom(ComplexHeatmap,HeatmapAnnotation)
-importFrom(ComplexHeatmap,anno_mark)
-importFrom(ComplexHeatmap,draw)
-importFrom(ComplexHeatmap,ht_opt)
-importFrom(ComplexHeatmap,rowAnnotation)
 importFrom(DESeq2,DESeq)
 importFrom(DESeq2,DESeqDataSetFromMatrix)
 importFrom(DESeq2,counts)
@@ -95,7 +85,6 @@ importFrom(WGCNA,cor)
 importFrom(WGCNA,moduleEigengenes)
 importFrom(WGCNA,orderMEs)
 importFrom(basetheme,num2col)
-importFrom(circlize,colorRamp2)
 importFrom(cli,cli_alert_danger)
 importFrom(cli,cli_alert_info)
 importFrom(cli,cli_alert_success)
@@ -127,9 +116,6 @@ importFrom(ggplot2,ggsave)
 importFrom(ggplot2,ggtitle)
 importFrom(ggpubr,ggscatter)
 importFrom(ggrepel,geom_text_repel)
-importFrom(grDevices,colorRampPalette)
-importFrom(grDevices,dev.off)
-importFrom(grDevices,pdf)
 importFrom(httr,POST)
 importFrom(jsonlite,fromJSON)
 importFrom(limma,contrasts.fit)
@@ -161,15 +147,13 @@ importFrom(purrr,map)
 importFrom(purrr,map_chr)
 importFrom(purrr,map_dfr)
 importFrom(purrr,possibly)
-importFrom(readr,parse_number)
 importFrom(reshape2,melt)
 importFrom(slingshot,as.SlingshotDataSet)
 importFrom(slingshot,slingshot)
 importFrom(stats,as.formula)
 importFrom(stats,model.matrix)
 importFrom(stats,prcomp)
 importFrom(stats,reorder)
-importFrom(stringr,str_split)
 importFrom(stringr,str_wrap)
 importFrom(tibble,enframe)
 importFrom(tidyr,pivot_wider)

diff --git a/R/DE_formating.R b/R/DE_formating.R
@@ -22,8 +22,7 @@ format_res_limma <- function(dt,
                              gene_id_conversion = NULL,
                              log2FoldChange = 0,
                              n_label = 20,
-                             padj = 0.05,
-                             ...) {
+                             padj = 0.05) {
   if (!is.null(gene_id_conversion)) {
     dt$gene_name <- gene_id_conversion$gene_name[
       match(dt$Identifier, gene_id_conversion$uniprot_id)]

diff --git a/R/clusters_DE.R b/R/clusters_DE.R
@@ -1,8 +1,8 @@
 #' Performs differential expression between clusters
 #'
+#' @param normalized_data Normalized rna/protein data
 #' @param colData The coldadata variable
 #' @param dependent The dependent variable
-#' @param covariates The covariates
 #' @param levels Levels to compare
 #' @param log2FoldChange logFC cutoff
 #'

diff --git a/R/enrichment.R b/R/enrichment.R
@@ -3,13 +3,16 @@
 #'
 #' Performs impacted pathway analysis with a list of genes.
 #'
-#' @param interest_genes vector containing genes of interest
+#' @param interest_gene vector containing genes of interest
 #' Column names should be gene, logFC, pval and padj respectively.
+#' @param project_name The name of the project to save.
 #' @param enrichment_database Name of the database for enrichment. User can
 #' specify one or more database names from [enrichR::listEnrichrDbs()].
 #' @param is_output If TRUE a folder will be created and results of enrichment
 #' analysis will be saved otherwise a R list will be returned. Default FALSE
 #' @param output_dir Path for the output directory. Default is current dir.
+#' @param min_overlap minimum overlap between genes and genesets to keep.
+#' @param plot_n number of genesets to plot
 #'
 #' @return enrichment_result a list of data.frames containing enrichment output
 #' and a list of plots of top 10 significant genesets.

diff --git a/R/integrative_models.R b/R/integrative_models.R
@@ -7,7 +7,8 @@
 #' integration are `cor`(strength of correlation between PC1 of omics layers)
 #' or `full` (The strength of all relationships between dataframes is
 #' maximised (= 1) – a “fully connected” design)
-#' @param range
+#' @param range a named list specifying the number of rna and protein.
+#' @param list.keepX list to keep rows
 #'
 #' @return a list object with `multimodal_object` and `model` slots
 #'
@@ -27,7 +28,7 @@ integrate_with_DIABLO <- function(multimodal_omics,
                                     mRNA = seq(5, 100, by = 10),
                                     proteins = seq(5, 100, by = 10)
                                   ),
-                                  list.keepX, ...) {
+                                  list.keepX) {
   multimodal_omics <- lapply(multimodal_omics, t)
   X <- list(
     mRNA = multimodal_omics[[1]],
@@ -117,8 +118,8 @@ integrate_with_sMBPLS <- function(multimodal_omics,
                                   design,
                                   ncomp,
                                   list.keepX = list(mRNA = c(50),
-                                                    proteins = c(50)),
-                                  ...) {
+                                                    proteins = c(50))
+                                  ) {
   multimodal_omics <- lapply(multimodal_omics, t)
   X <- list(
     mRNA = multimodal_omics[[1]],
@@ -192,7 +193,7 @@ integrate_with_sMBPLS <- function(multimodal_omics,
 integrate_with_MBPLS <- function(multimodal_omics,
                                  Y,
                                  design,
-                                 ncomp, ...) {
+                                 ncomp) {
   multimodal_omics <- lapply(multimodal_omics, t)
   X <- list(
     mRNA = multimodal_omics[[1]],
@@ -248,7 +249,7 @@ integrate_with_MBPLS <- function(multimodal_omics,
 #' @param multimodal_omics  multimodal_object generated by `get_multimodal_object`
 #' @param num_factors Number of factors
 #' @param scale_views Logical whether to scale omics layers
-#' @param metadata
+#' @param metadata sample metadata
 #'
 #' @return a list object with `multimodal_object` and `model` slots
 #'
@@ -261,7 +262,7 @@ integrate_with_MBPLS <- function(multimodal_omics,
 integrate_with_MOFA <- function(multimodal_omics,
                                 num_factors = 5,
                                 scale_views = TRUE,
-                                metadata, ...) {
+                                metadata) {
   python_path <- Sys.which("python")
   reticulate::use_python(python_path, required = NULL)
 
@@ -323,8 +324,8 @@ integrate_with_MEIFESTO <- function(multimodal_omics,
                                     num_factors = 5,
                                     scale_views = TRUE,
                                     metadata,
-                                    time = "pseudotime",
-                                    ...) {
+                                    time = "pseudotime"
+                                    ) {
   # python_path <- Sys.which("python")
   # reticulate::use_python(python_path, required = NULL)
 
@@ -372,51 +373,3 @@ integrate_with_MEIFESTO <- function(multimodal_omics,
     model = model
   ))
 }
-
-
-#'  Vertical integration with iCluster
-#'
-#' @param multimodal_omics multimodal_object generated by `get_multimodal_object`
-#' @param try.N.clust Range of number of potential clusters in the optimisation step
-#'
-#' @return a list object with `multimodal_object` and `model` slots
-#'
-#' @family Multi-omic integration
-#'
-#'
-#' @export
-
-
-
-integrate_with_iCluster <- function(multimodal_omics,
-                                    try.N.clust = 2:4) {
-  cli::cli_alert_success("Optimising the number of cluster
-                         (this make take a while")
-
-  optk.i <- MOVICS::getClustNum(
-    data = multimodal_omics,
-    is.binary = c(FALSE, FALSE),
-    try.N.clust = try.N.clust,
-    fig.name = "Cluster number tuning"
-  )
-
-
-  optimal_n <- optk.i$N.clust
-
-  cli::cli_alert_success("Integration in progress (this make take a while)")
-  model <- MOVICS::getiClusterBayes(
-    data = multimodal_omics,
-    N.clust = optimal_n,
-    type = c("gaussian", "gaussian"),
-    n.burnin = 1800,
-    n.draw = 1200,
-    prior.gamma = c(0.5, 0.5),
-    sdev = 0.05,
-    thin = 3
-  )
-
-  return(list(
-    multimodal_object = multimodal_omics,
-    model = model
-  ))
-}