Merge pull request #89 from saezlab/development

Development into main

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 2.1.3
+current_version = 2.1.4
 commit = True
 tag = True
 files = DESCRIPTION README.md README.Rmd

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: MetaProViz
 Type: Package
 Title: METabolomics pre-PRocessing, functiOnal analysis and VIZualisation
-Version: 2.1.3
+Version: 2.1.4
 Authors@R: c(
     person("Christina", "Schmidt", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-3867-0881")),

NAMESPACE

@@ -1,12 +1,12 @@
 # Generated by roxygen2: do not edit by hand
 
-export(AddInfo)
-export(CleanMapping)
 export(ClusterORA)
 export(DMA)
+export(LoadGaude)
 export(LoadHallmarks)
 export(LoadKEGG)
 export(LoadMetalinks)
+export(LoadRAMP)
 export(MCA_2Cond)
 export(MCA_CoRe)
 export(MCA_rules)
@@ -27,11 +27,6 @@ export(VizHeatmap)
 export(VizPCA)
 export(VizSuperplot)
 export(VizVolcano)
-importFrom(magrittr,"%>%")
-importFrom(magrittr,extract2)
-importFrom(readr,cols)
-importFrom(readr,read_csv)
-importFrom(tibble,column_to_rownames)
 export(metaproviz_config_path)
 export(metaproviz_load_config)
 export(metaproviz_reset_config)
@@ -170,8 +165,6 @@ importFrom(tidyr,replace_na)
 importFrom(tidyr,separate)
 importFrom(tidyr,separate_rows)
 importFrom(tidyr,unite)
-importFrom(tidyselect,everything)
-importFrom(tidyselect,starts_with)
 importFrom(tidyselect,all_of)
 importFrom(utils,combn)
 importFrom(utils,head)

R/GetPriorKnoweldge.R

@@ -50,14 +50,6 @@ LoadKEGG <- function(){
     KEGG_Metabolite <- readRDS(File_path)
     message("Cached file loaded from: ", File_path)
   }else{# load from KEGG
-    RequiredPackages <- c("KEGGREST", "tidyverse")
-    new.packages <- RequiredPackages[!(RequiredPackages %in% installed.packages()[,"Package"])]
-    if(length(new.packages)) install.packages(new.packages)
-
-    suppressMessages(library(KEGGREST))
-    suppressMessages(library(tidyverse))
-
-    #--------------------------------------------------------------------------------------------
     # 1. Make a list of all available human pathways in KEGG
     Pathways_H <- as.data.frame(keggList("pathway", "hsa"))  # hsa = human
 
@@ -126,10 +118,10 @@ LoadKEGG <- function(){
 ### ### ### Load Hallmark prior knowledge ### ### ###
 ##########################################################################################
 #'
-#' @title Toy Data Import
-#' @description Import and process .csv file to create toy data.
+#' @title Prior Knowledge Import
+#' @description Import and process .csv file to create Prior Knowledge.
 #' @importFrom utils read.csv
-#' @return A data frame containing the toy data.
+#' @return A data frame containing the Prior Knowledge.
 #' @export
 #'
 LoadHallmarks <- function() {
@@ -144,6 +136,118 @@ LoadHallmarks <- function() {
   assign("Hallmark_Pathways", Hallmark, envir=.GlobalEnv)
 }
 
+##########################################################################################
+### ### ### Load Gaude Metabolic Signature prior knowledge ### ### ###
+##########################################################################################
+#'
+#' @title Prior Knowledge Import
+#' @description Import and process .csv file to create Prior Knowledge.
+#' @importFrom utils read.csv
+#' @return A data frame containing the Prior Knowledge.
+#' @export
+#'
+LoadGaude <- function() {
+  ## ------------ Create log file ----------- ##
+  MetaProViz_Init()
+
+  # Read the .csv files
+  MetabolicSig <- system.file("data", "Compilled_MetabolicSig_2025-01-07.csv", package = "MetaProViz")
+  MetabolicSig <- read.csv(MetabolicSig, check.names=FALSE)
+
+  # Return into environment
+  assign("Gaude_Pathways", MetabolicSig, envir=.GlobalEnv)
+}
+
+##########################################################################################
+### ### ### Load RaMP prior knowledge ### ### ###
+##########################################################################################
+#'
+#' @title Prior Knowledge Import
+#' @param version \emph{Optional: } Version of the RaMP database loaded from OmniPathR. \strong{default: "2.5.4"}
+#' @param SaveAs_Table \emph{Optional: } File types for the analysis results are: "csv", "xlsx", "txt". \strong{Default = "csv"}
+#' @param FolderPath {Optional:} String which is added to the resulting folder name \strong{default: NULL}
+
+#' @description Import and process file to create Prior Knowledge.
+#'
+#' @importFrom  OmnipathR ramp_table
+#' @importFrom rappdirs user_cache_dir
+#' @importFrom dplyr filter select group_by summarise mutate
+#' @importFrom stringr str_remove
+#'
+#' @return A data frame containing the Prior Knowledge.
+#'
+#' @examples
+#' ChemicalClass <- MetaProViz::LoadRAMP()
+#'
+#' @export
+#'
+LoadRAMP <- function(version = "2.5.4",
+                     SaveAs_Table="csv",
+                     FolderPath=NULL){
+  ## ------------ Create log file ----------- ##
+  MetaProViz_Init()
+
+  ## ------------ Folder ----------- ##
+  if(is.null(SaveAs_Table)==FALSE){
+    Folder <- SavePath(FolderName= "PriorKnowledge",
+                       FolderPath=FolderPath)
+
+    SubFolder <- file.path(Folder, "MetaboliteSet")
+    if (!dir.exists(SubFolder)) {dir.create(SubFolder)}
+  }
+
+
+  ######################################################
+  #Get the directory and filepath of cache results of R
+  directory <- rappdirs::user_cache_dir()#get chache directory
+  File_path <-paste(directory, "/RaMP-ChemicalClass_Metabolite.rds", sep="")
+
+  if(file.exists(File_path)==TRUE){# First we will check the users chache directory and weather there are rds files with KEGG_pathways already:
+    HMDB_ChemicalClass <- readRDS(File_path)
+    message("Cached file loaded from: ", File_path)
+  }else{# load from OmniPath
+  # Get RaMP via OmnipathR and extract ClassyFire classes
+  Structure <- OmnipathR::ramp_table( "metabolite_class" , version = version)
+  Class <- OmnipathR::ramp_table( "chem_props" , version = version)
+
+  HMDB_ChemicalClass <- merge(Structure, Class[,c(1:3,10)], by="ramp_id", all.x=TRUE)%>%
+    dplyr::filter(stringr::str_starts(class_source_id, "hmdb:"))%>% # Select HMDB only!
+    dplyr::filter(stringr::str_starts(chem_source_id, "hmdb:"))%>% # Select HMDB only!
+    dplyr::select(-c("chem_data_source", "chem_source_id"))%>%
+    tidyr::pivot_wider(
+      names_from = class_level_name, # Use class_level_name as the new column names
+      values_from = class_name,      # Use class_name as the values for the new columns
+      values_fn = list(class_name = ~paste(unique(.), collapse = ", ")) # Combine duplicate values
+    )%>%
+    dplyr::group_by(across(-common_name))%>%
+    dplyr::summarise(
+      common_name = paste(unique(common_name), collapse = "; "), # Combine all common names into one
+      .groups = "drop"  # Ungroup after summarising
+    )%>%
+    dplyr::mutate(class_source_id = stringr::str_remove(class_source_id, "^hmdb:"))%>% # Remove 'hmdb:' prefix
+    dplyr::select(class_source_id, common_name, ClassyFire_class, ClassyFire_super_class, ClassyFire_sub_class) # Reorder columns
+
+  #Save the results as an RDS file in the Cache directory of R
+  if(!dir.exists(directory)) {dir.create(directory)}
+  saveRDS(HMDB_ChemicalClass, file = paste(directory, "/RaMP-ChemicalClass_Metabolite.rds", sep=""))
+
+  }
+
+  ##-------------- Save and return
+  DF_List <- list("ChemicalClass_MetabSet"=HMDB_ChemicalClass)
+  suppressMessages(suppressWarnings(
+    SaveRes(InputList_DF= DF_List,#This needs to be a list, also for single comparisons
+            InputList_Plot= NULL,
+            SaveAs_Table=SaveAs_Table,
+            SaveAs_Plot=NULL,
+            FolderPath= SubFolder,
+            FileName= "ChemicalClass",
+            CoRe=FALSE,
+            PrintPlot=FALSE)))
+
+  # Return into environment
+  assign("ChemicalClass_MetabSet", HMDB_ChemicalClass, envir=.GlobalEnv)
+}
 
 
 ##########################################################################################

R/ToyData.R

@@ -35,14 +35,16 @@
 #'         \item{\code{"Tissue_DMA"}: }
 #'         \item{\code{"Tissue_DMA_Old"}: }
 #'         \item{\code{"Tissue_DMA_Young"}: }
+#'         \item{\code{"Tissue_TvN_Proteomics"}: }
+#'         \item{\code{"Tissue_TvN_RNAseq"}: }
 #'     }
 #'
 #' @return A data frame containing the toy data.
 #'
 #' @description Import and process .csv file to create toy data DF.
 #'
 #' @examples
-#' Intra <- ToyData("IntraCells_Raw")
+#' Intra <- MetaProViz::ToyData("IntraCells_Raw")
 #'
 #' @importFrom readr read_csv cols
 #' @importFrom magrittr %>% extract2
@@ -66,6 +68,8 @@ ToyData <- function(Dataset) {
     Tissue_DMA = "Hakimi_ccRCC-Tissue_DMA_TvsN.csv",
     Tissue_DMA_Old ="Hakimi_ccRCC-Tissue_DMA_TvsN-Old.csv",
     Tissue_DMA_Young ="Hakimi_ccRCC-Tissue_DMA_TvsN-Young.csv",
+    Tissue_TvN_Proteomics ="ccRCC-Tissue_TvN_Proteomics.csv",
+    Tissue_TvN_RNAseq = "ccRCC-Tissue_TvN_RNAseq.csv",
     AlaninePathways = "AlaninePathways.csv"
   )
 

README.Rmd

@@ -214,7 +214,7 @@ GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007
   title = {MetaProViz: METabolomics pre-PRocessing, functiOnal analysis and VIZualisation},
   author = {Christina Schmidt, Dimitrios Prymidis, Macabe Daley, Denes Turei, Julio Saez-Rodriguez and Christian Frezza},
   year = {2023},
-  note = {R package version 2.1.3},
+  note = {R package version 2.1.4},
 }
 ```
 

README.md

@@ -160,5 +160,5 @@ GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007
       title = {MetaProViz: METabolomics pre-PRocessing, functiOnal analysis and VIZualisation},
       author = {Christina Schmidt, Dimitrios Prymidis, Macabe Daley, Denes Turei, Julio Saez-Rodriguez and Christian Frezza},
       year = {2023},
-      note = {R package version 2.1.3},
+      note = {R package version 2.1.4},
     }