1. StarBioTrek
  2. Commit ecb03afef5320039784a21bd02230ba57f0c652a
Browse code

'update'

claudia.cava authored on 15/04/2019 08:55:17
Showing 56 changed files
R/StarBioTrek.r
History View file @ ecb03af
... ... 
@@ -11,34 +11,64 @@
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 #' @name StarBioTrek
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 NULL
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-#' Pathway data from KEGG
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+
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+#' pathway data list
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 #' @docType data
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 #' @keywords internal
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 #' @name path
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-#' @format A data frame with rows and  variables
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+#' @format A list of dataframe
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 NULL
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-#' network data
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+#' pathway data list
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 #' @docType data
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 #' @keywords internal
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-#' @name netw
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+#' @name Data_CANCER_normUQ_fil
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+#' @format A dataframe with gene expression profiles
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+NULL
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+
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+
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+#' pathway data
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+#' @docType data
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+#' @keywords internal
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+#' @name pathway
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 #' @format A data frame with  rows and variables
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 NULL
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+#' network data for IPPI fucntion
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+#' @docType data
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+#' @keywords internal
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+#' @name netw_IPPI
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+#' @format A list
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+NULL
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+
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+#' network data
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+#' @docType data
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+#' @keywords internal
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+#' @name pathway_matrix
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+#' @format A data frame with  rows and variables
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+NULL
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+#' network data
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+#' @docType data
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+#' @keywords internal
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+#' @name netw
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+#' @format A data frame with  rows and variables
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+NULL
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-#' TCGA data
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+#' All pathways data from KEGG
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 #' @docType data
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 #' @keywords internal
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-#' @name Data_CANCER_normUQ_filt
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-#' @format A data frame with rows and variables
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+#' @name path_KEGG
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+#' @format A list of pathways with the involved genes
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 NULL
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+
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+
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 #' Score Matrix of pairwise pathway using euclidean distance
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 #' @docType data
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 #' @keywords internal
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-#' @name score_euc_dist
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+#' @name score_euc_dista
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 #' @format A data frame with rows and variables
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 NULL
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... ... 
@@ -55,10 +85,3 @@ NULL
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 #' @name tumo
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 #' @format A data frame with rows and variables
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 NULL
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-
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-#' A matrix of gene expression for pathways given by the user.
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-#' @docType data
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-#' @keywords internal
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-#' @name list_path_plot
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-#' @format A data frame with rows and variables
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-NULL
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\ No newline at end of file
R/getdata.R
History View file @ ecb03af
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@@ -1,225 +1,133 @@
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-#' @title Get human KEGG pathway data.
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-#' @description getKEGGdata creates a data frame with human KEGG pathway. Columns are the pathways and rows the genes inside those pathway
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-#' @param KEGG_path  variable
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+#' @title Get general information inside pathways.
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+#' @description GetData creates a list with genes inside the pathways.
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+#' @param species  variable. The user can select the species of interest from SELECT_path_species(path_spec)
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+#' @param pathwaydb variable. The user can select the pathway database of interest from SELECT_path_graphite(path_spec)
4 5 
 #' @export
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-#' @importFrom KEGGREST keggList keggGet
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-#' @importFrom org.Hs.eg.db org.Hs.egSYMBOL2EG
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-#' @importFrom AnnotationDbi mappedkeys as.list
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-#' @return dataframe with human pathway data
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+#' @importFrom graphite pathways pathwayTitle
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+#' @return a list of pathways
9 8 
 #' @examples
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-#' path<-getKEGGdata(KEGG_path="Transcript")
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-getKEGGdata<-function(KEGG_path){
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-if (KEGG_path=="Carb_met") {
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-  mer<-select_path_carb(Carbohydrate)
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-  c<-proc_path(mer)
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-  a<-c[[2]]
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+#' \dontrun{
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+#' species="hsapiens"
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+#' pathwaydb="pharmgkb"
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+#' path<-GetData(species,pathwaydb)}
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+GetData<-function(species,pathwaydb){
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+  humanpath <- pathways(species, pathwaydb)
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+  humanReactome<-humanpath
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+  le<-list()
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+  for (j in  1:length(humanReactome)){
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+    e<-humanReactome[[j]]
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+    print(paste0("Querying.............  ",pathwayTitle(e),"   ", j, " of ",length(humanReactome)," pathways"))
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+    le[[j]]<-e
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+  }
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+  names(le)<- names(humanReactome)
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+  return(le)
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 }
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-  if (KEGG_path=="Ener_met") {
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-    mer<-select_path_en(Energy)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="Lip_met") {
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-    mer<-select_path_lip(Lipid)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="Amn_met") {
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-    mer<-select_path_amn(Aminoacid)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="Gly_bio_met") {
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-    mer<-select_path_gly(Glybio_met)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="Cof_vit_met") {
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-    mer<-select_path_cofa(Cofa_vita_met)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="Transcript") {
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-    mer<-select_path_transc(Transcription)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="Transl") {
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-    mer<-select_path_transl(Translation)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="Fold_degr") {
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-    mer<-select_path_fold(Folding_sorting_and_degradation)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="Repl_repair") {
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-    mer<-select_path_repl(Replication_and_repair)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="sign_transd") {
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-    mer<-select_path_sign(Signal_transduction)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="sign_mol_int") {
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-    mer<-select_path_sign_mol(Signaling_molecules_and_interaction)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="Transp_cat") {
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-    mer<-select_path_transp_ca(Transport_and_catabolism)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="cell_grow_d") {
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-    mer<-select_path_cell_grow(Cell_growth_and_death)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="cell_comm") {
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-    mer<-select_path_cell_comm(Cellular_community)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="imm_syst") {
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-    mer<-select_path_imm_syst(Immune_system)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
92 
-  if (KEGG_path=="end_syst") {
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-    mer<-select_path_end_syst(Endocrine_system)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
97 
-  if (KEGG_path=="circ_syst") {
98 
-    mer<-select_path_circ_syst(Circulatory_system)
99 
-    c<-proc_path(mer)
100 
-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="dig_syst") {
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-    mer<-select_path_dig_syst(Digestive_system)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
106 
-  }
107 
-  if (KEGG_path=="exc_syst") {
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-    mer<-select_path_exc_syst(Excretory_system)
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-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="nerv_syst") {
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-    mer<-select_path_ner_syst(Nervous_system)
114 
-    c<-proc_path(mer)
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-    a<-c[[2]]
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-  }
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-  if (KEGG_path=="sens_syst") {
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-    mer<-select_path_sens_syst(Sensory_system)
119 
-    c<-proc_path(mer)
120 
-    a<-c[[2]]
121 
-  }
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-if (KEGG_path=="KEGG_path") {
123 
-  pathways.list <- keggList("pathway", "hsa")## returns the list of human pathways
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-pathway.codes <- sub("path:", "", names(pathways.list))
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-pathways.list<-list(pathways.list)
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-pathways.list<-pathways.list[lapply(pathways.list,length)!=0]
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-list_pathkeg<-do.call("cbind", pathways.list)
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-c<-list(pathway.codes,list_pathkeg)
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-a<-c[[2]]
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-}
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-pathway.codes<-c[[1]]
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-genes.by.pathway <- sapply(pathway.codes,
134 
-                           function(pwid){
135 
-                             pw <- keggGet(pwid)
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-                             pw[[1]]$GENE[c(TRUE, FALSE)]
137 
-                           })
138 
-x <- org.Hs.egSYMBOL2EG
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-mapped_genes <- mappedkeys(x)
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-xx <- as.list(x[mapped_genes])
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-top3 <- matrix(0, length(xx), length(genes.by.pathway))
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-rownames(top3) <- names(xx)
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-colnames(top3)<- names(genes.by.pathway)
144 
-
145 
-
146 
-
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-for (j in  1:length(xx)){
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-  for (k in  1:length(genes.by.pathway)){
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-    if (length(intersect(xx[[j]],genes.by.pathway[[k]])!=0)){
150 
-
151 
-
152 
-
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-       top3[j,k]<-names(xx[j])
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-    }
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-  }
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-}
157 26
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+#' @title Get  genes inside pathways.
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+#' @description GetPathData creates a list of genes inside the pathways.
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+#' @param path_ALL  variable. The user can select the variable as obtained by  GetData function
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+#' @export
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+#' @importFrom graphite nodes pathwayTitle
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+#' @return a list of pathways
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+#' @examples
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+#' pathway_ALL_GENE<-GetPathData(path_ALL=path[1:3])
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+GetPathData<-function(path_ALL){
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+  le<-list()
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+  for (j in  1:length(path_ALL)){
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+    e<-path_ALL[[j]]
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+    genes<-nodes(e,which = "proteins")
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+    print(paste0("Downloading.............  ",pathwayTitle(e),"   ", j, " of ",length(path_ALL)," pathways"))
41 
+    le[[j]]<-genes
42 
+  }
43 
+  names(le)<- names(path_ALL)
44 
+  return(le)
45 
+}
158 46
159 47
160 
-for (j in  1:length(xx)){
161 
-  for (k in  1:length(genes.by.pathway)){
162 
-    if (length(intersect(xx[[j]],genes.by.pathway[[k]])!=0)){
163 
-
164 48
165 49
166 
-     # top3[j,k]<-names(xx[j])
167 
-    }
168 
-  }
169 
-}
170 
-top3[top3 == 0] <- " "
171 
-#a<-data.frame(pathways.list)
172 
-#i <- sapply(a, is.factor)
173 
-#a[i] <- lapply(a[i], as.character)
174 
-rownames(a)<-sub("path:","",rownames(a))
175 
-PROVA<-top3
176 
-for( i in 1:ncol(PROVA)) {
177 
-  if (colnames(PROVA)[i]==rownames(a)[i]){
178 
-    colnames(PROVA)[i]<-a[i]
179 
-}
180 
-}
181 
-return(PROVA)
50 
+#' @title Get  interacting genes inside pathways.
51 
+#' @description GetPathNet creates a list of genes inside the pathways.
52 
+#' @param path_ALL  variable. The user can select the variable as obtained by  GetData function
53 
+#' @export
54 
+#' @importFrom graphite edges pathwayTitle
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+#' @return a list of pathways
56 
+#' @examples
57 
+#' pathway_net<-GetPathNet(path_ALL=path[1:3])
58 
+GetPathNet<-function(path_ALL){
59 
+  le<-list()
60 
+  for (j in  1:length(path_ALL)){
61 
+    e<-path_ALL[[j]]
62 
+    genes<-edges(e,which = "proteins")
63 
+    print(paste0("Downloading.............  ",pathwayTitle(e),"   ", j, " of ",length(path_ALL)," pathways"))
64 
+    le[[j]]<-genes
65 
+  }
66 
+  names(le)<- names(path_ALL)
67 
+  return(le)
182 68 
 }
183 69
184 70
185 
-#' @title Get network data.
71 
+
72 
+#' @title Get  interacting genes inside pathways.
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+#' @description GetPathNet creates a list of genes inside the pathways.
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+#' @param path_ALL  variable. The user can select the variable as obtained by  GetData function
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+#' @export
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+#' @importFrom graphite nodes pathwayTitle convertIdentifiers
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+#' @return a list of pathways
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+#' @examples
79 
+#' pathway<-ConvertedIDgenes(path_ALL=path[1:3])
80 
+ConvertedIDgenes<-function(path_ALL){
81 
+  le<-list()
82 
+  for (j in  1:length(path_ALL)){
83 
+    e<-path_ALL[[j]]
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+    s1<-convertIdentifiers(e, "symbol")
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+    genes<-nodes(s1,which = "proteins")
86 
+    er <- sapply(strsplit(genes, split=':', fixed=TRUE), function(x) (x[2]))
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+    print(paste0("Mapping Uniprot ID to Gene Symbol, using convertIdentifiers of graphite package..........  ",pathwayTitle(e),"   ", j, " of ",length(path_ALL)," pathways"))
88 
+    #attr(mm, "names")<-NULL
89 
+    le[[j]]<-er
90 
+  }
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+  names(le)<- names(path_ALL)
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+  return(le)
93 
+}
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+
95 
+
96 
+
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+
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+#' @title Get network data from GeneMania.
186 99 
 #' @description getNETdata creates a data frame with network data.
187 100 
 #' Network category can be filtered among: physical interactions, co-localization, genetic interactions and shared protein domain.
188 101 
 #' @param network  variable. The user can use the following parameters
189 102 
 #' based on the network types to be used. PHint for Physical_interactions,
190 103 
 #' COloc for Co-localization, GENint for Genetic_interactions and
191 104 
 #' SHpd for Shared_protein_domains
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-#' @param organism organism==NULL default value is homo sapiens
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+#' @param organismID organism==NULL default value is homo sapiens.
193 106 
 #' @export
194 
-#' @importFrom SpidermiR SpidermiRquery_species SpidermiRquery_spec_networks SpidermiRdownload_net SpidermiRprepare_NET
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-#' @return dataframe with gene-gene (or protein-protein interactions)
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+#' @importFrom SpidermiR  SpidermiRquery_spec_networks SpidermiRdownload_net SpidermiRprepare_NET
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+#' @return list with gene-gene (or protein-protein interactions)
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 #' @examples
197 
-#' organism="Saccharomyces_cerevisiae"
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-#' netw<-getNETdata(network="SHpd",organism)
199 
-getNETdata<-function(network,organism=NULL){
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-  org_shar_pro<-SpidermiRquery_species(species)
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-  if (is.null(organism)) {
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-  net_shar_prot<-SpidermiRquery_spec_networks(organismID = org_shar_pro[6,],network)
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-  out_net_shar_pro<-SpidermiRdownload_net(net_shar_prot)
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-  geneSymb_net_shar_pro<-SpidermiRprepare_NET(organismID = org_shar_pro[6,],data = out_net_shar_pro)
205 
-  }
206 
-  if( !is.null(organism) ){
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-    net_shar_prot<-SpidermiRquery_spec_networks(organismID = org_shar_pro[9,],network)
208 
-    out_net_shar_pro<-SpidermiRdownload_net(net_shar_prot)
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-    geneSymb_net_shar_pro<-SpidermiRprepare_NET(organismID = org_shar_pro[9,],data = out_net_shar_pro)
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-}
211 
-  ds_shar_pro<-do.call("rbind", geneSymb_net_shar_pro)
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-  data_shar_pro<-as.data.frame(ds_shar_pro[!duplicated(ds_shar_pro), ])
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-  sdc_shar_pro<-unlist(data_shar_pro$gene_symbolA,data_shar_pro$gene_symbolB)
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-  m_shar_pro<-c(data_shar_pro$gene_symbolA)
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-  m2_shar_pro<-c(data_shar_pro$gene_symbolB)
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-  ss_shar_pro<-cbind(m_shar_pro,m2_shar_pro)
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-  data_pr_shar_pro<-as.data.frame(ss_shar_pro[!duplicated(ss_shar_pro), ])
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-  colnames(data_pr_shar_pro) <- c("m_shar_pro", "m2_shar_pro")
219 
-return(data_pr_shar_pro)
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+#' \dontrun{
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+#' organismID="Saccharomyces_cerevisiae"
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+#' netw<-getNETdata(network="SHpd",organismID)}
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+getNETdata<-function(network,organismID=NULL){
114 
+  if( is.null(organismID) ){
115 
+    prr<-SpidermiRprepare_NET(organismID = 'Homo_sapiens',
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+                         data = SpidermiRdownload_net(data = SpidermiRquery_spec_networks(organismID = 'Homo_sapiens',network
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+                                                                                          )))
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+  }
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+  if( !is.null(organismID) ){
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+    prr<-SpidermiRprepare_NET(organismID,
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+                              data = SpidermiRdownload_net(data = SpidermiRquery_spec_networks(organismID ,
122 
+                                                                                               network)))
123 
+  }
124 
+  return(prr)
220 125 
 }
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+
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+
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+
R/internal.R
History View file @ ecb03af
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@@ -1,405 +1,48 @@
1 
-
2 
-
3 
-
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-select_path_carb<-function(Carbohydrate){
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-species<-c("- Homo sapiens (human)")
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-a<-paste("Glycolysis / Gluconeogenesis", species)
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-b<-paste("Citrate cycle (TCA cycle)", species)
8 
-c<-paste("Pentose phosphate pathway", species)
9 
-d<-paste("Pentose and glucuronate interconversions", species)
10 
-e<-paste("Fructose and mannose metabolism", species)
11 
-f<-paste("Galactose metabolism", species)
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-g<-paste("Ascorbate and aldarate metabolism", species)
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-h<-paste("Starch and sucrose metabolism", species)
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-i<-paste("Amino sugar and nucleotide sugar metabolism", species)
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-l<-paste("Pyruvate metabolism", species)
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-m<-paste("Glyoxylate and dicarboxylate metabolism", species)
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-n<-paste("Propanoate metabolism", species)
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-o<-paste("Butanoate metabolism", species)
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-p<-paste("C5-Branched dibasic acid metabolism", species)
20 
-q<-paste("Inositol phosphate metabolism", species)
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-r<-paste("Enzymes", species)
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-s<-paste("Compounds with biological roles",species)
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-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s)
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-return(mer)
25 
-}
26 
-
27 
-select_path_en<-function(Energy){
28 
-  species<-c("- Homo sapiens (human)")
29 
-  r<-paste("Oxidative phosphorylation", species)
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-  s<-paste("Photosynthesis", species)
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-  t<-paste("Photosynthesis - antenna proteins", species)
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-  v<-paste("Carbon fixation in photosynthetic organisms", species)
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-  u<-paste("Carbon fixation pathways in prokaryotes", species)
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-  z<-paste("Methane metabolism", species)
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-  aa<-paste("Nitrogen metabolism", species)
36 
-  ab<-paste("Sulfur metabolism", species)
37 
-  mer<-c(r,s,t,v,u,z,aa,ab)
38 
-  return(mer)
39 
-}
40 
-
41 
-
42 
-select_path_lip<-function(Lipid){
43 
-  species<-c("- Homo sapiens (human)")
44 
-ac<-paste("Fatty acid biosynthesis", species)
45 
-ad<-paste("Fatty acid elongation", species)
46 
-ae<-paste("Fatty acid degradation", species)
47 
-af<-paste("Synthesis and degradation of ketone bodies", species)
48 
-ag<-paste("Cutin, suberine and wax biosynthesis", species)
49 
-ah<-paste("Steroid biosynthesis", species)
50 
-ai<-paste("Primary bile acid biosynthesis", species)
51 
-al<-paste("Secondary bile acid biosynthesis", species)
52 
-am<-paste("Steroid hormone biosynthesis", species)
53 
-an<-paste("Glycerolipid metabolism", species)
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-ao<-paste("Glycerophospholipid metabolism", species)
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-ap<-paste("Ether lipid metabolism", species)
56 
-aq<-paste("Sphingolipid metabolism", species)
57 
-ar<-paste("Arachidonic acid metabolism", species)
58 
-as<-paste("Linoleic acid metabolism", species)
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-at<-paste("alpha-Linolenic acid metabolism", species)
60 
-av<-paste("Biosynthesis of unsaturated fatty acids", species)
61 
-
62 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap,aq,ar,as,at,av)
63 
-return(mer)
64 
-}
65 
-
66 
-
67 
-
68 
-
69 
-select_path_amn<-function(Aminoacid){
70 
-  species<-c("- Homo sapiens (human)")
71 
-ac<-paste("Alanine, aspartate and glutamate metabolism", species)
72 
-ad<-paste("Glycine, serine and threonine metabolism", species)
73 
-ae<-paste("Cysteine and methionine metabolism", species)
74 
-af<-paste("Valine, leucine and isoleucine degradation", species)
75 
-ag<-paste("Valine, leucine and isoleucine biosynthesis", species)
76 
-ah<-paste("Lysine biosynthesis", species)
77 
-ai<-paste("Lysine degradation", species)
78 
-al<-paste("Arginine biosynthesis", species)
79 
-am<-paste("Arginine and proline metabolism", species)
80 
-an<-paste("Histidine metabolism", species)
81 
-ao<-paste("Tyrosine metabolism", species)
82 
-ap<-paste("Phenylalanine metabolism", species)
83 
-aq<-paste("Tryptophan metabolism", species)
84 
-ar<-paste("Phenylalanine, tyrosine and tryptophan biosynthesis", species)
85 
-as<-paste("beta-Alanine metabolism", species)
86 
-at<-paste("Taurine and hypotaurine metabolism", species)
87 
-av<-paste("Phosphonate and phosphinate metabolism", species)
88 
-au<-paste("Selenocompound metabolism", species)
89 
-az<-paste("Cyanoamino acid metabolism", species)
90 
-a<-paste("D-Glutamine and D-glutamate metabolism", species)
91 
-b<-paste("D-Arginine and D-ornithine metabolism", species)
92 
-c<-paste("D-Alanine metabolism", species)
93 
-d<-paste("Glutathione metabolism", species)
94 
-
95 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap,aq,ar,as,at,av,au,az,a,b,c,d)
96 
-return(mer)
97 
-}
98 
-
99 
-select_path_gly<-function(Glybio_met){
100 
-  species<-c("- Homo sapiens (human)")
101 
-ac<-paste("N-Glycan biosynthesis", species)
102 
-ad<-paste("Various types of N-glycan biosynthesis", species)
103 
-ae<-paste("Mucin type O-Glycan biosynthesis", species)
104 
-af<-paste("Other types of O-glycan biosynthesis", species)
105 
-ag<-paste("Glycosaminoglycan biosynthesis - CS/DS", species)
106 
-ah<-paste("Glycosaminoglycan biosynthesis - HS/Hep", species)
107 
-ai<-paste("Glycosaminoglycan biosynthesis - KS", species)
108 
-al<-paste("Glycosaminoglycan degradation", species)
109 
-am<-paste("Glycosylphosphatidylinositol(GPI)-anchor biosynthesis", species)
110 
-an<-paste("Glycosphingolipid biosynthesis - lacto and neolacto series", species)
111 
-ao<-paste("Glycosphingolipid biosynthesis - globo series", species)
112 
-ap<-paste("Glycosphingolipid biosynthesis - ganglio series", species)
113 
-aq<-paste("Lipopolysaccharide biosynthesis", species)
114 
-ar<-paste("Peptidoglycan biosynthesis", species)
115 
-as<-paste("Other glycan degradation", species)
116 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap,aq,ar,as)
117 
-return(mer)
118 
-}
119 
-
120 
-
121 
-
122 
-select_path_cofa<-function(Cofa_vita_met){
123 
-  species<-c("- Homo sapiens (human)")
124 
-ac<-paste("Thiamine metabolism", species)
125 
-ad<-paste("Riboflavin metabolism", species)
126 
-ae<-paste("Vitamin B6 metabolism", species)
127 
-af<-paste("Nicotinate and nicotinamide metabolism", species)
128 
-ag<-paste("Pantothenate and CoA biosynthesis", species)
129 
-ah<-paste("Biotin metabolism", species)
130 
-ai<-paste("Lipoic acid metabolism", species)
131 
-al<-paste("Folate biosynthesis", species)
132 
-am<-paste("One carbon pool by folate", species)
133 
-an<-paste("Retinol metabolism", species)
134 
-ao<-paste("Porphyrin and chlorophyll metabolism", species)
135 
-ap<-paste("Ubiquinone and other terpenoid-quinone biosynthesis", species)
136 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap)
137 
-return(mer)
138 
-}
139 
-
140 
-select_path_transc<-function(Transcription){
141 
-  species<-c("- Homo sapiens (human)")
142 
-ac<-paste("RNA polymerase", species)
143 
-ad<-paste("Basal transcription factors", species)
144 
-ae<-paste("Spliceosome", species)
145 
-af<-paste("Transcription factors", species)
146 
-ag<-paste("Transcription machinery", species)
147 
-mer<-c(ac,ad,ae,af,ag)
148 
-return(mer)
149 
-}
150 
-
151 
-
152 
-
153 
-select_path_transl<-function(Translation){
154 
-  species<-c("- Homo sapiens (human)")
155 
-ac<-paste("Ribosome", species)
156 
-ad<-paste("Aminoacyl-tRNA biosynthesis", species)
157 
-ae<-paste("RNA transport", species)
158 
-af<-paste("mRNA surveillance pathway", species)
159 
-ag<-paste("Ribosome biogenesis in eukaryotes", species)
160 
-ah<-paste("Ribosomal proteins", species)
161 
-ai<-paste("Ribosome biogenesis", species)
162 
-al<-paste("Transfer RNA biogenesis", species)
163 
-am<-paste("Translation factors", species)
164 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am)
165 
-return(mer)
166 
-}
167 
-
168 
-select_path_fold<-function(Folding_sorting_and_degradation){
169 
-  species<-c("- Homo sapiens (human)")
170 
-ac<-paste("Protein export", species)
171 
-ad<-paste("Protein processing in endoplasmic reticulum", species)
172 
-ae<-paste("SNARE interactions in vesicular transport", species)
173 
-af<-paste("Ubiquitin mediated proteolysis", species)
174 
-ag<-paste("Sulfur relay system", species)
175 
-ah<-paste("RNA degradation", species)
176 
-ai<-paste("Chaperones and folding catalysts", species)
177 
-al<-paste("SNAREs", species)
178 
-am<-paste("Ubiquitin system", species)
179 
-an<-paste("Proteasome", species)
180 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an)
181 
-return(mer)
182 
-}
183 
-
184 
-
185 
-
186 
-
187 
-select_path_repl<-function(Replication_and_repair){
188 
-  species<-c("- Homo sapiens (human)")
189 
-ac<-paste("DNA replication", species)
190 
-ad<-paste("Base excision repair", species)
191 
-ae<-paste("Nucleotide excision repair", species)
192 
-af<-paste("Mismatch repair", species)
193 
-ag<-paste("Homologous recombination", species)
194 
-ah<-paste("Non-homologous end-joining", species)
195 
-ai<-paste("Fanconi anemia pathway", species)
196 
-al<-paste("DNA replication proteins", species)
197 
-am<-paste("Chromosome", species)
198 
-an<-paste("DNA repair and recombination", species)
199 
-ao<-paste("proteins", species)
200 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao)
201 
-return(mer)
202 
-}
203 
-
204 
-
205 
-
206 
-select_path_sign<-function(Signal_transduction){
207 
-  species<-c("- Homo sapiens (human)")
208 
-a<-paste("Ras signaling pathway", species)
209 
-b<-paste("Rap1 signaling pathway", species)
210 
-c<-paste("MAPK signaling pathway", species)
211 
-d<-paste("ErbB signaling pathway", species)
212 
-e<-paste("Wnt signaling pathway", species)
213 
-f<-paste("Notch signaling pathway", species)
214 
-g<-paste("Hedgehog signaling pathway", species)
215 
-h<-paste("TGF-beta signaling pathway", species)
216 
-i<-paste("Hippo signaling pathway", species)
217 
-l<-paste("VEGF signaling pathway", species)
218 
-m<-paste("Jak-STAT signaling pathway", species)
219 
-n<-paste("NF-kappa B signaling pathway", species)
220 
-o<-paste("TNF signaling pathway", species)
221 
-p<-paste("HIF-1 signaling pathway", species)
222 
-q<-paste("FoxO signaling pathway", species)
223 
-r<-paste("Calcium signaling pathway", species)
224 
-s<-paste("Phosphatidylinositol signaling system", species)
225 
-t<-paste("Phospholipase D signaling pathway", species)
226 
-v<-paste("Sphingolipid signaling pathway", species)
227 
-u<-paste("cAMP signaling pathway", species)
228 
-z<-paste("cGMP-PKG signaling pathway", species)
229 
-ab<-paste("PI3K-Akt signaling pathway", species)
230 
-ac<-paste("AMPK signaling pathway", species)
231 
-ad<-paste("mTOR signaling pathway", species)
232 
-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s,t,v,u,z,ab,ac,ad)
233 
-return(mer)
234 
-}
235 
-
236 
-
237 
-select_path_sign_mol<-function(Signaling_molecules_and_interaction){
238 
-  species<-c("- Homo sapiens (human)")
239 
-a<-paste("Neuroactive ligand-receptor interaction", species)
240 
-b<-paste("Cytokine-cytokine receptor interaction", species)
241 
-c<-paste("ECM-receptor interaction", species)
242 
-d<-paste("Cell adhesion molecules (CAMs)", species)
243 
-mer<-c(a,b,c,d)
244 
-return(mer)
245 
-}
246 
-
247 
-
248 
-select_path_transp_ca<-function(Transport_and_catabolism){
249 
-  species<-c("- Homo sapiens (human)")
250 
-a<-paste("Endocytosis", species)
251 
-b<-paste("Phagosome", species)
252 
-c<-paste("Lysosome", species)
253 
-d<-paste("Peroxisome", species)
254 
-e<-paste("Regulation of autophagy", species)
255 
-mer<-c(a,b,c,d,e)
256 
-return(mer)
257 
-}
258 
-
259 
-select_path_cell_grow<-function(Cell_growth_and_death){
260 
-  species<-c("- Homo sapiens (human)")
261 
-  a<-paste("Cell cycle", species)
262 
-b<-paste("Apoptosis", species)
263 
-c<-paste("p53 signaling pathway", species)
264 
-mer<-c(a,b,c)
265 
-return(mer)
266 
-}
267 
-
268 
-
269 
-select_path_cell_comm<-function(Cellular_community){
270 
-  species<-c("- Homo sapiens (human)")
271 
-  a<-paste("Focal adhesion", species)
272 
-b<-paste("Adherens junction", species)
273 
-c<-paste("Tight junction", species)
274 
-d<-paste("Gap junction", species)
275 
-e<-paste("Signaling pathways regulating pluripotency of stem cells ", species)
276 
-mer<-c(a,b,c,d,e)
277 
-return(mer)
278 
-}
279 
-
280 
-
281 
-select_path_imm_syst<-function(Immune_system){
282 
-  species<-c("- Homo sapiens (human)")
283 
-a<-paste("Hematopoietic cell lineage", species)
284 
-b<-paste("Complement and coagulation cascades", species)
285 
-c<-paste("Platelet activation", species)
286 
-d<-paste("Toll-like receptor signaling pathway", species)
287 
-e<-paste("Toll and Imd signaling pathway", species)
288 
-f<-paste("NOD-like receptor signaling pathway", species)
289 
-g<-paste("RIG-I-like receptor signaling pathway", species)
290 
-h<-paste("Cytosolic DNA-sensing pathway", species)
291 
-i<-paste("Natural killer cell mediated cytotoxicity", species)
292 
-l<-paste("Antigen processing and presentation", species)
293 
-m<-paste("T cell receptor signaling pathway", species)
294 
-n<-paste("B cell receptor signaling pathway", species)
295 
-o<-paste("Fc epsilon RI signaling pathway", species)
296 
-p<-paste("Fc gamma R-mediated phagocytosis", species)
297 
-q<-paste("Leukocyte transendothelial migration", species)
298 
-r<-paste("Intestinal immune network for IgA production", species)
299 
-s<-paste("Chemokine signaling pathway", species)
300 
-
301 
-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s)
302 
-return(mer)
1 
+#' @title List of species
2 
+#' @description List of species for network
3 
+#' @param path_spec variable
4 
+#' @importFrom graphite pathwayDatabases
5 
+#' @examples
6 
+#' \dontrun{m<-SELECTpathspecies(path_spec)
7 
+#' }
8 
+SELECTpathspecies<-function(path_spec){
9 
+  e<-pathwayDatabases()
10 
+  return(e)
303 11 
 }
304 12
13 
+Uniform<-function(pathwayfrom){
14 
+  mapped_genes<-pathwayfrom
15 
+  xx <- unique(unlist(mapped_genes))
16 
+  top3 <- matrix(0, length(xx), length(pathwayfrom))
17 
+  rownames(top3) <- xx
18 
+  colnames(top3)<- names(pathwayfrom)
19 
+  for (j in  1:length(xx)){
20 
+    for (k in  1:length(pathwayfrom)){
21 
+      if (length(intersect(xx[[j]],pathwayfrom[[k]])!=0)){
22 
+
23 
+        top3[j,k]<-xx[j]
24 
+      }
25 
+    }
26 
+  }
27 
+  top3[top3 == 0] <- " "
28 
+return(top3)
29 
+  }
305 30
306 31
307 32
308 
-select_path_end_syst<-function(Endocrine_system){
309 
-  species<-c("- Homo sapiens (human)")
310 
-a<-paste("Insulin secretion", species)
311 
-b<-paste("Insulin signaling pathway", species)
312 
-c<-paste("Glucagon signaling pathway", species)
313 
-d<-paste("Regulation of lipolysis in adipocytes", species)
314 
-e<-paste("Adipocytokine signaling pathway", species)
315 
-f<-paste("PPAR signaling pathway", species)
316 
-g<-paste("GnRH signaling pathway", species)
317 
-h<-paste("Ovarian steroidogenesis", species)
318 
-i<-paste("Estrogen signaling pathway", species)
319 
-l<-paste("Progesterone-mediated oocyte maturation", species)
320 
-m<-paste("Prolactin signaling pathway", species)
321 
-n<-paste("Oxytocin signaling pathway", species)
322 
-o<-paste("Thyroid hormone synthesis", species)
323 
-p<-paste("Thyroid hormone signaling pathway", species)
324 
-q<-paste("Melanogenesis", species)
325 
-r<-paste("Renin secretion", species)
326 
-s<-paste("Renin-angiotensin system", species)
327 
-t<-paste("Aldosterone synthesis and secretion", species)
328 
-
329 
-
330 
-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s,t)
331 
-return(mer)
332 
-}
333 33
334 34
335 
-select_path_circ_syst<-function(Circulatory_system){
336 
-  species<-c("- Homo sapiens (human)")
337 
-  a<-paste("Cardiac muscle contraction", species)
338 
-b<-paste("Adrenergic signaling in cardiomyocytes", species)
339 
-c<-paste("Vascular smooth muscle contraction", species)
340 
-mer<-c(a,b,c)
341 
-return(mer)
342 
-}
343 35
344 36
345 
-select_path_dig_syst<-function(Digestive_system){
346 
-  species<-c("- Homo sapiens (human)")
347 
-  a<-paste("Salivary secretion", species)
348 
-b<-paste("Gastric acid secretion", species)
349 
-c<-paste("Pancreatic secretion", species)
350 
-d<-paste("Bile secretion", species)
351 
-e<-paste("Carbohydrate digestion and absorption", species)
352 
-f<-paste("Protein digestion and absorption", species)
353 
-g<-paste("Fat digestion and absorption", species)
354 
-h<-paste("Vitamin digestion and absorption", species)
355 
-i<-paste("Mineral absorption", species)
356 
-
357 
-mer<-c(a,b,c,d,e,f,g,h,i)
358 
-return(mer)
359 
-}
360 37
361 38
362 39
363 
-select_path_exc_syst<-function(Excretory_system){
364 
-  species<-c("- Homo sapiens (human)")
365 
-  a<-paste("Vasopressin-regulated water reabsorption", species)
366 
-b<-paste("Aldosterone-regulated sodium reabsorption", species)
367 
-c<-paste("Endocrine and other factor-regulated calcium reabsorption", species)
368 
-d<-paste("Proximal tubule bicarbonate reclamation", species)
369 
-e<-paste("Collecting duct acid secretion", species)
370 40
371 41
372 
-mer<-c(a,b,c,d,e)
373 
-return(mer)
374 
-}
375 42
376 43
377 
-select_path_ner_syst<-function(Nervous_system){
378 
-  species<-c("- Homo sapiens (human)")
379 
-a<-paste("Glutamatergic synapse", species)
380 
-b<-paste("GABAergic synapse", species)
381 
-c<-paste("Cholinergic synapse", species)
382 
-d<-paste("Dopaminergic synapse", species)
383 
-e<-paste("Serotonergic synapse", species)
384 
-f<-paste("Long-term potentiation", species)
385 
-g<-paste("Long-term depression", species)
386 
-h<-paste("Retrograde endocannabinoid signaling", species)
387 
-i<-paste("Synaptic vesicle cycle", species)
388 
-l<-paste("Neurotrophin signaling pathway", species)
389 
-
390 
-mer<-c(a,b,c,d,e,f,g,h,i,l)
391 
-return(mer)
392 
-}
393 
-
394 
-
395 
-select_path_sens_syst<-function(Sensory_system){
396 
-  species<-c("- Homo sapiens (human)")
397 
-  a<-paste("Phototransduction", species)
398 
-b<-paste("Olfactory transduction", species)
399 
-c<-paste("Taste transduction", species)
400 
-d<-paste("Inflammatory mediator regulation of TRP channels", species)
401 
-mer<-c(a,b,c,d)
402 
-return(mer)
44 
+delete.NULLs  <-  function(xlist){   # delele null/empty entries in a list
45 
+  xlist[unlist(lapply(xlist, nrow) != 0)]
403 46 
 }
404 47
405 48
... ... 
@@ -411,9 +54,9 @@ return(mer)
411 54 
 #' @export
412 55 
 #' @return a gene expression matrix of the samples with specified label
413 56 
 #' @examples
414 
-#' tumo<-SelectedSample(Dataset=Data_CANCER_normUQ_filt,typesample="tumor")[,2]
57 
+#' tumo<-SelectedSample(Dataset=Data_CANCER_normUQ_fil,typesample="tumour")[,2]
415 58 
 SelectedSample <- function(Dataset,typesample){
416 
-  if( typesample =="tumor"){
59 
+  if( typesample =="tumour"){
417 60 
     Dataset <- Dataset[,which( as.numeric(substr(colnames(Dataset), 14, 15)) == 01) ]
418 61 
   }
419 62
... ... 
@@ -427,115 +70,284 @@ SelectedSample <- function(Dataset,typesample){
427 70
428 71
429 72 
 #' @title Select the class of TCGA data
430 
-#' @description select two labels from ID barcode
73 
+#' @description select best performance
74 
+#' @param performance_matrix  list of AUC value
431 75 
 #' @param cutoff cut-off for AUC value
432 
-#' @param auc.df list of AUC value
433 76 
 #' @return a gene expression matrix with only pairwise pathway with a particular cut-off
434 
-select_class<-function(auc.df,cutoff){
435 
-ds<-do.call("rbind", auc.df)
436 
-tmp_ordered <- as.data.frame(ds[order(ds,decreasing=TRUE),])
437 
-colnames(tmp_ordered)<-'pathway'
438 
-er<-as.data.frame(tmp_ordered$pathway>cutoff)
439 
-ase<-tmp_ordered[tmp_ordered$pathway>cutoff,]
440 
-rownames(er)<-rownames(tmp_ordered)
441 
-er[,2]<-tmp_ordered$pathway
442 
-lipid_metabolism<-er[1:length(ase),]
443 
-return(lipid_metabolism)
77 
+select_class<-function(performance_matrix,cutoff){
78 
+  tmp_ordered <- as.data.frame(performance_matrix[order(performance_matrix[,1],decreasing=TRUE),])
79 
+
80 
+  er<-tmp_ordered[tmp_ordered[,1]>cutoff,]
81 
+  #ase<-tmp_ordered[tmp_ordered$pathway>cutoff,]
82 
+  #rownames(er)<-rownames(tmp_ordered)
83 
+  #er[,2]<-tmp_ordered$pathway
84 
+  #lipid_metabolism<-er[1:length(ase),]
85 
+  return(er)
444 86 
 }
445 87
446 88
447 89
448 90
449 
-#' @title Process matrix TCGA data after the selection of pairwise pathway
450 
-#' @description processing gene expression matrix
451 
-#' @param measure matrix with measure of cross-talk among pathways
452 
-#' @param list_perf output of the function select_class
453 
-#' @return a gene expression matrix for case study 1
454 
-process_matrix<-function(measure,list_perf){
455 
-scoreMatrix <- as.data.frame(measure[,3:ncol(measure)])
456 
-for( i in 1: ncol(scoreMatrix)){
457 
-  scoreMatrix[,i] <- as.numeric(as.character(scoreMatrix[,i]))
458 
-}
459 
-measure[,1] <- gsub(" ", "_", measure[,1])
460 
-d<-sub('_-_Homo_sapiens_*', '', measure[,1])
461 
-d_pr<- gsub("(human)", "", d, fixed="TRUE")
462 
-d_pr <- gsub("_", "", d_pr)
463 
-d_pr <- gsub("-", "", d_pr)
464 
-measure[,2] <- gsub(" ", "_", measure[,2])
465 
-d2<-sub('_-_Homo_sapiens_(human)*', '', measure[,2])
466 
-d_pr2<- gsub("(human)", "", d2, fixed="TRUE")
467 
-d_pr2 <- gsub("_", "", d_pr2)
468 
-d_pr2 <- gsub("-", "", d_pr2)
469 
-PathwaysPair <- paste( as.matrix(d_pr), as.matrix(d_pr2),sep="_" )
470 
-rownames(scoreMatrix) <-PathwaysPair
471 
-intera<-intersect(rownames(scoreMatrix),rownames(list_perf))
472 
-path_bestlipd<-scoreMatrix[intera,]
473 
-return(path_bestlipd)
91 
+IPPIpath_net<-function(pathway,data){
92 
+  lista_int<-list()
93 
+  for (k in 1:ncol(pathway)){
94 
+    print(colnames(pathway)[k])
95 
+    currentPathway_genes<-pathway[,k]
96 
+    colnames(data) <- c("gene_symbolA", "gene_symbolB")
97 
+    i <- sapply(data, is.factor)
98 
+    data[i] <- lapply(data[i], as.character)
99 
+    ver<-unlist(data)
100 
+    n<-unique(ver)
101 
+    s<-intersect(n,currentPathway_genes)
102 
+    g <- graph.data.frame(data,directed=FALSE)
103 
+    g2 <- induced.subgraph(graph=g,vids=s)
104 
+    aaa<-get.data.frame(g2)
105 
+    colnames(aaa)[1] <- 'V1'
106 
+    colnames(aaa)[2] <- 'V2'
107 
+    lista_int[[k]]<-aaa
108 
+    names(lista_int)[k]<-colnames(pathway)[k]
109 
+  }
110 
+  return(lista_int)
474 111 
 }
475 112
476 113
477 114
478 
-process_matrix_cell_process<-function(measure_cell_process){
479 
-score__cell_grow_d <- as.data.frame(measure_cell_process[,3:ncol(measure_cell_process)])
480 
-for( i in 1: ncol(score__cell_grow_d)){
481 
-  score__cell_grow_d[,i] <- as.numeric(as.character(score__cell_grow_d[,i]))
482 
-}
483 
-
484 
-measure_cell_process[,1] <- gsub(" ", "_", measure_cell_process[,1])
485 
-d<-sub('_-_Homo_sapiens_*', '', measure_cell_process[,1])
486 
-
487 
-d_pr<- gsub("(human)", "", d, fixed="TRUE")
488 
-d_pr <- gsub("_", "", d_pr)
489 
-d_pr <- gsub("-", "", d_pr)
490 115
491 
-measure_cell_process[,2] <- gsub(" ", "_", measure_cell_process[,2])
492 
-d2<-sub('_-_Homo_sapiens_(human)*', '', measure_cell_process[,2])
493 
-d_pr2<- gsub("(human)", "", d2, fixed="TRUE")
494 
-d_pr2 <- gsub("_", "", d_pr2)
495 
-d_pr2 <- gsub("-", "", d_pr2)
496 116
497 
-PathwaysPair <- paste( as.matrix(d_pr), as.matrix(d_pr2),sep="_" )
498 
-rownames(score__cell_grow_d) <-PathwaysPair
499 
-return(score__cell_grow_d)
500 
-}
117 
+IPPIlist_path_net<-function(lista_net,pathway){
118 
+  v=list()
119 
+  bn=list()
120 
+  for (j in 1:length(lista_net)){
121 
+    cf<-lista_net[[j]]
122 
+    i <- sapply(cf, is.factor)
123 
+    cf[i] <- lapply(cf[i], as.character)
124 
+    colnames(cf) <- c("m_shar_pro", "m2_shar_pro")
125 
+    m<-c(cf$m_shar_pro)
126 
+    m2<-c(cf$m2_shar_pro)
127 
+    s<-c(m,m2)
128 
+    fr<- unique(s)
129 
+    n<-as.data.frame(fr)
130 
+    if(length(n)==0){
131 
+      v[[j]]<-NULL
132 
+
133 
+    }
134 
+    if(length(n)!=0){
135 
+      i <- sapply(n, is.factor)
136 
+      n[i] <- lapply(n[i], as.character)
137 
+      #for (k in  1:ncol(pathway)){
138 
+      if (length(intersect(n$fr,pathway[,j]))==nrow(n)){
139 
+        print(paste("List of genes interacting in the same pathway:",colnames(pathway)[j]))
140 
+        aa<-intersect(n$fr,pathway[,j])
141 
+        v[[j]]<-aa
142 
+        names(v)[j]<-colnames(pathway)[j]
143 
+      }
144 
+    }}
145 
+  return(v)}
501 146
502 147
503 
-#' @title Get human KEGG pathway data.
504 
-#' @description getKEGGdata creates a data frame with human KEGG pathway. Columns are the pathways and rows the genes inside those pathway
505 
-#' @param mer  output for example of select_path_carb
506 
-#' @export
507 
-#' @importFrom KEGGREST keggList
508 
-#' @return dataframe with human pathway data
509 
-proc_path<-function(mer){
510 
-pathways.list <- keggList("pathway", "hsa")## returns the list of human pathways
511 
-common<-intersect(pathways.list,mer)
512 
-lo<-list()
513 
-for (i in 1:length(pathways.list)){
514 
-  if (length(intersect(pathways.list[[i]],common)!=0)){
515 
-    lo[[i]]<-pathways.list[[i]]
516 
-    names(lo)[[i]]<-names(pathways.list)[[i]]
517 
-  }
518 
-}
519 
-pathways.list<-lo[lapply(lo,length)!=0]
520 
-pathway.codes <- sub("path:", "", names(pathways.list))
521 
-b<-do.call("rbind", pathways.list)
522 
-list_pathkegg<-list(pathway.codes,b)
523 
-return(list_pathkegg)
524 
-}
525 148
526 149
527 150
528 
-delete.NULLs  <-  function(xlist){   # delele null/empty entries in a list
529 
-  xlist[unlist(lapply(xlist, nrow) != 0)]
151 
+check_chord <- function(mat, limit){
152 
+
153 
+  if(all(colSums(mat) >= limit[2]) & all(rowSums(mat) >= limit[1])) return(mat)
154 
+
155 
+  tmp <- mat[(rowSums(mat) >= limit[1]),]
156 
+  mat <- tmp[,(colSums(tmp) >= limit[2])]
157 
+
158 
+  mat <- check_chord(mat, limit)
159 
+  return(mat)
530 160 
 }
531 161
162 
+bezier <- function(data, process.col){
163 
+  x <- c()
164 
+  y <- c()
165 
+  Id <- c()
166 
+  sequ <- seq(0, 1, by = 0.01)
167 
+  N <- dim(data)[1]
168 
+  sN <- seq(1, N, by = 2)
169 
+  if (process.col[1] == '') col_rain <- grDevices::rainbow(N) else col_rain <- process.col
170 
+  for (n in sN){
171 
+    xval <- c(); xval2 <- c(); yval <- c(); yval2 <- c()
172 
+    for (t in sequ){
173 
+      xva <- (1 - t) * (1 - t) * data$x.start[n] + t * t * data$x.end[n]
174 
+      xval <- c(xval, xva)
175 
+      xva2 <- (1 - t) * (1 - t) * data$x.start[n + 1] + t * t * data$x.end[n + 1]
176 
+      xval2 <- c(xval2, xva2)
177 
+      yva <- (1 - t) * (1 - t) * data$y.start[n] + t * t * data$y.end[n]
178 
+      yval <- c(yval, yva)
179 
+      yva2 <- (1 - t) * (1 - t) * data$y.start[n + 1] + t * t * data$y.end[n + 1]
180 
+      yval2 <- c(yval2, yva2)
181 
+    }
182 
+    x <- c(x, xval, rev(xval2))
183 
+    y <- c(y, yval, rev(yval2))
184 
+    Id <- c(Id, rep(n, 2 * length(sequ)))
185 
+  }
186 
+  df <- data.frame(lx = x, ly = y, ID = Id)
187 
+  return(df)
188 
+}
532 189
533 190
534 
-
535 
-
536 
-
537 
-
538 
-
539 
-
191 
+#' @name GOChord
192 
+#' @title Displays the relationship between genes and terms.
193 
+#' @description The GOChord function generates a circularly composited overview
194 
+#'   of selected/specific genes and their assigned processes or terms. More
195 
+#'   generally, it joins genes and processes via ribbons in an intersection-like
196 
+#'   graph.
197 
+#' @param data The matrix represents the binary relation (1= is related to, 0=
198 
+#'   is not related to) between a set of genes (rows) and processes (columns); a
199 
+#'   column for the logFC of the genes is optional
200 
+#' @param title The title (on top) of the plot
201 
+#' @param space The space between the chord segments of the plot
202 
+#' @param gene.order A character vector defining the order of the displayed gene
203 
+#'   labels
204 
+#' @param gene.size The size of the gene labels
205 
+#' @param gene.space The space between the gene labels and the segement of the
206 
+#'   logFC
207 
+#' @param nlfc Defines the number of logFC columns (default=1)
208 
+#' @param lfc.col The fill color for the logFC specified in the following form:
209 
+#'   c(color for low values, color for the mid point, color for the high values)
210 
+#' @param lfc.min Specifies the minimium value of the logFC scale (default = -3)
211 
+#' @param lfc.max Specifies the maximum value of the logFC scale (default = 3)
212 
+#' @param ribbon.col The background color of the ribbons
213 
+#' @param border.size Defines the size of the ribbon borders
214 
+#' @param process.label The size of the legend entries
215 
+#' @param limit A vector with two cutoff values (default= c(0,0)).
216 
+#' @import ggplot2
217 
+#' @import grDevices
218 
+GOChord <- function(data, title, space, gene.order, gene.size, gene.space, nlfc = 1, lfc.col, lfc.min, lfc.max, ribbon.col, border.size, process.label, limit){
219 
+  y <- id <- xpro <- ypro <- xgen <- ygen <- lx <- ly <- ID <- logFC <- NULL
220 
+  Ncol <- dim(data)[2]
221 
+
222 
+  if (missing(title)) title <- ''
223 
+  if (missing(space)) space = 0
224 
+  if (missing(gene.order)) gene.order <- 'none'
225 
+  if (missing(gene.size)) gene.size <- 3
226 
+  if (missing(gene.space)) gene.space <- 0.2
227 
+  if (missing(lfc.col)) lfc.col <- c('brown1', 'azure', 'cornflowerblue')
228 
+  if (missing(lfc.min)) lfc.min <- -10
229 
+  if (missing(lfc.max)) lfc.max <- 10
230 
+  if (missing(border.size)) border.size <- 0.5
231 
+  if (missing (process.label)) process.label <- 11
232 
+  if (missing(limit)) limit <- c(0, 0)
233 
+
234 
+  if (gene.order == 'logFC') data <- data[order(data[, Ncol], decreasing = T), ]
235 
+  if (gene.order == 'alphabetical') data <- data[order(rownames(data)), ]
236 
+  if (sum(!is.na(match(colnames(data), 'logFC'))) > 0){
237 
+    if (nlfc == 1){
238 
+      cdata <- check_chord(data[, 1:(Ncol - 1)], limit)
239 
+      lfc <- sapply(rownames(cdata), function(x) data[match(x,rownames(data)), Ncol])
240 
+    }else{
241 
+      cdata <- check_chord(data[, 1:(Ncol - nlfc)], limit)
242 
+      lfc <- sapply(rownames(cdata), function(x) data[, (Ncol - nlfc + 1)])
243 
+    }
244 
+  }else{
245 
+    cdata <- check_chord(data, limit)
246 
+    lfc <- 0
247 
+  }
248 
+  if (missing(ribbon.col)) colRib <- grDevices::rainbow(dim(cdata)[2]) else colRib <- ribbon.col
249 
+  nrib <- colSums(cdata)
250 
+  ngen <- rowSums(cdata)
251 
+  Ncol <- dim(cdata)[2]
252 
+  Nrow <- dim(cdata)[1]
253 
+  colRibb <- c()
254 
+  for (b in 1:length(nrib)) colRibb <- c(colRibb, rep(colRib[b], 202 * nrib[b]))
255 
+  r1 <- 1; r2 <- r1 + 0.1
256 
+  xmax <- c(); x <- 0
257 
+  for (r in 1:length(nrib)){
258 
+    perc <- nrib[r] / sum(nrib)
259 
+    xmax <- c(xmax, (pi * perc) - space)
260 
+    if (length(x) <= Ncol - 1) x <- c(x, x[r] + pi * perc)
261 
+  }
262 
+  xp <- c(); yp <- c()
263 
+  l <- 50
264 
+  for (s in 1:Ncol){
265 
+    xh <- seq(x[s], x[s] + xmax[s], length = l)
266 
+    xp <- c(xp, r1 * sin(x[s]), r1 * sin(xh), r1 * sin(x[s] + xmax[s]), r2 * sin(x[s] + xmax[s]), r2 * sin(rev(xh)), r2 * sin(x[s]))
267 
+    yp <- c(yp, r1 * cos(x[s]), r1 * cos(xh), r1 * cos(x[s] + xmax[s]), r2 * cos(x[s] + xmax[s]), r2 * cos(rev(xh)), r2 * cos(x[s]))
268 
+  }
269 
+  df_process <- data.frame(x = xp, y = yp, id = rep(c(1:Ncol), each = 4 + 2 * l))
270 
+  xp <- c(); yp <- c(); logs <- NULL
271 
+  x2 <- seq(0 - space, -pi - (-pi / Nrow) - space, length = Nrow)
272 
+  xmax2 <- rep(-pi / Nrow + space, length = Nrow)
273 
+  for (s in 1:Nrow){
274 
+    xh <- seq(x2[s], x2[s] + xmax2[s], length = l)
275 
+    if (nlfc <= 1){
276 
+      xp <- c(xp, (r1 + 0.05) * sin(x2[s]), (r1 + 0.05) * sin(xh), (r1 + 0.05) * sin(x2[s] + xmax2[s]), r2 * sin(x2[s] + xmax2[s]), r2 * sin(rev(xh)), r2 * sin(x2[s]))
277 
+      yp <- c(yp, (r1 + 0.05) * cos(x2[s]), (r1 + 0.05) * cos(xh), (r1 + 0.05) * cos(x2[s] + xmax2[s]), r2 * cos(x2[s] + xmax2[s]), r2 * cos(rev(xh)), r2 * cos(x2[s]))
278 
+    }else{
279 
+      tmp <- seq(r1, r2, length = nlfc + 1)
280 
+      for (t in 1:nlfc){
281 
+        logs <- c(logs, data[s, (dim(data)[2] + 1 - t)])
282 
+        xp <- c(xp, (tmp[t]) * sin(x2[s]), (tmp[t]) * sin(xh), (tmp[t]) * sin(x2[s] + xmax2[s]), tmp[t + 1] * sin(x2[s] + xmax2[s]), tmp[t + 1] * sin(rev(xh)), tmp[t + 1] * sin(x2[s]))
283 
+        yp <- c(yp, (tmp[t]) * cos(x2[s]), (tmp[t]) * cos(xh), (tmp[t]) * cos(x2[s] + xmax2[s]), tmp[t + 1] * cos(x2[s] + xmax2[s]), tmp[t + 1] * cos(rev(xh)), tmp[t + 1] * cos(x2[s]))
284 
+      }}}
285 
+  if(lfc[1] != 0){
286 
+    if (nlfc == 1){
287 
+      df_genes <- data.frame(x = xp, y = yp, id = rep(c(1:Nrow), each = 4 + 2 * l), logFC = rep(lfc, each = 4 + 2 * l))
288 
+    }else{
289 
+      df_genes <- data.frame(x = xp, y = yp, id = rep(c(1:(nlfc*Nrow)), each = 4 + 2 * l), logFC = rep(logs, each = 4 + 2 * l))
290 
+    }
291 
+  }else{
292 
+    df_genes <- data.frame(x = xp, y = yp, id = rep(c(1:Nrow), each = 4 + 2 * l))
293 
+  }
294 
+  aseq <- seq(0, 180, length = length(x2)); angle <- c()
295 
+  for (o in aseq) if((o + 270) <= 360) angle <- c(angle, o + 270) else angle <- c(angle, o - 90)
296 
+  df_texg <- data.frame(xgen = (r1 + gene.space) * sin(x2 + xmax2/2),ygen = (r1 + gene.space) * cos(x2 + xmax2 / 2),labels = rownames(cdata), angle = angle)
297 
+  df_texp <- data.frame(xpro = (r1 + 0.15) * sin(x + xmax / 2),ypro = (r1 + 0.15) * cos(x + xmax / 2), labels = colnames(cdata), stringsAsFactors = FALSE)
298 
+  cols <- rep(colRib, each = 4 + 2 * l)
299 
+  x.end <- c(); y.end <- c(); processID <- c()
300 
+  for (gs in 1:length(x2)){
301 
+    val <- seq(x2[gs], x2[gs] + xmax2[gs], length = ngen[gs] + 1)
302 
+    pros <- which((cdata[gs, ] != 0) == T)
303 
+    for (v in 1:(length(val) - 1)){
304 
+      x.end <- c(x.end, sin(val[v]), sin(val[v + 1]))
305 
+      y.end <- c(y.end, cos(val[v]), cos(val[v + 1]))
306 
+      processID <- c(processID, rep(pros[v], 2))
307 
+    }
308 
+  }
309 
+  df_bezier <- data.frame(x.end = x.end, y.end = y.end, processID = processID)
310 
+  df_bezier <- df_bezier[order(df_bezier$processID,-df_bezier$y.end),]
311 
+  x.start <- c(); y.start <- c()
312 
+  for (rs in 1:length(x)){
313 
+    val<-seq(x[rs], x[rs] + xmax[rs], length = nrib[rs] + 1)
314 
+    for (v in 1:(length(val) - 1)){
315 
+      x.start <- c(x.start, sin(val[v]), sin(val[v + 1]))
316 
+      y.start <- c(y.start, cos(val[v]), cos(val[v + 1]))
317 
+    }
318 
+  }
319 
+  df_bezier$x.start <- x.start
320 
+  df_bezier$y.start <- y.start
321 
+  df_path <- bezier(df_bezier, colRib)
322 
+  if(length(df_genes$logFC) != 0){
323 
+    tmp <- sapply(df_genes$logFC, function(x) ifelse(x > lfc.max, lfc.max, x))
324 
+    logFC <- sapply(tmp, function(x) ifelse(x < lfc.min, lfc.min, x))
325 
+    df_genes$logFC <- logFC
326 
+  }
327 
+
328 
+  g<- ggplot() +
329 
+    geom_polygon(data = df_process, aes(x, y, group=id), fill='gray70', inherit.aes = F,color='black') +
330 
+    geom_polygon(data = df_process, aes(x, y, group=id), fill=cols, inherit.aes = F,alpha=0.6,color='black') +
331 
+    geom_point(aes(x = xpro, y = ypro, size = factor(labels, levels = labels), shape = NA), data = df_texp) +
332 
+    guides(size = guide_legend("Pathway", ncol = 4, byrow = T, override.aes = list(shape = 22, fill = unique(cols), size = 8))) +
333 
+    theme(legend.text = element_text(size = process.label)) +
334 
+    geom_text(aes(xgen, ygen, label = labels, angle = angle), data = df_texg, size = gene.size) +
335 
+    geom_polygon(aes(x = lx, y = ly, group = ID), data = df_path, fill = colRibb, color = 'black', size = border.size, inherit.aes = F) +
336 
+    labs(title = title) + theme(axis.line = element_blank(), axis.text.x = element_blank(),
337 
+                                axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(),
338 
+                                axis.title.y = element_blank(), panel.background = element_blank(), panel.border = element_blank(),
339 
+                                panel.grid.major = element_blank(), panel.grid.minor = element_blank(), plot.background = element_blank())
340 
+
341 
+
342 
+  if (nlfc >= 1){
343 
+    g + geom_polygon(data = df_genes, aes(x, y, group = id, fill = logFC), inherit.aes = F, color = 'black') +
344 
+      scale_fill_gradient2('score', space = 'Lab', low = lfc.col[3], mid = lfc.col[2], high = lfc.col[1], guide = guide_colorbar(title.position = "top", title.hjust = 0.5),
345 
+                           breaks = c(min(df_genes$logFC), max(df_genes$logFC)), labels = c(round(min(df_genes$logFC)), round(max(df_genes$logFC)))) +
346 
+      theme(legend.position = 'bottom', legend.background = element_rect(fill = 'transparent'), legend.box = 'horizontal', legend.direction = 'horizontal')
347 
+  }else{
348 
+    g + geom_polygon(data = df_genes, aes(x, y, group = id), fill = 'gray50', inherit.aes = F, color = 'black')+
349 
+      theme(legend.position = 'bottom', legend.background = element_rect(fill = 'transparent'), legend.box = 'horizontal', legend.direction = 'horizontal')
350 
+  }
351 
+}
540 352
541 353
R/path_star.R
History View file @ ecb03af
... ... 
@@ -1,17 +1,28 @@
1 
-#' @title Get human KEGG pathway data and network data in order to define the common gene.
2 
-#' @description path_net creates a list of network data for each human pathway. The network data will be generated when interacting genes belong to that pathway.
3 
-#' @param data  network data as provided by getNETdata
4 
-#' @param pathway  pathway data as provided by getKEGGdata
1 
+#' @title Get human KEGG pathway data and creates a network data.
2 
+#' @description pathnet creates a list of network data for each human pathway. The network data will be generated when interacting genes belong to that pathway.
3 
+#' @param data  a list of network data as provided by getNETdata
4 
+#' @param genes.by.pathway  a list of pathway data as provided by ConvertedIDgenes
5 5 
 #' @importFrom igraph graph.data.frame induced.subgraph get.data.frame
6 6 
 #' @export
7 7 
 #' @return a list of network data for each pathway (interacting genes belong to that pathway)
8 8 
 #' @examples
9 
-#' lista_net<-path_net(pathway=path,data=netw)
10 
-path_net<-function(pathway,data){
9 
+#' lista_net<-pathnet(genes.by.pathway=pathway[1:5],data=netw)
10 
+pathnet<-function(genes.by.pathway,data){
11 
+  geneSymb_net_shar_pro<-data
12 
+  ds_shar_pro<-do.call("rbind", geneSymb_net_shar_pro)
13 
+  data_shar_pro<-as.data.frame(ds_shar_pro[!duplicated(ds_shar_pro), ])
14 
+  sdc_shar_pro<-unlist(data_shar_pro$gene_symbolA,data_shar_pro$gene_symbolB)
15 
+  m_shar_pro<-c(data_shar_pro$gene_symbolA)
16 
+  m2_shar_pro<-c(data_shar_pro$gene_symbolB)
17 
+  ss_shar_pro<-cbind(m_shar_pro,m2_shar_pro)
18 
+  data_pr_shar_pro<-as.data.frame(ss_shar_pro[!duplicated(ss_shar_pro), ])
19 
+  pathwayx<-  Uniform(genes.by.pathway)
20 
+  data<-data_pr_shar_pro
21 
+
11 22 
   lista_int<-list()
12 
-  for (k in 1:ncol(pathway)){
13 
-    print(colnames(pathway)[k])
14 
-    currentPathway_genes<-pathway[,k]
23 
+  for (k in 1:ncol(pathwayx)){
24 
+    print(colnames(pathwayx)[k])
25 
+    currentPathway_genes<-pathwayx[,k]
15 26 
     colnames(data) <- c("gene_symbolA", "gene_symbolB")
16 27 
     i <- sapply(data, is.factor)
17 28 
     data[i] <- lapply(data[i], as.character)
... ... 
@@ -24,7 +35,7 @@ path_net<-function(pathway,data){
24 35 
     colnames(aaa)[1] <- 'V1'
25 36 
     colnames(aaa)[2] <- 'V2'
26 37 
     lista_int[[k]]<-aaa
27 
-    names(lista_int)[k]<-colnames(pathway)[k]
38 
+    names(lista_int)[k]<-colnames(pathwayx)[k]
28 39 
   }
29 40 
   return(lista_int)
30 41 
 }
... ... 
@@ -32,16 +43,18 @@ path_net<-function(pathway,data){
32 43
33 44
34 45
35 
-#' @title Get human KEGG pathway data and output of path_net in order to define the common genes.
36 
-#' @description list_path_net creates a list of interacting genes for each human pathway.
46 
+#' @title Get human KEGG pathway data and the output of list_path_net  define the common genes.
47 
+#' @description listpathnet creates a list of interacting genes for each human pathway.
37 48 
 #' @param lista_net  output of path_net
38 
-#' @param pathway  pathway data as provided by getKEGGdata
49 
+#' @param pathway_exp  pathway data as provided by getKEGGdata
39 50 
 #' @export
40 51 
 #' @return a list of genes for each pathway (interacting genes belong to that pathway)
41 52 
 #' @examples
42 
-#' lista_netw<-path_net(pathway=path,data=netw)
43 
-#' list_path<-list_path_net(lista_net=lista_netw,pathway=path)
44 
-list_path_net<-function(lista_net,pathway){
53 
+#' lista_network<-pathnet(genes.by.pathway=pathway[1:5],data=netw)
54 
+#' list_path<-listpathnet(lista_net=lista_network,pathway=pathway[1:5])
55 
+listpathnet<-function(lista_net,pathway_exp){
56 
+  top3<-  Uniform(pathway_exp)
57 
+  pathway<-top3
45 58 
 v=list()
46 59 
 bn=list()
47 60 
 for (j in 1:length(lista_net)){
... ... 
@@ -56,9 +69,7 @@ for (j in 1:length(lista_net)){
56 69 
   n<-as.data.frame(fr)
57 70 
   if(length(n)==0){
58 71 
     v[[j]]<-NULL
59 
-
60 72 
   }
61 
-  if(length(n)!=0){
62 73 
   i <- sapply(n, is.factor)
63 74 
   n[i] <- lapply(n[i], as.character)
64 75 
   #for (k in  1:ncol(pathway)){
... ... 
@@ -68,31 +79,30 @@ for (j in 1:length(lista_net)){
68 79 
     v[[j]]<-aa
69 80 
     names(v)[j]<-colnames(pathway)[j]
70 81 
   }
71 
-}}
82 
+}
72 83 
 return(v)}
73 84
74 85
75 86
76 87
77 
-#' @title Get human KEGG pathway data and a gene expression matrix in order to obtain a matrix with the gene expression for only pathways given in input .
78 
-#' @description GE_matrix creates a matrix of gene expression for pathways given by the user.
88 
+#' @title Get human KEGG pathway data and a gene expression matrix in order to obtain a list with the gene expression for only pathways given in input .
89 
+#' @description GE_matrix creates a list of gene expression for pathways given by the user.
79 90 
 #' @param DataMatrix  gene expression matrix (eg.TCGA data)
80 
-#' @param pathway  pathway data as provided by getKEGGdata
91 
+#' @param genes.by.pathway a list of pathway data as provided by GetData and ConvertedID_genes
81 92 
 #' @export
82 
-#' @return a matrix for each pathway ( gene expression level belong to that pathway)
93 
+#' @return a list for each pathway ( gene expression level belong to that pathway)
83 94 
 #' @examples
84 
-#' list_path_gene<-GE_matrix(DataMatrix=tumo[,1:2],pathway=path)
85 
-GE_matrix<-function(DataMatrix,pathway) {
86 
-  path_name<-sub(' ', '_',colnames(pathway))
87 
-d_pr<- gsub(" - Homo sapiens (human)", "", path_name, fixed="TRUE")
88 
-colnames(pathway)<-d_pr
89 
-#zz<-as.data.frame(rowMeans(DataMatrix))
95 
+#' list_path_gene<-GE_matrix(DataMatrix=tumo[,1:2],genes.by.pathway=pathway[1:5])
96 
+GE_matrix<-function(DataMatrix,genes.by.pathway) {
97 
+  pathwayfrom<-genes.by.pathway
98 
+  top3<-  Uniform(pathwayfrom)
99 
+  pathway<-top3
90 100 
 zz<-as.data.frame(DataMatrix)
91 101 
 v<-list()
92 102 
 for ( k in 1: ncol(pathway)){
93 103 
   #k=2
94 104 
   if (length(intersect(rownames(zz),pathway[,k])!=0)){
95 
-    print(colnames(path)[k])
105 
+    print(colnames(pathway)[k])
96 106 
   currentPathway_genes_list_common <- intersect(rownames(zz), currentPathway_genes<-pathway[,k])
97 107 
   currentPathway_genes_list_commonMatrix <- as.data.frame(zz[currentPathway_genes_list_common,])
98 108 
   rownames(currentPathway_genes_list_commonMatrix)<-currentPathway_genes_list_common
... ... 
@@ -100,27 +110,23 @@ for ( k in 1: ncol(pathway)){
100 110 
   names(v)[k]<-colnames(pathway)[k]
101 111 
   }
102 112 
 }
103 
-#PEAmatrix <- matrix( 0,nrow(DataMatrix),ncol(pathway))
104 
-#rownames(PEAmatrix) <- as.factor(rownames(DataMatrix))
105 
-#colnames(PEAmatrix) <-  as.factor(colnames(pathway))
106 
-#for (i in 1:length(v)){
107 
-#PEAmatrix[v[[i]],i]<-zz[v[[i]],]
108 
-#}
109 
-#PEAmatrix<-PEAmatrix[which(rowSums(PEAmatrix) > 0),]
110 113 
 return(v)
111 114 
 }
112 115
113 116
114 117
115 118 
 #' @title Get human KEGG pathway data and a gene expression matrix in order to obtain a matrix with the mean gene expression for only pathways given in input .
116 
-#' @description GE_matrix creates a matrix of mean gene expression for pathways given by the user.