1. StarBioTrek
  2. Commit 4b11e5c2fad0121aa75632db83e71a41b083afeb
Browse code

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git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/branches/RELEASE_3_4/madman/Rpacks/StarBioTrek@125023 bc3139a8-67e5-0310-9ffc-ced21a209358

Claudia Cava authored on 12/12/2016 11:51:28
Showing 42 changed files
DESCRIPTION
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-Package: StarBioTrek
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-Type: Package
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-Title: StarBioTrek
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-Version: 1.0.0
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-Date: 10-16-2016
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-Author: Claudia Cava,
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-    Isabella Castiglioni
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-Maintainer: Claudia Cava <claudia.cava@ibfm.cnr.it>
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-Depends:
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-    R (>= 3.3)
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-Imports:
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-    SpidermiR,
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-	KEGGREST,
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-	org.Hs.eg.db,
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-	AnnotationDbi,
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-	e1071,
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-	ROCR,
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-	grDevices
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-Description: This tool StarBioTrek presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network data.
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-License: GPL (>= 3)
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-biocViews: GeneRegulation,
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-    Network,
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-	Pathways,
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-	KEGG
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-Suggests:
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-    BiocStyle,
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-    knitr,
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-    rmarkdown,
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-    testthat,
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-	devtools,
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-	roxygen2,
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-	qgraph,
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-	png,
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-	grid
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-VignetteBuilder: knitr
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-LazyData: true
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-URL: https://github.com/claudiacava/StarBioTrek
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-BugReports: https://github.com/claudiacava/StarBioTrek/issues
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-RoxygenNote: 5.0.1
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NAMESPACE
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-# Generated by roxygen2: do not edit by hand
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-
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-export(GE_matrix)
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-export(SelectedSample)
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-export(average)
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-export(ds_score_crtlk)
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-export(euc_dist_crtlk)
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-export(getKEGGdata)
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-export(getNETdata)
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-export(list_path_net)
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-export(plotting_cross_talk)
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-export(proc_path)
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-export(st_dv)
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-export(svm_classification)
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-importFrom(AnnotationDbi,as.list)
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-importFrom(AnnotationDbi,mappedkeys)
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-importFrom(KEGGREST,keggGet)
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-importFrom(KEGGREST,keggList)
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-importFrom(ROCR,performance)
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-importFrom(ROCR,prediction)
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-importFrom(SpidermiR,SpidermiRdownload_net)
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-importFrom(SpidermiR,SpidermiRquery_spec_networks)
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-importFrom(SpidermiR,SpidermiRquery_species)
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-importFrom(e1071,svm)
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-importFrom(e1071,tune)
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-importFrom(grDevices,rainbow)
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-importFrom(org.Hs.eg.db,org.Hs.egSYMBOL2EG)
NEWS
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-  StarBioTrek
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-  FIRST VERSION - FEATURES
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-
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-* getKEGGdata	Searching by KEGG data.
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-* getNETdata	Searching by network data.
R/StarBioTrek.r
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-#' Download data
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-#'
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-#' StarBioTrek allows you to Download data of samples from StarBioTrek
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-#'
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-#' The functions you're likely to need from \pkg{StarBioTrek} is
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-#' \code{path_star}
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-#'Otherwise refer to the vignettes to see
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-#' how to format the documentation.
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-#'
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-#' @docType package
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-#' @name StarBioTrek
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-NULL
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-
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-#' Pathway data from KEGG
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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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-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 netw
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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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-
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-
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-
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-#' TCGA data
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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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-NULL
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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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-#' @format A data frame with rows and variables
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-NULL
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-
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-#' TCGA data with normal samples
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-#' @docType data
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-#' @keywords internal
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-#' @name norm
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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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-#' TCGA data with tumour samples
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-#' @docType data
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-#' @keywords internal
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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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R/getdata.R
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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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-#' @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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-#' @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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-}
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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_gly(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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-  }
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-  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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-  }
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-  if (KEGG_path=="circ_syst") {
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-    mer<-select_path_circ_syst(Circulatory_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=="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]]
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-  }
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-  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)
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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=="sens_syst") {
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-    mer<-select_path_sens_syst(Sensory_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=="KEGG_path") {
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-  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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-a<-do.call("cbind", pathways.list)
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-}
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-pathway.codes<-c[[1]]
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-genes.by.pathway <- sapply(pathway.codes,
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-                           function(pwid){
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-                             pw <- keggGet(pwid)
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-                             pw[[1]]$GENE[c(TRUE, FALSE)]
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-                           })
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-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)
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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)){
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-
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-      top3[j,k]<-names(xx[j])
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-    }
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-  }
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-}
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-top3[top3 == 0] <- " "
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-#a<-data.frame(pathways.list)
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-#i <- sapply(a, is.factor)
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-#a[i] <- lapply(a[i], as.character)
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-rownames(a)<-sub("path:","",rownames(a))
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-PROVA<-top3
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-for( i in 1:ncol(PROVA)) {
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-  if (colnames(PROVA)[i]==rownames(a)[i]){
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-    colnames(PROVA)[i]<-a[i]
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-}
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-}
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-return(PROVA)
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-}
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-
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-
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-#' @title Get network data.
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-#' @description getNETdata creates a data frame with network data.
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-#' Network category can be filtered among: physical interactions, co-localization, genetic interactions and shared protein domain.
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-#' @param network  variable. The user can use the following parameters
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-#' based on the network types to be used. PHint for Physical_interactions,
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-#' COloc for Co-localization, GENint for Genetic_interactions and
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-#' SHpd for Shared_protein_domains
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-#' @param organism organism==NULL default value is homo sapiens
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-#' @export
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-#' @importFrom SpidermiR SpidermiRquery_species SpidermiRquery_spec_networks SpidermiRdownload_net
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-#' @return dataframe with gene-gene (or protein-protein interactions)
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-#' @examples
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-#' organism="Saccharomyces_cerevisiae"
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-#' netw<-getNETdata(network="SHpd",organism)
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-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)
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-  }
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-  if( !is.null(organism) ){
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-    net_shar_prot<-SpidermiRquery_spec_networks(organismID = org_shar_pro[9,],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[9,],data = out_net_shar_pro)
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-}
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-  #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$gene_symbolB)
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-  #ss_shar_pro<-cbind(m_shar_pro,m2_shar_pro)
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-  #data_pr_shar_pro<-as.data.frame(ss_shar_pro[!duplicated(ss_shar_pro), ])
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-  #colnames(data_pr_shar_pro) <- c("m_shar_pro", "m2_shar_pro")
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-return(out_net_shar_pro)
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-}
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-
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-
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-
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-
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-
R/internal.R
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-#overlap <- function(net_type,x,currentPathway_genes){
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- # de<-net_type[which(net_type$m_shar_pro==x),]
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-#  fr<-intersect(de$m2_shar_pro,currentPathway_genes)
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- # go=list()
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-  #if(length(fr)!=0)    {
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-   # for (i in 1:length(fr)){
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-   #   de2<-de[which(de$m2_shar_pro==fr[i]),]
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-    #  go[[i]]<-de2
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-    #}
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-  #}
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-#  dst<-do.call("rbind", go)
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- # return(dst)
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-#}
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-
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-
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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)
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-c<-paste("Pentose phosphate pathway", species)
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-d<-paste("Pentose and glucuronate interconversions", species)
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-e<-paste("Fructose and mannose metabolism", species)
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-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)
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-q<-paste("Inositol phosphate metabolism", species)
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-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q)
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-return(mer)
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-}
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-
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-select_path_en<-function(Energy){
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-  species<-c("- Homo sapiens (human)")
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-  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)
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-  ab<-paste("Sulfur metabolism", species)
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-  mer<-c(r,s,t,v,u,z,aa,ab)
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-  return(mer)
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-}
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-
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-
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-select_path_lip<-function(Lipid){
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-  species<-c("- Homo sapiens (human)")
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-ac<-paste("Fatty acid biosynthesis", species)
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-ad<-paste("Fatty acid elongation", species)
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-ae<-paste("Fatty acid degradation", species)
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-af<-paste("Synthesis and degradation of ketone bodies", species)
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-ag<-paste("Cutin, suberine and wax biosynthesis", species)
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-ah<-paste("Steroid biosynthesis", species)
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-ai<-paste("Primary bile acid biosynthesis", species)
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-al<-paste("Secondary bile acid biosynthesis", species)
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-am<-paste("Steroid hormone biosynthesis", species)
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-an<-paste("Glycerolipid metabolism", species)
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-ao<-paste("Glycerophospholipid metabolism", species)
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-ap<-paste("Ether lipid metabolism", species)
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-aq<-paste("Sphingolipid metabolism", species)
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-ar<-paste("Arachidonic acid metabolism", species)
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-as<-paste("Linoleic acid metabolism", species)
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-at<-paste("alpha-Linolenic acid metabolism", species)
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-av<-paste("Biosynthesis of unsaturated fatty acids", species)
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-
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-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap,aq,ar,as,at,av)
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-return(mer)
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-}
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-
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-
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-
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-
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-select_path_amn<-function(Aminoacid){
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-  species<-c("- Homo sapiens (human)")
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-ac<-paste("Alanine, aspartate and glutamate metabolism", species)
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-ad<-paste("Glycine, serine and threonine metabolism", species)
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-ae<-paste("Cysteine and methionine metabolism", species)
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-af<-paste("Valine, leucine and isoleucine degradation", species)
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-ag<-paste("Valine, leucine and isoleucine biosynthesis", species)
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-ah<-paste("Lysine biosynthesis", species)
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-ai<-paste("Lysine degradation", species)
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-al<-paste("Arginine biosynthesis", species)
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-am<-paste("Arginine and proline metabolism", species)
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-an<-paste("Histidine metabolism", species)
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-ao<-paste("Tyrosine metabolism", species)
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-ap<-paste("Phenylalanine metabolism", species)
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-aq<-paste("Tryptophan metabolism", species)
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-ar<-paste("Phenylalanine, tyrosine and tryptophan biosynthesis", species)
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-as<-paste("beta-Alanine metabolism", species)
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-at<-paste("Taurine and hypotaurine metabolism", species)
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-av<-paste("Phosphonate and phosphinate metabolism", species)
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-au<-paste("Selenocompound metabolism", species)
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-az<-paste("Cyanoamino acid metabolism", species)
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-a<-paste("D-Glutamine and D-glutamate metabolism", species)
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-b<-paste("D-Arginine and D-ornithine metabolism", species)
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-c<-paste("D-Alanine metabolism", species)
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-d<-paste("Glutathione metabolism", species)
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-
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-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)
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-return(mer)
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-}
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-
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-select_path_gly<-function(Glybio_met){
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-ac<-paste("N-Glycan biosynthesis", species)
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-ad<-paste("Various types of N-glycan biosynthesis", species)
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-ae<-paste("Mucin type O-Glycan biosynthesis", species)
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-af<-paste("Other types of O-glycan biosynthesis", species)
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-ag<-paste("Glycosaminoglycan biosynthesis - CS/DS", species)
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-ah<-paste("Glycosaminoglycan biosynthesis - HS/Hep", species)
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-ai<-paste("Glycosaminoglycan biosynthesis - KS", species)
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-al<-paste("Glycosaminoglycan degradation", species)
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-am<-paste("Glycosylphosphatidylinositol(GPI)-anchor biosynthesis", species)
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-an<-paste("Glycosphingolipid biosynthesis - lacto and neolacto series", species)
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-ao<-paste("Glycosphingolipid biosynthesis - globo series", species)
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-ap<-paste("Glycosphingolipid biosynthesis - ganglio series", species)
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-aq<-paste("Lipopolysaccharide biosynthesis", species)
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-ar<-paste("Peptidoglycan biosynthesis", species)
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-as<-paste("Other glycan degradation", species)
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-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap,aq,ar,as)
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-return(mer)
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-}
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-
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-
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-
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-select_path_cofa<-function(Cofa_vita_met){
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-  species<-c("- Homo sapiens (human)")
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-ac<-paste("Thiamine metabolism", species)
134 
-ad<-paste("Riboflavin metabolism", species)
135 
-ae<-paste("Vitamin B6 metabolism", species)
136 
-af<-paste("Nicotinate and nicotinamide metabolism", species)
137 
-ag<-paste("Pantothenate and CoA biosynthesis", species)
138 
-ah<-paste("Biotin metabolism", species)
139 
-ai<-paste("Lipoic acid metabolism", species)
140 
-al<-paste("Folate biosynthesis", species)
141 
-am<-paste("One carbon pool by folate", species)
142 
-an<-paste("Retinol metabolism", species)
143 
-ao<-paste("Porphyrin and chlorophyll metabolism", species)
144 
-ap<-paste("Ubiquinone and other terpenoid-quinone biosynthesis", species)
145 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap)
146 
-return(mer)
147 
-}
148 
-
149 
-select_path_transc<-function(Transcription){
150 
-  species<-c("- Homo sapiens (human)")
151 
-ac<-paste("RNA polymerase", species)
152 
-ad<-paste("Basal transcription factors", species)
153 
-ae<-paste("Spliceosome", species)
154 
-af<-paste("Transcription factors", species)
155 
-ag<-paste("Transcription machinery", species)
156 
-mer<-c(ac,ad,ae,af,ag)
157 
-return(mer)
158 
-}
159 
-
160 
-
161 
-
162 
-select_path_transl<-function(Translation){
163 
-  species<-c("- Homo sapiens (human)")
164 
-ac<-paste("Ribosome", species)
165 
-ad<-paste("Aminoacyl-tRNA biosynthesis", species)
166 
-ae<-paste("RNA transport", species)
167 
-af<-paste("mRNA surveillance pathway", species)
168 
-ag<-paste("Ribosome biogenesis in eukaryotes", species)
169 
-ah<-paste("Ribosomal proteins", species)
170 
-ai<-paste("Ribosome biogenesis", species)
171 
-al<-paste("Transfer RNA biogenesis", species)
172 
-am<-paste("Translation factors", species)
173 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am)
174 
-return(mer)
175 
-}
176 
-
177 
-select_path_fold<-function(Folding_sorting_and_degradation){
178 
-  species<-c("- Homo sapiens (human)")
179 
-ac<-paste("Protein export", species)
180 
-ad<-paste("Protein processing in endoplasmic reticulum", species)
181 
-ae<-paste("SNARE interactions in vesicular transport", species)
182 
-af<-paste("Ubiquitin mediated proteolysis", species)
183 
-ag<-paste("Sulfur relay system", species)
184 
-ah<-paste("RNA degradation", species)
185 
-ai<-paste("Chaperones and folding catalysts", species)
186 
-al<-paste("SNAREs", species)
187 
-am<-paste("Ubiquitin system", species)
188 
-an<-paste("Proteasome", species)
189 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an)
190 
-return(mer)
191 
-}
192 
-
193 
-
194 
-
195 
-
196 
-select_path_repl<-function(Replication_and_repair){
197 
-  species<-c("- Homo sapiens (human)")
198 
-ac<-paste("DNA replication", species)
199 
-ad<-paste("Base excision repair", species)
200 
-ae<-paste("Nucleotide excision repair", species)
201 
-af<-paste("Mismatch repair", species)
202 
-ag<-paste("Homologous recombination", species)
203 
-ah<-paste("Non-homologous end-joining", species)
204 
-ai<-paste("Fanconi anemia pathway", species)
205 
-al<-paste("DNA replication proteins", species)
206 
-am<-paste("Chromosome", species)
207 
-an<-paste("DNA repair and recombination", species)
208 
-ao<-paste("proteins", species)
209 
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao)
210 
-return(mer)
211 
-}
212 
-
213 
-
214 
-
215 
-select_path_sign<-function(Signal_transduction){
216 
-  species<-c("- Homo sapiens (human)")
217 
-a<-paste("Ras signaling pathway", species)
218 
-b<-paste("Rap1 signaling pathway", species)
219 
-c<-paste("MAPK signaling pathway", species)
220 
-d<-paste("ErbB signaling pathway", species)
221 
-e<-paste("Wnt signaling pathway", species)
222 
-f<-paste("Notch signaling pathway", species)
223 
-g<-paste("Hedgehog signaling pathway", species)
224 
-h<-paste("TGF-beta signaling pathway", species)
225 
-i<-paste("Hippo signaling pathway", species)
226 
-l<-paste("VEGF signaling pathway", species)
227 
-m<-paste("Jak-STAT signaling pathway", species)
228 
-n<-paste("NF-kappa B signaling pathway", species)
229 
-o<-paste("TNF signaling pathway", species)
230 
-p<-paste("HIF-1 signaling pathway", species)
231 
-q<-paste("FoxO signaling pathway", species)
232 
-r<-paste("Calcium signaling pathway", species)
233 
-s<-paste("Phosphatidylinositol signaling system", species)
234 
-t<-paste("Phospholipase D signaling pathway", species)
235 
-v<-paste("Sphingolipid signaling pathway", species)
236 
-u<-paste("cAMP signaling pathway", species)
237 
-z<-paste("cGMP-PKG signaling pathway", species)
238 
-ab<-paste("PI3K-Akt signaling pathway", species)
239 
-ac<-paste("AMPK signaling pathway", species)
240 
-ad<-paste("mTOR signaling pathway", species)
241 
-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)
242 
-return(mer)
243 
-}
244 
-
245 
-
246 
-select_path_sign_mol<-function(Signaling_molecules_and_interaction){
247 
-  species<-c("- Homo sapiens (human)")
248 
-a<-paste("Neuroactive ligand-receptor interaction", species)
249 
-b<-paste("Cytokine-cytokine receptor interaction", species)
250 
-c<-paste("ECM-receptor interaction", species)
251 
-d<-paste("Cell adhesion molecules (CAMs)", species)
252 
-mer<-c(a,b,c,d)
253 
-return(mer)
254 
-}
255 
-
256 
-
257 
-select_path_transp_ca<-function(Transport_and_catabolism){
258 
-  species<-c("- Homo sapiens (human)")
259 
-a<-paste("Endocytosis", species)
260 
-b<-paste("Phagosome", species)
261 
-c<-paste("Lysosome", species)
262 
-d<-paste("Peroxisome", species)
263 
-e<-paste("Regulation of autophagy", species)
264 
-mer<-c(a,b,c,d,e)
265 
-return(mer)
266 
-}
267 
-
268 
-select_path_cell_grow<-function(Cell_growth_and_death){
269 
-  species<-c("- Homo sapiens (human)")
270 
-  a<-paste("Cell cycle", species)
271 
-b<-paste("Apoptosis", species)
272 
-c<-paste("p53 signaling pathway", species)
273 
-mer<-c(a,b,c)
274 
-return(mer)
275 
-}
276 
-
277 
-
278 
-select_path_cell_comm<-function(Cellular_community){
279 
-  species<-c("- Homo sapiens (human)")
280 
-  a<-paste("Focal adhesion", species)
281 
-b<-paste("Adherens junction", species)
282 
-c<-paste("Tight junction", species)
283 
-d<-paste("Gap junction", species)
284 
-e<-paste("Signaling pathways regulating pluripotency of stem cells ", species)
285 
-mer<-c(a,b,c,d,e)
286 
-return(mer)
287 
-}
288 
-
289 
-
290 
-select_path_imm_syst<-function(Immune_system){
291 
-  species<-c("- Homo sapiens (human)")
292 
-a<-paste("Hematopoietic cell lineage", species)
293 
-b<-paste("Complement and coagulation cascades", species)
294 
-c<-paste("Platelet activation", species)
295 
-d<-paste("Toll-like receptor signaling pathway", species)
296 
-e<-paste("Toll and Imd signaling pathway", species)
297 
-f<-paste("NOD-like receptor signaling pathway", species)
298 
-g<-paste("RIG-I-like receptor signaling pathway", species)
299 
-h<-paste("Cytosolic DNA-sensing pathway", species)
300 
-i<-paste("Natural killer cell mediated cytotoxicity", species)
301 
-l<-paste("Antigen processing and presentation", species)
302 
-m<-paste("T cell receptor signaling pathway", species)
303 
-n<-paste("B cell receptor signaling pathway", species)
304 
-o<-paste("Fc epsilon RI signaling pathway", species)
305 
-p<-paste("Fc gamma R-mediated phagocytosis", species)
306 
-q<-paste("Leukocyte transendothelial migration", species)
307 
-r<-paste("Intestinal immune network for IgA production", species)
308 
-s<-paste("Chemokine signaling pathway", species)
309 
-
310 
-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s)
311 
-return(mer)
312 
-}
313 
-
314 
-
315 
-
316 
-
317 
-select_path_end_syst<-function(Endocrine_system){
318 
-  species<-c("- Homo sapiens (human)")
319 
-a<-paste("Insulin secretion", species)
320 
-b<-paste("Insulin signaling pathway", species)
321 
-c<-paste("Glucagon signaling pathway", species)
322 
-d<-paste("Regulation of lipolysis in adipocytes", species)
323 
-e<-paste("Adipocytokine signaling pathway", species)
324 
-f<-paste("PPAR signaling pathway", species)
325 
-g<-paste("GnRH signaling pathway", species)
326 
-h<-paste("Ovarian steroidogenesis", species)
327 
-i<-paste("Estrogen signaling pathway", species)
328 
-l<-paste("Progesterone-mediated oocyte maturation", species)
329 
-m<-paste("Prolactin signaling pathway", species)
330 
-n<-paste("Oxytocin signaling pathway", species)
331 
-o<-paste("Thyroid hormone synthesis", species)
332 
-p<-paste("Thyroid hormone signaling pathway", species)
333 
-q<-paste("Melanogenesis", species)
334 
-r<-paste("Renin secretion", species)
335 
-s<-paste("Renin-angiotensin system", species)
336 
-t<-paste("Aldosterone synthesis and secretion", species)
337 
-
338 
-
339 
-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s,t)
340 
-return(mer)
341 
-}
342 
-
343 
-
344 
-select_path_circ_syst<-function(Circulatory_system){
345 
-  species<-c("- Homo sapiens (human)")
346 
-  a<-paste("Cardiac muscle contraction", species)
347 
-b<-paste("Adrenergic signaling in cardiomyocytes", species)
348 
-c<-paste("Vascular smooth muscle contraction", species)
349 
-mer<-c(a,b,c)
350 
-return(mer)
351 
-}
352 
-
353 
-
354 
-select_path_dig_syst<-function(Digestive_system){
355 
-  species<-c("- Homo sapiens (human)")
356 
-  a<-paste("Salivary secretion", species)
357 
-b<-paste("Gastric acid secretion", species)
358 
-c<-paste("Pancreatic secretion", species)
359 
-d<-paste("Bile secretion", species)
360 
-e<-paste("Carbohydrate digestion and absorption", species)
361 
-f<-paste("Protein digestion and absorption", species)
362 
-g<-paste("Fat digestion and absorption", species)
363 
-h<-paste("Vitamin digestion and absorption", species)
364 
-i<-paste("Mineral absorption", species)
365 
-
366 
-mer<-c(a,b,c,d,e,f,g,h,i)
367 
-return(mer)
368 
-}
369 
-
370 
-
371 
-
372 
-select_path_exc_syst<-function(Excretory_system){
373 
-  species<-c("- Homo sapiens (human)")
374 
-  a<-paste("Vasopressin-regulated water reabsorption", species)
375 
-b<-paste("Aldosterone-regulated sodium reabsorption", species)
376 
-c<-paste("Endocrine and other factor-regulated calcium reabsorption", species)
377 
-d<-paste("Proximal tubule bicarbonate reclamation", species)
378 
-e<-paste("Collecting duct acid secretion", species)
379 
-
380 
-
381 
-mer<-c(a,b,c,d,e)
382 
-return(mer)
383 
-}
384 
-
385 
-
386 
-select_path_ner_syst<-function(Nervous_system){
387 
-  species<-c("- Homo sapiens (human)")
388 
-a<-paste("Glutamatergic synapse", species)
389 
-b<-paste("GABAergic synapse", species)
390 
-c<-paste("Cholinergic synapse", species)
391 
-d<-paste("Dopaminergic synapse", species)
392 
-e<-paste("Serotonergic synapse", species)
393 
-f<-paste("Long-term potentiation", species)
394 
-g<-paste("Long-term depression", species)
395 
-h<-paste("Retrograde endocannabinoid signaling", species)
396 
-i<-paste("Synaptic vesicle cycle", species)
397 
-l<-paste("Neurotrophin signaling pathway", species)
398 
-
399 
-mer<-c(a,b,c,d,e,f,g,h,i,l)
400 
-return(mer)
401 
-}
402 
-
403 
-
404 
-select_path_sens_syst<-function(Sensory_system){
405 
-  species<-c("- Homo sapiens (human)")
406 
-  a<-paste("Phototransduction", species)
407 
-b<-paste("Olfactory transduction", species)
408 
-c<-paste("Taste transduction", species)
409 
-d<-paste("Inflammatory mediator regulation of TRP channels", species)
410 
-mer<-c(a,b,c,d)
411 
-return(mer)
412 
-}
413 
-
414 
-
415 
-
416 
-#' @title Select the class of TCGA data
417 
-#' @description select two labels from ID barcode
418 
-#' @param Dataset gene expression matrix
419 
-#' @param typesample the labels of the samples (e.g. tumor,normal)
420 
-#' @export
421 
-#' @return a gene expression matrix of the samples with specified label
422 
-#' @examples
423 
-#' tumo<-SelectedSample(Dataset=Data_CANCER_normUQ_filt,typesample="tumor")[,2]
424 
-SelectedSample <- function(Dataset,typesample){
425 
-  if( typesample =="tumor"){
426 
-    Dataset <- Dataset[,which( as.numeric(substr(colnames(Dataset), 14, 15)) == 01) ]
427 
-  }
428 
-
429 
-  if( typesample =="normal"){
430 
-    Dataset <- Dataset[,which( as.numeric(substr(colnames(Dataset), 14, 15)) >= 10) ]
431 
-  }
432 
-
433 
-  return(Dataset)
434 
-
435 
-}
436 
-
437 
-
438 
-#' @title Select the class of TCGA data
439 
-#' @description select two labels from ID barcode
440 
-#' @param cutoff cut-off for AUC value
441 
-#' @param auc.df list of AUC value
442 
-#' @return a gene expression matrix with only pairwise pathway with a particular cut-off
443 
-select_class<-function(auc.df,cutoff){
444 
-ds<-do.call("rbind", auc.df)
445 
-tmp_ordered <- as.data.frame(ds[order(ds,decreasing=TRUE),])
446 
-colnames(tmp_ordered)<-'pathway'
447 
-er<-as.data.frame(tmp_ordered$pathway>cutoff)
448 
-ase<-tmp_ordered[tmp_ordered$pathway>cutoff,]
449 
-rownames(er)<-rownames(tmp_ordered)
450 
-er[,2]<-tmp_ordered$pathway
451 
-lipid_metabolism<-er[1:length(ase),]
452 
-return(lipid_metabolism)
453 
-}
454 
-
455 
-
456 
-
457 
-
458 
-#' @title Process matrix TCGA data after the selection of pairwise pathway
459 
-#' @description processing gene expression matrix
460 
-#' @param measure matrix with measure of cross-talk among pathways
461 
-#' @param list_perf output of the function select_class
462 
-#' @return a gene expression matrix for case study 1
463 
-process_matrix<-function(measure,list_perf){
464 
-scoreMatrix <- as.data.frame(measure[,3:ncol(measure)])
465 
-for( i in 1: ncol(scoreMatrix)){
466 
-  scoreMatrix[,i] <- as.numeric(as.character(scoreMatrix[,i]))
467 
-}
468 
-measure[,1] <- gsub(" ", "_", measure[,1])
469 
-d<-sub('_-_Homo_sapiens_*', '', measure[,1])
470 
-d_pr<- gsub("(human)", "", d, fixed="TRUE")
471 
-d_pr <- gsub("_", "", d_pr)
472 
-d_pr <- gsub("-", "", d_pr)
473 
-measure[,2] <- gsub(" ", "_", measure[,2])
474 
-d2<-sub('_-_Homo_sapiens_(human)*', '', measure[,2])
475 
-d_pr2<- gsub("(human)", "", d2, fixed="TRUE")
476 
-d_pr2 <- gsub("_", "", d_pr2)
477 
-d_pr2 <- gsub("-", "", d_pr2)
478 
-PathwaysPair <- paste( as.matrix(d_pr), as.matrix(d_pr2),sep="_" )
479 
-rownames(scoreMatrix) <-PathwaysPair
480 
-intera<-intersect(rownames(scoreMatrix),rownames(list_perf))
481 
-path_bestlipd<-scoreMatrix[intera,]
482 
-return(path_bestlipd)
483 
-}
484 
-
485 
-
486 
-
487 
-process_matrix_cell_process<-function(measure_cell_process){
488 
-score__cell_grow_d <- as.data.frame(measure_cell_process[,3:ncol(measure_cell_process)])
489 
-for( i in 1: ncol(score__cell_grow_d)){
490 
-  score__cell_grow_d[,i] <- as.numeric(as.character(score__cell_grow_d[,i]))
491 
-}
492 
-
493 
-measure_cell_process[,1] <- gsub(" ", "_", measure_cell_process[,1])
494 
-d<-sub('_-_Homo_sapiens_*', '', measure_cell_process[,1])
495 
-
496 
-d_pr<- gsub("(human)", "", d, fixed="TRUE")
497 
-d_pr <- gsub("_", "", d_pr)
498 
-d_pr <- gsub("-", "", d_pr)
499 
-
500 
-measure_cell_process[,2] <- gsub(" ", "_", measure_cell_process[,2])
501 
-d2<-sub('_-_Homo_sapiens_(human)*', '', measure_cell_process[,2])
502 
-d_pr2<- gsub("(human)", "", d2, fixed="TRUE")
503 
-d_pr2 <- gsub("_", "", d_pr2)
504 
-d_pr2 <- gsub("-", "", d_pr2)
505 
-
506 
-PathwaysPair <- paste( as.matrix(d_pr), as.matrix(d_pr2),sep="_" )
507 
-rownames(score__cell_grow_d) <-PathwaysPair
508 
-return(score__cell_grow_d)
509 
-}
510 
-
511 
-
512 
-#' @title Get human KEGG pathway data.
513 
-#' @description getKEGGdata creates a data frame with human KEGG pathway. Columns are the pathways and rows the genes inside those pathway
514 
-#' @param mer  output for example of select_path_carb
515 
-#' @export
516 
-#' @importFrom KEGGREST keggList
517 
-#' @return dataframe with human pathway data
518 
-proc_path<-function(mer){
519 
-pathways.list <- keggList("pathway", "hsa")## returns the list of human pathways
520 
-common<-intersect(pathways.list,mer)
521 
-lo<-list()
522 
-for (i in 1:length(pathways.list)){
523 
-  if (length(intersect(pathways.list[[i]],common)!=0)){
524 
-    lo[[i]]<-pathways.list[[i]]
525 
-    names(lo)[[i]]<-names(pathways.list)[[i]]
526 
-  }
527 
-}
528 
-
529 
-pathways.list<-lo[lapply(lo,length)!=0]
530 
-pathway.codes <- sub("path:", "", names(pathways.list))
531 
-b<-do.call("rbind", pathways.list)
532 
-list_pathkegg<-list(pathway.codes,b)
533 
-return(list_pathkegg)
534 
-}
R/path_star.R
History View file @ 4b11e5c
535 0 
deleted file mode 100644
... ... 
@@ -1,405 +0,0 @@
1 
-#' @title Get human KEGG pathway data and network data in order to define the common gene.
2 
-#' @description list_path_net creates a list of interacting genes for each human pathway.
3 
-#' @param net_type  network data as provided by getNETdata
4 
-#' @param pathway  pathway data as provided by getKEGGdata
5 
-#' @export
6 
-#' @return a list of genes for each pathway (interacting genes belong to that pathway)
7 
-#' @examples
8 
-#' list_path<-list_path_net(net_type=netw,pathway=path)
9 
-list_path_net<-function(net_type,pathway){
10 
-  i <- sapply(net_type, is.factor)
11 
-  net_type[i] <- lapply(net_type[i], as.character)
12 
-  m<-c(net_type$m_shar_pro)
13 
-  m2<-c(net_type$m2_shar_pro)
14 
-  s<-c(m,m2)
15 
-  fr<- unique(s)
16 
-  n<-as.data.frame(fr)
17 
-  i <- sapply(n, is.factor)
18 
-  n[i] <- lapply(n[i], as.character)
19 
-  v=list()
20 
-    for (k in  1:ncol(pathway)){
21 
-      if (length(intersect(n$fr,pathway[,k])!=0)){
22 
-        print(colnames(pathway)[k])
23 
-        aa<-intersect(n$fr,pathway[,k])
24 
-        v[[k]]<-aa
25 
-        names(v)[k]<-colnames(pathway)[k]
26 
-      }
27 
-    }
28 
-
29 
-  return(v)
30 
-}
31 
-
32 
-
33 
-#' @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 .
34 
-#' @description GE_matrix creates a matrix of gene expression for pathways given by the user.
35 
-#' @param DataMatrix  gene expression matrix (eg.TCGA data)
36 
-#' @param pathway  pathway data as provided by getKEGGdata
37 
-#' @export
38 
-#' @return a matrix for each pathway ( gene expression level belong to that pathway)
39 
-#' @examples
40 
-#' list_path_plot<-GE_matrix(DataMatrix=tumo[,1:2],pathway=path)
41 
-GE_matrix<-function(DataMatrix,pathway) {
42 
-  path_name<-sub(' ', '_',colnames(pathway))
43 
-d_pr<- gsub(" - Homo sapiens (human)", "", path_name, fixed="TRUE")
44 
-colnames(pathway)<-d_pr
45 
-zz<-as.data.frame(rowMeans(DataMatrix))
46 
-v<-list()
47 
-for ( k in 1: ncol(pathway)){
48 
-  #k=2
49 
-  if (length(intersect(rownames(zz),pathway[,k])!=0)){
50 
-    print(colnames(path)[k])
51 
-  currentPathway_genes_list_common <- intersect(rownames(zz), currentPathway_genes<-pathway[,k])
52 
-  currentPathway_genes_list_commonMatrix <- as.data.frame(zz[currentPathway_genes_list_common,])
53 
-  rownames(currentPathway_genes_list_commonMatrix)<-currentPathway_genes_list_common
54 
-  v[[k]]<- currentPathway_genes_list_common
55 
-  names(v)[k]<-colnames(pathway)[k]
56 
-  }
57 
-}
58 
-PEAmatrix <- matrix( 0,nrow(DataMatrix),ncol(pathway))
59 
-rownames(PEAmatrix) <- as.factor(rownames(DataMatrix))
60 
-colnames(PEAmatrix) <-  as.factor(colnames(pathway))
61 
-for (i in 1:length(v)){
62 
-PEAmatrix[v[[i]],i]<-zz[v[[i]],]
63 
-}
64 
-PEAmatrix<-PEAmatrix[which(rowSums(PEAmatrix) > 0),]
65 
-return(PEAmatrix)
66 
-}
67 
-
68 
-
69 
-#' @title Get human KEGG pathway data and a gene expression matrix we obtain a matrix with the gene expression for only pathways given in input .
70 
-#' @description plotting_matrix creates a matrix of gene expression for pathways given by the user.
71 
-#' @param DataMatrix  gene expression matrix (eg.TCGA data)
72 
-#' @param pathway  pathway data as provided by getKEGGdata
73 
-#' @param path_matrix  output of the function GE_matrix
74 
-#' @export
75 
-#' @return a plot for pathway cross talk
76 
-#' @examples
77 
-#' mt<-plotting_cross_talk(DataMatrix=tumo[,1:2],pathway=path,path_matrix=list_path_plot)
78 
-plotting_cross_talk<-function(DataMatrix,pathway,path_matrix){
79 
-  zz<-as.data.frame(rowMeans(DataMatrix))
80 
-  v<-list()
81 
-  for ( k in 1: ncol(pathway)){
82 
-    path_name<-sub(' ', '_',colnames(pathway))
83 
-    d_pr<- gsub(" - Homo sapiens (human)", "", path_name, fixed="TRUE")
84 
-    colnames(pathway)<-d_pr
85 
-    if (length(intersect(rownames(zz),pathway[,k])!=0)){
86 
-      print(colnames(path)[k])
87 
-      currentPathway_genes_list_common <- intersect(rownames(zz), currentPathway_genes<-pathway[,k])
88 
-      currentPathway_genes_list_commonMatrix <- as.data.frame(zz[currentPathway_genes_list_common,])
89 
-      rownames(currentPathway_genes_list_commonMatrix)<-currentPathway_genes_list_common
90 
-      v[[k]]<- as.factor(currentPathway_genes_list_common)
91 
-      names(v)[k]<-colnames(pathway)[k]
92 
-    }
93 
-  }
94 
-  vv<-list()
95 
-  dc<-cor(t(path_matrix))
96 
-  for ( k in 1: length(v)){
97 
-    currentPathway_genes_list_common <- intersect(rownames(dc), v[[k]])
98 
-    a<-match(currentPathway_genes_list_common,rownames(dc))
99 
-    vv[[k]]<- a
100 
-    names(vv)[k]<-colnames(pathway)[k]
101 
-  }
102 
-  list_plt=list(corr=dc,gruppi=vv)
103 
- #r<-qgraph(list_plt$corr, groups=list_plt$gruppi, mar=c(1,1,1,1),minimum=0.6)
104 
-  return(list_plt)
105 
-}
106 
-
107 
-
108 
-
109 
-
110 
-#' @title For TCGA data get human pathway data and creates a matrix with the average of genes for each pathway.
111 
-#' @description average creates a matrix with a summarized value for each pathway
112 
-#' @param dataFilt TCGA matrix
113 
-#' @param pathway pathway data
114 
-#' @export
115 
-#' @return a matrix value for each pathway
116 
-#' @examples
117 
-#' score_mean<-average(dataFilt=tumo[,1:2],path)
118 
-average<-function(dataFilt,pathway){
119 
-  DataMatrix<-dataFilt
120 
-  #dataFilt[ , "new.col"] <- gsub("\\|.*", "", rownames(dataFilt))
121 
-  #DataMatrix<-dataFilt[which(dataFilt$new.col!="?"),]
122 
-  #DataMatrix <- subset(DataMatrix, !duplicated(DataMatrix$new.col))
123 
-  #rownames(DataMatrix)<-DataMatrix$new.col
124 
-  #DataMatrix$new.col<-NULL
125 
-
126 
-PEAmatrix <- matrix( 0, ncol(pathway),ncol(DataMatrix))
127 
-rownames(PEAmatrix) <- colnames(pathway)
128 
-colnames(PEAmatrix) <-  colnames(DataMatrix)
129 
-listIPA_pathways<-colnames(pathway)
130 
-for ( k in 1: nrow(PEAmatrix)){
131 
-  #k=1
132 
-  currentPathway <- colnames(pathway)[k]
133 
-  currentPathway_genes_list_common <- intersect(rownames(DataMatrix), currentPathway_genes<-pathway[,k])
134 
-  currentPathway_genes_list_commonMatrix <- DataMatrix[currentPathway_genes_list_common,]
135 
-  SumGenes <- colSums(currentPathway_genes_list_commonMatrix)
136 
-  AverageGenes <- SumGenes / length(currentPathway_genes_list_common)
137 
-  PEAmatrix[k,] <- AverageGenes
138 
-}
139 
-return(PEAmatrix)
140 
-}
141 
-
142 
-
143 
-
144 
-
145 
-
146 
-
147 
-
148 
-
149 
-
150 
-
151 
-
152 
-
153 
-#' @title For TCGA data get human pathway data and creates a measure of cross-talk among pathways
154 
-#' @description euc_dist_crtlk creates a matrix with euclidean distance for pairwise pathways
155 
-#' @param dataFilt TCGA matrix
156 
-#' @param pathway pathway data
157 
-#' @export
158 
-#' @return a matrix value for each pathway
159 
-#' @examples
160 
-#' score_euc_dista<-euc_dist_crtlk(dataFilt=tumo[,1:2],path)
161 
-euc_dist_crtlk <- function(dataFilt,pathway){
162 
-  PEAmatrix<-average(dataFilt,pathway)
163 
-  #step 5 distance
164 
-  # EUCLIDEA DISTANCE
165 
-  df=combn(rownames(PEAmatrix),2) # possibili relazioni tra i pathway
166 
-  df=t(df)
167 
-  ma_d<-matrix(0,nrow(df),ncol(PEAmatrix)) # creo matrix che conterr? le distanze
168 
-  colnames(ma_d)<-colnames(PEAmatrix) # colnames conterr? il nome dei pazienti
169 
-  for ( p in 1: ncol(PEAmatrix)){ # per ogni paziente
170 
-    patients <- (PEAmatrix)[,p]
171 
-    distance<-dist(patients) # calcolo distanza EUCLIDEA tra le possibile combinazioni
172 
-    ma_d[,p]<-distance
173 
-  }
174 
-  euc_dist<-cbind(df,ma_d) # inserisco label con le relazioni tra i pathway
175 
-  return(euc_dist)
176 
-}
177 
-
178 
-
179 
-
180 
-
181 
-#' @title For TCGA data get human pathway data and creates a measure of standard deviations among pathways
182 
-#' @description st_dv creates a matrix with standard deviation for pathways
183 
-#' @param DataMatrix TCGA matrix
184 
-#' @param pathway pathway data
185 
-#' @export
186 
-#' @return a matrix value for each pathway
187 
-#' @examples
188 
-#' stand_dev<-st_dv(DataMatrix=tumo[,1:2],pathway=path)
189 
-st_dv<-function(DataMatrix,pathway){
190 
-#DataMatrix<-dataFilt
191 
-
192 
-#dataFilt[ , "new.col"] <- gsub("\\|.*", "", rownames(dataFilt))
193 
-#DataMatrix<-dataFilt[which(dataFilt$new.col!="?"),]
194 
-#DataMatrix <- subset(DataMatrix, !duplicated(DataMatrix$new.col))
195 
-#rownames(DataMatrix)<-DataMatrix$new.col
196 
-#DataMatrix$new.col<-NULL
197 
-
198 
-PEAmatrix_sd <- matrix( 0, ncol(pathway),ncol(DataMatrix))
199 
-rownames(PEAmatrix_sd) <- colnames(pathway)
200 
-colnames(PEAmatrix_sd) <-  colnames(DataMatrix)
201 
-for ( k in 1: nrow(PEAmatrix_sd)){
202 
-  print(colnames(pathway)[k])
203 
-  currentPathway <- colnames(pathway)[k]
204 
-  currentPathway_genes_list_common <- intersect( rownames(DataMatrix), currentPathway_genes<-pathway[,k])
205 
-  currentPathway_genes_list_commonMatrix <- DataMatrix[currentPathway_genes_list_common,]
206 
-  stdev<-apply(currentPathway_genes_list_commonMatrix,2,sd) #deviazione standard dei pathway
207 
-  PEAmatrix_sd[k,] <- stdev
208 
-  }
209 
-return(PEAmatrix_sd)
210 
-}
211 
-
212 
-
213 
-
214 
-
215 
-
216 
-
217 
-#' @title For TCGA data get human pathway data and creates a measure of discriminating score among pathways
218 
-#' @description ds_score_crtlk creates a matrix with  discriminating score for pathways
219 
-#' @param dataFilt TCGA matrix
220 
-#' @param pathway pathway data
221 
-#' @export
222 
-#' @return a matrix value for each pathway
223 
-#' @examples
224 
-#' cross_talk_st_dv<-ds_score_crtlk(dataFilt=tumo[,1:2],pathway=path)
225 
-ds_score_crtlk<-function(dataFilt,pathway){
226 
-  PEAmatrix<-average(dataFilt,pathway)
227 
-  #step 5 distance
228 
-  # EUCLIDEA DISTANCE
229 
-  df=combn(rownames(PEAmatrix),2) # possibili relazioni tra i pathway
230 
-  df=t(df)
231 
-  ma_d<-matrix(0,nrow(df),ncol(PEAmatrix)) # creo matrix che conterr? le distanze
232 
-  colnames(ma_d)<-colnames(PEAmatrix) # colnames conterr? il nome dei pazienti
233 
-  for ( p in 1: ncol(PEAmatrix)){ # per ogni paziente
234 
-    patients <- (PEAmatrix)[,p]
235 
-    distance<-dist(patients) # calcolo distanza EUCLIDEA tra le possibile combinazioni
236 
-    ma_d[,p]<-distance
237 
-  }
238 
-  PEAmatrix_sd<-st_dv(dataFilt,pathway)
239 
-  df=combn(rownames(PEAmatrix_sd),2)
240 
-  df=t(df)
241 
-  ma<-matrix(0,nrow(df),ncol(PEAmatrix_sd)) # creo matrix che conterr? le somme delle dev st
242 
-  colnames(ma)<-colnames(PEAmatrix_sd) # colnames conterr? il nome dei pazienti
243 
-  for ( p in 1: ncol(PEAmatrix_sd)){ # per ogni paziente
244 
-    patients <- (PEAmatrix_sd)[,p]
245 
-    out <- apply(df, 1, function(x) sum(patients[x])) # calcolo somma delle dev standard tra le possibili combinazioni
246 
-    ma[,p]<-out
247 
-  }
248 
-  score<-ma_d/ma # discriminating score M1-M2/S1+S2
249 
-  score<- cbind(df,score)
250 
-return(score)
251 
-}
252 
-
253 
-
254 
-
255 
-#' @title SVM classification for each feature
256 
-#' @description svm class creates a list with auc value
257 
-#' @param TCGA_matrix gene expression matrix
258 
-#' @param nfs nfs split data into a training  and test set
259 
-#' @param tumour barcode samples for a class
260 
-#' @param normal barcode samples for another class
261 
-#' @export
262 
-#' @importFrom e1071 tune svm
263 
-#' @importFrom ROCR prediction performance
264 
-#' @importFrom  grDevices rainbow
265 
-#' @return a list with AUC value for pairwise pathway
266 
-#' @examples
267 
-#' nf <- 60
268 
-#' res_class<-svm_classification(TCGA_matrix=score_euc_dist,nfs=nf,
269 
-#' normal=colnames(norm[,1:10]),tumour=colnames(tumo[,1:10]))
270 
-svm_classification<-function(TCGA_matrix,tumour,normal,nfs){
271 
-  #library("e1071")
272 
-  #library(ROCR)
273 
-
274 
-  scoreMatrix <- as.data.frame(TCGA_matrix[,3:ncol(TCGA_matrix)])
275 
-  scoreMatrix <-as.data.frame(scoreMatrix)
276 
-  for( i in 1: ncol(scoreMatrix)){
277 
-    scoreMatrix[,i] <- as.numeric(as.character(scoreMatrix[,i]))
278 
-  }
279 
-
280 
-  TCGA_matrix[,1] <- gsub(" ", "_", TCGA_matrix[,1])
281 
-  d<-sub('_-_Homo_sapiens_*', '', TCGA_matrix[,1])
282 
-  #d_pr<-sub(')*', '', DataMatrix[,1])
283 
-
284 
-  d_pr<- gsub("(human)", "", d, fixed="TRUE")
285 
-  d_pr <- gsub("_", "", d_pr)
286 
-  d_pr <- gsub("-", "", d_pr)
287 
-
288 
-  TCGA_matrix[,2] <- gsub(" ", "_", TCGA_matrix[,2])
289 
-  d2<-sub('_-_Homo_sapiens_(human)*', '', TCGA_matrix[,2])
290 
-  d_pr2<- gsub("(human)", "", d2, fixed="TRUE")
291 
-  d_pr2 <- gsub("_", "", d_pr2)
292 
-  d_pr2 <- gsub("-", "", d_pr2)
293 
-
294 
-  PathwaysPair <- paste( as.matrix(d_pr), as.matrix(d_pr2),sep="_" )
295 
-
296 
-  rownames(scoreMatrix) <-PathwaysPair
297 
-
298 
-
299 
-  tDataMatrix<-as.data.frame(t(scoreMatrix))
300 
-  #tDataMatrix$Target[,1]<-0
301 
-
302 
-  tDataMatrix<-cbind(Target=0,tDataMatrix )
303 
-
304 
-  tum<-intersect(rownames(tDataMatrix),tumour)
305 
-  nor<-intersect(rownames(tDataMatrix),normal)
306 
-  #tDataMatrix$
307 
-
308 
-  Dataset_g1<-tDataMatrix[nor,]
309 
-  Dataset_g3<- tDataMatrix[tum,]
310 
-
311 
-
312 
-#training=read.table('C:/Users/UserInLab05/Desktop/trai.txt',header = TRUE)
313 
-#testset=read.table('C:/Users/UserInLab05/Desktop/test.txt',header = TRUE)
314 
-
315 
-  Dataset_g1$Target <- 0
316 
-  Dataset_g3$Target<-1
317 
-#Dataset_g3 <- Dataset_g3[Dataset_g3$Target <- 1, ]
318 
-
319 
-tab_g1_training <- sample(rownames(Dataset_g1),round(nrow(Dataset_g1) / 100 * nfs ))
320 
-tab_g3_training <- sample(rownames(Dataset_g3),round(nrow(Dataset_g3) / 100 * nfs ))
321 
-tab_g1_testing <- setdiff(rownames(Dataset_g1),tab_g1_training)
322 
-tab_g3_testing <- setdiff(rownames(Dataset_g3),tab_g3_training)
323 
-
324 
-FR<-intersect(rownames(Dataset_g1),tab_g1_training)
325 
-
326 
-#rownames(Dataset_g1)<-Dataset_g1[,1]
327 
-G1<-Dataset_g1[FR,]
328 
-
329 
-FR1<-intersect(rownames(Dataset_g3),tab_g3_training)
330 
-#rownames(Dataset_g3)<-Dataset_g3$ID
331 
-
332 
-G3<-Dataset_g3[FR1,]
333 
-training<-rbind(G1,G3)
334 
-
335 
-inter1<-intersect(rownames(Dataset_g1),tab_g1_testing)
336 
-#rownames(Dataset_g1)<-Dataset_g1$ID
337 
-
338 
-G1_testing<-Dataset_g1[inter1,]
339 
-
340 
-inter2<-intersect(rownames(Dataset_g3),tab_g3_testing)
341 
-#rownames(Dataset_g3)<-Dataset_g3$ID
342 
-G3_testing<-Dataset_g3[inter2,]
343 
-
344 
-testing<-rbind(G1_testing,G3_testing)
345 
-
346 
-x <- subset(training, select=-Target)
347 
-y <- training$Target
348 
-#testing[,2]<-NULL
349 
-z<-subset(testing, select=-Target)
350 
-
351 
-zi<-testing$Target
352 
-
353 
-auc.df<-list()
354 
-svm_model_after_tune_COMPL<-list()
355 
-for( k in 2: ncol(training)){
356 
-  print(colnames(training)[k])
357 
-  svm_tune <- tune(svm, train.x=x, train.y=y,
358 
-                   kernel="radial", ranges=list(cost=10^(-1:2), gamma=c(.5,1,2)),cross=10)
359 
-  #print(svm_tune)
360 
-
361 
-  svm_model_after_tune <- svm(Target ~ ., data=training[,c(1,k)], kernel="radial", cost=svm_tune$best.parameters$cost, gamma=svm_tune$best.parameters$gamma,cross=10,probability = TRUE)
362 
-
363 
-
364 
-  #svm_model_after_tune <- svm(Target ~ ., data=training[,c(1,k)], kernel="radial", cost=svm_tune$best.parameters[1], gamma=svm_tune$best.parameters[2],cross=10,probability = TRUE)
365 
-  #summary(svm_model_after_tune)
366 
-
367 
-  j=k-1
368 
-  z2=z[,j]
369 
-  z3<-as.data.frame(z2)
370 
-  #rownames(z3)<-rownames(z)
371 
-  #colnames(z3)<-as.character(paste("X",j,sep = ""))
372 
-  colnames(z3)<-colnames(z)[j]
373 
-  #classifiersMatrix <- c(classifiersMatrix,svm_model_after_tune)
374 
-  pred <- predict(svm_model_after_tune,z3,decision.values=TRUE,cross=10)
375 
-
376 
-  #a<-table(pred,zi)
377 
-  svm.roc <- prediction(attributes(pred)$decision.values, zi)
378 
-  svm.auc <- performance(svm.roc, 'tpr', 'fpr')
379 
-
380 
-  perf <- performance(svm.roc, "auc")
381 
-  auc<-perf@y.values[[1]]
382 
-
383 
-  auc.df[[j]]<- auc
384 
-  svm_model_after_tune_COMPL[[j]]<-svm_model_after_tune
385 
-
386 
-  palette <- as.matrix(rainbow(ncol(z)))
387 
-  #print(j)
388 
-  if (j >1 & j < 6) {
389 
-    plot(svm.auc,col=palette[j], add=TRUE)
390 
-    legend('bottomright', colnames(z),
391 
-           lty=1, col=palette, bty='n', cex=.90,pch = 20,ncol=1)
392 
-
393 
-
394 
-  }
395 
-  else {
396 
-    plot(svm.auc, col=palette[j])
397 
-
398 
-
399 
-  }
400 
-
401 
-}
402 
-names(auc.df) <- colnames(z)
403 
-return(auc.df)
404 
-}
405 
-
README.md
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1 
-# StarBioTrek
2 
-
3 
-### Installation ###
4 
-```R
5 
-source("https://bioconductor.org/biocLite.R")
6 
-biocLite("StarBioTrek")
7 
-```
8 
-
data/Data_CANCER_normUQ_filt.rda
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inst/CITATION
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-citHeader("To cite StarBioTrek in publications use:")
2 
-
3 
-citEntry(entry = "article",
4 
-         title        = "StarBioTrek",
5 
-         author       = personList(as.person("Claudia Cava"),
6 
-                                   as.person("Isabella Castiglioni")
7 
-         ),
8 
-         journal = "manuscript in preparation",
9 
-         year         = "2016",
10 
-         textVersion  =
11 
-             paste("Claudia Cava, Isabella Castiglioni (2016).",
12 
-                   "StarBioTrek")
13 
-)
14 
-
15 
-citEntry(entry = "article",
16 
-         title        = "Integrating genetics and epigenetics in
17 
-breast cancer: biological insights, experimental, computational methods and
18 
-therapeutic potential.",
19 
-         author       = personList(as.person("Claudia Cava"),
20 
-                                   as.person("Gloria Bertoli"),
21 
-                                   as.person("Isabella Castiglioni")
22 
-         ),
23 
-         journal = "BMC Syst Biol",
24 
-         year         = "2015",
25 
-		 	volume = 9,
26 
-	number = 62,
27 
-         textVersion  =
28 
-             paste("Cava C, Bertoli G, Castiglioni I. Integrating genetics and epigenetics in
29 
-breast cancer: biological insights, experimental, computational methods and
30 
-therapeutic potential. BMC Syst Biol.2015;9:62")
31 
-)
man/Data_CANCER_normUQ_filt.Rd
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-% Generated by roxygen2: do not edit by hand
2 
-% Please edit documentation in R/StarBioTrek.r
3 
-\docType{data}
4 
-\name{Data_CANCER_normUQ_filt}
5 
-\alias{Data_CANCER_normUQ_filt}
6 
-\title{TCGA data}
7 
-\format{A data frame with rows and variables}
8 
-\description{
9 
-TCGA data
10 
-}
11 
-\keyword{internal}
12 
-
man/GE_matrix.Rd
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-% Generated by roxygen2: do not edit by hand
2 
-% Please edit documentation in R/path_star.R
3 
-\name{GE_matrix}
4 
-\alias{GE_matrix}
5 
-\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 .}
6 
-\usage{
7 
-GE_matrix(DataMatrix, pathway)
8 
-}
9 
-\arguments{
10