1. StarBioTrek
  2. Commit cec98acadc4fc5321fcefd0a1d20520c41336c0a
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git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/StarBioTrek@122485 bc3139a8-67e5-0310-9ffc-ced21a209358

Claudia Cava authored on 16/10/2016 09:58:12
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: 0.99.28
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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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+----------------------------------------------------------------
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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)
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+ad<-paste("Riboflavin metabolism", species)
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+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 @ cec98ac
0 535 
new file mode 100644
... ... 
@@ -0,0 +1,405 @@
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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data/list_path_plot.rda
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data/netw.rda
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data/norm.rda
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data/path.rda
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data/score_euc_dist.rda
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data/tumo.rda
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inst/CITATION
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1 
+citHeader("To cite StarBioTrek in publications use:")
2 
+
3 
+citEntry(entry = "article",
4 
+         title        = "StarBioTrek
5 
+    miRNA data",
6 
+         author       = personList(as.person("Claudia Cava"),
7 
+                                   as.person("Isabella Castiglioni")
8 
+         ),
9 
+         journal = "manuscript in preparation",
10 
+         year         = "2016",
11 
+         textVersion  =
12 
+             paste("Claudia Cava, Isabella Castiglioni (2016).",
13 
+                   "StarBioTrek")
14 
+)
15 
+
16 
+citEntry(entry = "article",
17 
+         title        = "Integrating genetics and epigenetics in
18 
+breast cancer: biological insights, experimental, computational methods and
19 
+therapeutic potential.",
20 
+         author       = personList(as.person("Claudia Cava"),
21 
+                                   as.person("Gloria Bertoli"),
22 
+                                   as.person("Isabella Castiglioni")
23 
+         ),
24 
+         journal = "BMC Syst Biol",
25 
+         year         = "2015",
26 
+		 	volume = 9,
27 
+	number = 62,
28 
+         textVersion  =
29 
+             paste("Cava C, Bertoli G, Castiglioni I. Integrating genetics and epigenetics in
30 
+breast cancer: biological insights, experimental, computational methods and
31 
+therapeutic potential. BMC Syst Biol.2015;9:62")
32 
+)
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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1 
+% 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)