Found 47746 results in 5072 files, showing top 50 files (show more).
seq2pathway:R/seq2pathway.r: [ ] |
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928: path <-paste(system.file(package="seq2pathway"),
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856: get_python3_command_path <- function()
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859: python3_command_path <- Sys.which2("python")
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1029: script_path <- file.path(tempdir(), name)
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275: pathwaygene<-length(intersect(toupper(gene_list[[i]]),
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484: pathwaygene<-length(intersect(toupper(gsmap$genesets[[i]]),
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843: cmdpath <- Sys.which(cmdname)
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1051: runseq2pathway<-function(inputfile,
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1161: gene2pathway_result<-list()
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1310: gene2pathway_test<-function(dat,DataBase="GOterm",FisherTest=TRUE,
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1344: gene2pathway_result<-list()
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854: #get_python3_command_path: funtion from Herve Pages, Bioconductor Maintainance Team, Oct 9 2020
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858: # python3_command_path <- Sys.which2("python3") #3/3/2021 by Holly
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860: if (python3_command_path != "")
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864: return(python3_command_path)}
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873: # python3_command_path <- Sys.which2("python")
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874: python3_command_path <- Sys.which2("python3") #3/3/2021 by Holly
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875: if (python3_command_path != ""){
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876: print(paste0("python3 found: ",python3_command_path))
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877: return(python3_command_path)}
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880: " 'python3' (or 'python') executable is in your PATH.")
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924: ### assign the path of main function
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932: path <-paste(system.file(package="seq2pathway"),
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976: sink(file.path(tempdir(),name,fsep = .Platform$file.sep))}
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994: cat("'", path, "').load_module()",sep="")
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1030: if (!file.exists(script_path))
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1032: mypython <- get_python3_command_path()
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1034: response <- system2(mypython, args=script_path,
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75: data(gencode_coding,package="seq2pathway.data")
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155: data(gencode_coding,package="seq2pathway.data")
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214: ####load GP pathway information
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216: data(GO_BP_list,package="seq2pathway.data")
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217: data(GO_MF_list,package="seq2pathway.data")
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218: data(GO_CC_list,package="seq2pathway.data")
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219: data(Des_BP_list,package="seq2pathway.data")
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220: data(Des_MF_list,package="seq2pathway.data")
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221: data(Des_CC_list,package="seq2pathway.data")
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223: data(GO_BP_list,package="seq2pathway.data")
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224: data(Des_BP_list,package="seq2pathway.data")
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226: data(GO_MF_list,package="seq2pathway.data")
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227: data(Des_MF_list,package="seq2pathway.data")
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229: data(GO_CC_list,package="seq2pathway.data")
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230: data(Des_CC_list,package="seq2pathway.data")
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237: data(GO_GENCODE_df_hg_v36,package="seq2pathway.data")
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240: data(GO_GENCODE_df_hg_v19,package="seq2pathway.data")
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243: data(GO_GENCODE_df_mm_vM25,package="seq2pathway.data")
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246: data(GO_GENCODE_df_mm_vM1,package="seq2pathway.data")
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280: c<-pathwaygene-a
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289: mdat[i,7]<-pathwaygene
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321: pathwaygene<-length(intersect(toupper(GO_BP_list[[i]]),
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326: c<-pathwaygene-a
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335: mdat[i,7]<-pathwaygene
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367: pathwaygene<-length(intersect(toupper(GO_CC_list[[i]]),
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372: c<-pathwaygene-a
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381: mdat[i,7]<-pathwaygene
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413: pathwaygene<-length(intersect(toupper(GO_MF_list[[i]]),
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418: c<-pathwaygene-a
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427: mdat[i,7]<-pathwaygene
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455: data(Msig_GENCODE_df_hg_v36,package="seq2pathway.data")
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458: data(Msig_GENCODE_df_hg_v19,package="seq2pathway.data")
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461: data(Msig_GENCODE_df_mm_vM25,package="seq2pathway.data")
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464: data(Msig_GENCODE_df_mm_vM1,package="seq2pathway.data")
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489: c<-pathwaygene-a
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498: mdat[i,7]<-pathwaygene
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549: data(gencode_coding,package="seq2pathway.data")
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647: rungene2pathway <-
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704: colnames(res) <- c(paste(colnames(dat),"2pathscore",sep=""))
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705: print("gene2pathway calculates score....... done")
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711: rungene2pathway_EmpiricalP <-
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770: colnames(res) <- c(paste(colnames(dat),"2pathscore",sep=""))
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829: colnames(res_p) <- c(paste(colnames(dat),"2pathscore_Pvalue",sep=""))
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832: print("pathwayscore Empirical Pvalue calculation..........done")
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849: success <- grepl(pattern1, cmdpath, fixed=TRUE) ||
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850: grepl(pattern2, cmdpath, fixed=TRUE)
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851: if (success) cmdpath else ""
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1007: #cat(paste("inputpath=","'",inputpath,"/'",sep=""),sep="\n")
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1009: #cat(paste("outputpath=","'",outputpath,"/'",sep=""),sep="\n")
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1018: cat(paste("pwd=","'",system.file(package="seq2pathway.data"),"/extdata/'",sep=""),sep="\n")
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1103: data(GO_BP_list,package="seq2pathway.data")
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1104: data(GO_MF_list,package="seq2pathway.data")
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1105: data(GO_CC_list,package="seq2pathway.data")
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1106: data(Des_BP_list,package="seq2pathway.data")
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1107: data(Des_CC_list,package="seq2pathway.data")
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1108: data(Des_MF_list,package="seq2pathway.data")
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1134: #############################rungene2pathway,normalization,empiricalP,summary table
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1166: GO_BP_FAIME<-rungene2pathway(dat=dat_CP,gsmap=GO_BP_list,alpha=alpha,logCheck=logCheck,
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1171: GO_BP_FAIME_Pvalue<-rungene2pathway_EmpiricalP(dat=dat_CP,gsmap=GO_BP_list,
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1174: ########gene2pathway table
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1190: gene2pathway_result[[n.list]]<-GO_BP_N_P
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1191: names(gene2pathway_result)[n.list]<-c("GO_BP")
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1195: GO_MF_FAIME<-rungene2pathway(dat=dat_CP,gsmap=GO_MF_list,alpha=alpha,logCheck=logCheck,
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1198: GO_MF_FAIME_Pvalue<-rungene2pathway_EmpiricalP(dat=dat_CP,gsmap=GO_MF_list,
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1215: gene2pathway_result[[n.list]]<-GO_MF_N_P
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1216: names(gene2pathway_result)[n.list]<-c("GO_MF")
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1220: GO_CC_FAIME<-rungene2pathway(dat=dat_CP,gsmap=GO_CC_list,alpha=alpha,logCheck=logCheck,
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1223: GO_CC_FAIME_Pvalue<-rungene2pathway_EmpiricalP(dat=dat_CP,gsmap=GO_CC_list,
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1241: gene2pathway_result[[n.list]]<-GO_CC_N_P
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1242: names(gene2pathway_result)[n.list]<-c("GO_CC")
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1245: dat_FAIME<-rungene2pathway(dat=dat_CP,gsmap=DataBase,alpha=alpha,logCheck=logCheck,
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1248: dat_FAIME_Pvalue<-rungene2pathway_EmpiricalP(dat=dat_CP,gsmap=DataBase,
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1255: colnames(DB_N_P)<-c("score2pathscore_Normalized","score2pathscore_Pvalue")
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1274: gene2pathway_result<-DB_N_P[,c(ncol(DB_N_P),1:(ncol(DB_N_P)-1))]
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1276: print("gene2pathway analysis is done")
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1279: if(exists("gene2pathway_result")&exists("FS_test")){
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1283: TotalResult[[2]]<-gene2pathway_result
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1284: names(TotalResult)[2]<-"gene2pathway_result.FAIME"
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1286: names(TotalResult)[3]<-"gene2pathway_result.FET"
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1289: }else if(exists("gene2pathway_result")&exists("FS_test")==FALSE){
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1293: TotalResult[[2]]<-gene2pathway_result
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1294: names(TotalResult)[2]<-"gene2pathway_result.FAIME"
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1298: else if(exists("gene2pathway_result")==FALSE&exists("FS_test")){
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1303: names(TotalResult)[2]<-"gene2pathway_result.FET"
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1326: data(GO_BP_list,package="seq2pathway.data")
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1327: data(GO_MF_list,package="seq2pathway.data")
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1328: data(GO_CC_list,package="seq2pathway.data")
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1329: data(Des_BP_list,package="seq2pathway.data")
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1330: data(Des_CC_list,package="seq2pathway.data")
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1331: data(Des_MF_list,package="seq2pathway.data")
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1346: #############################rungene2pathway,normalization,empiricalP,summary table
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1348: gene2pathway_result<-list()
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1352: GO_BP_method<-rungene2pathway(dat=dat,gsmap=GO_BP_list,alpha=alpha,logCheck=logCheck,
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1358: GO_BP_method_Pvalue<-rungene2pathway_EmpiricalP(dat=dat,gsmap=GO_BP_list,alpha=alpha,
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1364: ########gene2pathway table
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1376: gene2pathway_result[[n.list]]<-GO_BP_N_P
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1377: names(gene2pathway_result)[n.list]<-c("GO_BP")
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1380: GO_MF_method<-rungene2pathway(dat=dat,gsmap=GO_MF_list,alpha=alpha,logCheck=logCheck,
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1384: GO_MF_method_Pvalue<-rungene2pathway_EmpiricalP(dat=dat,gsmap=GO_MF_list,alpha=alpha,
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1402: gene2pathway_result[[n.list]]<-GO_MF_N_P
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1403: names(gene2pathway_result)[n.list]<-c("GO_MF")
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1406: GO_CC_method<-rungene2pathway(dat=dat,gsmap=GO_CC_list,alpha=alpha,logCheck=logCheck,
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1410: GO_CC_method_Pvalue<-rungene2pathway_EmpiricalP(dat=dat,gsmap=GO_CC_list,alpha=alpha,
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1427: gene2pathway_result[[n.list]]<-GO_CC_N_P
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1428: names(gene2pathway_result)[n.list]<-c("GO_CC")
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1431: dat_method<-rungene2pathway(dat=dat,gsmap=DataBase,alpha=alpha,logCheck=logCheck,
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1435: dat_method_Pvalue<-rungene2pathway_EmpiricalP(dat=dat,gsmap=DataBase,alpha=alpha,
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1443: colnames(DB_N_P)<-c("score2pathscore_Normalized","score2pathscore_Pvalue")
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1464: gene2pathway_result<-DB_N_P[,c(ncol(DB_N_P),1:(ncol(DB_N_P)-1))]
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1466: print("gene2pathway analysis is done")
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1470: if(exists("gene2pathway_result")&exists("FS_test")){
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1472: TResult[[1]]<-gene2pathway_result
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1473: names(TResult)[1]<-"gene2pathway_result.2"
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1475: names(TResult)[2]<-"gene2pathway_result.FET"
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1476: }else if(exists("gene2pathway_result")&exists("FS_test")==FALSE){
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1477: TResult<-gene2pathway_result
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1479: else if(exists("gene2pathway_result")==FALSE&exists("FS_test")){
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CHRONOS:R/pathwayToGraph.R: [ ] |
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101: path <- paste(dir, file, sep='//')
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34: paths <- list.files(xmlDir)
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83: pathwayToGraph <- function (i, ...)
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3: createPathwayGraphs <- function(org, pathways, edgeTypes, doubleEdges, choice,
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141: getPathwayType <- function(filepath, file)
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159: metabolicPathwayToGraph <- function(filepath)
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347: nonMetabolicPathwayToGraph <- function(filepath, doubleEdges, groupMode)
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102: gr <- metabolicPathwayToGraph(path)
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119: path <- paste(dir, file, sep='//')
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120: gr <- nonMetabolicPathwayToGraph(path, doubleEdges, groupMode)
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225: removeCompoundsMetabolicGraph <- function(path)
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228: if(path$name != gsub('ec','',path$name)) { nodeType<-"enzyme" }
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229: enzymes <- which(path$vertices$type == nodeType)
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230: vid <- path$vertices$id
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233: if ( length(path$edges) > 0 )
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243: for (r1 in path$edges[path$edges$e1 ==
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244: path$vertices[,'id'][enzymes[j]],]$e2)
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247: for (r2 in path$edges[path$edges$e1 ==
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248: path$vertices[,'id'][which(vid == r1)],]$e2)
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252: nid <- vid[which(path$vertices$id == r2)]
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267: xid <- path$vertices$id[enzymes]
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268: names <- path$vertices$names[enzymes]
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513: removeCompoundsNonMetabolicGraph <- function(path, unique, edgeTypes)
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515: if (is.null(path)) return(NULL)
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516: vid <- as.numeric(path$vertices$id)
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517: etype <- path$vertices$type
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519: if(path$name != gsub('ko','',path$name)) { nodeType <- "ortholog" }
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522: genesIndx <- which(path$vertices$type == nodeType)
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528: neighbors <- path$edges$e2[path$edges$e1 == vid[gi]]
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546: idx1 <- which( path$edges$e1 == vid[gi] )
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547: idx2 <- which( path$edges$e2 == vid[nbrId] )
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549: TT <- c( TT, paste((path$edges$type[idx]), collapse='_') )
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557: cpdNeighbors <- path$edges$e2[
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558: which(path$edges$e1 == vid[nbrId]) ]
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586: names <- unique(path$vertices$names[genesIndx])
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598: idx1 <- which(path$vertices$id == source[i])
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599: idx2 <- which(path$vertices$id == destin[i])
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600: source[i] <- names[ names == path$vertices$names[idx1] ]
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601: destin[i] <- names[ names == path$vertices$names[idx2] ]
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623: gids <- path$vertices$id[genesIndx]
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624: names <- unname(path$vertices$names[genesIndx])
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31: # Choose valid pathways
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32: if (missing(pathways))
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37: if (!missing(pathways))
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39: paths <- paste(org, pathways, '.xml', sep='')
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44: # Create compact adjacency matrices for given pathways.
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45: types <- getPathwayType(paste(xmlDir, paths, sep='//'))
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46: N <- length(paths)
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56: funcName=pathwayToGraph,
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59: N=length(paths),
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61: xmlDir, paths, types, FALSE, edgeTypes,
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64: names(cAdjMats) <- gsub('.xml', '', paths)
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67: eAdjMats <- .doSafeParallel(funcName=pathwayToGraph,
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70: N=length(paths),
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72: xmlDir, paths, types, TRUE, edgeTypes,
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75: names(eAdjMats) <- gsub('.xml', '', paths)
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143: types <- vector(mode='numeric', length=length(filepath))
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144: for (i in 1:length(filepath))
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146: num <- tail(unlist(strsplit(filepath[i], '//')), 1)
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156: # Graph from Metabolic Pathways
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161: xmlDoc <- tryCatch(xmlTreeParse(filepath,error=NULL),
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344: # Graph from Mon Metabolic Pathways
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350: xmlDoc <- tryCatch(xmlTreeParse(filepath,error=NULL),
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49: 'nonMetabolicPathwayToGraph', 'expandMetabolicGraph',
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51: 'metabolicPathwayToGraph', 'expandNonMetabolicGraph',
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609: # Set new interaction types to apathetic
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732: # apathetic 3
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biodbKegg:R/KeggPathwayConn.R: [ ] |
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61: path <- self$getEntry(path.id)
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59: for (path.id in id) {
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127: for (path.id in id) {
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304: path_idx <- sub('^[^0-9]+', '', id)
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322: path_idx <- sub('^[^0-9]+', '', id)
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29: KeggPathwayConn <- R6::R6Class("KeggPathwayConn",
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40: super$initialize(db.name='pathway', db.abbrev='path', ...)
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62: if ( ! is.null(path) && path$hasField('kegg.module.id')) {
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65: for (mod.id in path$getFieldValue('kegg.module.id')) {
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144: graph[[path.id]]=list(vertices=vert, edges=edg)
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147: graph[[path.id]]=NULL
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309: params=c(org_name='map', mapno=path_idx,
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325: img_filename <- paste0('pathwaymap-', path_idx)
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331: biodb::error0('Impossible to find pathway image path inside',
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335: tmp_file <- file.path(cache$getTmpFolderPath(),
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2: #' The connector class to KEGG Pathway database.
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16: #' conn=mybiodb$getFactory()$createConn('kegg.pathway')
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18: #' # Retrieve all reactions related to a mouse pathway:
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21: #' # Get a pathway graph
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44: #' Retrieves all reactions part of a KEGG pathway. Connects to
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45: #' KEGG databases, and walk through all pathways submitted, and
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58: # Loop on all Pathway IDs
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89: #' Takes a list of pathways IDs and converts them to the specified organism,
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92: #' @param org The organism in which to search for pathways, as a KEGG organism
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113: #' Builds a pathway graph in the form of two tables of vertices and edges,
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115: #' @param id A character vector of KEGG pathway entry IDs.
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120: #' @return A named list whose names are the pathway IDs, and values are lists
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126: # Loop on all pathway IDs
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158: #' Builds a pathway graph, as an igraph object, using KEGG database.
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159: #' @param id A character vector of KEGG pathway entry IDs.
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196: #' Create a pathway graph picture, with some of its elements colorized.
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197: #' @param id A KEGG pathway ID.
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227: #' Extracts shapes from a pathway map image.
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228: #' @param id A KEGG pathway ID.
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303: # Extract pathway number
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308: 'show_pathway'),
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329: 'src="([^"]+)"(\\s+.*)?\\s+(name|id)="pathwayimage"')
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332: ' HTML page for pathway ID ', id, '.')
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342: img_file <- cache$getFilePath(cid, img_filename, 'png')
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22: #' graph=conn$buildPathwayGraph('mmu00260')
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98: convertToOrgPathways=function(id, org) {
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122: buildPathwayGraph=function(id, directed=FALSE, drop=TRUE) {
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166: getPathwayIgraph=function(id, directed=FALSE, drop=TRUE) {
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173: g <- self$buildPathwayGraph(id=id, directed=directed, drop=FALSE)
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210: pix <- private$getPathwayImage(id)
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213: shapes <- self$extractPathwayMapShapes(id=id, color2ids=color2ids)
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233: ,extractPathwayMapShapes=function(id, color2ids) {
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237: html <- private$getPathwayHtml(id)
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301: ,getPathwayHtml=function(id) {
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319: getPathwayImage=function(id) {
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321: html <- private$getPathwayHtml(id)
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NoRCE:R/pathway.R: [ ] |
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353: path <- merge(merge1, symb, by = "gene")
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66: pathTable <- unique(keggPathwayDB(org_assembly))
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72: pathfreq <- as.data.frame(table(annot$pathway))
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100: pathT <- as.character(freq$Var1[enrich])
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119: pathways <- data.frame(unique(pathT))
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205: pathTable <- unique(reactomePathwayDB(org_assembly))
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211: pathfreq <- as.data.frame(table(annot$pathway))
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237: pathT <- as.character(freq$Var1[enrich])
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542: pathTable <- unique(WikiPathwayDB(org_assembly))
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547: pathfreq <- as.data.frame(table(annot$pathID))
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573: pathT <- as.character(freq$Var1[enrich])
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580: pathTerms <- as.character(r$pathTerm[match(pathT, r$pathID)])
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630: pathwayEnrichment <- function(genes,
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679: pathfreq <- as.data.frame(table(annot$pathTerm))
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711: pathT <- as.character(freq$Var1[enrich])
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719: pathTerms <- as.character(r$pathTerm[match(pathT, r$pathID)])
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272: reactomePathwayDB <- function(org_assembly = c("hg19",
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359: keggPathwayDB <- function(org_assembly = c("hg19",
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435: WikiPathwayDB <- function(org_assembly = c("hg19",
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15: #' @param gmtFile File path of the gmt file
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92: file.path(x[1], x[2]))
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96: file.path(x[1], x[2]))
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156: #' @param gmtFile File path of the gmt file
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230: file.path(x[1], x[2]))
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233: file.path(x[1], x[2]))
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355: return(path)
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501: #' @param gmtFile File path of the gmt file
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565: file.path(x[1], x[2]))
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569: file.path(x[1], x[2]))
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610: #' @param gmtFile File path of the gmt file
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704: file.path(x[1], x[2]))
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707: file.path(x[1], x[2]))
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1: #' KEGG pathway enrichment
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22: #' @return KEGG pathway enrichment results
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69: annot <- pathTable[which(pathTable$symbol %in% genes$g),]
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73: pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
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76: geneSize = length(unique(pathTable$symbol))
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78: bckfreq <- as.data.frame(table(pathTable$pathway))
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79: notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
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80: freq <- merge(pathfreq, notGene, by = "Var1")
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105: r <- annot[annot$pathway %in% pathT,]
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107: for (i in seq_along(pathT))
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109: if (length(which(pathT[i] == r$pathway)) > 0)
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114: as.character(r[which(pathT[i] == r$pathway),]$symbol)),
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115: paste(pathT[i])))
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120: tmp <- character(length(pathT))
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121: if (nrow(pathways) > 0) {
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123: unlist(lapply(seq_len(nrow(pathways)), function(x)
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124: tmp[x] <- try(KEGGREST::keggGet(pathT[x])[[1]]$NAME)
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130: ID = pathT,
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142: #' Reactome pathway enrichment
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164: #' @return Reactome pathway enrichment results
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208: annot <- pathTable[which(pathTable$symbol %in% genes$g),]
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212: pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
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214: geneSize = length(unique(pathTable$symbol))
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216: bckfreq <- as.data.frame(table(pathTable$pathway))
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217: notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
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218: freq <- merge(pathfreq, notGene, by = "Var1")
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242: r <- annot[annot$pathway %in% pathT,]
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246: for (i in seq_along(pathT))
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248: if (length(which(pathT[i] == r$pathway)) > 0)
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253: list(as.character(r[which(pathT[i] == r$pathway),]$symbol)),
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254: paste(pathT[i])))
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260: ID = pathT,
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261: Term = as.character(rt[order(match(rt$pathway, pathT)), ]$name),
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281: table1 <- data.frame(pathway = rep(names(xx), lapply(xx, length)),
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284: pn <- data.frame(pathway = rep(names(pn), lapply(pn, length)),
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290: ty <- table1[grepl("^R-HSA", table1$pathway),]
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291: pn1 <- pn[grepl("^R-HSA", pn$pathway),]
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298: ty <- table1[grepl("^R-MMU", table1$pathway),]
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299: pn1 <- pn[grepl("^R-MMU", pn$pathway),]
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306: ty <- table1[grepl("^R-DRE", table1$pathway),]
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307: pn1 <- pn[grepl("^R-DRE", pn$pathway),]
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314: ty <- table1[grepl("^R-RNO", table1$pathway),]
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315: pn1 <- pn[grepl("^R-RNO", pn$pathway),]
|
322: ty <- table1[grepl("^R-CEL", table1$pathway),]
|
323: pn1 <- pn[grepl("^R-CEL", pn$pathway),]
|
330: ty <- table1[grepl("^R-SCE", table1$pathway),]
|
331: pn1 <- pn[grepl("^R-SCE", pn$pathway),]
|
344: ty <- table1[grepl("^R-DME", table1$pathway),]
|
345: pn1 <- pn[grepl("^R-DME", pn$pathway),]
|
351: by = "pathway",
|
371: kegg <- org.Hs.eg.db::org.Hs.egPATH2EG
|
379: kegg <- org.Mm.eg.db::org.Mm.egPATH2EG
|
387: kegg <- org.Dr.eg.db::org.Dr.egPATH2EG
|
395: kegg <- org.Rn.eg.db::org.Rn.egPATH2EG
|
403: kegg <- org.Ce.eg.db::org.Ce.egPATH2EG
|
411: kegg <- org.Sc.sgd.db::org.Sc.sgdPATH2ORF
|
419: kegg <- org.Dm.eg.db::org.Dm.egPATH2EG
|
425: pathTable <-
|
426: data.frame(pathway = paste0(prefix, rep(names(kegg2),
|
431: pathTable <- merge(pathTable, x, by = "gene")
|
432: return(pathTable)
|
474: do.call(rbind, strsplit(as.character(gmtFile$pathTerm), '%'))
|
480: pathID = tmp[, 3],
|
481: pathTerm = tmp[, 1]
|
508: #' @return Wiki Pathway Enrichment
|
545: annot <- pathTable[which(pathTable$gene %in% genes$g),]
|
548: pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
|
550: geneSize = length(unique(pathTable$gene))
|
551: bckfreq <- as.data.frame(table(pathTable$pathID))
|
552: notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
|
553: freq <- merge(pathfreq, notGene, by = "Var1")
|
578: r <- annot[annot$pathID %in% pathT,]
|
581: for (i in seq_along(pathT))
|
583: if (length(which(pathT[i] == r$pathID)) > 0)
|
587: list(as.character(r[which(pathT[i] == r$pathID),]$gene)),
|
588: paste(pathT[i])))
|
595: ID = pathT,
|
596: Term = pathTerms,
|
606: #' For a given gmt file of a specific pathway database, pathway enrichment
|
628: #' @return Pathway Enrichment
|
671: pathTable <-
|
676: pathTable <- geneListEnrich(f = gmtFile, isSymbol = isSymbol)
|
678: annot <- pathTable[which(pathTable$symbol %in% genes$g),]
|
680: pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
|
684: geneSize = length(unique(pathTable$symbol))
|
689: bckfreq <- as.data.frame(table(pathTable$pathTerm))
|
691: notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
|
692: freq <- merge(pathfreq, notGene, by = "Var1")
|
717: r <- annot[annot$pathTerm %in% pathT,]
|
721: for (i in seq_along(pathT))
|
723: if (length(which(pathT[i] == r$pathTerm)) > 0)
|
726: list(as.character(r[which(pathT[i] == r$pathTerm),]$symbol)),
|
727: paste(pathT[i])))
|
732: ID = pathT,
|
733: Term = pathTerms,
|
743: #' Convert gmt formatted pathway file to the Pathway ID, Entrez, symbol
|
746: #' @param gmtName Custom pathway gmt file
|
815: colnames(f) <- c('pathTerm', 'Entrez', 'symbol')
|
830: colnames(f) <- c('pathTerm', 'symbol', 'Entrez')
|
852: colnames(f) <- c('pathTerm', 'Entrez', 'symbol')
|
863: colnames(f) <- c('pathTerm', 'symbol', 'Entrez')
|
280: xx <- as.list(reactome.db::reactomePATHID2EXTID)
|
283: pn <- as.list(reactome.db::reactomePATHID2NAME)
|
445: rWikiPathways::downloadPathwayArchive(organism = "Homo sapiens",
|
449: rWikiPathways::downloadPathwayArchive(organism = "Mus musculus",
|
453: rWikiPathways::downloadPathwayArchive(organism = "Danio rerio",
|
457: rWikiPathways::downloadPathwayArchive(organism = "Rattus norvegicus",
|
461: rWikiPathways::downloadPathwayArchive(
|
465: rWikiPathways::downloadPathwayArchive(
|
469: rWikiPathways::downloadPathwayArchive(
|
487: #' WikiPathways Enrichment
|
maigesPack:R/plot-methods.R: [ ] |
---|
190: Path <- list(Type1=new("graphNEL", vertices, edgeL=arestas1),
|
326: Path <- new("graphNEL", vertices, edgeL=arestas)
|
167: graphPath <- function(data=NULL, cuttoffPvalue) {
|
309: graphPath <- function(data=NULL, cuttoffCor=NULL, cuttoffP=NULL) {
|
194: return(Path)
|
327: return(Path)
|
287: graph <- graphPath(x, cutPval)
|
426: graph <- graphPath(x, cutCor, NULL)
|
431: graph <- graphPath(x, NULL, cutPval)
|
pcaExplorer:R/pcaExplorer.R: [ ] |
---|
928: path <- system.file("doc", "pcaExplorer.html", package = "pcaExplorer")
|
929: if (path == "") {
|
932: browseURL(path)
|
936: path <- system.file("doc", "upandrunning.html", package = "pcaExplorer")
|
937: if (path == "") {
|
940: browseURL(path)
|
261: # Use web vignette, with varying paths depending on whether we're release or devel.
|
271: # Use web vignette, with varying paths depending on whether we're release or devel.
|
1047: cm <- utils::read.delim(input$uploadcmfile$datapath, header = TRUE,
|
1081: coldata <- utils::read.delim(input$uploadmetadatafile$datapath, header = TRUE,
|
1114: annodata <- utils::read.delim(input$uploadannotationfile$datapath, header = TRUE,
|
ReactomeContentService4R:R/getContent.R: [ ] |
---|
13: path <- "data/discover"
|
92: path <- "data/eventsHierarchy"
|
115: path <- "data/orthology"
|
151: path <- "data/participants"
|
254: path <- "data/pathways/low/entity"
|
324: path <- "data/person"
|
364: path <- "search/facet"
|
410: path <- "data/query/enhanced"
|
452: path <- "data/schema"
|
542: path <- "search/query"
|
586: path <- "data/species"
|
261: pathways <- .retrieveData(url, as="text")
|
272: top.pathways <- foreach(id=pathways$dbId, .export=c(".retrieveData", ".checkStatus"), .combine=dfcomb) %dopar% {
|
253: getPathways <- function(id, species=NULL, allForms=FALSE, top.level=FALSE) {
|
332: authoredPathways <- .retrieveData(ap.url, as="text")
|
14: url <- file.path(getOption("base.address"), path, event.id)
|
61: url <- file.path(getOption("base.address"), "data/complex", id,
|
64: url <- file.path(getOption("base.address"), "data/complexes", resource, id)
|
66: url <- file.path(getOption("base.address"), "data/entity", id, retrieval)
|
94: url <- file.path(getOption("base.address"), path, taxon.id)
|
118: url <- file.path(getOption("base.address"), path, id, "species", species.id)
|
154: url <- file.path(getOption("base.address"), path, event.id) #all participants
|
162: url <- file.path(url, "participatingPhysicalEntities")
|
164: url <- file.path(url, "referenceEntities")
|
166: # in a different path/method - /data/pathway/{id}/containedEvents
|
167: url <- file.path(getOption("base.address"), "data/pathway", event.id, "containedEvents")
|
256: url <- file.path(getOption("base.address"), path, id)
|
257: if (allForms) url <- file.path(url, "allForms")
|
274: ancestors.url <- file.path(getOption("base.address"), "data/event", id, "ancestors")
|
327: url <- file.path(getOption("base.address"), path, id)
|
331: ap.url <- file.path(url, "authoredPathways")
|
338: tmp.url <- file.path(getOption("base.address"), path, id, attribute)
|
371: url <- file.path(getOption("base.address"), path)
|
411: url <- file.path(getOption("base.address"), path, id)
|
453: url <- file.path(getOption("base.address"), path, class)
|
457: cnt.url <- file.path(url, "count")
|
484: if (minimised) url <- file.path(url, "min")
|
485: if (reference) url <-file.path(url, "reference")
|
543: url <- file.path(getOption("base.address"), paste0(path, "?query=", gsub("\\s", "%20", query)))
|
589: file.path(getOption("base.address"), path, "main"),
|
590: file.path(getOption("base.address"), path, "all"))
|
75: #' Events (Pathways and Reactions) in Reactome are organized in a hierarchical
|
129: #' Data in Reactome are organized in a hierarchical manner - Pathways contain Reactions,
|
136: ...(11 bytes skipped)...g{ReferenceEntities}: retrieves the ReferenceEntities for all PhysicalEntities in every constituent Pathway/Reaction
|
139: #' @param event.id a stable or db id of an Event (pathways and reactions)
|
168: msg <- "'Events' are found in the 'hasEvent' attribute of Pathways"
|
234: #' Pathway related queries
|
238: #' @param id a stable or db id of a PhysicalEntity or Event present in the pathways
|
240: #' @param allForms if set to \code{TRUE}, all low level pathways that contain the given PhysicalEntity (not Event) in all forms returned
|
241: #' @param top.level if set to \code{TRUE}, only top-level pathways returned
|
242: #' @return a dataframe containing requested pathways
|
263: # map to top level pathways
|
281: rownames(top.pathways) <- seq(1, nrow(top.pathways))
|
282: return(top.pathways)
|
284: return(pathways)
|
330: # add authored pathways if any
|
532: #' searchQuery(query="Biological oxidation", species="Mus musculus", types=c("Pathway", "Reaction"))
|
576: #' either manually curated or computationally inferred pathways
|
137: #' - \strong{EventsInPathways}: recursively retrieves all the Events contained in any given Event
|
140: ...(26 bytes skipped)...ipants to be retrieved, including "AllInstances", "PhysicalEntities", "ReferenceEntities", "EventsInPathways"
|
144: #' getParticipants("R-HSA-69306", "EventsInPathways")
|
150: "ReferenceEntities", "EventsInPathways")) {
|
165: } else if (retrieval == "EventsInPathways") {
|
236: #' To get the Events that contain the given PhysicalEntity or Event (i.e. subpathway).
|
244: #' getPathways("R-HSA-199420", "Homo sapiens")
|
245: #' @rdname getPathways
|
277: ancestors[ancestors$schemaClass == "TopLevelPathway",]
|
333: if (length(authoredPathways) != 0) all.info[["authoredPathways"]] <- authoredPathways
|
MetaboSignal:R/General_internal_functions.R: [ ] |
---|
185: path = all_paths[maxBW, ]
|
140: path_individual = as.character(row)
|
278: path_as_network = function(path) {
|
123: shortpath = rownames(as.matrix(unlist(ASP)))
|
347: pathM = convertTable(response)
|
122: ASP_paths = function (ASP) {
|
360: all_pathsGM_names = all_pathsGM
|
341: MS_FindPathway = function(match = NULL, organism_code = NULL) {
|
141: BW = sapply(path_individual, get_bw_score, BW_matrix)
|
180: ## Get global BW score for each path
|
186: path = as.character(path)
|
187: all_paths = matrix(path, ncol = length(path))
|
277: ##################### path_as_network ######################
|
280: for (i in 1:(length(path) - 1)) {
|
281: edge = c(path[i], path[i + 1])
|
348: colnames(pathM) = c("path_ID", "path_Description")
|
17: #metabolite is a substrate. It is used to calculate shortest paths with SP mode.
|
121: ####################### ASP_paths #######################
|
124: return(shortpath)
|
150: BW_ranked_SP = function (all_paths, BW_matrix, networkBW_i, mode) {
|
152: all_nodes = unique(as.vector(all_paths))
|
181: Global_BW_score = sapply (split(all_paths, row(all_paths)), get_global_BW_score,
|
189: return(all_paths)
|
342: file = paste("https://rest.kegg.jp/list/pathway/", organism_code, sep = "")
|
345: stop("A valid organism_code is required for KEGG_entry = pathway")
|
349: rownames(pathM) = NULL
|
351: target_matrix = pathM
|
352: target_column = pathM[, 2]
|
355: } else (return(pathM))
|
359: network_names = function(all_pathsGM, organism_code) {
|
361: all_nodes = unique(as.vector(all_pathsGM[, 1:2]))
|
365: all_pathsGM_names[all_pathsGM_names == all_nodes[i]] = all_names[i]
|
367: return(all_pathsGM_names)
|
340: #################### MS_FindPathway ####################
|
MSstatsBig:R/backends.R: [ ] |
---|
11: sparklyr::spark_read_csv(connection, "mstinput", path = input_file,
|
epivizrStandalone:R/startStandalone.R: [ ] |
---|
95: path=paste0("/", index_file),
|
217: path <- NULL
|
92: server <- epivizrServer::createServer(static_site_path = webpath, non_interactive=non_interactive, ...)
|
219: server <- epivizrServer::createServer(static_site_path = webpath, non_interactive=non_interactive, ...)
|
84: webpath <- system.file("www", package = "epivizrStandalone")
|
215: webpath <- ""
|
222: path=path,
|
TIN:R/correlationPlot.R: [ ] |
---|
104: path<-getwd()
|
105: cat("Plot was saved in ",paste(path,"/",fileName,sep=""),"\n")
|
PloGO2:inst/script/WGCNA_proteomics.R: [ ] |
---|
106: path <- system.file("files", package="PloGO2")
|
107: allDat = read.csv(file.path(path,"rice.csv") )
|
108: Group = read.csv(file.path(path, "group_rice.csv") ) [,2]
|
scde:R/functions.R: [ ] |
---|
5439: path <- env[['PATH_INFO']]
|
6044: path <- env[['PATH_INFO']]
|
2147: pathsizes <- unlist(tapply(vi, gcll, length))
|
5059: pathway.pc.correlation.distance <- function(pcc, xv, n.cores = 10, target.ndf = NULL) {
|
6173: pathcl <- ifelse(is.null(req$params()$pathcl), 1, as.integer(req$params()$pathcl))
|
1877: pagoda.pathway.wPCA <- function(varinfo, setenv, n.components = 2, n.cores = detectCores(), min.pathway.size = 10, max.pathway.size = 1e3, n.randomizations = 10, n.internal.shuffles = 0, n.starts = 10, center = TRUE, batch....(55 bytes skipped)...
|
5558: t.view.pathways <- function(pathways, mat, matw, env, proper.names = rownames(mat), colcols = NULL, zlim = NULL, labRow = NA, vhc = ...(145 bytes skipped)...
|
5684: pagoda.show.pathways <- function(pathways, varinfo, goenv = NULL, n.genes = 20, two.sided = FALSE, n.pc = rep(1, length(pathways)), colcols = NULL, zlim = NULL, showRowLabels = FALSE, cexCol = 1, cexRow = 1, nstarts = 10, ce...(117 bytes skipped)...
|
5698: c.view.pathways <- function(pathways, mat, matw, goenv = NULL, batch = NULL, n.genes = 20, two.sided = TRUE, n.pc = rep(1, length(pathways)), colcols = NULL, zlim = NULL, labRow = NA, vhc = NULL, cexCol = 1, cexRow = 1, nstarts = 50, ...(93 bytes skipped)...
|
461: ##' @param name URL path name for this app
|
5443: switch(path,
|
6047: switch(path,
|
1: ##' Single-cell Differential Expression (with Pathway And Gene set Overdispersion Analysis)
|
6: ##' The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis
|
9: ##' See vignette("pagoda") for a brief tutorial on pathway and gene set overdispersion analysis to identify and characterize cell subpopulations.
|
1292: ...(39 bytes skipped)...lowed for the estimated adjusted variance (capping of adjusted variance is recommended when scoring pathway overdispersion relative to randomly sampled gene sets)
|
1789: ##' such as ribosomal pathway variation) and subtracts it from the data so that it is controlled
|
1815: ##' cc.pattern <- pagoda.show.pathways(ls(go.env)[1:2], varinfo, go.env, show.cell.dendrogram = TRUE, showRowLabels = TRUE) # Look at...(30 bytes skipped)...
|
1845: ##' @param min.pathway.size minimum number of observed genes that should be contained in a valid gene set
|
1846: ##' @param max.pathway.size maximum number of observed genes in a valid gene set
|
1847: ...(103 bytes skipped)...ith each gene set (can be kept at 5 or 10, but should be increased to 50-100 if the significance of pathway overdispersion will be determined relative to random gene set models)
|
1873: ##' pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)
|
1901: gsl <- gsl[gsl.ng >= min.pathway.size & gsl.ng<= max.pathway.size]
|
1905: message("processing ", length(gsl), " valid pathways")
|
1954: ##' @param pwpca result of the pagoda.pathway.wPCA() call with n.randomizations > 1
|
1965: ##' pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)
|
2148: names(pathsizes) <- pathsizes
|
2151: rsdv <- unlist(lapply(names(pathsizes), function(s) {
|
2156: return(data.frame(n = as.integer(pathsizes), var = unlist(sdv), round = i, rvar = rsdv))
|
2160: data.frame(n = as.integer(pathsizes), var = unlist(sdv), round = i)
|
2216: ##' @param pwpca output of pagoda.pathway.wPCA()
|
2242: ##' pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)
|
2393: # determine genes driving significant pathways
|
2435: ##' @param pwpca output of pagoda.pathway.wPCA()
|
2453: ##' pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)
|
2460: pclc <- pathway.pc.correlation.distance(c(pwpca, clpca$cl.goc), tam$xv, target.ndf = 100, n.cores = n.cores)
|
2521: ##' pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)
|
2603: ##' pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)
|
2654: ##' @param tamr Combined pathways that show similar expression patterns. Output of \code{\link{pagoda.reduce.redundancy}}
|
2655: ##' @param row.clustering Dendrogram of combined pathways clustering
|
2667: ##' pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)
|
2724: ##' @param tamr Combined pathways that show similar expression patterns. Output of \code{\link{pagoda.reduce.redundancy}}
|
2725: ##' @param tam Combined pathways that are driven by the same gene sets. Output of \code{\link{pagoda.reduce.loading.redundancy}}...(0 bytes skipped)...
|
2728: ...(15 bytes skipped)...a Weighted PC magnitudes for each gene set provided in the \code{env}. Output of \code{\link{pagoda.pathway.wPCA}}
|
2732: ##' @param row.clustering Dendrogram of combined pathways clustering. Default NULL.
|
2733: ##' @param title Title text to be used in the browser label for the app. Default, set as 'pathway clustering'
|
2739: ...(47 bytes skipped)... env, pwpca, clpca = NULL, col.cols = NULL, cell.clustering = NULL, row.clustering = NULL, title = "pathway clustering", zlim = c(-1, 1)*quantile(tamr$xv, p = 0.95)) {
|
2764: # prepare pathway df
|
3559: .onUnload <- function(libpath) {
|
3560: library.dynam.unload("scde", libpath, verbose = TRUE)
|
5108: #set scale at top pathway?
|
5560: lab <- which(proper.names %in% na.omit(unlist(mget(pathways, envir = env, ifnotfound = NA))))
|
5564: lab <- which(proper.names %in% pathways)
|
5569: #table(rownames(mat) %in% mget(pathways, envir = env))
|
5660: ##' View pathway or gene weighted PCA
|
5662: ##' Takes in a list of pathways (or a list of genes), runs weighted PCA, optionally showing the result.
|
5663: ##' @param pathways character vector of pathway or gene names
|
5665: ##' @param goenv environment mapping pathways to genes
|
5668: ...(5 bytes skipped)...param n.pc optional integer vector giving the number of principal component to show for each listed pathway
|
5681: ##' @param ... additional arguments are passed to the \code{c.view.pathways}
|
5687: x <- c.view.pathways(pathways, varinfo$mat, varinfo$matw, goenv, batch = varinfo$batch, n.genes = n.genes, two.sided = two.si...(213 bytes skipped)...
|
5695: # takes in a list of pathways with a list of corresponding PC numbers
|
5696: # recalculates PCs for each individual pathway, weighting gene loading in each pathway and then by total
|
5697: # pathway variance over the number of genes (rough approximation)
|
5699: # are these genes or pathways being passed?
|
5701: x <- pathways %in% ls(goenv)
|
5703: x <- rep(FALSE, length(pathways))
|
5705: if(sum(x) > 0) { # some pathways matched
|
5707: message("WARNING: partial match to pathway names. The following entries did not match: ", paste(pathways[!x], collapse = " "))
|
5709: # look up genes for each pathway
|
5710: pathways <- pathways[x]
|
5711: p.genes <- mget(pathways, goenv, ifnotfound = NA)
|
5713: x <- pathways %in% rownames(mat)
|
5716: ...(9 bytes skipped)... message("WARNING: partial match to gene names. The following entries did not match: ", paste(pathways[!x], collapse = " "))
|
5718: p.genes <- list("genes" = pathways[x])
|
5719: pathways <- c("genes");
|
5720: } else { # neither genes nor pathways are passed
|
5721: stop("ERROR: provided names do not match either gene nor pathway names (if the pathway environment was provided)")
|
5728: # recalculate wPCA for each pathway
|
5729: ppca <- pagoda.path...(130 bytes skipped)...s), n.cores = 1, n.randomizations = 0, n.starts = 2, n.components = max(n.pc), verbose = FALSE, min.pathway.size = 0, max.pathway.size = Inf, n.internal.shuffles = 0)
|
5731: if(length(ppca) > 1) { # if more than one pathway was supplied, combine genes using appropriate loadings and use consensus PCA (1st PC) as a patte...(2 bytes skipped)...
|
5733: scaled.gene.loadings <- unlist(lapply(seq_along(pathways), function(i) {
|
5734: gl <- ppca[[pathways[i]]]$xp$rotation[, n.pc[i], drop = TRUE]*as.numeric(ppca[[pathways[i]]]$xp$sd)[n.pc[i]]/sqrt(ppca[[pathways[i]]]$n)
|
5735: names(gl) <- rownames(ppca[[pathways[i]]]$xp$rotation)
|
5777: } else { # only one pathway was provided
|
5970: ##' @field results Output of the pathway clustering and redundancy reduction
|
5972: ##' @field pathways
|
5983: fields = c('results', 'genes', 'pathways', 'mat', 'matw', 'goenv', 'renv', 'name', 'trim', 'batch'),
|
5986: initialize = function(results, pathways, genes, mat, matw, goenv, batch = NULL, name = "pathway overdispersion", trim = 1.1/ncol(mat)) {
|
5995: pathways <<- pathways
|
6012: gcl <- t.view.pathways(genes, mat = mat, matw = matw, env = goenv, vhc = results$hvc, plot = FALSE, trim = ltrim)
|
6058: ...(21 bytes skipped)... <link rel = "stylesheet" type = "text/css" href = "http://pklab.med.harvard.edu/sde/pathcl.css" / >
|
6064: ...(10 bytes skipped)... <script type = "text/javascript" src = "http://pklab.med.harvard.edu/sde/pathcl.js" > </script >
|
6072: '/pathcl.json' = { # report pathway clustering heatmap data
|
6126: '/pathwaygenes.json' = { # report heatmap data for a selected set of genes
|
6133: x <- c.view.pathways(gsub("^#PC\\d+# ", "", pws), mat, matw, goenv = goenv, n.pc = n.pcs, n.genes = ngenes, two.side...(75 bytes skipped)...
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6134: #x <- t.view.pathways(gsub("^#PC\\d+# ", "", pws), mat, matw, env = goenv, vhc = results$hvc, plot = FALSE, trim = lt...(14 bytes skipped)...
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6174: ii <- which(results$ct == pathcl)
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6251: '/pathways.json' = {
|
6252: lgt <- pathways
|
cTRAP:R/shinyInterface.R: [ ] |
---|
475: if (is.function(path)) path <- path()
|
418: path=".", globalUI=FALSE) {
|
476: ENCODEsamples <- loadENCODEsamples(ENCODEmetadata, path=path)
|
1194: inputFile <- file.path(token, sprintf("input_%s.Rda", rand))
|
1195: outputFile <- file.path(token, sprintf("output_%s.rds", rand))
|
1380: .predictTargetingDrugsServer <- function(id, x, path=".", globalUI=FALSE,
|
1405: corMatrix, path=path)
|
1469: .drugSetEnrichmentAnalyserServer <- function(id, x, path=NULL) {
|
1492: path=path)
|
1665: file="ENCODEmetadata.rds", path=".") {
|
1670: .diffExprENCODEloaderServer(id, metadata, cellLine, gene, path=path)
|
RBGL:R/interfaces.R: [ ] |
---|
308: path <- f
|
305: extractPath <- function(s, f, pens) {
|
307: # linear path from node s to node f
|
311: while (path[1] != s) {
|
312: if (i > maxl) # no path available
|
314: path <- "NA"
|
317: path <- c(pens[f], path)
|
321: as.numeric(path)
|
373: # obtain weights in g for path of nodes in char vec nl
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394: path_detail=as.vector(ans[[i]]),
|
182: ans <- .Call("BGL_dijkstra_shortest_paths_D",
|
365: nG[extractPath(nodeind(thiss), nodeind(thisf[j]), curdi)]
|
410: ans <- .Call("BGL_johnson_all_pairs_shortest_paths_D",
|
428: ans <- .Call("BGL_floyd_warshall_all_pairs_shortest_paths_D",
|
450: ans <- .Call("BGL_bellman_ford_shortest_paths",
|
478: ans <- .Call("BGL_dag_shortest_paths",
|
methylPipe:R/Allfunctions.R: [ ] |
---|
223: path <- paste0(files_location,"/",all_files[[i]])
|
28: filename <- file.path(output_folder,
|
31: filename <- file.path(output_folder,
|
58: all_files <- list.files(path = files_location, pattern = ".sam")
|
63: output.files[[read.context]] <- file.path(output_folder,
|
66: read.files <- file.path(files_location, all_files)
|
97: asBam(file.path(files_location, all_files[i]), destination=file.path(temp_folder, sample_name[i]), overwrite=TRUE)
|
101: bam.files <- file.path(path = temp_folder, paste(sample_name, ".bam", sep=""))
|
112: filename <- file.path(output_folder, paste0(sample_name[[i]],"_uncov", ".Rdata"))
|
220: all_files <- list.files(path = files_location, pattern = ".txt")
|
224: temp_data <- fread(path,nrows=10)
|
227: cmd <- paste("sed -i 's/\"//g'",path)
|
229: cmd <- paste("sed -i 's/^/chr/'",path)
|
268: Tabix_files <- list.files(path = files_location, pattern = "_tabix.txt")
|
11: output_folder <- normalizePath(output_folder)
|
50: files_location <- normalizePath(files_location)
|
51: output_folder <- normalizePath(output_folder)
|
132: files_location <- normalizePath(files_location)
|
186: BSprepare <- function(files_location, output_folder, tabixPath, bc=1.5/100) {
|
191: if(!is.character(tabixPath))
|
192: stop('tabixPath has to be of class character ..')
|
193: if(!file.exists(paste(tabixPath, '/tabix', sep='')))
|
194: stop('tabix not found at tabixPath ..')
|
195: if(!file.exists(paste(tabixPath, '/bgzip', sep='')))
|
196: stop('bgzip not found at tabixPath ..')
|
198: files_location <- normalizePath(files_location)
|
199: output_folder <- normalizePath(output_folder)
|
200: tabixPath <- normalizePath(tabixPath)
|
276: str <- paste0(tabixPath, '/bgzip ', output_folder, "/", filetb_name,"_out.txt")
|
280: str <- paste(tabixPath, '/tabix -s 1 -b 2 -e 2 -f ', fileoutgz, sep='')
|
SMITE:R/SMITE.R: [ ] |
---|
1277: path <- goseq::goseq(pwf, "hg19", "knownGene",
|
1196: PATHID2NAME <- AnnotationDbi::as.list(reactome.db::reactomePATHID2NAME)
|
1210: pathways <- KEGGREST::keggList("pathway", "hsa") ## returns the list of human pathways
|
1279: path <- cbind(path,(as.character(sapply(
|
1280: path$category, function(i){PATHID2NAME[[i]]}))))
|
1281: colnames(path)[6] <- "cat_name"
|
1282: subset(path, path$over_represented_pvalue < p_thresh)
|
1189: AnnotationDbi::as.list(reactome.db::reactomeEXTID2PATHID)
|
1201: link_kegg<- KEGGREST::keggLink("pathway", "hsa") ## returns pathways for each kegg gene
|
1202: list_link <- split(unname(link_kegg), names(link_kegg)) ## combines each pathway into list object for each gene
|
1211: PATHID2NAME <- as.list(pathways)
|
pathVar:R/pipeline.final.R: [ ] |
---|
634: path <- pvalue_results@pwayCounts[[pathway]]
|
780: path1 <- pvalue_results@pwayCounts1[[pathway]]
|
781: path2 <- pvalue_results@pwayCounts2[[pathway]]
|
99: pathVarOneSample <- function(dat.mat, pways, test = c("chisq", "exact"), varStat = c("sd",
|
131: pathwayCounts <- lapply(lapply(olap.pways, function(x) table(x, deparse.level = 0)), function(x) if (len...(10 bytes skipped)...
|
206: pathVarTwoSamplesCont <- function(dat.mat, pways, groups, boot = 1000, varStat = c("sd", "mean",
|
290: pathVarTwoSamplesDisc <- function(dat.mat, pways, groups, perc = c(1/3, 2/3), test = c("chisq",
|
344: pathwayCounts1 <- lapply(lapply(olap.pways1, function(x) table(x, deparse.level = 0)),
|
354: pathwayCounts2 <- lapply(lapply(olap.pways2, function(x) table(x, deparse.level = 0)),
|
853: pathDat1 <- as.data.frame(table(mixDat1))
|
855: pathDat2 <- as.data.frame(table(mixDat2))
|
943: pathname <- sapply(listPath, function(x) if (length(unlist(strsplit(x, "/"))) > 1) {
|
796: plotPath1 <- ggplot(path1, aes(x = Cluster, y = Number_of_genes, fill = Cluster)) + geom_bar(stat = "identity",
|
800: plotPath2 <- ggplot(path2, aes(x = Cluster, y = Number_of_genes, fill = Cluster)) + geom_bar(stat = "identity",
|
659: plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
738: plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
806: plotPathway1 <- plotPath1 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
809: plotPathway2 <- plotPath2 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
871: plotPathDat1 <- ggplot(path...(136 bytes skipped)...("Number of genes") + theme(legend.position = "none") + ggtitle("Group 1") + xlab("") + ylim(0, max(pathDat1[,2], pathDat2[,2]))
|
872: plotPathDat2 <- ggplot(path...(136 bytes skipped)...("Number of genes") + theme(legend.position = "none") + ggtitle("Group 2") + xlab("") + ylim(0, max(pathDat1[,2], pathDat2[,2]))
|
26: ...(25 bytes skipped)...ptions are TRUE or FALSE. If TRUE then the first column of the tab delimited file is expected to be path IDs. If FALSE, then the first column is expected to be pathway names.
|
645: path <- as.data.frame(path)
|
646: colnames(path) <- c("Cluster", "Number_of_genes")
|
653: d <- ggplot(path, aes(x = Cluster, y = Number_of_genes, fill = Cluster)) + geom_bar(stat = "identity",
|
663: plotPathway <- plotPathway + annotate("text", x = sigCat, y = path[sigCat + 0.1,
|
789: path1 <- as.data.frame(path1)
|
790: colnames(path1) <- c("Cluster", "Number_of_genes")
|
792: path2 <- as.data.frame(path2)
|
793: colnames(path2) <- c("Cluster", "Number_of_genes")
|
794: yLimMax <- max(path1[, 2], path2[, 2])
|
813: plotPathway1 <- plotPathway1 + annotate("text", x = sigCat, y = path1[sigCat +
|
815: plotPathway2 <- plotPathway2 + annotate("text", x = sigCat, y = path2[sigCat +
|
20: #makeDBList put your pathways text file into a list
|
21: #pway$PATHNAME is the pathway names from the file
|
22: #pway$PATHID is a vector of pathway ID numbers is there are any. Otherwise it will be a vector filled with NA
|
23: #pway$GENES is a list of vectors, where each vector are the genes for a single pathway
|
25: #file is a tab delimited text file, where first and second columns are pathwayID and pathway name. The third (or last column is the genes associated with each pathway, seperated by commas.
|
33: pways$PATHNAME <- as.vector(pwayTable[, 2])
|
34: pways$PATHID <- as.vector(pwayTable[, 1])
|
35: pways$GENES <- list(length(pways$PATHID))
|
37: for (i in 1:length(pways$PATHID)) {
|
43: pways$PATHID <- pways$PATHID[-i]
|
44: pways$PATHNAME <- pways$PATHNAME[-i]
|
49: pways$PATHNAME <- as.vector(pwayTable[, 1])
|
50: pways$PATHID <- rep("NA", length(pways$PATHNAME))
|
51: pways$GENES <- list(length(pways$PATHID))
|
53: for (i in 1:length(pways$PATHID)) {
|
59: pways$PATHID <- pways$PATHID[-i]
|
60: pways$PATHNAME <- pways$PATHNAME[-i]
|
64: names(pways$GENES) <- pways$PATHNAME
|
69: #pathVarOneSample
|
73: # 3. For each pathway, we extract the gene in our dataset and in which cluster they belong.
|
74: # 4. For each pathway, we look how the gene counts in each category and compare it to the reference counts with all th...(59 bytes skipped)...
|
78: # Output 1: tablePway columns are :pathway name, path...(46 bytes skipped)...or exact test,the percentage of genes from our dataset related to the total number of genes in each pathway, the number of genes from our dataset inside the pathway and the total number of genes inside the pathway
|
79: #Output 2: NAPways corresponds to the pathway names of the pathway having less than 10 genes for the Chi-Squared or also more than 500 genes for the exact tes.
|
80: # Output 3: genesInPway correspond to each pathway with the genes from the datasets belonging to it and in which cluster they were classsify.
|
83: # Output 6: pwayCounts is the genes counts of the each pathway in each cluster.
|
91: #Input 2: pways contains the pathways of interest (KEGG, REACTOME, etc...) in the same format that makeDBList
|
103: # check if any GENES are in the pathway.
|
106: stop("None of the genes in the data set are found in the given gene set or pathway")
|
125: # olap.pways contains the genes are in each pathway with their cluster number
|
127: names(olap.pways) <- pways$PATHNAME
|
130: # list of tables of the number of genes in each cluster per pathway
|
140: # Chi-Square or Exact test to compare the reference and the pathway distribution
|
142: # chisq test and ajustment of the pvalue for each pathway
|
143: pvals.pways <- sapply(pathwayCounts, function(x) if (sum(x) >= 10) {
|
154: # Exact test and ajustment of the pvalue for each pathway
|
157: # We perform the multinomial test on the pathway containing between 10 and 500 genes because a bigger number will involve too many possibilities ...(11 bytes skipped)...
|
158: pvals.pways <- sapply(pathwayCounts, function(x) if (sum(x) >= 10 & sum(x) < 500) {
|
170: xtab <- data.table(PwayName = pways$PATHNAME[not_na], PwayID = pways$PATHID[not_na], APval = apvals.pways,
|
172: NumOfGenesFromDataSetInPathway = lengths(olap.pways[not_na]), PathwaySize = pways$SIZE[not_na])
|
176: ...(50 bytes skipped)...ab, NAPways=pval.NA, genesInPway=olap.pways, refProb=pexp, refCounts=pexp * length(mix), pwayCounts=pathwayCounts, numOfClus=nmix, varStat=varStat, genesInClus=mix, var=vs)
|
181: #pathVarTwoSamplesCont
|
184: # 2. For each pathway, we extract the gene in our dataset.
|
185: # 3. For each pathway, we look how its genes are distributed and compare the 2 groups using the bootstrap Kolmogorov-S...(12 bytes skipped)...
|
189: # Output 1: tablePway columns are :pathway name, pathway IDs, adjusted p-value ffrom the boot KS test, the number of genes from our dataset inside the pathway and the total number of genes inside the pathway.
|
190: #Output 2: NAPways corresponds to the pathway names of the pathway having no genes inside the dataset.
|
191: # Output 3: genesInPway correspond to the genes from the dataset belonging to each pathway
|
200: #Input 2: pways contains the pathways of interest (KEGG, REACTOME, etc...) in the same format that makeDBList
|
209: # check if any GENES are in the pathway
|
212: stop("None of the genes in the data set are found in the given gene set or pathway")
|
236: # olap.pways contains the genes from the dataset in each pathway
|
238: names(olap.pways) <- pways$PATHNAME
|
239: # We compare the two densities (one for each group) of the genes of each pathway with the Kolmogorov-Smirnow test. Â Â Â Â
|
251: xtab <- data.table(PwayName = pways$PATHNAME[not_na], PwayID = pways$PATHID[not_na], APval = apvals,
|
253: NumOfGenesFromDataSetInPathway = lengths(olap.pways[not_na]), PathwaySize = pways$SIZE[not_na])
|
261: #pathVarTwoSamplesDisc
|
265: # 3. For each pathway, we extract the gene in our dataset and in which cluster they belong.
|
266: # 4. For each pathway, we look at the gene counts in each category and compare the 2 samples to each other with all th...(60 bytes skipped)...
|
269: # Output 1: tablePway columns are :pathway name, path...(11 bytes skipped)...justed p-value, the percentage of genes in our dataset related to the total number of genes in each pathway, the number of genes from our dataset inside the pathway and the total number of genes inside the pathway.
|
270: #Output 2: NAPways corresponds to the pathway names of the pathway having no genes inside the dataset.
|
271: # Output 3: genesInPway1 corresponds to the genes from the dataset belonging to each pathway in the first sample
|
272: # Output 4: genesInPway2 corresponds to the genes from the dataset belonging to each pathway in the second sample
|
273: # Output 5: pwayCounts1 corresponds to a list of tables of the number of genes in each cluster per pathway for group 1
|
274: # Output 6: pwayCounts2 corresponds to a list of tables of the number of genes in each cluster per pathway for group 2
|
283: #Input 2: pways contains the pathways of interest (KEGG, REACTOME, etc...) in the same format that makeDBList
|
294: # check if any GENES are in the pathway
|
297: stop("None of the genes in the data set are found in the given gene set or pathway")
|
338: # olap.pways contains the genes from the dataset in each pathway
|
340: names(olap.pways1) <- pways$PATHNAME
|
342: names(olap.pways2) <- pways$PATHNAME
|
343: # list of tables of the number of genes in each cluster per pathway
|
353: # list of tables of the number of genes in each cluster per pathway
|
364: # chisq test and ajustment of the pvalue for each pathway
|
365: pvals.pways <- sapply(pways$PATHNAME, function(x) if (sum(pathwayCounts1[x][[1]]) >=
|
367: exp.val <- pathwayCounts1[x][[1]] #forgot the.val
|
368: chi <- sum((pathwayCounts2[x][[1]] - exp.val)^2/exp.val)
|
374: pval.NA <- pways$PATHNAME[-not_na]
|
377: # Exact test and ajustment of the pvalue for each pathway
|
380: # We perform the multinomial test on the pathway containing between 10 and 500 genes because a bigger number will involve too many possibilities ...(11 bytes skipped)...
|
381: pvals.pways <- sapply(pways$PATHNAME, function(x) if (sum(pathwayCounts1[x][[1]]) >=
|
382: 10 & sum(pathwayCounts1[x][[1]]) < 500) {
|
383: pexp <- pathwayCounts1[x][[1]]/sum(pathwayCounts1[x][[1]])
|
384: multinomial.test(as.vector(pathwayCounts2[x][[1]]), as.vector(pexp), useChisq = FALSE)$p.value
|
391: pval.NA <- pways$PATHNAME[-not_na]
|
395: xtab <- data.table(PwayName = pways$PATHNAME[not_na], PwayID = pways$PATHID[not_na], APval = apvals.pways,
|
397: NumOfGenesFromDataSetInPway = lengths(olap.pways1[not_na]), PathwaySize = pways$SIZE[not_na])
|
402: ...(36 bytes skipped)...", tablePway=xtab, NAPways=pval.NA, genesInPway1=olap.pways1, genesInPway2=olap.pways2, pwayCounts1=pathwayCounts1, pwayCounts2=pathwayCounts2, groups=groups, groupNames=groupNames, var1=var_1, var2=var_2, varStat=varStat)
|
409: #It is a function that returns the significant pathway(s),which category(ies) from this pathway are significant and which gene(s) belongs to this(ese) category(ies).
|
413: # Output 1: genesInSigPways1 contains the genes per significant pathway belonging to the significant category.
|
414: #Output 2: sigCatPerPway contains the category(ies) per pathway that are significant.
|
418: #Input 1: pvalue_results is result from the pathVarOneSample function
|
430: warning("There are no significant pathways. Quitting significant_category function and returning empty object")
|
434: # PathName that were significant in xtab.
|
436: # The list of table with the number of genes in each cluster from the significant pathways
|
442: # results contain the p-value for each category in each pathway computed with the binomial test.
|
450: # For each significant pathway we look which category(ies) is are significant and the genes
|
477: #It is a function that returns the significant pathways and which genes belongs to these #pathways.
|
481: # Output 1: genesInSigPways1 contains the genes belonging to each significant pathway
|
485: #Input 1: pvalue_results is result from the pathVarTwoSamplesCont function
|
493: warning("There are no significant pathways. Quitting significant_category function and returning empty object")
|
497: # Pathways that were significant in xtab.
|
499: # Genes from the dataset inside each significant pathway
|
507: #It is a function that returns the significant pathways and which genes belong to these pathways
|
511: # Output 1: genesInSigPways1 contains the genes belonging to each significant pathway in significant categories in the first sample
|
512: # Output 2: genesInSigPways2 contains the genes belonging to each significant pathway in significant categories in the second sample
|
513: # Output 3: sigCatPerPway contains the significant categories in each pathway
|
517: #Input 1: pvalue_results is result from the pathVarTwoSamplesDisc function
|
527: warning("There are no significant pathways. Quitting significant_category function and returning empty object")
|
531: # PathName that were significant in xtab.
|
533: # The list of table with the number of genes in each cluster from the significant pathways
|
540: # results contain the p-value for each category in each pathway computed with the binomial
|
550: # For each significant pathway we look which category(ies) are significant and the genes belonging to this(ese) category(ies). ...(81 bytes skipped)...
|
576: ...(77 bytes skipped)...es cases and then use sigOneSample, sigTwoSamplesCont, or sigTwoSamplesDisc to find the significant pathways.
|
582: #Input 1: pvalue_results is result from the pathVarOneSample, pathVarTwoSamplesCont, or pathVarTwoSamplesDisc function.
|
602: #It is a function that returns the plot of the reference counts along with the plot of a chosen #pathway. This function is made for output from pathVarOneSample.
|
605: # plot of the reference and a pathway counts
|
608: #Input 1: pvalue_results is result from the pathVarOneSample function
|
609: #Input 2: pathway is the chosen pathway you want to plot.
|
613: #If sig is not NULL, the function will check if the pathway is a significant one and if yes the
|
619: plotOneSample <- function(pvalue_results, pathway, sig) {
|
620: mp <- pathway
|
621: # If the name of the pathway is two long it will cut it into two lines in the plot.
|
644: # data frame for the pathway distribution
|
656: # If the pathway is one of the significant ones, the title will be in red. and the categories, if any, we be high...(24 bytes skipped)...
|
657: if (pathway %in% names(category)) {
|
658: sigCat <- category[[pathway]]
|
670: # plot the reference and pathway counts side by side
|
676: ...(34 bytes skipped)... plot of the two densities (one for each group) of the statistics (sd, mad, cv or mean) of a chosen pathway. This function is made for output from pathVarTwoSamplesCont.
|
682: #Input 1: pvalue_results is result from the pathVarTwoSamplesCont function
|
683: #Input 2: pathway is the chosen pathway you want to plot.
|
687: #If sig is not NULL, the function will check if the pathway is a significant one and if yes the title will be printed in red.
|
690: plotTwoSamplesCont <- function(pvalue_results, pathway, sig) {
|
691: mp <- pathway
|
692: # If the name of the pathway is two long it will cut it into two lines in the plot.
|
705: # If the number of genes of the pathway is less than 3, it is not possible to draw a density and it will return an empty plot with this ...(8 bytes skipped)...
|
706: if (xtab[PwayName == pathway, NumOfGenesFromDataSetInPathway] < 3) {
|
717: genes <- pvalue_results@genesInPway[[pathway]]
|
726: ...(0 bytes skipped)... # Plot of the two densities (one for each group) of the variability of the genes inside the pathway.
|
736: # If we included the results of sigTwoSamplesCont, it will verify if the pathway is one of them and if yes the title will be printed in red.
|
737: if (pathway %in% significant) {
|
752: ##It is a function that returns 2 plots of the 2 samples for a chosen pathway. This function is made for output from pathVarTwoSamplesDisc.
|
755: # plot of the 2 samples for a significant pathway
|
758: #Input 1: pvalue_results is result from the pathVarTwoSamplesDisc function
|
759: #Input 2: pathway is the chosen pathway you want to plot.
|
763: #If sig is not NULL, the function will check if the pathway is a significant one and if yes the title will be printed in red.
|
766: plotTwoSamplesDisc <- function(pvalue_results, pathway, sig) {
|
767: mp <- pathway
|
768: # If the name of the pathway is two long it will cut it into two lines in the plot.
|
791: # data frame for the pathway distribution
|
803: # If the pathway is one of the significant ones, the title will be in red. and the categories, if any, we be high...(24 bytes skipped)...
|
804: if (pathway %in% names(category)) {
|
805: sigCat <- category[[pathway]]
|
819: plotPathway1 <- plotPath1 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
821: plotPathway2 <- plotPath2 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
824: # plot the reference and pathway counts side by side
|
854: colnames(pathDat1) <- c("Cluster", "Number_of_genes")
|
856: colnames(pathDat2) <- c("Cluster", "Number_of_genes")
|
862: results <- apply(rbind(pathDat2[,2],pathDat1[,2]/pathDat1[,2]/sum(pathDat1[,2])), 2, function(y) multinomial.test(c(y[1], sum(pathDat2[,2]) - y[1]), prob = c(y[2],1 - y[2]))$p.value)
|
874: plotPathDat1 <- plotPathDat1 + annotate("text", x = category, y = pathDat1[category +
|
876: plotPathDat2 <- plotPathDat2 + annotate("text", x = category, y = pathDat2[category +
|
895: ...(46 bytes skipped)...rom the one sample or two samples cases and then use plotOneSample or plotTwoSamples for the chosen pathway.
|
898: # plot of the results of the one or two samples case for a chosen pathway.
|
901: #Input 1: pvalue_results is the result from the pathVarOneSample, pathVarTwoSamplesCont, or pathVarTwoSamplesDisc function
|
902: #Input 2: pathway is the chosen pathway you want to plot.
|
906: #If sig is not NULL, the function will check if the pathway is a significant one. And they will be highlighted in the resulting plot (see plotOneSample or p...(14 bytes skipped)...
|
909: plotPway <- function(pvalue_results, pathway, sig = NULL) {
|
912: plotOneSample(pvalue_results, pathway, sig)
|
914: plotTwoSamplesCont(pvalue_results, pathway, sig)
|
916: plotTwoSamplesDisc(pvalue_results, pathway, sig)
|
923: #Save as a pdf the plots for the one or two samples case of the significant pathway or a chosen list of pathway..
|
926: # Save as a pdf the plots of the significant pathway or a chosen list of pathway.
|
929: #Input 1: pvalue_results is the result from the pathVarOneSample, pathVarTwoSamplesCont, or pathVarTwoSamplesDisc function
|
931: #Input 3: listPath is "significant" if you want to save the plots of the significant pathways or can be a list of names of pathway of interest.
|
934: #If sig is not NULL, the function will check if the pathway is a significant one. And they will be highlighted in the resulting plot (see plotOneSample or p...(14 bytes skipped)...
|
937: saveAsPDF <- function(pvalue_results, sig, listPath = "significant") {
|
938: # If listPath='significant' we will save as pdf all the plots corresponding to the significant pathway from sig. Other wise it will save the pathways given to listPath.
|
939: if (listPath[1] == "significant") {
|
940: listPath <- names(sig@genesInSigPways1)
|
942: # The name of the file will be the pathname where we replace '/' by '_'
|
948: # save as PDF all the pathways significant or given in listPath
|
949: for (i in 1:length(pathname)) {
|
950: pdf(file = paste(pathname[i], ".pdf", sep = ""), width = 10, height = 7)
|
951: plotPway(pvalue_results, listPath[i], sig)
|
959: #It is a function that returns one list of genes for group 1 and one for group 2 of a chosen pathway having their statistics (sd, mad, cv or mean) inside a chosen interval.
|
962: # Output 1: genes1 contains the genes belonging to the pathway in the given window for group 1.
|
963: # Output 2: genes2 contains the genes belonging to the pathway in the given window for group 2.
|
964: # Output 3: genesAll contains the genes from the dataset belonging to the pathway
|
967: #Input 1: pvalue_results is result from the pathVarTwoSamplesCont function
|
968: #Input 2: pathway is the chosen pathway.
|
973: getGenes <- function(pvalue_results, pathway, window) {
|
978: genes <- olap.pways[[pathway]]
|
981: # Take the genes from group 1 from the pathway belonging to the window
|
983: # Take the genes from group 3 from the pathway belonging to the window
|
985: # Take all the genes from the pathway
|
431: sig <- new("significantPathway", genesInSigPways1=list(), sigCatPerPway=list(), thresPValue=numeric())
|
471: sig <- new("significantPathway", genesInSigPways1=genes, sigCatPerPway=category, thresPValue=pvalue)
|
494: sig <- new("significantPathway2", genesInSigPways1=list(), thresPValue=numeric())
|
501: sig <- new("significantPathway2", genesInSigPways1=genes, thresPValue=pvalue)
|
528: sig <- new("significantPathway3", genesInSigPways1=list(), genesInSigPways2=list(), sigCatPerPway=list(), thresPValue=numeric()...(1 bytes skipped)...
|
570: sig <- new("significantPathway3", genesInSigPways1=genes1, genesInSigPways2=genes2, sigCatPerPway=category, thresPValue=pvalue)...(0 bytes skipped)...
|
667: plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
671: grid.arrange(arrangeGrob(plotRef, plotPathway, nrow = 1))
|
742: plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
746: plot(plotPathway)
|
825: grid.arrange(arrangeGrob(plotPathway1, plotPathway2, nrow = 1))
|
879: plotPathDat1 <- plotPathDat1 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background...(63 bytes skipped)...
|
880: plotPathDat2 <- plotPathDat2 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background...(63 bytes skipped)...
|
884: grid.arrange(arrangeGrob(plotPathDat1, plotPathDat2, nrow = 1))
|
887: grid.arrange(arrangeGrob(plotPathDat1, plotPathDat2, nrow = 1))
|
ggtree:R/tree-utilities.R: [ ] |
---|
726: path <- c(anc_from[1:i], rev(anc_to[1:(j-1)]))
|
732: path <- get.path(phylo, from, to)
|
700: path_length <- sapply(1:(root-1), function(x) get.path_length(tr, root, x))
|
716: get.path <- function(phylo, from, to) {
|
980: pathLength <- sapply(1:length(tr$tip.label), function(i) {
|
731: get.path_length <- function(phylo, from, to, weight=NULL) {
|
701: i <- which.max(path_length)
|
702: return(get.path(tr, root, i))
|
705: ##' path from start node to end node
|
708: ##' @title get.path
|
727: return(path)
|
734: return(length(path)-1)
|
749: for(i in 1:(length(path)-1)) {
|
750: ee <- get_edge_index(df, path[i], path[i+1])
|
981: get.path_length(tr, i, root, yscale)
|
984: ordered_tip <- order(pathLength, decreasing = TRUE)
|
ACE:R/ACE.R: [ ] |
---|
134: readCounts <- QDNAseq::binReadCounts(bins, path = inputdir)
|
960: if (dirname(filename)==".") {newpath <- file.path(outputdir,filename)}
|
131: currentdir <- file.path(outputdir,paste0(b,"kbp"))
|
136: saveRDS(readCounts, file = file.path(outputdir, paste0(b, "kbp-raw.rds")))
|
146: saveRDS(copyNumbersSegmented, file = file.path(outputdir,paste0(b,"kbp.rds")))
|
157: currentdir <- file.path(outputdir,paste0(substr(files[f],0,nchar(files[f])-4)))
|
159: copyNumbersSegmented <- readRDS(file.path(inputdir,files[f]))
|
166: write.table(parameters, file=file.path(outputdir,"parameters.tsv"), quote = FALSE, sep = "\t", na = "", row.names = FALSE)
|
182: qdir <- file.path(currentdir,paste0(q,"N"))
|
189: dir.create(file.path(qdir,"likelyfits"))
|
258: fp <- file.path(qdir,pd$name[a])
|
263: dir.create(file.path(fp,"graphs"))
|
284: imagefunction(file.path(fp,paste0(pd$name[a],"_errorlist.",imagetype)))
|
320: fn <- file.path(fp,"graphs",paste0(pd$name[a], " - ",q,"N fit ", m, ".",imagetype))
|
348: imagefunction(file.path(qdir,"likelyfits",paste0(pd$name[a],"_bestfit_",q,"N.",imagetype)),width=10.5)
|
350: imagefunction(file.path(qdir,"likelyfits",paste0(pd$name[a],"_bestfit_",q,"N.",imagetype)),width=720)
|
358: imagefunction(file.path(qdir,"likelyfits",paste0(pd$name[a],"_lastminimum_",q,"N.",imagetype)),width=10.5)
|
360: imagefunction(file.path(qdir,"likelyfits",paste0(pd$name[a],"_lastminimum_",q,"N.",imagetype)),width=720)
|
377: pdf(file.path(fp,paste0("summary_",pd$name[a],".pdf")),width=10.5)
|
382: imagefunction(file.path(fp,paste0("summary_",pd$name[a],".",imagetype)), width = 720)
|
386: imagefunction(file.path(fp,paste0("summary_",pd$name[a],".",imagetype)), width = 2160, height = 480*ceiling(length(plots)/3...(3 bytes skipped)...
|
399: pdf(file.path(qdir,"summary_likelyfits.pdf"),width=10.5)
|
402: pdf(file.path(qdir,"summary_errors.pdf"))
|
406: imagefunction(file.path(qdir,paste0("summary_likelyfits.",imagetype)), width = 2160, height = 480*length(pd$name))
|
409: imagefunction(file.path(qdir,paste0("summary_errors.",imagetype)), width = 1920, height = 480*ceiling(length(pd$name)/4))
|
415: pdf(file.path(qdir,"summary_errors.pdf"))
|
419: imagefunction(file.path(qdir,paste0("summary_errors.",imagetype)), width = 1920, height = 480*ceiling(length(pd$name)/4))
|
425: write.table(fitpicker, file=file.path(qdir,paste0("fitpicker_",q,"N.tsv")), quote = FALSE, sep = "\t", na = "", row.names = FALSE)
|
830: # frequency in percentage). It can also be a file path to a tab-delimited
|
832: # by getadjustedsegments. Again, this can be either a data frame or a file path
|
1003: copyNumbersSegmented <- readRDS(file.path(inputdir,files[1]))
|
1005: if(missing(modelsfile)){models <- try(read.table(file.path(inputdir,"models.tsv"), header = TRUE, comment.char = "", sep = "\t"))
|
1009: if (dir.exists(file.path(inputdir,"variantdata"))) {
|
1010: variantdata <- file.path(inputdir,"variantdata")
|
1072: if (!dir.exists(file.path(outputdir,"newplots"))) {dir.create(file.path(outputdir,"newplots"))}
|
1074: imagefunction(file.path(outputdir,"newplots",paste0(pd$name[a],".",imagetype)),width=10.5)
|
1078: imagefunction(file.path(outputdir,"newplots",paste0(pd$name[a],".",imagetype)), width=720)
|
1091: variantfile <- file.path(variantdata,paste0(prefix,pd$name[a],postfix,varext))
|
1092: folder <- file.path(outputdir,"variantdata")
|
1104: if (!dir.exists(file.path(outputdir,"segmentfiles"))) {dir.create(file.path(outputdir,"segmentfiles"))}
|
1105: fn <- file.path(outputdir,"segmentfiles",paste0(pd$name[a],"_segments.",segext))
|
961: else {newpath <- sub(dirname(filename),outputdir,filename)}
|
962: fn <- gsub(".csv","_ACE.csv",newpath)
|
peakPantheR:R/methods_peakPantheRAnnotation.R: [ ] |
---|
1724: device = "png", path = saveFolder, dpi = 100, width = 21,
|
1853: path_cpdMeta <- paste(saveFolder, "/", annotationName,
|
1871: path_specMeta <- paste(saveFolder, "/", annotationName,
|
1883: path_var <- paste(saveFolder, "/", annotationName, "_", i, ".csv",
|
1918: path_summary <- paste(saveFolder, "/", annotationName, "_summary.csv",
|
1116: .filepath <- x@filepath[i]
|
1863: tmp_filepath <- filepath(object)
|
2045: .spectraPaths <- resSpectra$spectraPaths
|
2182: resetAnnot_spectraPathMetadata <- function(previousAnnotation, spectraPaths,
|
2186: .spectraPaths <- filepath(previousAnnotation)
|
1076: return(tools::file_path_sans_ext(basename(object@filepath)))
|
1386: #' @param saveFolder (str) Path of folder where annotationParameters_summary.csv
|
1399: #' spectraPaths <- c('./path/file1', './path/file2', './path/file3')
|
1610: #' @param saveFolder (str) Path of folder where annotationParameters_summary.csv
|
1703: # @param saveFolder (str) Path where plots will be saved
|
1727: # output path
|
1758: #' @param saveFolder (str) Path of folder where the annotation result csv will
|
1855: utils::write.csv(tmp_outCpdMeta, file = path_cpdMeta, row.names = FALSE,
|
1858: if (verbose) { message("Compound metadata saved at ", path_cpdMeta) }
|
1873: utils::write.csv(tmp_outSpecMeta, file = path_specMeta, row.names = FALSE,
|
1876: if (verbose) { message("Spectra metadata saved at ", path_specMeta) }
|
1885: utils::write.csv(tmp_var, file = path_var, row.names = TRUE,
|
1889: message("Peak measurement \"", i, "\" saved at ", path_var)
|
1920: utils::write.csv(tmp_summary, file = path_summary, row.names = TRUE,
|
1923: if (verbose) { message("Summary saved at ", path_summary) }
|
24: length(object@filepath), " samples. \n", sep = "")
|
53: # @filepath length. Slot type is not checked as \code{setClass} enforces it.
|
78: #' # Paths to spectra files
|
119: #' # Paths to spectra files
|
160: #' # Paths to spectra files
|
206: #' # Paths to spectra files
|
253: #' # Paths to spectra files
|
286: # filepath
|
287: setGeneric("filepath", function(object, ...) standardGeneric("filepath"))
|
288: #' filepath accessor
|
290: #' @return (str) A character vector of file paths, of length number of spectra
|
293: #' @aliases filepath
|
300: #' # Paths to spectra files
|
319: #' filepath(annotation)
|
324: setMethod("filepath", "peakPantheRAnnotation", function(object) {
|
325: object@filepath
|
343: #' # Paths to spectra files
|
386: #' # Paths to spectra files
|
430: #' # Paths to spectra files
|
472: #' # Paths to spectra files
|
512: #' # Paths to spectra files
|
552: #' # Paths to spectra files
|
593: #' # Paths to spectra files
|
636: #' # Paths to spectra files
|
695: #' # Paths to spectra files
|
742: #' # Paths to spectra files
|
788: #' # Paths to spectra files
|
817: #' nbSamples accessor established on filepath
|
828: #' # Paths to spectra files
|
852: return(length(object@filepath))
|
868: #' # Paths to spectra files
|
913: #' # Paths to spectra files
|
946: nbSample <- length(object@filepath)
|
953: rownames(tmpAnnotation) <- object@filepath
|
961: rownames(tmpAnnotation) <- object@filepath
|
977: rownames(tmpAnnotation) <- object@filepath
|
1000: #' # Paths to spectra files
|
1041: #' filename accessor by spliting filepath
|
1052: #' # Paths to spectra files
|
1134: FIR = .FIR, uROI = .uROI, filepath = .filepath,
|
1214: #' # Paths to spectra files
|
1398: #' # Paths to spectra files
|
1416: #' savePath <- tempdir()
|
1419: #' outputAnnotationParamsCSV(emptyAnnotation, saveFolder=savePath, verbose=TRUE)
|
1488: #' # Paths to spectra files
|
1627: #' # Paths to spectra files
|
1652: #' savePath1 <- tempdir()
|
1653: #' outputAnnotationDiagnostic(annotation, saveFolder=savePath1, savePlots=FALSE,
|
1771: #' # Paths to spectra files
|
1796: #' savePath1 <- tempdir()
|
1797: #' outputAnnotationResult(annotation, saveFolder=savePath1,
|
1867: tmp_outSpecMeta <- data.frame(filepath = tmp_filepath,
|
1941: #' the slots (\code{filepath} (from \code{spectraPaths}), \code{ROI},
|
1947: #' @param spectraPaths NULL or a character vector of spectra file paths, to set
|
1972: #' (\code{cpdID}, \code{cpdName}, \code{ROI}, \code{filepath}, \code{TIC},
|
1984: #' # Paths to spectra files
|
2188: message(" Previous \"filepath\" value kept")
|
2415: #' # Paths to spectra files
|
80: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
95: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
121: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
136: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
162: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
177: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
208: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
223: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
255: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
270: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
302: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
317: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
345: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
360: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
388: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
403: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
432: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
447: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
474: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
489: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
514: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
529: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
554: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
569: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
595: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
610: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
638: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
653: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
697: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
712: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
744: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
759: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
790: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
805: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
830: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
845: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
870: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
885: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
915: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
930: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
1002: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
1017: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
1054: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
1069: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
1216: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
1231: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
1412: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
1490: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
1505: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
1629: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
1644: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
1773: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
1788: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
1931: function(previousAnnotation, spectraPaths = NULL, targetFeatTable = NULL,
|
1939: #' \code{spectraPaths}) or compounds (\code{targetFeatTable}) are passed, the
|
1986: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
2000: #' smallAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
2011: #' newSpectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
2015: #' spectraPaths=newSpectraPaths,
|
2026: function(previousAnnotation, spectraPaths, targetFeatTable, uROI, FIR,
|
2042: # spectraPaths, spectraMetadata
|
2043: resSpectra <- resetAnnot_spectraPathMetadata(previousAnnotation,
|
2044: spectraPaths, spectraMetadata, verbose)
|
2061: # Create new object In all case (old or new value) spectraPaths and
|
2063: peakPantheRAnnotation(spectraPaths = .spectraPaths,
|
2181: # resetAnnotation spectraPaths, spectraMetadata
|
2185: if (all(is.null(spectraPaths))) {
|
2208: .spectraPaths <- spectraPaths
|
2210: message(" New \"spectraPaths\" value set")
|
2229: return(list(spectraPaths = .spectraPaths,
|
2417: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
|
2431: #' smallAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
|
dir.expiry:R/clearDirectories.R: [ ] |
---|
99: path <- file.path(dir, version)
|
111: acc.path <- file.path(dir, expfile)
|
5: #' @param dir String containing the path to a package cache containing any number of versioned directories.
|
16: #' If the last access date is too old, the corresponding subdirectory in \code{path} is treated as expired and is deleted.
|
40: #' version.dir <- file.path(cache.dir, version)
|
70: plock <- .plock_path(dir)
|
100: vlock <- .vlock_path(path)
|
112: last.used <- as.integer(read.dcf(acc.path)[,"AccessDate"])
|
116: unlink(acc.path, force=TRUE)
|
117: unlink(paste0(acc.path, lock.suffix), force=TRUE)
|
118: unlink(path, recursive=TRUE, force=TRUE)
|
snapcount:R/basic_query_functions.R: [ ] |
---|
273: path <- paste(compilation, paste0(endpoint, "?"), sep = "/")
|
319: paste0(pkg_globals$snaptron_host, path, paste(query, collapse = "&"))
|
recountmethylation:inst/extdata/scripts/data_analyses.R: [ ] |
---|
26: path <- system.file("extdata", "metadata", package = "recountmethylation")
|
20: savepath <- paste(dfp, env.name, sep = "/")
|
27: mdpath <- paste(path, list.files(path)[1], sep = "/")
|
327: save(ds, file = file.path("data_analyses", "df-l2med-signals.rda"))
|
28: md <- get(load(mdpath))
|
qusage:R/qusage.R: [ ] |
---|
532: path = q$pathways[[i]]
|
537: path = QSarray$pathways[[i]]
|
379: PDFs = pathMeans = Sizes = NULL
|
792: SDPath = apply(calcBayesCI(QSarray,low=0.5,up=0.8413448)[,path.index,drop=F],2,function(x)x[2]-x[1])
|
800: XPath = getXcoords(QSarray,i,addVIF=addVIF)
|
808: PDFPath<-approx( XPath, QSarray$path.PDF[,i],X_Sample,rule=2)$y
|
47: qs.results.comb$path.mean = 2 * qs.results.comb$path.mean
|
59: for(i in c("var.method","labels","pairVector","pathways","path.size")){
|
408: geneResults$path.mean = pathMeans
|
409: geneResults$path.size = Sizes
|
412: geneResults$path.PDF = PDFs
|
503: if(!is.null(geneResults$path.PDF)){ ##if defined, rescale the pdf with the new vif values
|
504: geneResults$path.PDF = t(t(geneResults$path.PDF) / pdfScaleFactor(geneResults))
|
524: if(is.null(QSarray$path.PDF)){stop("convolution results not found.")}
|
525: p = sapply(1:ncol(QSarray$path.PDF), function(i){
|
526: if(!is.null(QSarray$path.size) && QSarray$path.size[i]==0){return(NA)}
|
533: mean(q$mean[-path])
|
538: null.hyp = mean(QSarray$mean[-path])
|
545: # path = QSarray$pathways[[i]]
|
546: # null.hyp=mean(getExAbs(QSarray$dof[path])*QSarray$SD[path])
|
551: PDF_NORM<-QSarray$path.PDF[,i]/sum(QSarray$path.PDF[,i])
|
552: # sum(QSarray$path.PDF[1:findInterval(0,x),i]) / sum(QSarray$path.PDF[,i])
|
571: twoWay.pVal <- function(grp1, grp2, path.index1 = 1:numPathways(grp1), path.index2 = 1:numPathways(grp2),
|
575: return(twoCurve.pVal(grp1, grp2, path.index1, path.index2, alternative, direction,addVIF))
|
582: path.index1 = 1:numPathways(grp1),
|
583: path.index2 = 1:numPathways(grp2),
|
589: # if(ncol(grp1$path.PDF)!=ncol(grp2$path.PDF) | all(colnames(grp1$path.PDF) != colnames(grp2$path.PDF))){
|
592: if(length(path.index1)!=length(path.index2)){
|
595: # if(sum(names(grp1$path.mean[path.index1])!=names(grp2$path.mean[path.index2]))){
|
599: x1 = sapply(path.index1,function(i){getXcoords(grp1,i,addVIF=addVIF)})
|
600: x2 = sapply(path.index2,function(i){getXcoords(grp2,i,addVIF=addVIF)})
|
605: p = sapply(1:length(path.index1), function(i){
|
607: PDF1<-approx( x1[,i], grp1$path.PDF[,path.index1[i]],seq(Min[i],Max[i],length.out=Length1+Length2),rule=2)$y
|
608: PDF2<-approx( x2[,i], grp2$path.PDF[,path.index2[i]],seq(Min[i],Max[i],length.out=Length1+Length2),rule=2)$y
|
615: ## path.index can either be an integer between 1 and length(path.means), or the name of the pathway.
|
618: getXcoords = function(QSarray,path.index=1, addVIF=!is.null(QSarray$vif)){ #,absolute=FALSE){
|
619: if(length(path.index)>1){stop("path.index must be of length 1")}
|
622: sif = ifelse(addVIF,sqrt(QSarray$vif[path.index]),1)
|
626: seq(-1,1,length.out=QSarray$n.points)* QSarray$ranges[path.index]* sif + QSarray$path.mean[path.index]
|
630: # MeanAbs<-mean(abs(QSarray$mean[QSarray$pathways[[path.index]]]))
|
631: # seq(-1,1,length.out=QSarray$n.points)* QSarray$ranges[path.index]* sif / QSarray$path.size[path.index] + MeanAbs
|
642: pdfSum = colSums(QSarray$path.PDF)
|
655: cis = sapply(1:ncol(QSarray$path.PDF), function(i){
|
657: any(is.na(QSarray$path.PDF[,i]))){return(c(NA,NA))}
|
659: cdf = cumsum(QSarray$path.PDF[,i])
|
668: colnames(cis) = colnames(QSarray$path.PDF)
|
771: path.index=1:numPathways(QSarray), ##The pathways to calculate the pVals for.
|
778: ##check path.index
|
779: if(is.character(path.index)){
|
780: path.index = match(path.index, names(QSarray$pathways))
|
793: if(!addVIF)SDPath = SDPath / sqrt(QSarray$vif[path.index])
|
799: for(i in path.index){
|
824: TMP<-pnorm( ( Means[j] - QSarray$path.mean[i] ) / sqrt( SDPath[i]^2 + (DOF[j])/(DOF[j]-2)*SD[j]^2) )
|
835: for(i in path.index){
|
842: if(compareTo=="mean")SUBSTRACT=QSarray$path.mean[i]
|
875: if(!CompareWithZero)SUBSTRACT=QSarray$path.mean[i]
|
17: geneSets, ##a list of pathways to be compared. Each item in the list is a vector of names that correspond to the row names of ...(5 bytes skipped)...
|
74: geneSets, ##a list of pathways to be compared. Each item in the list is a vector of names that correspond to the row names of ...(5 bytes skipped)...
|
308: if(!is.null(QSarray$pathways)){stop("too late...aggregateGeneSet already being called")}
|
320: ##Simple function to read in a .gmt file and return a list of pathways
|
322: if(!grepl("\\.gmt$",file)[1]){stop("Pathway information must be a .gmt file")}
|
331: #######Combine individual gene differential expresseion for each pathway (Neg) ~ 1 minute
|
334: geneSets, ##a list of pathways to be compared, each item in the list is a vector of names that correspond to the gene names fr...(25 bytes skipped)...
|
336: silent=TRUE ##If false, print a "." every fifth pathway, as a way to keep track of progress
|
401: pathMeans = c(pathMeans, mean(Means[Indexes]))
|
405: colnames(PDFs) = names(pathMeans) = names(Sizes) = names(geneSets)
|
407: geneResults$pathways = geneSets
|
425: # geneSets=NULL, ##a list of pathways calculate the vif for, each item in the list is a vector of names that correspond to the gene n...(32 bytes skipped)...
|
430: if(is.null(geneResults$pathways)){stop("Pathway Information not found. Please provide a list of gene sets.")}
|
431: geneSets = geneResults$pathways
|
439: # geneResults$pathways = geneSets
|
516: ## function for calculating a p-value for each pathway convolution as output by aggregateGeneSet.
|
519: ...(59 bytes skipped)... true (and alternative="two.sided"), p-values will be returned as eiter positive or negative if the pathway is greater or less than 0, respectively.
|
521: ...(15 bytes skipped)... selfContained=TRUE ##If false, rather than comparing to 0, it will compare the pathway mean to the mean of all genes not in the pathway.
|
578: ## A method to compare the pathway convolutions in two QSarray objects.
|
588: ##if the names of the pathways don't match,
|
590: # stop("Pathways in grp1 do not match pathways in grp2")
|
593: stop("Number of pathways in grp1 do not match number of pathways in grp2")
|
596: # warning("Some of the comparisons are made between different pathways")
|
614: ## Calculates the x-coordinates for the PDF of a given pathway.
|
629: # ###First calculate the new mean of the pathway based on the absolute values of the means
|
656: if( (!is.null(QSarray$pathways) && length(QSarray$pathways[[i]])==0 ) ||
|
772: silent=TRUE, ##If false, print a "." every fifth pathway, as a way to keep track of progress
|
784: if(is.null(QSarray$pathways)){stop("Pathway Information not found. Please run aggregateGeneSet first.")}
|
785: geneSets = QSarray$pathways
|
805: Min<-min(c(XGene[1]+ Means[Indexes],XPath[1]))
|
806: Max<-max(c(XGene[NPoints]+ Means[Indexes],XPath[QSarray$n.points]))
|
815: PS<-c(PS,compareTwoDistsFaster(PDFGene,PDFPath, alternative="two.sided"))
|
860: ...(17 bytes skipped)... CompareWithZero=TRUE ###Logical, if TRUE compares with mean of zero, else with mean of pathway
|
862: if(is.null(QSarray$pathways)){stop("Pathway Information not found. Please provide a list of gene sets.")}
|
863: geneSets = QSarray$pathways
|
898: # geneSets=NULL, ##a list of pathways calculate the vif for, each item in the list is a vector of names that correspond to the gene n...(32 bytes skipped)...
|
901: if(is.null(geneResults$pathways)){stop("Pathway Information not found. Please provide a list of gene sets.")}
|
902: geneSets = geneResults$pathways
|
640: sif = sapply(1:numPathways(QSarray),function(i){ifelse(addVIF,sqrt(QSarray$vif[i]),1)})
|
AffiXcan:R/AffiXcan.R: [ ] |
---|
642: for(path in tbaPaths) {
|
643: tba <- readRDS(path)
|
6: #' @param tbaPaths A vector of strings, which are the paths to
|
186: #' @param tbaPaths A vector of strings, which are the paths to
|
623: #' @param tbaPaths, A vector of strings, which are the paths to
|
654: #' @param tbaPaths A vector of strings, which are the paths to
|
1064: #' @param tbaPaths A vector of strings, which are the paths to
|
38: #' objects listed in the param tbaPaths. Each of these lists contain two
|
92: #' trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
102: #' tbaPaths=trainingTbaPaths, regionAssoc=regionAssoc, cov=trainingCovariates,
|
105: affiXcanTrain <- function(exprMatrix, assay, tbaPaths, regionAssoc, cov=NULL,
|
107: regionsCount <- overlookRegions(tbaPaths)
|
131: pca <- affiXcanPca(tbaPaths, varExplained, scale, regionsCount,
|
141: pcs <- affiXcanPcs(tbaPaths, affiXcanTraining, scale, BPPARAM,
|
194: #' every MultiAssayExperiment RDS object indicated in the param tbaPaths; it is
|
200: #' of MultiAssayExperiment objects from tbaPaths) of the samples that have to
|
205: #' listed in the param tbaPaths. Each of these lists contain two objects:
|
221: #' tbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
223: #' regionsCount <- overlookRegions(tbaPaths)
|
235: #' pca <- affiXcanPca(tbaPaths=tbaPaths, varExplained=80, scale=TRUE,
|
238: affiXcanPca <- function(tbaPaths, varExplained=80, scale=TRUE, regionsCount,
|
244: for(i in seq(1,length(tbaPaths))) {
|
246: tbaMatrixMAE <- readRDS(tbaPaths[i])
|
376: #' of MultiAssayExperiment objects from tbaPaths) of the samples that have to
|
401: #' tbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
403: #' regionsCount <- overlookRegions(tbaPaths)
|
416: #' pca <- affiXcanPca(tbaPaths=tbaPaths, varExplained=80, scale=TRUE,
|
516: #' tbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
518: #' regionsCount <- overlookRegions(tbaPaths)
|
531: #' pca <- affiXcanPca(tbaPaths=tbaPaths, varExplained=80, scale=TRUE,
|
627: #' MultiAssayExperiment RDS object indicated in the param tbaPaths
|
633: #' testingTbaPaths <- system.file("extdata","testing.tba.toydata.rds",
|
636: #' regionsCount <- overlookRegions(tbaPaths=testingTbaPaths)
|
638: overlookRegions <- function(tbaPaths) {
|
663: #' of MultiAssayExperiment objects from tbaPaths) of the samples that have not
|
680: #' trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
682: #' testingTbaPaths <- system.file("extdata","testing.tba.toydata.rds",
|
688: #' tbaPaths=trainingTbaPaths, regionAssoc=regionAssoc, cov=trainingCovariates,
|
691: #' pcs <- affiXcanPcs(tbaPaths=testingTbaPaths, affiXcanTraining=training,
|
694: affiXcanPcs <- function(tbaPaths, affiXcanTraining, scale, BPPARAM=bpparam(),
|
700: for(i in seq(1,length(tbaPaths))) {
|
703: tbaMatrixMAE <- readRDS(tbaPaths[i])
|
746: #' trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
756: #' tbaPaths=trainingTbaPaths, regionAssoc=regionAssoc, cov=trainingCovariates,
|
801: #' trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
811: #' tbaPaths=trainingTbaPaths, regionAssoc=regionAssoc, cov=trainingCovariates,
|
814: #' testingTbaPaths <- system.file("extdata","testing.tba.toydata.rds",
|
817: #' pcs <- affiXcanPcs(tbaPaths=testingTbaPaths, affiXcanTraining=training,
|
863: #' trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
873: #' tbaPaths=trainingTbaPaths, regionAssoc=regionAssoc, cov=trainingCovariates,
|
876: #' testingTbaPaths <- system.file("extdata","testing.tba.toydata.rds",
|
879: #' pcs <- affiXcanPcs(tbaPaths=testingTbaPaths, affiXcanTraining=training,
|
967: #' trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
977: #' tbaPaths=trainingTbaPaths, regionAssoc=regionAssoc, cov=trainingCovariates,
|
980: #' imputedExpr <- affiXcanImpute(tbaPaths=trainingTbaPaths,
|
1029: #' trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
1039: #' tbaPaths=trainingTbaPaths, regionAssoc=regionAssoc, cov=trainingCovariates,
|
1042: #' imputedExpr <- affiXcanImpute(tbaPaths=trainingTbaPaths,
|
1078: #' trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
|
1088: #' tbaPaths=trainingTbaPaths, regionAssoc=regionAssoc,
|
1091: #' testingTbaPaths <- system.file("extdata","testing.tba.toydata.rds",
|
1094: #' exprmatrix <- affiXcanImpute(tbaPaths=testingTbaPaths,
|
1096: affiXcanImpute <- function(tbaPaths, affiXcanTraining, scale=TRUE,
|
1098: regionsCount <- overlookRegions(tbaPaths)
|
1104: tbaPaths refers to ", regionsCount, " regions\n"))
|
1109: pcs <- affiXcanPcs(tbaPaths, affiXcanTraining, scale, BPPARAM)
|
BiocBook:R/init.R: [ ] |
---|
217: path <- file.path("inst", "assets", "_book.yml")
|
246: gert::git_init(path = repo)
|
207: file.path(tmpdir, 'BiocBook.template'),
|
218: .fix_placeholders(file.path(repo, path), pkg = repo, usr = user)
|
219: cli::cli_alert_success(cli::col_grey("Filled out `{cli::col_cyan(path)}` fields"))
|
222: path <- "README.md"
|
223: .fix_placeholders(file.path(repo, path), pkg = repo, usr = user)
|
224: cli::cli_alert_success(cli::col_grey("Filled out `{cli::col_cyan(path)}` fields"))
|
227: path <- "DESCRIPTION"
|
228: .fix_placeholders(file.path(repo, path), pkg = repo, usr = user)
|
229: cli::cli_alert_success(cli::col_grey("Filled out `{cli::col_cyan(path)}` fields"))
|
230: cli::cli_alert_info(cli::col_grey("Please finish editing the `{cli::col_cyan(path)}` fields, including:"))
|
236: path <- file.path("inst", "index.qmd")
|
237: .fix_placeholders(file.path(repo, path), pkg = repo, usr = user)
|
238: cli::cli_alert_success(cli::col_grey("Filled out `{cli::col_cyan(path)}` fields"))
|
239: cli::cli_alert_info(cli::col_grey("Please finish editing the `{cli::col_cyan(path)}` fields, including the `Welcome` section"))
|
255: version <- read.dcf(file.path(repo, "DESCRIPTION"))[1,"BiocBookTemplate"]
|
325: path = "_temp",
|
362: charToRaw('{ "source": { "branch": "gh-pages", "path": "/docs" } }'),
|
386: file.path("inst", "assets", "cover.png")
|
R453Plus1Toolbox:R/methods-AVASet.R: [ ] |
---|
270: path = file.path(dirname, s, r)
|
370: path = file.path(dirname, s, r)
|
774: path = unique(c(subset(RData, sample==s)$currentPath, subset(RData, sample==s)$currentPath))
|
23: dir_root = file.path(dirname, "Amplicons")
|
24: dir_results = file.path(dir_root,"Results")
|
25: dir_projectDef = file.path(dir_root,"ProjectDef")
|
26: dir_variants = file.path(dir_results, "Variants")
|
27: dir_align = file.path(dir_results, "Align")
|
37: | !file.exists(file.path(dir_variants, "currentVariantDefs.txt"))
|
38: | !file.exists(file.path(dir_projectDef, "ampliconsProject.txt"))
|
95: doAmplicon = file.path(avaBin, "doAmplicon")
|
217: file_sample = file.path(dirname, file_sample)
|
218: file_amp = file.path(dirname, file_amp)
|
219: file_reference = file.path(dirname, file_reference)
|
220: file_variant = file.path(dirname, file_variant)
|
221: file_variantHits = file.path(dirname, file_variantHits)
|
271: files = list.files(path)
|
281: amps_align[[i]]= readLines(file.path(path, file))
|
325: file_sample = file.path(dirname, file_sample)
|
326: file_amp = file.path(dirname, file_amp)
|
327: file_reference = file.path(dirname, file_reference)
|
371: files = list.files(path)
|
381: amps_align[[i]]= readLines(file.path(path, file))
|
675: text = readLines(file.path(dir_projectDef, "ampliconsProject.txt"))
|
689: warning(paste("sample information missing in", file.path(dir_projectDef, "ampliconsProject.txt")))
|
757: warning(paste("Read data or MID entries missing in", file.path(dir_projectDef, "ampliconsProject.txt")))
|
775: if(!any(is.na(path))){
|
776: path = sapply(strsplit(path, split="\\."), function(x)x[1])
|
777: ptp = paste(substr(path, 1, nchar(path)-2), collapse=",")
|
778: lane = paste(substr(path, nchar(path)-1, nchar(path)), collapse=",")
|
822: variantDefs=read.table(file=file.path(dir_variants, "currentVariantDefs.txt"), sep="\t",
|
902: if(file.exists(file.path(dir_variants, s_id))){
|
903: detections = dir(file.path(dir_variants, s_id), pattern=".txt$", ignore.case=FALSE)
|
905: det = read.table(file.path(dir_variants, s_id, d), sep="\t",
|
949: pfLines=readLines(file.path(dir_projectDef, "ampliconsProject.txt"))
|
997: pfLines=readLines(file.path(dir_projectDef, "ampliconsProject.txt"))
|
1052: thisSampleDir=file.path(dir_align, samples$SampleID[s])
|
1055: thisRefDir=file.path(thisSampleDir, r)
|
1056: alignFile=file.path(thisRefDir, paste(samples$SampleID[s],
|
700: "annotation", "currentPath", "name", "originalPath", "readDataGroup", "sequenceBlueprint"),
|
760: RData = data.frame(sample=samples, currentPath=rep(NA, numSamples),
|
TPP2D:R/import_funcs.R: [ ] |
---|
348: Experiment <- Path <- Compound <- NULL
|
570: Path <- label <- conc <- Compound <- Experiment <-
|
371: givenPaths <- NULL
|
253: "Experiment", "Path", "Path",
|
254: "Path", "Condition", "Replicate",
|
334: #' @param infoTable character string of a file path to
|
372: if (any("Path" %in% colnames(infoTable))) {
|
373: if (all(infoTable$Path == "") || all(is.na(infoTable$Path))) {
|
374: message("Removing empty 'Path' column from config table")
|
375: infoTable <- infoTable %>% select(-Path)
|
378: givenPaths <- infoTable$Path
|
443: #' @param configTable character string of a file path to a config table
|
485: files <- configTable$Path
|
511: "RefCol", "Path", "Condition")
|
573: if(any(grepl("Path", colnames(configWide)))){
|
575: dplyr::select(-Path) %>%
|
756: #' @param configTable character string of a file path to a config table
|
433: expCond = infoTable$Condition, files = givenPaths,
|
MuData:R/write_h5mu.R: [ ] |
---|
313: path <- paste(H5Iget_name(parent), key, sep="/")
|
315: filepath=file, name=path, chunkdim=chunkdim, parent=parent, datasetname=key)
|
crlmm:R/cnrma-functions.R: [ ] |
---|
42: path <- system.file("extdata", package=pkgname)
|
1391: path <- system.file("extdata", package=pkgname)
|
43: ##multiple.builds <- length(grep("hg19", list.files(path)) > 0)
|
44: snp.file <- list.files(path, pattern="snpProbes_hg")
|
47: snp.file <- list.files(path, pattern="snpProbes.rda")
|
51: snp.file <- list.files(path, pattern="snpProbes_hg")
|
61: ## load(file.path(path, "snpProbes.rda"))
|
62: ## } else load(file.path(path, paste("snpProbes_", genome, ".rda", sep="")))
|
63: load(file.path(path, snp.file))
|
71: load(file.path(path, cn.file))
|
73: ## load(file.path(path, "cnProbes.rda"))
|
74: ## } else load(file.path(path, paste("cnProbes_", genome, ".rda", sep="")))
|
1392: load(file.path(path, "cnProbes.rda"))
|
1393: load(file.path(path, "snpProbes.rda"))
|
1465: path,
|
1468: load(file.path(path, "snpFile.rda"))
|
1470: load(file.path(path, "cnFile.rda"))
|
gDRstyle:R/build_tools.R: [ ] |
---|
105: remotes::install_local(path = repo_path)
|
15: #' @param base_dir String, path to dir with token file
|
24: gh_access_token_file <- file.path(base_dir, filename)
|
89: #' @param repo_path String of repository directory.
|
100: installLocalPackage <- function(repo_path,
|
133: deps_yaml <- file.path(base_dir, "/dependencies.yaml")
|
ZygosityPredictor:R/fncts.R: [ ] |
---|
1465: path <- find_path(sub_checked_read_presence %>%
|
1220: find_path <- function(connections, mut_id1, mut_id2) {
|
1234: phase_along_path <- function(path, sub_checked_read_presence, bam_raw,
|
1223: shortest_paths(graph, from = mut_id1, to = mut_id2, mode = "all")$vpath
|
1222: shortest_path <-
|
1224: if (length(shortest_path) > 0) {
|
1225: return(names(unlist(shortest_path)))
|
1237: rel_sub_combs <- lapply(seq_len(length(path)), function(N){
|
1238: if(N==length(path)){
|
1243: (path[[N]]==mut_id1|path[[N]]==mut_id2)&
|
1244: (path[[N+1]]==mut_id1|path[[N+1]]==mut_id2)
|
1463: # path <- find_path(still_present_muts, sub_checked_read_presence,
|
1468: if(!is.null(path)){
|
1469: catt(printLog, 5, c("path found:",
|
1470: paste(unlist(path), collapse=" - ")))
|
1475: which(vars_in_between$mut_id %in% unlist(path)),],
|
1482: sub_fin_comb <- phase_along_path(path, sub_checked_read_presence,
|
1490: "all combinations of path have assigned status")
|
1503: "recombination impossible: more than one diff status in path"
|
1506: ext_snp_info <- "recombination imposiible: null status in path"
|
1509: ext_snp_info <- "no connecting path between main muts"
|
1926: #' @param bamDna path to bam-file
|
1927: #' @param bamRna optional; path to rna file (bam format)
|
1959: #' @param vcf character; path to variant call file (.vcf.gz format).
|
1219: #' @importFrom igraph graph_from_data_frame shortest_paths
|
ELMER:R/plots.R: [ ] |
---|
924: readr::write_delim(pairs, path = filename, append = TRUE)
|
614: write_tsv(cbind(as.data.frame(pairs)[,c(1:3,6)],corretlation.tab),path = file.name.table)
|
628: write_tsv(cbind(as.data.frame(pairs)[,c(1:3,6)],corretlation.tab),path = file.name.table)
|
974: filename <- file.path(dir,track.names[idx])
|
976: filename <- file.path(dir,paste0(sample,".bw"))
|
994: #' dir(path = "analysis",
|
1068: ret <- summarizeTF(path = x,
|
1103: function(path){
|
1104: TF <- readr::read_csv(dir(path = path, pattern = ".significant.TFs.with.motif.summary.csv",
|
1106: motif <- readr::read_csv(dir(path = path, pattern = ".motif.enrichment.csv",
|
1129: function(path){
|
1130: TF <- readr::read_csv(dir(path = path,
|
1133: motif <- readr::read_csv(dir(path = path,
|
1141: TF.meth.cor <- get(load(dir(path = path, pattern = ".TFs.with.motif.pvalue.rda", recursive = T, full.names = T)))
|
1185: function(path){
|
1186: TF <- readr::read_csv(dir(path = path,
|
1190: motif <- readr::read_csv(dir(path = path,
|
1200: TF.meth.cor <- get(load(dir(path = path,
|
1214: TF.meth.cor$analysis <- path
|
famat:R/compl_data.R: [ ] |
---|
568: path<-h[2]
|
710: path<-paste(stringr::str_sub(k, 1, 3),
|
858: path<-stringr::str_split(s[1], "__")[[1]]
|
366: notin_path<-vapply(elem_names, function(e){
|
369: nb_path<-length(first_item[first_item %in% "X"])
|
387: kegg_path<-pathways[stringr::str_sub(pathways, 1, 3) == "hsa"]
|
390: path_walks_k<-vapply(kegg_path, function(x){
|
396: wp_path<-pathways[stringr::str_sub(pathways, 1, 2) == "WP"]
|
397: path_walks_w<-vapply(wp_path, function(x){
|
404: path_walks_r<-vapply(first_walks_r, function(x){
|
425: path_walks<-rbind(final_walks_r, path_walks_k,path_walks_w)
|
536: cluster_elem<-save_cluster_elem<-listele[[1]];notin_path<-listele[[2]]
|
585: path_inter<-tagged[tagged$path == path,]
|
601: heatmap<-listhtmp[[1]]; notin_path<-listhtmp[[2]]; hierapath<-listhtmp[[3]]
|
670: rea_path<-sorted_path[stringr::str_sub(sorted_path, 1, 3) == "R-H"]
|
708: kegg_path<-sorted_path[stringr::str_sub(sorted_path, 1, 3) == "hsa"]
|
740: wp_path<-sorted_path[stringr::str_sub(sorted_path, 1, 2) == "WP"]
|
771: type_path<-function(sorted_path, hierapath){
|
800: path_types<-unique(types$root)
|
802: type_path<-types[types[, 2] %in% p, 1]#concerned pathways
|
948: filter_path<-function(tagged,size){
|
949: path_inter<-as.vector(tagged[,4])
|
950: sorted_path<-apply(size,1,function(x){ #sort pathways obtained
|
951: path_elem<-as.integer(x[4])+as.integer(x[8])
|
965: central<-listparam[[5]]; no_path<-listparam[[6]];
|
969: sorted_path<-filter_path(tagged,size)
|
971: path_walks<-listpath[[1]]; max<-listpath[[2]]
|
977: heatmap<-listtab[[1]]; notin_path<-listtab[[2]]; hierapath<-listtab[[3]]
|
996: path_cat<-stringr::str_split(i[6], ", ")[[1]]
|
435: pathidtoname <- as.list(reactome.db::reactomePATHID2NAME)
|
516: hierapath<-vapply(root_ids, function(r){
|
542: heatmap<-listhiera[[1]]; hierapath<-listhiera[[2]]
|
963: size<-listparam[[1]]; pathways<-listparam[[2]]; tagged<-listparam[[3]];
|
970: listpath<-sort_hiera(sorted_path)
|
370: if(element == TRUE && nb_path == 0){list(e)}
|
373: notin_path<-unname(unlist(notin_path))
|
381: if (element == TRUE){return(list(cluster, notin_path))}
|
388: kegg_path<-paste(stringr::str_sub(kegg_path,1,3),
|
389: stringr::str_sub(kegg_path,5),sep="")
|
393: path_walks_k<-as.data.frame(sort(unlist(path_walks_k)))
|
394: if(ncol(path_walks_k) == 0){path_walks_k<-data.frame(walks=character())}
|
400: path_walks_w<-as.data.frame(sort(unlist(path_walks_w)))
|
401: if(ncol(path_walks_w) == 0){path_walks_w<-data.frame(walks=character())}
|
412: path_walks_r<-rm_vector(unname(unlist(path_walks_r)))
|
413: path_walks_r<-path_walks_r[stringr::str_detect(path_walks_r, ">")]
|
415: final_walks_r<-vapply(path_walks_r, function(x){
|
416: dupl<-which(stringr::str_detect(path_walks_r, x))
|
417: dupl<-dupl[-which(dupl == which(path_walks_r == x))]
|
424: names(final_walks_r)<-names(path_walks_w)<-names(path_walks_k)<-"walks"
|
426: max<-max(stringr::str_count(path_walks[,1],">"))+1
|
427: return(list(path_walks, max))
|
434: treeview, no_path, list_elem){
|
455: paste("'", size[size$path == node, 2], "/",
|
456: size[size$path == node, 4], sep=""),
|
457: paste("'", size[size$path == node, 6], "/",
|
458: size[size$path == node, 8], sep=""),NA)
|
463: colnames(heatmap)<-c("path_name", "path_id", "meta_ratio", "gene_ratio",
|
466: heatmap[which(heatmap[, 4] == "'/"), 4]<-"'0/0";tags<-no_path$tag
|
480: cluster_hiera<-function(heatmap, size, tagged, no_path){
|
532: cluster_htmp<-function(heatmap, tags, size, tagged, no_path){
|
538: cluster_elem<-cluster_elem[!(cluster_elem %in% notin_path)]
|
539: heatmap<-heatmap[,c("path_name", "path_id", "meta_ratio", "gene_ratio",
|
541: listhiera<-cluster_hiera(heatmap, size, tagged, no_path)
|
550: if(x[2] %in% tagged$path){
|
556: names(heatmap)<-c("path_name", "path_id", "meta_ratio", "gene_ratio",
|
558: return(list(heatmap, notin_path, hierapath, save_cluster_elem))
|
562: final_tab<-function(build_hm, pathways, size, sorted_path, no_path,
|
564: heatmap<-hiera_info(pathways, size, sorted_path, build_hm,
|
565: no_path, list_elem)
|
566: sub_htmp<-heatmap[2:nrow(heatmap),]; tags<-no_path$tag #direct interactions
|
569: pre_elem<-c(size[size$path %in% path, 3], size[size$path %in% path, 7])
|
586: path_inter<-path_inter[path_inter$tag ==
|
588: if(nrow(path_inter)>0){list("X")}
|
600: listhtmp<-cluster_htmp(heatmap, tags, size, tagged, no_path)
|
603: return(list(heatmap, notin_path, hierapath, save_cluster_elem))
|
625: infos_elem<-function(genes, notin_path, meta, keggchebiname, no_path,
|
634: genetab<-pre_genetab[which(!(pre_genetab[,1] %in% notin_path)),]
|
635: gene_notin<-pre_genetab[which(pre_genetab[,1] %in% notin_path),]
|
653: intetab<-apply(no_path, 1, function(p){
|
664: "go", "path", "type")
|
669: type_reactome<-function(sorted_path){
|
672: rea_types<-vapply(rea_path,function(r){
|
707: type_kegg<-function(sorted_path){
|
709: kegg_types<-vapply(kegg_path, function(k){
|
712: hiera<-kegg_hiera[stringr::str_detect(kegg_hiera[, 1], path), ]
|
725: else if (path == "hsa01100"){
|
739: type_wp<-function(sorted_path){
|
741: wp_types<-vapply(wp_path, function(w){
|
772: kegg_type<-type_kegg(sorted_path)#kegg types
|
773: rea_types<-type_reactome(sorted_path)#Reactome types
|
774: wp_types<-type_wp(sorted_path)#wikipathways types
|
801: hieratypes<-vapply(path_types, function(p){
|
804: if(length(intersect(type_path, h[["name"]]))>0){h[["index"]]}
|
807: if(length(intersect(type_path, h[["name"]]))>0){h[["name"]]}
|
872: list(paste("x : ",element,"\ny : ",path[length(path)],
|
881: list(paste("x : ", element, "\ny : ", path[length(path)],
|
952: if (path_elem>0){
|
954: if(num/path_elem>=0.2){x[1]}
|
957: sorted_path<-unname(unlist(sorted_path))
|
958: sorted_path<-rm_vector(c(sorted_path[!is.na(sorted_path)],path_inter))
|
959: return(sorted_path)
|
972: path_walks<-tidyr::separate(path_walks, 1, as.character(c(seq_len(max))),
|
974: treeview<-tree_view(path_walks);names(treeview)<-c(seq_len(ncol(treeview)))
|
975: listtab<-final_tab(treeview, pathways, size, sorted_path, no_path,
|
981: listelm<-infos_elem(gene_list, notin_path, meta_list, keggchebiname,
|
982: no_path, go_genelist)
|
986: listype<-type_path(sorted_path, hierapath)
|
998: %in% path_cat),2]), collapse=", ")
|
1002: names(intetab)<-c("tag", "first_item", "link", "sec_item", "go", "path",
|
341: ##find which elements are found in the same pathways, and put them together
|
342: ##find which pathways contain the same elements also
|
343: ##if element=T, also return user's elements which aren't in pathways
|
385: #filter entire pathways hierarchy to build a hierarchy concerning our pathways
|
386: sort_hiera<-function(pathways){
|
406: if(length(pathways[pathways %in% rea_walks])>0){
|
407: rea_walks<-rm_vector(rea_walks[c(1, which(rea_walks%in%pathways))])
|
430: #add informations about pathway hierarchies to the final heatmap
|
432: #names and ids of pathways in hierarchies
|
433: hiera_info<-function(pathways, size, sorted_pathways,
|
443: name<-pathways[pathways[,2] == node, 1]
|
447: pathways[pathways[,2] == node, 1], sep=""))
|
450: htmp<-c(htmp,paste(space, stringr::str_sub(pathidtoname[[node]],
|
451: 15, nchar(pathidtoname[[node]])), sep=""))
|
479: ##the hierarchy pathways are added to the root
|
527: hierapath[length(hierapath)]=NULL
|
528: return(list(heatmap, hierapath))
|
544: hierapath<-lapply(hierapath,function(x){
|
549: heatmap<-apply(heatmap,1,function(x){#pathway with direct interaction ?
|
561: ##build heatmap of hierarchies of pathways and elements included in them
|
668: #reactome pathways types
|
706: #kegg pathways types
|
738: #wikipathways pathways types
|
747: if(root%in%c("classic metabolic pathway", "regulatory pathway")
|
770: #pathways types=roots of pathways hierarchy
|
799: ##list of concerned hierarchies by pathways types
|
803: index<-lapply(hierapath, function(h){
|
806: name<-lapply(hierapath, function(h){
|
947: #filter pathways regarding user's element ratio and direct interactions
|
1010: hierapath, save_cluster_elem, centrality, inter_values,
|
671: mapnameid <- as.list(reactome.db::reactomePATHID2NAME) #id-name mapping
|
biodb:R/BiodbConn.R: [ ] |
---|
734: path <- cch$getFilePath(self$getCacheId(), name='download', ext=ext)
|
147: #' Get the path to the persistent cache file.
|
727: #' Gets the path where the downloaded content is written.
|
728: #' @return The path where the downloaded database is written.
|
736: logDebug0('Download path of ', self$getId(), ' is "', path, '".')
|
738: return(path)
|
743: #' @param src Path to the downloaded file.
|
151: #' containing the paths to the cache files corresponding to the requested
|
156: fp <- c$getFilePath(self$getCacheId(), entry.id, self$getEntryFileExt())
|
729: getDownloadPath=function() {
|
793: if ( ! file.exists(self$getDownloadPath()))
|
795: self$getDownloadPath())
|
EpiMix:R/TCGA_Download_Preprocess.R: [ ] |
---|
1643: path <- eh[[hub_id]]
|
176: nameForDownloadedFileFullPath <- paste0(saveDir, nameForDownloadedFile)
|
41: #' @param saveDir path to directory to save downloaded files.
|
50: #' @return DownloadedFile path to directory with downloaded files.
|
405: #' @param METdirectory path to the 27K or 450K data
|
1497: #' @param TargetDirectory Path to save the sample.info. Default: ''.
|
1636: #' @return local file path where the lncRNA expression data are saved
|
1644: return(path)
|
7: #' @return list with paths to downloaded files for both 27k and 450k methylation data.
|
116: # warnMessage <- paste0('\nNot returning any viable url data paths
|
193: untar(nameForDownloadedFileFullPath, exdir = saveDir)
|
965: #' @return list with paths to downloaded files for gene expression.
|
SpliceWiz:R/BuildRef.R: [ ] |
---|
1311: path <- tryCatch(BiocFileCache::bfcadd(bfc, url),
|
600: .validate_path <- function(reference_path, subdirs = NULL) {
|
683: map_path <- file.path(normalizePath(reference_path), "Mappability")
|
1116: r_path <- file.path(reference_path, "resource")
|
1117: gtf_path <- file.path(r_path, "transcripts.gtf.gz")
|
1287: .get_cache_file_path <- function(cache, rpath) {
|
11: #' subdirectory within the given `reference_path`. Resources are retrieved via
|
13: #' 1. User-supplied FASTA and GTF file. This can be a file path, or a web link
|
29: #' of the specified reference path. If `use_STAR_mappability` is set to `TRUE`
|
34: #' `getNonPolyARef()` returns the path of the non-polyA reference file for the
|
56: #' file, open the file specified in the path returned by
|
75: #' @param reference_path (REQUIRED) The directory path to store the generated
|
77: #' @param fasta The file path or web link to the user-supplied genome
|
80: #' been run using the same `reference_path`.
|
81: #' @param gtf The file path or web link to the user-supplied transcript
|
85: #' `reference_path`.
|
91: #' the file `SpliceWiz.ref.gz` is present inside `reference_path`.
|
158: #' * `reference_path/resource/genome.2bit`: Local copy of the genome sequences
|
160: #' * `reference_path/resource/transcripts.gtf.gz`: Local copy of the gene
|
164: #' which is written to the given directory specified by `reference_path`.
|
166: #' * `reference_path/settings.Rds`: An RDS file containing parameters used
|
168: #' * `reference_path/SpliceWiz.ref.gz`: A gzipped text file containing collated
|
170: #' * `reference_path/fst/`: Contains fst files for subsequent easy access to
|
172: #' * `reference_path/cov_data.Rds`: An RDS file containing data required to
|
176: #' subdirectory inside the designated `reference_path`
|
178: #' For `getNonPolyARef()`: Returns the file path to the BED file for
|
184: #' example_ref <- file.path(tempdir(), "Reference")
|
186: #' reference_path = example_ref,
|
191: #' reference_path = example_ref
|
196: #' example_ref <- file.path(tempdir(), "Reference")
|
198: #' reference_path = example_ref,
|
203: #' # Get the path to the Non-PolyA BED file for hg19
|
213: #' ont_ref <- file.path(tempdir(), "Reference_withGO")
|
215: #' reference_path = ont_ref,
|
229: #' reference_path = "./Reference_user",
|
241: #' reference_path = "./Reference_FTP",
|
257: #' reference_path = "./Reference_AH",
|
269: #' reference_path = "./Reference_UCSC",
|
279: #' # inside the given `reference_path`.
|
284: #' reference_path = "./Reference_with_STAR",
|
294: #' reference_path = "./Reference_with_STAR",
|
298: #' reference_path = reference_path,
|
303: #' reference_path = "./Reference_with_STAR",
|
322: #' of the given reference path
|
325: reference_path = "./Reference",
|
331: reference_path = reference_path,
|
341: #' given reference path
|
344: reference_path = "./Reference",
|
353: .validate_path(reference_path, subdirs = "resource")
|
355: file.exists(file.path(reference_path, "SpliceWiz.ref.gz"))) {
|
369: extra_files <- .fetch_genome_defaults(reference_path,
|
374: reference_path = reference_path,
|
384: .process_gtf(reference_data$gtf_gr, reference_path, verbose = verbose)
|
390: .process_ontology(reference_path, ontologySpecies, verbose)
|
396: reference_data$genome <- .check_2bit_performance(reference_path,
|
403: saveRDS(chromosomes, file.path(reference_path, "chromosomes.Rds"))
|
405: .process_introns(reference_path, reference_data$genome,
|
409: .gen_irf(reference_path, extra_files, reference_data$genome, chromosomes,
|
413: if(file.exists(file.path(reference_path, "fst", "Proteins.fst"))) {
|
417: .gen_nmd(reference_path, reference_data$genome,
|
421: .gen_nmd(reference_path, reference_data$genome,
|
430: .gen_splice(reference_path, verbose = verbose)
|
433: file.exists(file.path(reference_path, "fst", "Splice.fst")) &
|
434: file.exists(file.path(reference_path, "fst", "Proteins.fst"))
|
437: .gen_splice_proteins(reference_path, reference_data$genome,
|
445: cov_data <- .prepare_covplot_data(reference_path)
|
446: saveRDS(cov_data, file.path(reference_path, "cov_data.Rds"))
|
450: settings.list <- readRDS(file.path(reference_path, "settings.Rds"))
|
459: saveRDS(settings.list, file.path(reference_path, "settings.Rds"))
|
479: reference_path = "./Reference",
|
492: file.exists(file.path(reference_path, "SpliceWiz.ref.gz"))) {
|
500: reference_path = reference_path,
|
506: reference_path = reference_path,
|
513: reference_path = reference_path,
|
526: #' @describeIn Build-Reference-methods Returns the path to the BED file
|
559: Get_Genome <- function(reference_path, validate = TRUE,
|
561: if (validate) .validate_reference(reference_path)
|
562: twobit <- file.path(reference_path, "resource", "genome.2bit")
|
565: } else if (file.exists(file.path(reference_path, "settings.Rds"))) {
|
566: settings <- readRDS(file.path(reference_path, "settings.Rds"))
|
569: .log("In Get_Genome, invalid reference_path supplied")
|
577: Get_GTF_file <- function(reference_path) {
|
578: .validate_reference(reference_path)
|
579: if (file.exists(file.path(reference_path,
|
581: return(file.path(reference_path, "resource", "transcripts.gtf.gz"))
|
583: .log("In Get_GTF_file, invalid reference_path supplied")
|
602: reference_path != "" &&
|
604: ifelse(normalizePath(dirname(reference_path)) != "", TRUE, TRUE),
|
610: .log(paste("Error in 'reference_path',",
|
611: paste0("base path of '", reference_path, "' does not exist")
|
615: base <- normalizePath(dirname(reference_path))
|
616: if (!dir.exists(file.path(base, basename(reference_path))))
|
617: dir.create(file.path(base, basename(reference_path)))
|
621: dir_to_make <- file.path(base, basename(reference_path), subdirs)
|
625: return(file.path(base, basename(reference_path)))
|
630: .validate_reference_resource <- function(reference_path, from = "") {
|
631: resourceDir <- normalizePath(file.path(reference_path, "resource"))
|
636: genomeFile <- file.path(resourceDir, "genome.2bit")
|
637: gtfFile <- file.path(resourceDir, "transcripts.gtf.gz")
|
647: .validate_reference <- function(reference_path, from = "") {
|
648: ref <- normalizePath(reference_path)
|
653: "in reference_path =", reference_path,
|
654: ": this path does not exist"))
|
656: if (!file.exists(file.path(ref, "settings.Rds"))) {
|
658: "in reference_path =", reference_path,
|
661: settings.list <- readRDS(file.path(ref, "settings.Rds"))
|
665: "in reference_path =", reference_path,
|
675: .fetch_genome_defaults <- function(reference_path, genome_type,
|
681: prev_NPA_file <- file.path(reference_path, "resource",
|
684: map_file <- file.path(map_path, "MappabilityExclusion.bed.gz")
|
685: prev_map_file <- file.path(normalizePath(reference_path),
|
687: prev_BL_file <- file.path(reference_path, "resource",
|
727: path = map_path, overwrite = TRUE
|
765: local.nonPolyAFile <- file.path(reference_path, "resource",
|
767: local.MappabilityFile <- file.path(reference_path, "resource",
|
769: local.BlacklistFile <- file.path(reference_path, "resource",
|
848: .get_reference_data <- function(reference_path, fasta, gtf,
|
856: .validate_path(reference_path, subdirs = "resource")
|
858: twobit <- file.path(reference_path, "resource", "genome.2bit")
|
865: gtf <- file.path(reference_path, "resource", "transcripts.gtf.gz")
|
886: reference_path = reference_path,
|
893: reference_path = reference_path,
|
913: settingsFile <- file.path(reference_path, "settings.Rds")
|
943: reference_path = reference_path
|
948: saveRDS(settings.list, file.path(reference_path, "settings.Rds"))
|
950: settings.list <- readRDS(file.path(reference_path, "settings.Rds"))
|
983: reference_path = "./Reference",
|
993: .fetch_fasta_save_2bit(genome, reference_path, overwrite)
|
998: twobit <- file.path(reference_path, "resource", "genome.2bit")
|
1004: genome <- Get_Genome(reference_path, validate = FALSE,
|
1014: twobit <- file.path(reference_path, "resource", "genome.2bit")
|
1020: genome <- Get_Genome(reference_path, validate = FALSE,
|
1038: .fetch_fasta_save_2bit(genome, reference_path, overwrite)
|
1046: genome <- Get_Genome(reference_path, validate = FALSE,
|
1070: genome, reference_path, overwrite, verbose = TRUE
|
1072: genome.fa <- file.path(reference_path, "resource", "genome.fa")
|
1085: genome, reference_path, overwrite, verbose = TRUE
|
1087: genome.2bit <- file.path(reference_path, "resource", "genome.2bit")
|
1089: normalizePath(rtracklayer::path(genome)) ==
|
1099: file.exists(rtracklayer::path(genome))) {
|
1100: file.copy(rtracklayer::path(genome), genome.2bit, overwrite = TRUE)
|
1111: reference_path = "./Reference",
|
1123: if (overwrite || !file.exists(gtf_path)) {
|
1127: if (file.exists(gtf_path)) file.remove(gtf_path)
|
1128: file.copy(cache_loc, gtf_path)
|
1133: if (file.exists(gtf_path)) {
|
1138: gtf_gr <- rtracklayer::import(gtf_path, "gtf")
|
1149: if (file.exists(gtf_path)) {
|
1154: gtf_gr <- rtracklayer::import(gtf_path, "gtf")
|
1168: if (!file.exists(gtf_path) ||
|
1169: normalizePath(gtf_file) != normalizePath(gtf_path)) {
|
1170: if (overwrite || !file.exists(gtf_path)) {
|
1175: if (file.exists(gtf_path)) file.remove(gtf_path)
|
1176: file.copy(gtf_file, gtf_path, overwrite = TRUE)
|
1178: gzip(filename = gtf_file, destname = gtf_path,
|
1308: return(.get_cache_file_path(cache, res$rpath[nrow(res)]))
|
1318: if (identical(path, NA)) {
|
1322: return(.get_cache_file_path(cache, res$rpath[nrow(res)]))
|
1328: return(.get_cache_file_path(cache, res$rpath[nrow(res)]))
|
1507: .process_gtf <- function(gtf_gr, reference_path, verbose = TRUE) {
|
1509: .validate_path(reference_path, subdirs = "fst")
|
1513: file.path(reference_path, "fst", "gtf_fixed.fst"))
|
1517: Genes_group <- .process_gtf_genes(gtf_gr, reference_path, verbose)
|
1519: .process_gtf_transcripts(gtf_gr, reference_path, verbose)
|
1521: .process_gtf_misc(gtf_gr, reference_path, verbose)
|
1523: .process_gtf_exons(gtf_gr, reference_path, Genes_group, verbose)
|
1529: .process_gtf_genes <- function(gtf_gr, reference_path, verbose = TRUE) {
|
1565: file.path(reference_path, "fst", "Genes.fst")
|
1574: .process_gtf_transcripts <- function(gtf_gr, reference_path, verbose = TRUE) {
|
1588: file.path(reference_path, "fst", "Transcripts.fst")
|
1592: .process_gtf_misc <- function(gtf_gr, reference_path, verbose = TRUE) {
|
1623: file.path(reference_path, "fst", "Proteins.fst")
|
1636: file.path(reference_path, "fst", "Misc.fst")
|
1641: gtf_gr, reference_path, Genes_group, verbose = TRUE
|
1681: file.path(reference_path, "fst", "Exons.fst"))
|
1684: file.path(reference_path, "fst", "Exons.Group.fst")
|
1746: .check_2bit_performance <- function(reference_path, genome, verbose = TRUE) {
|
1749: read.fst(file.path(reference_path, "fst", "Exons.fst")),
|
1772: reference_path, genome, useExtendedTranscripts = TRUE, verbose = TRUE
|
1777: data <- .process_introns_data(reference_path, genome,
|
1791: file.path(reference_path, "fst", "junctions.fst"))
|
1796: .process_introns_data <- function(reference_path, genome,
|
1799: read.fst(file.path(reference_path, "fst", "Exons.fst")),
|
1802: read.fst(file.path(reference_path, "fst", "Transcripts.fst")),
|
1804: if(file.exists(file.path(reference_path, "fst", "Proteins.fst"))) {
|
1806: read.fst(file.path(reference_path, "fst", "Proteins.fst")),
|
1813: read.fst(file.path(reference_path, "fst", "Exons.Group.fst")),
|
2131: reference_path, extra_files, genome, chromosome_aliases, verbose = TRUE
|
2137: data <- .gen_irf_prep_data(reference_path)
|
2149: ), stranded = TRUE, reference_path, data2[["introns.unique"]]
|
2156: ), stranded = FALSE, reference_path, data2[["introns.unique"]]
|
2159: ref.cover <- .gen_irf_refcover(reference_path)
|
2161: ref.ROI <- .gen_irf_ROI(reference_path, extra_files, genome,
|
2164: readcons <- .gen_irf_readcons(reference_path,
|
2167: ref.sj <- .gen_irf_sj(reference_path)
|
2169: ref.tj <- .gen_irf_tj(reference_path)
|
2181: .gen_irf_final(reference_path, ref.cover, readcons, ref.ROI,
|
2189: .gen_irf_prep_data <- function(reference_path) {
|
2191: read.fst(file.path(reference_path, "fst", "Genes.fst")),
|
2204: read.fst(file.path(reference_path, "fst", "junctions.fst"))
|
2207: read.fst(file.path(reference_path, "fst", "Exons.fst")),
|
2211: read.fst(file.path(reference_path, "fst", "Transcripts.fst")),
|
2428: reference_path, introns.unique) {
|
2483: rtracklayer::export(IntronCover, file.path(reference_path,
|
2486: write.fst(IntronCover.summa, file.path(
|
2487: reference_path, "fst",
|
2544: .gen_irf_refcover <- function(reference_path) {
|
2545: tmpdir.IntronCover <- fread(file.path(
|
2546: reference_path, "tmpdir.IntronCover.bed"
|
2549: tmpnd.IntronCover <- fread(file.path(
|
2550: reference_path, "tmpnd.IntronCover.bed"
|
2563: .gen_irf_ROI <- function(reference_path, extra_files, genome,
|
2628: .gen_irf_readcons <- function(reference_path,
|
2657: .gen_irf_sj <- function(reference_path) {
|
2661: read.fst(file.path(reference_path, "fst", "junctions.fst"))
|
2682: .gen_irf_tj <- function(reference_path) {
|
2686: read.fst(file.path(reference_path, "fst", "junctions.fst"))
|
2745: .gen_irf_final <- function(reference_path,
|
2749: IRF_file <- file.path(reference_path, "SpliceWiz.ref")
|
2793: if (file.exists(file.path(reference_path, "tmpdir.IntronCover.bed"))) {
|
2794: file.remove(file.path(reference_path, "tmpdir.IntronCover.bed"))
|
2796: if (file.exists(file.path(reference_path, "tmpnd.IntronCover.bed"))) {
|
2797: file.remove(file.path(reference_path, "tmpnd.IntronCover.bed"))
|
2804: .gen_nmd <- function(reference_path, genome, verbose = TRUE,
|
2808: Exons.tr <- .gen_nmd_exons_trimmed(reference_path)
|
2809: protein.introns <- .gen_nmd_protein_introns(reference_path, Exons.tr)
|
2823: write.fst(NMD.Table, file.path(reference_path, "fst", "IR.NMD.fst"))
|
2828: .gen_nmd_exons_trimmed <- function(reference_path) {
|
2830: read.fst(file.path(reference_path, "fst", "Exons.fst"))
|
2833: read.fst(file.path(reference_path, "fst", "Misc.fst"))
|
2866: .gen_nmd_protein_introns <- function(reference_path, Exons.tr) {
|
2868: read.fst(file.path(reference_path, "fst", "junctions.fst"))
|
2871: read.fst(file.path(reference_path, "fst", "Misc.fst"))
|
3298: .gen_splice <- function(reference_path, verbose = TRUE) {
|
3301: read.fst(file.path(reference_path, "fst", "junctions.fst"))
|
3304: reference_path, candidate.introns)
|
3338: introns_found_RI <- .gen_splice_RI(candidate.introns, reference_path)
|
3353: .gen_splice_save(AS_Table, candidate.introns, reference_path)
|
3365: .gen_splice_skipcoord <- function(reference_path, candidate.introns) {
|
3367: read.fst(file.path(reference_path, "fst", "Genes.fst"))
|
4025: .gen_splice_RI <- function(candidate.introns, reference_path) {
|
4027: read.fst(file.path(reference_path, "fst", "Exons.fst")),
|
4031: read.fst(file.path(reference_path, "fst", "Introns.Dir.fst")))
|
4069: .gen_splice_save <- function(AS_Table, candidate.introns, reference_path) {
|
4081: reference_path)
|
4082: AS_Table <- .gen_splice_name_events(AS_Table, reference_path)
|
4112: reference_path) {
|
4114: read.fst(file.path(reference_path, "fst", "Exons.fst")),
|
4202: file.path(reference_path, "fst", "Splice.options.fst"))
|
4208: .gen_splice_name_events <- function(AS_Table, reference_path) {
|
4256: file.path(reference_path, "fst", "Splice.fst"))
|
4264: .gen_splice_proteins <- function(reference_path, genome, verbose = TRUE) {
|
4269: read.fst(file.path(reference_path, "fst", "Splice.fst"))
|
4272: read.fst(file.path(reference_path, "fst", "Proteins.fst"))
|
4311: file.path(reference_path, "fst", "Splice.Extended.fst"))
|
15: #' to specify the files or web paths to use.
|
1090: normalizePath(genome.2bit)) {
|
1288: if(grepl(cache, rpath, fixed = TRUE)) {
|
1289: return(rpath)
|
1291: return(paste(cache, rpath, sep = "/"))
|
1306: res <- BiocFileCache::bfcquery(bfc, url, "fpath", exact = TRUE)
|
1325: res <- BiocFileCache::bfcquery(bfc, url, "fpath", exact = TRUE)
|
nempi:other/TCGA.r: [ ] |
---|
23: path <- "mutclust/"
|
31: if (file.exists(paste0(path, type, "_final.rda")) & !newmut & !newllr & !newsave) {
|
32: load(paste0(path, type, "_final.rda"))
|
39: if (file.exists(paste0(path, type, "_meth.rda"))) {
|
40: load(paste0(path, type, "_meth.rda"))
|
80: save(meth, file = paste0(path, type, "_meth.rda"))
|
86: if (file.exists(paste0(path, type, "_cnv.rda"))) {
|
87: load(paste0(path, type, "_cnv.rda"))
|
102: save(cnv, file = paste0(path, type, "_cnv.rda"))
|
108: if (file.exists(paste0(path, type, "_query.rda"))) {
|
109: load(paste0(path, type, "_query.rda"))
|
128: save(data, file = paste0(path, type, "_query.rda"))
|
137: if (file.exists(paste0(path, type, "_mut.rda"))) {
|
138: load(paste0(path, type, "_mut.rda"))
|
155: save(mut, file = paste0(path, type, "_mut.rda"))
|
159: if (file.exists(paste0(path, type, "_clin.rda"))) {
|
160: load(paste0(path, type, "_clin.rda"))
|
163: save(clinical, file = paste0(path, type, "_clin.rda"))
|
167: if (file.exists(paste0(path, type, "_mut0.rda")) & !newmut) {
|
168: load(paste0(path, type, "_mut0.rda"))
|
234: save(M0, Mtype0, file = paste0(path, type, "_mut0.rda"))
|
263: if (file.exists(paste0(path, type, "_llr.rda")) & !newllr) {
|
264: load(paste0(path, type, "_llr.rda"))
|
345: save(tmp, DF, file = paste0(path, type, "_llr.rda"))
|
381: save(clinical, D, M, Mtype, DF, class, meth, cnv, file = paste0(path, type, "_final.rda"))
|
406: path <- "mutclust/"
|
408: load(paste0(path, type, "_final.rda"))
|
545: path <- ""
|
552: load(paste0(path, type, "_nempi.rda"))
|
570: load(paste0(path, type, "_nempi.rda"))
|
588: load(paste0(path, type, "_nempi.rda"))
|
611: load(paste0(path, type, "_nempi.rda"))
|
629: load(paste0(path, type, "_nempi.rda"))
|
683: ## save(nempires, knnres, rfres, mfres, svmres, nnres, Rho, D2, Pmut, Pmeth, Pcnv, file = paste0(path, type, "_nempi.rda"))
|
685: path <- "mutclust/"; type <- "TCGA-BRCA"
|
687: load(paste0(path, type, "_nempi.rda"))
|
908: load(paste0(path, type, "_nempi.rda"))
|
TrajectoryGeometry:R/TrajectoryGeometry.R: [ ] |
---|
796: path = samplePath(attributes, pseudotime, nWindows = nWindows)
|
302: pathToSphericalData = function(path,from,to,d,statistic)
|
431: randomPath = path
|
492: randomPath = matrix(0,nrow=n,ncol=d)
|
628: pathProgression = function(path,from=1,to=nrow(path),d=ncol(path),
|
670: samplePath = function(attributes, pseudotime, nWindows = 10){
|
678: pathLength = end - start
|
681: sampledPath = matrix(, nrow = 0, ncol = ncol(attributes))
|
1135: pathLineWidth = 3
|
45: testPathForDirectionality = function(path,from=1,to=nrow(path),d=ncol(path),
|
61: randomPaths = generateRandomPaths(path,
|
106: projectPathToSphere = function(path,from=1,to=nrow(path),d=ncol(path))
|
374: generateRandomPaths = function(path,from=1,to=nrow(path),d=ncol(path),
|
421: randomPathList = list()
|
475: generateRandomPathsBySteps = function(path,randomizationParams,N)
|
489: randomPathList = list()
|
564: getDistanceDataForPaths = function(paths,statistic)
|
1114: plotPathProjectionCenterAndCircle = function(path,
|
7: #' Test a path for directionality
|
9: #' This is the core function of this package. It takes a path, and a
|
11: #' for the directionality of that path.
|
13: #' @param path - An n x m matrix representing a series of n points in
|
15: #' @param from - The starting place along the path which will be
|
17: #' @param to - The end point of the path. This defaults to
|
18: #' nrow(path).
|
20: #' ncol(path)
|
25: #' comparison to the given path.
|
27: #' pValue - the p-value for the path and statistic in question;
|
28: #' sphericalData - a list containing the projections of the path to
|
39: #' p = testPathForDirectionality(path=straight_path,
|
42: #' q = testPathForDirectionality(path=crooked_path,from=6,
|
48: testPathForDirectionalityTest(path,from,to,d,
|
53: path = path[from:to,seq_len(d)]
|
57: sphericalData = getSphericalData(path,statistic)
|
86: #' Project a path onto the unit sphere
|
88: #' This function takes a path in d dimensional space and projects it onto
|
92: #' @param path - This is an mxn dimensional matrix. Each row is
|
94: #' @param from - The starting place along the path which will be
|
96: #' @param to - The end point of the path. This defaults to
|
97: #' nrow(path).
|
99: #' ncol(path)
|
100: #' @return This returns a projection of the path onto the d-1 sphere
|
104: #' projection1 = projectPathToSphere(straight_path)
|
105: #' projection2 = projectPathToSphere(crooked_path,from=6)
|
108: projectPathToSphereTest(path,from,to,d)
|
112: path = path[from:to,seq_len(d)]
|
113: n = nrow(path)
|
121: v = path[i,] - path[1,]
|
150: #' projection = projectPathToSphere(straight_path)
|
221: #' distances = findSphericalDistance(straight_path_center,
|
222: #' straight_path_projection)
|
253: #' given by the dimensions of the path
|
255: #' @param path - an m x n matrix. Each row is considered a point
|
258: #' projections of the path to the sphere, the center for those
|
263: #' sphericalData = getSphericalData(straight_path,'max')
|
264: getSphericalData = function(path,statistic)
|
266: getSphericalDataTest(path,statistic)
|
269: to = nrow(path)
|
270: d = ncol(path)
|
272: return(pathToSphericalData(path,from,to,d,statistic))
|
277: #' Find the spherical data for a given path
|
279: #' This function takes a path and returns a list containing
|
284: #' @param path - This is an mxn dimensional matrix. Each row is
|
286: #' @param from - The starting place along the path which will be
|
288: #' @param to - The end point of the path. This defaults to
|
289: #' nrow(path).
|
291: #' ncol(path)
|
294: #' projections of the path to the sphere, the center for those
|
299: #' sphericalData = pathToSphericalData(straight_path,from=1,
|
300: #' to=nrow(straight_path), d=3,
|
304: pathToSphericalDataTest(path,from,to,d,statistic)
|
309: path = path[from:to,seq_len(d)]
|
310: n = nrow(path)
|
313: ## Get the projections of the path to the sphere
|
314: projections = projectPathToSphere(path)
|
345: #' Produce random paths modeled on a given path
|
347: #' This function takes a path and produces N random paths of the same
|
349: #' permuting the entries in path or by taking steps from the initial
|
350: #' point of path. Exact behaviour is controlled by
|
353: #' @param path - This is an mxn dimensional matrix. Each row is
|
355: #' @param from - The starting place along the path which will be
|
357: #' @param to - The end point of the path. This defaults to
|
358: #' nrow(path).
|
360: #' ncol(path)
|
366: #' @return This function returns a list of random paths. Each path is
|
371: #' randomPaths = generateRandomPaths(crooked_path,from=6,to=nrow(crooked_path),
|
372: #' d=ncol(crooked_path),randomizationParams=randomizationParams,
|
377: generateRandomPathsTest(path,from,to,d,randomizationParams,N)
|
388: path = path[from:to,seq_len(d)]
|
391: return(generateRandomPathsByPermutation(path,
|
396: return(generateRandomPathsBySteps(path,
|
407: ## ' This function produces randomized paths from a given path via
|
413: ## ' @param path - An n x d matrix.
|
419: function(path,randomizationParams,N)
|
422: n = nrow(path)
|
423: d = ncol(path)
|
447: a = as.numeric(path)
|
450: b = as.numeric(path)
|
463: ## ' This function produces randomized paths from a given path by taking
|
465: ## ' have the same Euclidean length as those of the original path or
|
467: ## ' requiring the path to stay in the non-negative orthant or allowing
|
470: ## ' @param path - An n x d matrix.
|
477: n = nrow(path)
|
478: d = ncol(path)
|
484: stepLengths = getStepLengths(path)
|
493: randomPath[1,] = path[1,]
|
515: #' This finds the lengths of the steps along a path
|
517: #' @param path - This is an mxn dimensional matrix. Each row is
|
519: #' @param from - The starting place along the path which will be
|
521: #' @param to - The end point of the path. This defaults to
|
522: #' nrow(path).
|
524: #' ncol(path)
|
525: #' @return This function returns the length of each step in a path.
|
528: #' stepLengths = getStepLengths(path=crooked_path)
|
529: #' stepLengths = getStepLengths(path=crooked_path,from=4)
|
530: getStepLengths = function(path,from=1,to=nrow(path),d=ncol(path))
|
532: getStepLengthsTest(path,from,to,d)
|
536: path = path[from:to,seq_len(d)]
|
537: n = nrow(path)
|
541: stepLengths[i] = Norm(path[i+1,] - path[i,])
|
551: #' for the appropriate center for each path. Each path
|
562: #' generateRandomPaths(path=straight_path,randomizationParam='bySteps',N=5)
|
604: #' Measure a path's progression
|
606: #' This function measures the progress of a path in a specified
|
611: #' @param path - An n x d matrix
|
612: #' @param from - The point along the path to be taken as the starting
|
614: #' @param to - The point along the path to be used as the end point.
|
615: #' This defaults to nrow(path).
|
616: #' @param d - The dimension to be used. This defaults to ncol(path).
|
620: #' of the path along the line through its starting point in the
|
625: #' pathProgression(straight_path,direction=straight_path_center)
|
627: #' pathProgression(crooked_path,from=6,direction=crooked_path_center)
|
631: pathProgressionTest(path,from,to,d,direction)
|
633: path = path[from:to,seq_len(d)]
|
635: distance = numeric(nrow(path)-1)
|
636: for(i in 2:nrow(path))
|
638: delta = path[i,] - path[1,]
|
646: #' Sample a path from single cell data
|
652: #' coordinates of the sampled path. The matrix of attribute values should
|
657: #' the sampled path give the window number a cell was sampled from.
|
664: #' @return sampledPath - A path consisting of a matrix of attributes of sampled
|
675: ## Set parameters for path.
|
726: #' and comparing each path to random paths.
|
731: #' The function returns a list of Answers for each comparison of a sampled path
|
732: #' to a random path.
|
746: #' comparison to the given path (defaults to 1000).
|
748: #' information comparing a sampled path to random paths.
|
750: #' pValue - the p-value for the path and statistic in question;
|
751: #' sphericalData - a list containing the projections of the path to
|
797: answers[[i]] = testPathForDirectionality(path, randomizationParams =
|
835: #' comparison to the given path (defaults to 1000).
|
839: #' entry for each sampled path.
|
841: #' sampled path in question
|
843: #' pValue - the p-value for the path and statistic in question;
|
844: #' sphericalData - a list containing the projections of the path to
|
1070: #' Plot a path, its projection, its center and its circle
|
1072: #' This function assumes you have a path in dimension 3 and you have
|
1075: #' appropriate statistic. Scales the path to keep it comparable to
|
1079: #' @param path - A path of dimension 3 in the form of an N x 3 matrix.
|
1083: #' @param to - Likewise. It defaults to nrow(path).
|
1085: #' path.
|
1088: #' @param color - The color to use for this path and its associated
|
1093: #' path. Defaults to 8.
|
1095: #' projected path. Defaults to 8.
|
1096: #' @param scale - The path will be start (its actual start) at 0 and
|
1108: #' plotPathProjectionCenterAndCircle(path=straight_path,
|
1109: #' projection=straight_path_projection,
|
1110: #' center=straight_path_center,
|
1111: #' radius=straight_path_radius,
|
1116: to=nrow(path),
|
1127: plotPathProjectionCenterAndCircleTest(path,from,to,projection,
|
1143: ## Translate the path to begin at the origin and scale:
|
1144: N = nrow(path)
|
1146: offset = path[1,]
|
1149: path[i,] = path[i,] - offset
|
1150: distances[i] = Norm(path[i,])
|
1152: path = (scale / max(distances)) * path
|
1163: ## Plot the path and mark the relevant portion:
|
1164: points3d(path,size=pathPointSize,color=color)
|
1165: lines3d(path,lwd=pathLineWidth,color=color)
|
1167: points3d(path[from:to,],size=pathPointSize+relevantPortionPointHump,
|
1169: lines3d(path[from:to,],lwd=pathLineWidth+relevantPortionLineHump,
|
4: ## Code for testing directionality in paths:
|
23: #' control the production of randomized paths for comparison.
|
24: #' @param N - The number of random paths to generated for statistical
|
32: #' paths;
|
60: ## Generate random paths:
|
66: ## Compute the distance statistics for random paths:
|
250: #' This is a simplified wrapper for pathToSphericalData
|
365: #' @param N - The number of random paths required.
|
404: ## ' Produce randomized paths by permutation
|
416: ## ' @param N - The number of paths required.
|
417: ## ' @return This function returns a list of random paths.
|
435: randomPath[,j] = randomPath[perm,j]
|
437: randomPathList[[i]] = randomPath
|
460: ## ' Produce randomized paths by steps
|
473: ## ' @param N - The number of paths required.
|
474: ## ' @return This function returns a list of random paths.
|
488: ## Generate the random paths:
|
496: randomPath[j+1,] = randomPath[j,] +
|
501: idx = randomPath < 0
|
502: randomPath[idx] = - randomPath[idx]
|
505: randomPathList[[i]] = randomPath
|
547: #' Produce distance statistics for random paths
|
549: #' This function takes a list of paths and a choice of
|
553: #' will be the randomized paths. It is therefore assumed
|
556: #' @param paths - A list of paths. Each of these is an n x d matrix.
|
561: #' paths =
|
563: #' distance = getDistanceDataForPaths(paths=paths,statistic='max')
|
566: getDistanceDataForPathsTest(paths,statistic)
|
568: n = nrow(paths[[1]])
|
569: N = length(paths)
|
573: ## Iterate over paths:
|
576: sphericalData = getSphericalData(paths[[i]],statistic)
|
668: #' samplePath(chol_attributes, chol_pseudo_time_normalised)
|
669: #' samplePath(hep_attributes, hep_pseudo_time_normalised)
|
679: windowSize = pathLength/nWindows
|
710: sampledPath = rbind(sampledPath, chosenAttributes)
|
717: rownames(sampledPath) = windowNumber
|
718: return(sampledPath)
|
725: #' This function analyses a single cell trajectory by sampling multiple paths
|
738: #' control the production of randomized paths for comparison.
|
740: #' @param nSamples - The number of sampled paths to generate (default 1000).
|
745: #' @param N - The number of random paths to generated for statistical
|
755: #' paths;
|
794: ## Sample paths and test each one for directionality
|
800: print(paste(i, "sampled paths analysed"))
|
818: #' control the production of randomized paths for comparison.
|
829: #' @param nSamples - The number of sampled paths to generate (defaults to 1000).
|
834: #' @param N - The number of random paths to generated for statistical
|
842: #' to random paths. The entries consist of:
|
848: #' paths;
|
967: ## Code for plotting paths and their spherical data:
|
1077: #' called repeatedly to add additional paths in different colors.
|
1092: #' @param pathPointSize - Sets the size of points which represent the
|
1100: #' here when plotting multiple paths.
|
1104: #' additional paths to the same figure.
|
1122: pathPointSize = 8,
|
1129: pathPointSize,projectionPointSize,
|
1194: #' metrics for sampled paths to random paths. It can also create plots for
|
1195: #' comparing two sets of sampled paths by providing the traj2Data argument.
|
1205: #' stats - output of wilcox test (paired if comparing sampled to random paths,
|
1206: #' unpaired if comparing sampled paths for two different trajectories)
|
1270: ## Code for comparing sampled pathways to random pathways
|
1286: ## Use paired wilcox test to compare values sampled and random pathways,
|
1287: ## as random trajectories are parametised based on the sampled pathways
|
1317: #' paths for different trajectory different starting points
|
1319: #' comparing sampled paths to random paths for different trajectory starting
|
67: distances = getDistanceDataForPaths(randomPaths,statistic)
|
418: generateRandomPathsByPermutation =
|
439: return(randomPathList)
|
453: randomPathList[[i]] = matrix(b,nrow=n)
|
455: return(randomPathList)
|
508: return(randomPathList)
|
672: samplePathTest(attributes, pseudotime, nWindows)
|
matter:R/signal.R: [ ] |
---|
213: path <- data.frame(x=lx, y=ly)
|
264: path <- .Call(C_warpDTW, x, y, tx, ty,
|
315: path <- .Call(C_warpCOW, x, y, tx, ty,
|
231: path <- NULL
|
236: attr(xout, "path") <- path
|
266: i <- rev(path[!is.na(path[,1L]),1L]) + 1L
|
267: j <- rev(path[!is.na(path[,2L]),2L]) + 1L
|
268: path <- data.frame(x=tx[i], y=ty[j])
|
270: tout <- approx(ty[path$y], tx[path$x],
|
273: attr(xout, "path") <- path
|
318: i <- path[,1L] + 1L
|
319: j <- path[,2L] + 1L
|
320: path <- data.frame(x=tx[i], y=ty[j])
|
326: attr(xout, "path") <- path
|
HiCBricks:R/Brick_functions.R: [ ] |
---|
2002: Path <- Create_Path(c(Root.folders['matrices'],chr,chr))
|
498: Bintable.group.path <- Create_Path(
|
1361: Group.path <- Create_Path(c(Reference.object$hdf.matrices.root,chr1,chr2))
|
1456: Group.path <- Create_Path(c(Reference.object$hdf.matrices.root,chr,chr))
|
2522: Group.path <- Create_Path(c(Reference.object$hdf.matrices.root,
|
2585: Group_path <- Create_Path(c(Reference_object$hdf.matrices.root,
|
2678: Group.path <- Create_Path(c(Reference.object$hdf.matrices.root, chr1,
|
205: Config_filepath <- .make_configuration_path(output_directory)
|
895: Brick_filepath <- BrickContainer_get_path_to_file(Brick,
|
964: Brick_filepath <- BrickContainer_get_path_to_file(Brick,
|
1339: Brick_filepath <- BrickContainer_get_path_to_file(Brick,
|
1434: Brick_filepath <- BrickContainer_get_path_to_file(Brick,
|
1849: Brick_filepath <- BrickContainer_get_path_to_file(Brick = Brick,
|
2034: Brick_filepath <- BrickContainer_get_path_to_file(Brick = Brick,
|
2524: Brick_filepath <- BrickContainer_get_path_to_file(Brick = Brick,
|
2587: Brick_filepath <- BrickContainer_get_path_to_file(Brick = Brick,
|
2676: Brick_filepath <- BrickContainer_get_path_to_file(Brick = Brick,
|
703: Brick_paths <- BrickContainer_get_path_to_file(Brick = Brick,
|
760: Brick_filepaths <- BrickContainer_list_files(Brick = Brick,
|
812: Brick_filepaths <- BrickContainer_get_path_to_file(Brick,
|
18: #' A string containing the path to the file to load as the binning table for
|
141: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
143: #' out_dir <- file.path(tempdir(), "Creator_test")
|
145: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
178: "without an explicit path", "definition!", "If you want to create",
|
180: "please provide", "the complete path. Or, you can",
|
182: paste("file.path(getwd())", sep = ""),
|
300: #' the function will provide the path to the created/tracked HDF file.
|
306: #' out_dir <- file.path(tempdir(),"mcool_test_dir")
|
307: #' dir.create(path = out_dir)
|
311: #' destfile = file.path(out_dir,"H1-hESC-HiC-4DNFI7JNCNFB.mcool"))
|
313: #' mcool <- file.path(out_dir,"H1-hESC-HiC-4DNFI7JNCNFB.mcool")
|
332: stop("mcool must be provided as mcool= /path/to/something")
|
339: mcool.version <- GetAttributes(Path = Create_Path(
|
355: mcool.version <- GetAttributes(Path = NULL, File=mcool,
|
391: #' out_dir <- file.path(tempdir(),"mcool_test_dir")
|
392: #' dir.create(path = out_dir)
|
397: #' destfile = file.path(out_dir,"H1-hESC-HiC-4DNFI7JNCNFB.mcool"))
|
399: #' mcool <- file.path(out_dir,"H1-hESC-HiC-4DNFI7JNCNFB.mcool")
|
452: #' out_dir <- file.path(tempdir(), "mcool_test_dir")
|
453: #' dir.create(path = out_dir)
|
457: #' destfile = file.path(out_dir, "H1-hESC-HiC-4DNFI7JNCNFB.mcool"))
|
459: #' mcool <- file.path(out_dir, "H1-hESC-HiC-4DNFI7JNCNFB.mcool")
|
476: mcool.version <- GetAttributes(Path = NULL, File=mcool,
|
502: Bintable.group.path <- Create_Path(Bintable.group)
|
504: Handler <- ._Brick_Get_Something_(Group.path = Bintable.group.path,
|
517: #' A string specifying the path to the Brick store created with
|
534: #' Bintable_path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
537: #' out_dir <- file.path(tempdir(), "HiCBricks_chrominfo_test")
|
541: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable_path,
|
670: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
673: #' out_dir <- file.path(tempdir(), "add_ranges_test")
|
677: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
709: bplapply(Brick_paths, function(Brick_path){
|
710: ._Brick_Add_Ranges_(Brick = Brick_path,
|
711: Group.path = Create_Path(c(Reference.object$hdf.ranges.root,
|
739: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
742: #' out_dir <- file.path(tempdir(), "list_matrices_test")
|
746: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
766: Path = Create_Path(
|
794: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
797: #' out_dir <- file.path(tempdir(), "list_rangekeys_test")
|
801: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
815: Group.path = Create_Path(Reference.object$hdf.ranges.root),
|
836: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
839: #' out_dir <- file.path(tempdir(), "list_rangekeys_exists_test")
|
843: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
876: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
879: #' out_dir <- file.path(tempdir(), "list_ranges_mcols_test")
|
883: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
936: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
939: #' out_dir <- file.path(tempdir(), "list_get_ranges_test")
|
943: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
971: chromosomes <- ._Brick_Get_Something_(Group.path = Create_Path(
|
980: Group.path = Create_Path(
|
985: Lengths <- ._Brick_Get_Something_(Group.path = Create_Path(
|
995: Dataset <- ._Brick_Get_Something_(Group.path = Create_Path(
|
1020: Group.path = Create_Path(
|
1038: Group.path = Create_Path(c(
|
1071: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1074: #' out_dir <- file.path(tempdir(), "list_get_bintable_test")
|
1077: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1134: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1137: #' out_dir <- file.path(tempdir(), "fetch_range_index_test")
|
1140: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1232: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1235: #' out_dir <- file.path(tempdir(), "region_position_test")
|
1238: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1303: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1306: #' out_dir <- file.path(tempdir(), "matrix_load_test")
|
1309: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1314: #' Matrix_file <- system.file(file.path("extdata",
|
1367: Matrix.file = matrix_file, delim = delim, Group.path = Group.path,
|
1400: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1403: #' out_dir <- file.path(tempdir(), "matrix_load_dist_test")
|
1406: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1411: #' Matrix_file <- system.file(file.path("extdata",
|
1462: Matrix.file = matrix_file, delim = delim, Group.path = Group.path,
|
1481: #' @param mcool \strong{Required}. Path to an mcool file.
|
1504: #' out_dir <- file.path(tempdir(),"mcool_load_test")
|
1505: #' dir.create(path = out_dir)
|
1509: #' destfile = file.path(out_dir,"H1-hESC-HiC-4DNFI7JNCNFB.mcool"))
|
1511: #' mcool <- file.path(out_dir,"H1-hESC-HiC-4DNFI7JNCNFB.mcool")
|
1568: RetVar <- .process_mcool(Brick = Brick, mcool_path = mcool,
|
1585: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1588: #' out_dir <- file.path(tempdir(), "matrix_isdone_test")
|
1591: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1596: #' Matrix_file <- system.file(file.path("extdata",
|
1629: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1632: #' out_dir <- file.path(tempdir(), "matrix_issparse_test")
|
1635: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1640: #' Matrix_file <- system.file(file.path("extdata",
|
1681: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1684: #' out_dir <- file.path(tempdir(), "matrix_maxdist_test")
|
1687: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1692: #' Matrix_file <- system.file(file.path("extdata",
|
1736: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1739: #' out_dir <- file.path(tempdir(), "matrix_exists_test")
|
1742: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1747: #' Matrix_file <- system.file(file.path("extdata",
|
1775: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1778: #' out_dir <- file.path(tempdir(), "matrix_minmax_test")
|
1781: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1786: #' Matrix_file <- system.file(file.path("extdata",
|
1821: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1824: #' out_dir <- file.path(tempdir(), "matrix_dimension_test")
|
1827: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1832: #' Matrix_file <- system.file(file.path("extdata",
|
1851: Extents <- ._GetDimensions(group.path = Create_Path(
|
1853: dataset.path = Reference.object$hdf.matrix.name,
|
1870: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1873: #' out_dir <- file.path(tempdir(), "matrix_filename_test")
|
1876: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1881: #' Matrix_file <- system.file(file.path("extdata",
|
1936: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
1939: #' out_dir <- file.path(tempdir(), "val_by_dist_test")
|
1942: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
1947: #' Matrix_file <- system.file(file.path("extdata",
|
2040: diag(._Brick_Get_Something_(Group.path = Path, Brick = Brick_filepath,
|
2087: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2090: #' out_dir <- file.path(tempdir(), "get_matrix_coords_test")
|
2093: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2098: #' Matrix_file <- system.file(file.path("extdata",
|
2200: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2203: #' out_dir <- file.path(tempdir(), "get_matrix_test")
|
2206: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2211: #' Matrix_file <- system.file(file.path("extdata",
|
2309: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2312: #' out_dir <- file.path(tempdir(), "get_row_vector_test")
|
2315: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2320: #' Matrix_file <- system.file(file.path("extdata",
|
2463: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2466: #' out_dir <- file.path(tempdir(), "get_vector_val_test")
|
2472: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2477: #' Matrix_file <- system.file(file.path("extdata",
|
2526: Vector <- ._Brick_Get_Something_(Group.path = Group.path,
|
2550: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2553: #' out_dir <- file.path(tempdir(), "get_vector_val_test")
|
2558: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2563: #' Matrix_file <- system.file(file.path("extdata",
|
2589: dataset_handle <- ._Brick_Get_Something_(Group.path = Group_path,
|
2625: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2628: #' out_dir <- file.path(tempdir(), "get_matrix_mcols_test")
|
2631: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2636: #' Matrix_file <- system.file(file.path("extdata",
|
2680: Vector <- ._Brick_Get_Something_(Group.path = Group.path,
|
2694: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2697: #' out_dir <- file.path(tempdir(), "list_matrix_mcols_test")
|
2700: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2717: #' BrickContainer, a string of length 1 as resolution and a path specifying
|
2724: #' @param out_file Path to the output file to write.
|
2735: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2738: #' out_dir <- file.path(tempdir(), "write_file")
|
2741: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2746: #' Matrix_file <- system.file(file.path("extdata",
|
2755: #' out_file = file.path(out_dir, "example_out.txt"),
|
2768: stop(out_file, " already exists at path")
|
2820: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2823: #' out_dir <- file.path(tempdir(), "get_vector_val_test")
|
2828: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2833: #' Matrix_file <- system.file(file.path("extdata",
|
2870: #' @param table_file Path to the file that will be loaded
|
2887: #' Bintable.path <- system.file(file.path("extdata", "Bintable_100kb.bins"),
|
2890: #' out_dir <- file.path(tempdir(), "get_vector_val_test")
|
2895: #' My_BrickContainer <- Create_many_Bricks(BinTable = Bintable.path,
|
2900: #' Matrix_file <- system.file(file.path("extdata",
|
2909: #' out_file = file.path(out_dir, "example_out.txt"),
|
2914: #' table_file = file.path(out_dir, "example_out.txt"),
|
188: output_directory <- normalizePath(output_directory)
|
219: if(file.exists(Config_filepath)){
|
223: Container <- load_BrickContainer(Config_filepath)
|
282: Config_filepath)
|
283: .write_configuration_file(Container, Config_filepath)
|
907: mcol.df <- .prepare_ranges_metadata_mcols(Brick = Brick_filepath,
|
973: Brick = Brick_filepath,
|
982: Brick = Brick_filepath,
|
987: Brick = Brick_filepath,
|
997: Brick = Brick_filepath,
|
1022: Brick = Brick_filepath, Name = FUN_name, Start = m.start,
|
1040: rangekey)), Brick = Brick_filepath,
|
1341: if(length(Brick_filepath) == 0){
|
1366: RetVar <- ._ProcessMatrix_(Brick = Brick_filepath,
|
1436: if(length(Brick_filepath) == 0){
|
1461: RetVar <- ._Process_matrix_by_distance(Brick = Brick_filepath,
|
1854: File = Brick_filepath, return.what = "size")
|
2527: Brick = Brick_filepath, Name = Reference.object$hdf.matrix.name,
|
2590: Brick = Brick_filepath, Name = Reference_object$hdf.matrix.name,
|
2681: Brick = Brick_filepath, Name = what, return.what = "data")
|
2781: Brick_filepath = a_row$filepath,
|
763: chr1.list <- lapply(seq_len(nrow(Brick_filepaths)), function(i){
|
764: Row <- Brick_filepaths[i,]
|
770: File = Row$filepaths,
|
816: Brick = Brick_filepaths[1], return.what = "group_handle")
|
BiocFileCache:R/BiocFileCache-class.R: [ ] |
---|
497: path <- .sql_get_rpath(x, rids)
|
533: update_time_and_path <- function(x, i) {
|
542: locfile_path <- file.path(bfccache(x), id)
|
404: rpath <- .sql_add_resource(x, rname, rtype, fpath, ext, fname)
|
1009: fpath <- .sql_get_fpath(x, rid)
|
1193: paths <- .sql_get_rpath(x, bfcrid(x))
|
1320: newpath <- file.path(dir, basename(orig))
|
1393: exportPath <- file.path(exdir, "BiocFileCacheExport")
|
1139: rpaths <- .sql_get_rpath(x, rids)
|
1441: rpaths <- .sql_get_rpath(x, rids)
|
34: #' \item{'cache': }{character(1) on-disk location (directory path) of the
|
53: #' \item{'rpath': }{resource path. This is the path to the local
|
74: #' @param cache character(1) On-disk location (directory path) of
|
105: cache <- file.path(tempdir(), "BiocFileCache")
|
213: #' @describeIn BiocFileCache Get a file path for select resources from
|
228: #' @describeIn BiocFileCache Set the file path of selected resources
|
230: #' @param value character(1) Replacement file path.
|
279: #' @return For 'bfcnew': named character(1), the path to save your
|
283: #' path <- bfcnew(bfc0, "NewResource")
|
284: #' path
|
329: #' @param fpath For bfcadd(), character(1) path to current file
|
331: #' assumed to also be the path location. For bfcupdate()
|
334: #' if the resource is a local file, a relative path in the cache,
|
337: #' relative or web paths, based on the path prefix.
|
341: #' in current location but save the path in the cache. If 'rtype
|
357: #' @return For 'bfcadd': named character(1), the path to save your
|
484: #' @return For 'bfcpath': the file path location to load
|
498: path
|
517: #' in the cache the path is returned, if it is not it will try to
|
522: #' @return For 'bfcrpath': The local file path location to load.
|
543: locfile <- .lock2(locfile_path, exclusive = TRUE)
|
552: names(update_time_and_path(x, res))
|
561: .unlock2(locfile_path)
|
564: names(update_time_and_path(x, res))
|
591: update_time_and_path(x, rids)
|
678: "Setting a new remote path results in immediate\n",
|
1077: #' @return For 'bfcdownload': character(1) path to downloaded resource
|
1192: files <- file.path(bfccache(x), setdiff(dir(bfccache(x)),c(.CACHE_FILE, .CACHE_FILE_LOCK)))
|
1261: #' @return character(1) The outputFile path.
|
1283: dir <- file.path(tempdir(), "BiocFileCacheExport")
|
1324: newpath <- file.path(dir, filename)
|
1351: outputFile = file.path(origdir, outputFile)
|
1361: .util_unlink(file.path(dir, .CACHE_FILE_LOCK))
|
57: #' \item{'fpath': }{If rtype is "web", this is the link to the
|
217: #' @return For '[[': named character(1) rpath for the given resource
|
225: .sql_get_rpath(x, i)
|
240: .sql_set_rpath(x, i, value)
|
243: warning("updating rpath, changing rtype to 'local'")
|
304: x, rname, fpath = rname, rtype=c("auto", "relative", "local", "web"),
|
317: x, rname, fpath = rname, rtype=c("auto", "relative", "local", "web"),
|
323: bfcadd(x=BiocFileCache(), rname=rname, fpath=fpath, rtype=rtype,
|
339: #' \code{copy} of \code{fpath} in the cache directory; \code{move}
|
376: #' bfcadd(bfc0, "TestWeb", fpath=url)
|
381: x, rname, fpath = rname,
|
389: is.character(fpath), length(fpath) > 0L, !any(is.na(fpath))
|
395: stopifnot((length(action) == 1) || (length(action) == length(fpath)))
|
396: stopifnot((length(rtype) == 1) || (length(rtype) == length(fpath)))
|
397: if (length(action) == 1) action = rep(action, length(fpath))
|
398: if (length(rtype) == 1) rtype = rep(rtype, length(fpath))
|
400: rtype <- .util_standardize_rtype(rtype, fpath, action)
|
401: stopifnot(all(rtype == "web" | file.exists(fpath)))
|
405: rid <- names(rpath)
|
407: for(i in seq_along(rpath)){
|
411: copy = file.copy(fpath[i], rpath[i]),
|
412: move = file.rename(fpath[i], rpath[i]),
|
414: .sql_set_rpath(x, rid[i], fpath[i])
|
415: rpath[i] <- bfcrpath(x, rids = rid[i])
|
423: rpath
|
457: tbl <- mutate(tbl, rpath = unname(bfcrpath(x, rids=rids)))
|
469: setGeneric("bfcpath",
|
470: function(x, rids) standardGeneric("bfcpath"),
|
475: #' @aliases bfcpath,missing-method
|
476: #' @exportMethod bfcpath
|
477: setMethod("bfcpath", "missing",
|
480: bfcpath(x=BiocFileCache(), rids=rids)
|
486: #' bfcpath(bfc0, rid3)
|
487: #' @aliases bfcpath
|
488: #' @exportMethod bfcpath
|
489: setMethod("bfcpath", "BiocFileCacheBase",
|
502: setGeneric("bfcrpath",
|
503: function(x, rnames, ..., rids, exact = TRUE) standardGeneric("bfcrpath"),
|
508: #' @aliases bfcrpath,missing-method
|
509: #' @exportMethod bfcrpath
|
510: setMethod("bfcrpath", "missing",
|
513: bfcrpath(x=BiocFileCache(), rnames=rnames, ..., rids=rids, exact=exact)
|
516: #' @describeIn BiocFileCache display rpath of resource. If 'rnames' is
|
524: #' bfcrpath(bfc0, rids = rid3)
|
525: #' @aliases bfcrpath
|
526: #' @exportMethod bfcrpath
|
527: setMethod("bfcrpath", "BiocFileCacheBase",
|
534: .sql_get_rpath(x, i)
|
588: bfcrpath(x, rids = rids0)
|
611: #' @param rpath character() vector of replacement rpaths.
|
615: #' bfcupdate(bfc0, rid3, rpath=fl3, rname="NewRname")
|
617: #' bfcupdate(bfc0, "BFC5", fpath="http://google.com")
|
621: function(x, rids, ..., rname=NULL, rpath=NULL, fpath=NULL,
|
627: is.null(rpath) || (length(rids) == length(rpath)),
|
628: is.null(fpath) || (length(rids) == length(fpath))
|
632: is.null(rpath) || is.character(rpath),
|
633: is.null(fpath) || is.character(fpath)
|
636: if(is.null(rname) && is.null(rpath) && is.null(fpath)) {
|
638: "\n Please set rname, rpath, or fpath",
|
652: if (!is.null(rpath)) {
|
653: if (!file.exists(rpath[i]))
|
657: "\n rpath: ", sQuote(rpath[i]),
|
658: "\n reason: rpath does not exist.",
|
661: .sql_set_rpath(x, rids[i], rpath[i])
|
664: warning("updating rpath, changing rtype to 'local'")
|
669: if (!is.null(fpath)) {
|
687: x, rids[i], proxy, config, "bfcupdate()", fpath[i], ...
|
689: .sql_set_fpath(x, rids[i], fpath[i])
|
871: function(x, query, field=c("rname", "rpath", "fpath"), ..., exact = FALSE)
|
880: function(x, query, field=c("rname", "rpath", "fpath"), ..., exact = FALSE)
|
893: #' matches pattern agains rname, rpath, and fpath. If exact
|
898: #' \code{bfcrpath}, the default is \code{TRUE} (exact matching).
|
910: function(x, query, field=c("rname", "rpath", "fpath"), ..., exact = FALSE)
|
986: #' 'rid'. \code{TRUE}: fpath \code{etag} or \code{modified} time of
|
987: #' web resource more recent than in BiocFileCache; \code{FALSE}: fpath
|
1012: cache_info <- .httr_get_cache_info(fpath)
|
1094: if (ask && any(file.exists(.sql_get_rpath(x, rid)))) {
|
1105: bfcrpath(x, rids=rid)
|
1194: # normalizePath on windows
|
1197: files = normalizePath(files)
|
1198: paths = normalizePath(paths)
|
1200: untracked <- setdiff(files, paths)
|
1255: #' @param outputFile character(1) The <filepath>/basename for the
|
1319: orig <- .sql_get_rpath(x, i)
|
1321: if (file.exists(newpath)) {
|
1326: file.copy(orig, newpath)
|
1394: stopifnot(!dir.exists(exportPath))
|
1404: bfc = BiocFileCache(exportPath)
|
349: #' \code{httr::GET}. For 'bfcrpaths': Additional arguments passed
|
483: #' @describeIn BiocFileCache display rpaths of resource.
|
1140: cached <- startsWith(rpaths, bfccache(x))
|
1143: status <- .util_unlink(rpaths[cached])
|
1442: cached <- startsWith(rpaths, bfccache(x))
|
1445: txt0 <- paste("file ", sQuote(rpaths))
|
1454: .util_unlink(rpaths[cached])
|
TEQC:R/multiTEQCreport.R: [ ] |
---|
42: Path <- getwd()
|
44: print(paste("results and report are saved in folder", Path))
|
47: imgDir <- file.path(destDir, "image")
|
82: write.table(speci, file=file.path(destDir, "fractionReadsOnTarget.txt"), sep="\t", quote=FALSE)
|
86: write.table(targcov, file=file.path(destDir, "targetCoverageStats.txt"), sep="\t", quote=FALSE)
|
100: write.table(sensi, file=file.path(destDir, "sensitivity.txt"), sep="\t", row.names=FALSE, quote=FALSE)
|
103: write.table(perTargCov, file=file.path(destDir, "targetCoverage.txt"), sep="\t", quote=FALSE)
|
136: htmlFile <- file.path(destDir, "index.html")
|
150: file.copy(cssFile, file.path(destDir, names(cssFile)))
|
151: file.copy(system.file("template", "image", biocFile, package="TEQC"), file.path(destDir, "image", biocFile))
|
167: imgDir <- file.path(dir, "image")
|
170: jpeg(file.path(imgDir, figFile), ...)
|
172: png(file.path(imgDir, figFile), ...)
|
174: tiff(file.path(imgDir, figFile), ...)
|
178: pdf(file.path(imgDir, pdfFile), ...)
|
183: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
207: imgDir <- file.path(dir, "image")
|
210: jpeg(file.path(imgDir, figFile), ...)
|
212: png(file.path(imgDir, figFile), ...)
|
214: tiff(file.path(imgDir, figFile), ...)
|
218: pdf(file.path(imgDir, pdfFile), ...)
|
223: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
246: imgDir <- file.path(dir, "image")
|
249: jpeg(file.path(imgDir, figFile), ...)
|
251: png(file.path(imgDir, figFile), ...)
|
253: tiff(file.path(imgDir, figFile), ...)
|
257: pdf(file.path(imgDir, pdfFile), ...)
|
262: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
289: imgDir <- file.path(dir, "image")
|
292: jpeg(file.path(imgDir, figFile), width=1000, ...)
|
294: png(file.path(imgDir, figFile), width=1000, ...)
|
296: tiff(file.path(imgDir, figFile),width=1000, ...)
|
300: pdf(file.path(imgDir, pdfFile), width=14, ...)
|
305: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
335: imgDir <- file.path(dir, "image")
|
338: jpeg(file.path(imgDir, figFile), width=800, height=800, ...)
|
340: png(file.path(imgDir, figFile), width=800, height=800, ...)
|
342: tiff(file.path(imgDir, figFile), width=800, height=800, ...)
|
346: pdf(file.path(imgDir, pdfFile), width=12, height=12, ...)
|
351: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
MOGAMUN:R/MOGAMUN_FUN.R: [ ] |
---|
1232: for (Path in Dirs) {
|
1233: PathForPlots <- ExperimentsPath <- paste0(Path, "/") # path for plots
|
1659: GeneralPath <- ExperimentDir
|
1666: ExpPath <- paste0(GeneralPath, "/", d, "/")
|
1289: # get all files in the path that match the pattern
|
1763: # ExperimentsPath - path to the results
|
1234: Population <- GetIndividualsAllRuns(ExperimentsPath) # get results
|
1249: SimilarityBetweenRunsBoxplot(PathForPlots, Nodes1stRank_AllRuns)
|
1253: AccPF <- ObtainAccParetoFront(PathForPlots, Inds1stRank_AllRuns,
|
1258: FilterNetworks(ExperimentsPath, LoadedData, AccPF)
|
1265: SaveFilteredAccPF(DiversePopulation, ExperimentsPath, Threshold)
|
1278: # INPUT: ExperimentsPath - folder containing the results to be processed
|
1280: GetIndividualsAllRuns <- function(ExperimentsPath) {
|
1290: ResFiles <- list.files(ExperimentsPath, pattern = PatResFiles)
|
1295: con <- base::file(paste0(ExperimentsPath, ResFiles[counter]), open="r")
|
1328: # INPUTS: PathForPlots - folder to save the plot
|
1331: SimilarityBetweenRunsBoxplot <- function(PathForPlots, Nodes1stRank_AllRuns) {
|
1362: svg(paste0(PathForPlots, "A_Boxplot_similarities_between_runs.svg"))
|
1372: # INPUTS: PathForPlots - folder to save the plot
|
1378: ObtainAccParetoFront <- function(PathForPlots, Inds1stRank_AllRuns,
|
1389: svg(paste0(PathForPlots, "A_ScatterPlot_AccPF_ALL_INDS.svg"))
|
1397: svg(paste0(PathForPlots, "A_ScatterPlot_AccPF_LEGEND_ALL_INDS.svg"))
|
1409: svg(paste0(PathForPlots, "A_ScatterPlot_AccPF.svg"))
|
1466: # INPUTS: ExperimentsPath - folder to save the filtered networks
|
1471: FilterNetworks <- function(ExperimentsPath, LoadedData, AccPF) {
|
1495: write.table(MyFilteredNetwork, file = paste0(ExperimentsPath,
|
1501: myDataFiles <- list.files(ExperimentsPath, pattern = '_FILTERED.csv')
|
1508: data <- read.table(paste0(ExperimentsPath, myDataFiles[i]),
|
1516: write.table(myFullListOfInteractions, file = paste0(ExperimentsPath,
|
1614: # ExperimentsPath - folder to save the filtered networks
|
1617: SaveFilteredAccPF <- function(DiversePopulation, ExperimentsPath, Threshold) {
|
1621: write(paste(Ind, collapse=" ", sep=""), file = paste0(ExperimentsPath,
|
1643: write.csv(AllNodesDF_Acc, paste0(ExperimentsPath,
|
1662: Dirs <- list.dirs(GeneralPath, recursive = FALSE, full.names = FALSE)
|
1669: Network <- read.csv( paste0(ExpPath,
|
1698: CreateActiveModules(d, ExpPath)
|
1703: filename = paste0(ExpPath, "A_Acc_PF_", d))
|
1704: } else { saveSession(filename = paste0(ExpPath, "A_Acc_PF_", d)) }
|
1765: CreateActiveModules <- function(d, ExperimentsPath) {
|
1772: read.csv(paste0(ExperimentsPath, list.files(ExperimentsPath,
|
1799: MogamunBody <- function(RunNumber, LoadedData, BestIndsPath) {
|
1800: BestIndsFile <- paste0(BestIndsPath, "_Run_", RunNumber, ".txt")
|
1829: file = paste0(BestIndsPath,"StatisticsPerGeneration_Run", RunNumber,
|
ArrayExpress:R/parseMAGE.r: [ ] |
---|
522: path = mageFiles$path
|
30: isOneChannel = function(sdrf,path){
|
31: ph = try(read.AnnotatedDataFrame(sdrf, path = path, row.names = NULL, blank.lines.skip = TRUE, fill = TRUE, varMetadata.char = "$", quote="\""))
|
39: readPhenoData = function(sdrf,path){
|
42: ph = try(read.AnnotatedDataFrame(sdrf, path = path, row.names = NULL, blank.lines.skip = TRUE, fill = TRUE, varMetadata.char = "$", quote="\""))
|
105: readAEdata = function(path,files,dataCols,green.only){
|
108: source = getDataFormat(path,files)
|
122: rawdata = try(oligo::read.celfiles(filenames = file.path(path,unique(files), fsep='\\')))
|
125: rawdata = try(oligo::read.celfiles(filenames = file.path(path,unique(files))))
|
129: stop("Unable to read cel files in ",path)
|
136: dataCols= try(getDataColsForAE1(path,files))
|
139: rawdata = try(read.maimages(files=files,path=path,source="generic",columns=dataCols,annotation=headers$ae1))
|
144: rawdata = try(read.maimages(files=files,path=path,source=source,columns=dataCols,green.only=green.only))
|
148: rawdata = try(read.maimages(files=files,path=path,source="generic",columns=dataCols,green.only=green.only))
|
155: stop("Unable to read data files in",path)
|
175: readFeatures<-function(adf,path,procADFref=NULL){
|
179: lines2skip = skipADFheader(adf,path,!is.null(procADFref))
|
180: features = try(read.table(file.path(path, adf), row.names = NULL, blank.lines.skip = TRUE, fill = TRUE, sep="\t", na.strings=c('?','NA'), sk...(41 bytes skipped)...
|
229: readExperimentData = function(idf, path){
|
230: idffile = scan(file.path(path,idf),character(),sep = "\n",encoding="UTF-8")
|
270: skipADFheader<-function(adf,path,proc=F){
|
276: con = file(file.path(path, adf), "r")
|
325: getDataFormat=function(path,files){
|
332: allcnames = scan(file.path(path,files[1]),what = "",nlines = 200, sep = "\t",quiet=TRUE)
|
335: allcnames = scan(file.path(path,files[1]),what = "",nlines = 200, sep = "\t",quiet=TRUE,encoding="latin1")
|
351: allcnames = scan(file.path(path,files[1]),what = "",nlines = 1, sep = "\n",quiet=TRUE)
|
358: getDataColsForAE1 = function(path,files){
|
368: file.path(system.file("doc", package = "ArrayExpress"),"QT_list.txt"),
|
381: allcnames = scan(file.path(path,files[1]),what = "",nlines = 1, sep = "\t",quiet=TRUE)
|
436: if(!all(sapply(2:length(files), function(i) readLines(file.path(path,files[1]),1) == readLines(file.path(path,files[i]),1))))
|
529: try(file.remove(file.path(path, basename(mageFiles$rawFiles), fsep = sep)))
|
530: try(file.remove(file.path(path, basename(mageFiles$processedFiles), fsep = sep)))
|
532: try(file.remove(file.path(path, basename(mageFiles$sdrf), fsep = sep)))
|
533: try(file.remove(file.path(path, basename(mageFiles$idf), fsep = sep)))
|
534: try(file.remove(file.path(path, basename(mageFiles$adf), fsep = sep)))
|
535: try(file.remove(file.path(path, basename(mageFiles$rawArchive), fsep = sep)))
|
536: try(file.remove(file.path(path, basename(mageFiles$processedArchive), fsep = sep)))
|
EnMCB:R/utils.R: [ ] |
---|
221: path = getwd(),dpi = 300,units = "in",width = 10, height = 5,
|
277: path = getwd(),dpi = 300,units = "in",width = 5, height = 4.5,
|
390: path = getwd(),dpi = 300,units = "in",width = 5, height = 5,
|
193: path = getwd(),dpi = 300,units = "in",width = 5, height = 4.5,
|
323: path = getwd(),dpi = 300,units = "in",width = 5, height = 4.5,
|
8: # if (length(q$rpath)>0) return(readRDS(rev(q$rpath)[1])) # if multiple, use last
|
18: # BiocFileCache::bfcadd(ca, rname="IlluminaHumanMethylation450kanno.ilmn12.hg19", fpath=tf,
|
CBNplot:R/utilities.R: [ ] |
---|
310: path <- BiocFileCache::bfcrpath(bfc, url)
|
311: pathRel <- read.csv(path, sep="\t", header=FALSE)
|
312: pathRelG <- graph_from_edgelist(as.matrix(pathRel), directed = FALSE)
|
313: incPath <- V(pathRelG)[names(V(pathRelG)) %in% res$ID]
|
351: obtainPath <- function(res, geneSymbol) {
|
464: pathwayMatrix <- exp[ intersect(rownames(exp), genesInPathway),
|
469: pathwayMatrixPca <- prcomp(t(pathwayMatrix), scale. = FALSE)$x[,1]
|
314: incPathG <- igraph::subgraph(pathRelG, incPath)
|
423: bnpathtest <- function (results, exp, expSample=NULL, algo="hc",
|
455: genesInPathway <- strsplit(res[i, ]$geneID, "/")[[1]]
|
563: genesInPathway <- unlist(strsplit(res[pathNum, ]$geneID, "/"))
|
19: #' pathNum=1, returnNet=TRUE)
|
138: #' pathNum=1, returnNet=TRUE)
|
337: #' obtainPath
|
348: #' obtainPath(res = exampleEaRes, geneSymbol="ERCC7")
|
371: #' exp = exampleGeneExp, pathNum = 1, R = 10, returnNet=TRUE)
|
373: #' exp = exampleGeneExp, pathNum = 1, R = 10, returnNet=TRUE)
|
395: #' Testing various R for bayesian network between pathways
|
409: #' @param nCategory the number of pathways to be included
|
466: if (dim(pathwayMatrix)[1]==0) {
|
467: message("no gene in the pathway present in expression data")
|
470: avExp <- apply(pathwayMatrix, 2, mean)
|
471: corFlag <- cor(pathwayMatrixPca, avExp)
|
472: if (corFlag < 0){pathwayMatrixPca <- pathwayMatrixPca*-1}
|
473: # pathwayMatrixSum <- apply(pathwayMatrix, 2, sum)
|
475: pcs <- cbind(pcs, pathwayMatrixPca)
|
530: #' @param pathNum the pathway number (the number of row of the original result,
|
540: #' algo="hc", Rrange=seq(10, 30, 10), pathNum=1, scoreType="bge")
|
545: pathNum=NULL, convertSymbol=TRUE, expRow="ENSEMBL",
|
308: url <- "https://reactome.org/download/current/ReactomePathwaysRelation.txt"
|
315: incPathG <- set.vertex.attribute(incPathG, "name",
|
316: value=res[names(V(incPathG)), "Description"])
|
318: ovlG <- as_edgelist(igraph::intersection(incPathG, undirG))
|
393: #' bnpathtest
|
418: #' res <- bnpathtest(results = exampleEaRes, exp = exampleGeneExp,
|
458: genesInPathway <- clusterProfiler::bitr(genesInPathway,
|
566: genesInPathway <- clusterProfiler::bitr(genesInPathway,
|
573: pcs <- exp[ intersect(rownames(exp), genesInPathway), expSample ]
|
RepViz:R/plotGeneTrack.R: [ ] |
---|
125: host = "grch37.ensembl.org", path = "/biomart/martservice",
|
130: host = "grch38.ensembl.org", path = "/biomart/martservice",
|
138: host = "grch37.ensembl.org", path = "/biomart/martservice",
|
epivizrServer:R/middleware-plus-supporting.R: [ ] |
---|
438: path <- req$PATH_INFO
|
217: abs.path <- file.path(dir, relpath)
|
446: abs.path <- resolve(root, path)
|
367: pathPattern <- paste("^\\Q", prefix, "\\E/", sep = "")
|
371: origPath <- req$PATH_INFO
|
376: pathInfo <- substr(req$PATH_INFO, nchar(prefix)+1, nchar(req$PATH_INFO))
|
402: pathPattern <- paste("^\\Q", prefix, "\\E/", sep = "")
|
407: origPath <- req$PATH_INFO
|
412: pathInfo <- substr(req$PATH_INFO, nchar(prefix)+1, nchar(req$PATH_INFO))
|
205: # Attempt to join a path and relative path, and turn the result into a
|
206: # (normalized) absolute path. The result will only be returned if it is an
|
218: if (!file.exists(abs.path))
|
220: abs.path <- normalizePath(abs.path, winslash='/', mustWork=TRUE)
|
224: if (nchar(abs.path) <= nchar(dir) + 1)
|
226: if (substr(abs.path, 1, nchar(dir)) != dir ||
|
227: substr(abs.path, nchar(dir)+1, nchar(dir)+1) != '/') {
|
230: return(abs.path)
|
344: # `PATH_INFO` field, but since it's such a common need, let's make it simple by
|
349: # the route off of the `PATH_INFO` (and add it to the end of `SCRIPT_NAME`).
|
351: # path has already been matched via routing.
|
369: if (isTRUE(grepl(pathPattern, req$PATH_INFO))) {
|
374: req$PATH_INFO <- origPath
|
378: req$PATH_INFO <- pathInfo
|
405: if (isTRUE(grepl(pathPattern, req$PATH_INFO))) {
|
410: req$PATH_INFO <- origPath
|
414: req$PATH_INFO <- pathInfo
|
440: if (is.null(path))
|
443: if (path == '/')
|
444: path <- '/index.html'
|
447: if (is.null(abs.path))
|
450: content.type <- mime::guess_type(abs.path)
|
451: response.content <- readBin(abs.path, 'raw', n=file.info(abs.path)$size)
|
216: resolve <- function(dir, relpath) {
|
221: dir <- normalizePath(dir, winslash='/', mustWork=TRUE)
|
295: # representing paths to be used instead of handlers; any such strings we
|