Found 85001 results in 8409 files, showing top 50 files (show more).
NoRCE:R/pathway.R: [ ]
351:   path <- merge(merge1, symb, by = "gene")
65:     pathTable <- unique(keggPathwayDB(org_assembly))
71:     pathfreq <- as.data.frame(table(annot$pathway))
99:     pathT <- as.character(freq$Var1[enrich])
118:     pathways <- data.frame(unique(pathT))
203:     pathTable <- unique(reactomePathwayDB(org_assembly))
209:     pathfreq <- as.data.frame(table(annot$pathway))
235:     pathT <- as.character(freq$Var1[enrich])
540:   pathTable <- unique(WikiPathwayDB(org_assembly))
545:   pathfreq <- as.data.frame(table(annot$pathID))
571:   pathT <- as.character(freq$Var1[enrich])
578:   pathTerms <- as.character(r$pathTerm[match(pathT, r$pathID)])
628: pathwayEnrichment <- function(genes,
678:   pathfreq <- as.data.frame(table(annot$pathTerm))
710:   pathT <- as.character(freq$Var1[enrich])
718:   pathTerms <- as.character(r$pathTerm[match(pathT, r$pathID)])
270: reactomePathwayDB <- function(org_assembly = c("hg19",
357: keggPathwayDB <- function(org_assembly = c("hg19",
433: WikiPathwayDB <- function(org_assembly = c("hg19",
15: #' @param gmtFile File path of the gmt file
91:         file.path(x[1], x[2]))
95:         file.path(x[1], x[2]))
155: #' @param gmtFile File path of the gmt file
228:       file.path(x[1], x[2]))
231:       file.path(x[1], x[2]))
353:   return(path)
499: #' @param gmtFile File path of the gmt file
563:       file.path(x[1], x[2]))
567:       file.path(x[1], x[2]))
608: #' @param gmtFile File path of the gmt file
703:     file.path(x[1], x[2]))
706:     file.path(x[1], x[2]))
1: #' KEGG pathway enrichment
22: #' @return KEGG pathway enrichment results
68:     annot <- pathTable[which(pathTable$symbol %in% genes$g),]
72:     pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
75:     geneSize = length(unique(pathTable$symbol))
77:     bckfreq <- as.data.frame(table(pathTable$pathway))
78:     notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
79:     freq <- merge(pathfreq, notGene, by = "Var1")
104:     r <- annot[annot$pathway %in% pathT,]
106:     for (i in seq_along(pathT))
108:       if (length(which(pathT[i] == r$pathway)) > 0)
113:               as.character(r[which(pathT[i] == r$pathway),]$symbol)),
114:                      paste(pathT[i])))
119:     tmp <- character(length(pathT))
120:     if (nrow(pathways) > 0) {
122:         unlist(lapply(seq_len(nrow(pathways)), function(x)
123:           tmp[x] <- try(KEGGREST::keggGet(pathT[x])[[1]]$NAME)
129:         ID = pathT,
141: #' Reactome pathway enrichment
163: #' @return Reactome pathway enrichment results
206:     annot <- pathTable[which(pathTable$symbol %in% genes$g),]
210:     pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
212:     geneSize = length(unique(pathTable$symbol))
214:     bckfreq <- as.data.frame(table(pathTable$pathway))
215:     notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
216:     freq <- merge(pathfreq, notGene, by = "Var1")
240:     r <- annot[annot$pathway %in% pathT,]
244:     for (i in seq_along(pathT))
246:       if (length(which(pathT[i] == r$pathway)) > 0)
251:               list(as.character(r[which(pathT[i] == r$pathway),]$symbol)),
252:                      paste(pathT[i])))
258:         ID = pathT,
259:         Term = as.character(rt[order(match(rt$pathway, pathT)), ]$name),
279:   table1 <- data.frame(pathway = rep(names(xx), lapply(xx, length)),
282:   pn <- data.frame(pathway = rep(names(pn), lapply(pn, length)),
288:     ty <- table1[grepl("^R-HSA", table1$pathway),]
289:     pn1 <- pn[grepl("^R-HSA", pn$pathway),]
296:     ty <- table1[grepl("^R-MMU", table1$pathway),]
297:     pn1 <- pn[grepl("^R-MMU", pn$pathway),]
304:     ty <- table1[grepl("^R-DRE", table1$pathway),]
305:     pn1 <- pn[grepl("^R-DRE", pn$pathway),]
312:     ty <- table1[grepl("^R-RNO", table1$pathway),]
313:     pn1 <- pn[grepl("^R-RNO", pn$pathway),]
320:     ty <- table1[grepl("^R-CEL", table1$pathway),]
321:     pn1 <- pn[grepl("^R-CEL", pn$pathway),]
328:     ty <- table1[grepl("^R-SCE", table1$pathway),]
329:     pn1 <- pn[grepl("^R-SCE", pn$pathway),]
342:     ty <- table1[grepl("^R-DME", table1$pathway),]
343:     pn1 <- pn[grepl("^R-DME", pn$pathway),]
349:                   by = "pathway",
369:     kegg <- org.Hs.eg.db::org.Hs.egPATH2EG
377:     kegg <- org.Mm.eg.db::org.Mm.egPATH2EG
385:     kegg <- org.Dr.eg.db::org.Dr.egPATH2EG
393:     kegg <- org.Rn.eg.db::org.Rn.egPATH2EG
401:     kegg <- org.Ce.eg.db::org.Ce.egPATH2EG
409:     kegg <- org.Sc.sgd.db::org.Sc.sgdPATH2ORF
417:     kegg <- org.Dm.eg.db::org.Dm.egPATH2EG
423:   pathTable <-
424:     data.frame(pathway = paste0(prefix, rep(names(kegg2),
429:   pathTable <- merge(pathTable, x, by = "gene")
430:   return(pathTable)
472:     do.call(rbind, strsplit(as.character(gmtFile$pathTerm), '%'))
478:         pathID = tmp[, 3],
479:         pathTerm = tmp[, 1]
506: #' @return Wiki Pathway Enrichment
543:   annot <- pathTable[which(pathTable$gene %in% genes$g),]
546:   pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
548:   geneSize = length(unique(pathTable$gene))
549:   bckfreq <- as.data.frame(table(pathTable$pathID))
550:   notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
551:   freq <- merge(pathfreq, notGene, by = "Var1")
576:   r <- annot[annot$pathID %in% pathT,]
579:   for (i in seq_along(pathT))
581:     if (length(which(pathT[i] == r$pathID)) > 0)
585:           list(as.character(r[which(pathT[i] == r$pathID),]$gene)),
586:                           paste(pathT[i])))
593:       ID = pathT,
594:       Term = pathTerms,
604: #' For a given gmt file of a specific pathway database, pathway enrichment
626: #' @return Pathway Enrichment
670:     pathTable <-
675:     pathTable <- geneListEnrich(f = gmtFile, isSymbol = isSymbol)
677:   annot <- pathTable[which(pathTable$symbol %in% genes$g),]
679:   pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
683:     geneSize = length(unique(pathTable$symbol))
688:   bckfreq <- as.data.frame(table(pathTable$pathTerm))
690:   notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
691:   freq <- merge(pathfreq, notGene, by = "Var1")
716:   r <- annot[annot$pathTerm %in% pathT,]
720:   for (i in seq_along(pathT))
722:     if (length(which(pathT[i] == r$pathTerm)) > 0)
725:           list(as.character(r[which(pathT[i] == r$pathTerm),]$symbol)),
726:                           paste(pathT[i])))
731:       ID = pathT,
732:       Term = pathTerms,
742: #' Convert gmt formatted pathway file to the Pathway ID, Entrez, symbol
745: #' @param gmtName Custom pathway gmt file
814:     colnames(f) <- c('pathTerm', 'Entrez', 'symbol')
829:     colnames(f) <- c('pathTerm', 'symbol', 'Entrez')
851:     colnames(f) <- c('pathTerm', 'Entrez', 'symbol')
862:     colnames(f) <- c('pathTerm', 'symbol', 'Entrez')
278:   xx <- as.list(reactome.db::reactomePATHID2EXTID)
281:   pn <- as.list(reactome.db::reactomePATHID2NAME)
443:       rWikiPathways::downloadPathwayArchive(organism = "Homo sapiens",
447:       rWikiPathways::downloadPathwayArchive(organism = "Mus musculus",
451:       rWikiPathways::downloadPathwayArchive(organism = "Danio rerio",
455:       rWikiPathways::downloadPathwayArchive(organism = "Rattus norvegicus",
459:       rWikiPathways::downloadPathwayArchive(
463:       rWikiPathways::downloadPathwayArchive(
467:       rWikiPathways::downloadPathwayArchive(
485: #' WikiPathways Enrichment
seq2pathway:R/seq2pathway.r: [ ]
928:    path <-paste(system.file(package="seq2pathway"),
856: get_python3_command_path <- function()
859:   python3_command_path <- Sys.which2("python")
1029:     script_path <- file.path(tempdir(), name)
275: pathwaygene<-length(intersect(toupper(gene_list[[i]]),
484: pathwaygene<-length(intersect(toupper(gsmap$genesets[[i]]),
843:   cmdpath <- Sys.which(cmdname)
1051: runseq2pathway<-function(inputfile,
1161: gene2pathway_result<-list()
1310: gene2pathway_test<-function(dat,DataBase="GOterm",FisherTest=TRUE,
1344: gene2pathway_result<-list()
854: #get_python3_command_path: funtion from Herve Pages, Bioconductor Maintainance Team, Oct 9 2020
858: #  python3_command_path <- Sys.which2("python3") #3/3/2021 by Holly
860:   if (python3_command_path != "")
864:           return(python3_command_path)}
873: #  python3_command_path <- Sys.which2("python")
874:   python3_command_path <- Sys.which2("python3")  #3/3/2021 by Holly
875:   if (python3_command_path != ""){
876:     print(paste0("python3 found: ",python3_command_path))
877:     return(python3_command_path)}
880:        "  'python3' (or 'python') executable is in your PATH.")
924:     ### assign the path of main function
932:     path <-paste(system.file(package="seq2pathway"),
976: 		sink(file.path(tempdir(),name,fsep = .Platform$file.sep))} 
994:     cat("'", path, "').load_module()",sep="")
1030:     if (!file.exists(script_path))
1032:     mypython <- get_python3_command_path()
1034:     response <- system2(mypython, args=script_path,
75: data(gencode_coding,package="seq2pathway.data")
155: data(gencode_coding,package="seq2pathway.data")
214: ####load GP pathway information
216:    data(GO_BP_list,package="seq2pathway.data")
217:    data(GO_MF_list,package="seq2pathway.data")
218:    data(GO_CC_list,package="seq2pathway.data") 
219:    data(Des_BP_list,package="seq2pathway.data")
220:    data(Des_MF_list,package="seq2pathway.data")
221:    data(Des_CC_list,package="seq2pathway.data")
223:          data(GO_BP_list,package="seq2pathway.data") 
224:          data(Des_BP_list,package="seq2pathway.data")
226:               data(GO_MF_list,package="seq2pathway.data") 
227:               data(Des_MF_list,package="seq2pathway.data")
229:                   data(GO_CC_list,package="seq2pathway.data")
230:                   data(Des_CC_list,package="seq2pathway.data")
237: data(GO_GENCODE_df_hg_v36,package="seq2pathway.data")
240: data(GO_GENCODE_df_hg_v19,package="seq2pathway.data")
243: data(GO_GENCODE_df_mm_vM25,package="seq2pathway.data")
246: data(GO_GENCODE_df_mm_vM1,package="seq2pathway.data")
280: c<-pathwaygene-a
289: mdat[i,7]<-pathwaygene
321: pathwaygene<-length(intersect(toupper(GO_BP_list[[i]]),
326: c<-pathwaygene-a
335: mdat[i,7]<-pathwaygene
367: pathwaygene<-length(intersect(toupper(GO_CC_list[[i]]),
372: c<-pathwaygene-a
381: mdat[i,7]<-pathwaygene
413: pathwaygene<-length(intersect(toupper(GO_MF_list[[i]]),
418: c<-pathwaygene-a
427: mdat[i,7]<-pathwaygene
455: data(Msig_GENCODE_df_hg_v36,package="seq2pathway.data")
458: data(Msig_GENCODE_df_hg_v19,package="seq2pathway.data")
461: data(Msig_GENCODE_df_mm_vM25,package="seq2pathway.data")
464: data(Msig_GENCODE_df_mm_vM1,package="seq2pathway.data")
489: c<-pathwaygene-a
498: mdat[i,7]<-pathwaygene
549: data(gencode_coding,package="seq2pathway.data")
647: rungene2pathway <-
704: colnames(res) <- c(paste(colnames(dat),"2pathscore",sep=""))
705: print("gene2pathway calculates score....... done")
711: rungene2pathway_EmpiricalP <-
770: colnames(res) <- c(paste(colnames(dat),"2pathscore",sep=""))
829: colnames(res_p) <- c(paste(colnames(dat),"2pathscore_Pvalue",sep=""))
832: print("pathwayscore Empirical Pvalue calculation..........done")
849:   success <- grepl(pattern1, cmdpath, fixed=TRUE) ||
850:     grepl(pattern2, cmdpath, fixed=TRUE)
851:   if (success) cmdpath else ""
1007:     #cat(paste("inputpath=","'",inputpath,"/'",sep=""),sep="\n")
1009:     #cat(paste("outputpath=","'",outputpath,"/'",sep=""),sep="\n")
1018:     cat(paste("pwd=","'",system.file(package="seq2pathway.data"),"/extdata/'",sep=""),sep="\n")
1103: data(GO_BP_list,package="seq2pathway.data")
1104: data(GO_MF_list,package="seq2pathway.data")
1105: data(GO_CC_list,package="seq2pathway.data")
1106: data(Des_BP_list,package="seq2pathway.data")
1107: data(Des_CC_list,package="seq2pathway.data")
1108: data(Des_MF_list,package="seq2pathway.data")
1134: #############################rungene2pathway,normalization,empiricalP,summary table
1166: GO_BP_FAIME<-rungene2pathway(dat=dat_CP,gsmap=GO_BP_list,alpha=alpha,logCheck=logCheck,
1171: GO_BP_FAIME_Pvalue<-rungene2pathway_EmpiricalP(dat=dat_CP,gsmap=GO_BP_list,
1174: ########gene2pathway table
1190: gene2pathway_result[[n.list]]<-GO_BP_N_P
1191: names(gene2pathway_result)[n.list]<-c("GO_BP")
1195: GO_MF_FAIME<-rungene2pathway(dat=dat_CP,gsmap=GO_MF_list,alpha=alpha,logCheck=logCheck,
1198: GO_MF_FAIME_Pvalue<-rungene2pathway_EmpiricalP(dat=dat_CP,gsmap=GO_MF_list,
1215: gene2pathway_result[[n.list]]<-GO_MF_N_P
1216: names(gene2pathway_result)[n.list]<-c("GO_MF")
1220: GO_CC_FAIME<-rungene2pathway(dat=dat_CP,gsmap=GO_CC_list,alpha=alpha,logCheck=logCheck,
1223: GO_CC_FAIME_Pvalue<-rungene2pathway_EmpiricalP(dat=dat_CP,gsmap=GO_CC_list,
1241: gene2pathway_result[[n.list]]<-GO_CC_N_P
1242: names(gene2pathway_result)[n.list]<-c("GO_CC")
1245: dat_FAIME<-rungene2pathway(dat=dat_CP,gsmap=DataBase,alpha=alpha,logCheck=logCheck,
1248: dat_FAIME_Pvalue<-rungene2pathway_EmpiricalP(dat=dat_CP,gsmap=DataBase,
1255: colnames(DB_N_P)<-c("score2pathscore_Normalized","score2pathscore_Pvalue")
1274: gene2pathway_result<-DB_N_P[,c(ncol(DB_N_P),1:(ncol(DB_N_P)-1))]
1276: print("gene2pathway analysis is done")
1279: if(exists("gene2pathway_result")&exists("FS_test")){
1283: TotalResult[[2]]<-gene2pathway_result
1284: names(TotalResult)[2]<-"gene2pathway_result.FAIME"
1286: names(TotalResult)[3]<-"gene2pathway_result.FET"
1289: }else if(exists("gene2pathway_result")&exists("FS_test")==FALSE){
1293: TotalResult[[2]]<-gene2pathway_result
1294: names(TotalResult)[2]<-"gene2pathway_result.FAIME"
1298: else if(exists("gene2pathway_result")==FALSE&exists("FS_test")){
1303: names(TotalResult)[2]<-"gene2pathway_result.FET"
1326: data(GO_BP_list,package="seq2pathway.data")
1327: data(GO_MF_list,package="seq2pathway.data")
1328: data(GO_CC_list,package="seq2pathway.data")
1329: data(Des_BP_list,package="seq2pathway.data")
1330: data(Des_CC_list,package="seq2pathway.data")
1331: data(Des_MF_list,package="seq2pathway.data")
1346: #############################rungene2pathway,normalization,empiricalP,summary table
1348: gene2pathway_result<-list()
1352:   GO_BP_method<-rungene2pathway(dat=dat,gsmap=GO_BP_list,alpha=alpha,logCheck=logCheck,
1358:     GO_BP_method_Pvalue<-rungene2pathway_EmpiricalP(dat=dat,gsmap=GO_BP_list,alpha=alpha,
1364:   ########gene2pathway table
1376:   gene2pathway_result[[n.list]]<-GO_BP_N_P
1377:   names(gene2pathway_result)[n.list]<-c("GO_BP")
1380:     GO_MF_method<-rungene2pathway(dat=dat,gsmap=GO_MF_list,alpha=alpha,logCheck=logCheck,
1384:       GO_MF_method_Pvalue<-rungene2pathway_EmpiricalP(dat=dat,gsmap=GO_MF_list,alpha=alpha,
1402:   gene2pathway_result[[n.list]]<-GO_MF_N_P
1403:   names(gene2pathway_result)[n.list]<-c("GO_MF")
1406:    GO_CC_method<-rungene2pathway(dat=dat,gsmap=GO_CC_list,alpha=alpha,logCheck=logCheck,
1410:       GO_CC_method_Pvalue<-rungene2pathway_EmpiricalP(dat=dat,gsmap=GO_CC_list,alpha=alpha,
1427:   gene2pathway_result[[n.list]]<-GO_CC_N_P
1428:   names(gene2pathway_result)[n.list]<-c("GO_CC")
1431: dat_method<-rungene2pathway(dat=dat,gsmap=DataBase,alpha=alpha,logCheck=logCheck,
1435: dat_method_Pvalue<-rungene2pathway_EmpiricalP(dat=dat,gsmap=DataBase,alpha=alpha,
1443: colnames(DB_N_P)<-c("score2pathscore_Normalized","score2pathscore_Pvalue")
1464: gene2pathway_result<-DB_N_P[,c(ncol(DB_N_P),1:(ncol(DB_N_P)-1))]
1466: print("gene2pathway analysis is done")
1470: if(exists("gene2pathway_result")&exists("FS_test")){
1472: TResult[[1]]<-gene2pathway_result
1473: names(TResult)[1]<-"gene2pathway_result.2"
1475: names(TResult)[2]<-"gene2pathway_result.FET"
1476: }else if(exists("gene2pathway_result")&exists("FS_test")==FALSE){
1477: TResult<-gene2pathway_result
1479: else if(exists("gene2pathway_result")==FALSE&exists("FS_test")){
CHRONOS:R/pathwayToGraph.R: [ ]
101:         path <- paste(dir, file, sep='//')
34:         paths <- list.files(xmlDir) 
83: pathwayToGraph <- function (i, ...)
3: createPathwayGraphs <- function(org, pathways, edgeTypes, doubleEdges, choice,
141: getPathwayType                    <- function(filepath, file)
159: metabolicPathwayToGraph           <- function(filepath)
347: nonMetabolicPathwayToGraph <- function(filepath, doubleEdges, groupMode)
102:         gr   <- metabolicPathwayToGraph(path)
119:         path <- paste(dir, file, sep='//')
120:         gr   <- nonMetabolicPathwayToGraph(path, doubleEdges, groupMode)
225: removeCompoundsMetabolicGraph     <- function(path)
228:     if(path$name != gsub('ec','',path$name)) { nodeType<-"enzyme" }
229:     enzymes  <- which(path$vertices$type == nodeType)
230:     vid      <- path$vertices$id
233:     if ( length(path$edges) > 0 )
243:             for (r1 in path$edges[path$edges$e1 == 
244:                                 path$vertices[,'id'][enzymes[j]],]$e2)  
247:                 for (r2 in path$edges[path$edges$e1 == 
248:                                 path$vertices[,'id'][which(vid == r1)],]$e2)
252:                     nid <- vid[which(path$vertices$id == r2)]
267:     xid    <- path$vertices$id[enzymes]
268:     names  <- path$vertices$names[enzymes]       
513: removeCompoundsNonMetabolicGraph <- function(path, unique, edgeTypes)
515:     if (is.null(path)) return(NULL)
516:     vid      <- as.numeric(path$vertices$id)
517:     etype    <- path$vertices$type
519:     if(path$name != gsub('ko','',path$name)) { nodeType <- "ortholog" }
522:     genesIndx <- which(path$vertices$type == nodeType)
528:         neighbors <- path$edges$e2[path$edges$e1 == vid[gi]]
546:                 idx1 <- which( path$edges$e1 == vid[gi] )
547:                 idx2 <- which( path$edges$e2 == vid[nbrId] )
549:                 TT   <- c( TT, paste((path$edges$type[idx]), collapse='_') )
557:                 cpdNeighbors <- path$edges$e2[ 
558:                                         which(path$edges$e1 == vid[nbrId]) ]
586:         names            <- unique(path$vertices$names[genesIndx])
598:             idx1 <- which(path$vertices$id == source[i])
599:             idx2 <- which(path$vertices$id == destin[i])
600:             source[i] <- names[ names == path$vertices$names[idx1] ]
601:             destin[i] <- names[ names == path$vertices$names[idx2] ]
623:         gids                <- path$vertices$id[genesIndx]
624:         names               <- unname(path$vertices$names[genesIndx])
31:     # Choose valid pathways
32:     if (missing(pathways))  
37:     if (!missing(pathways)) 
39:         paths <- paste(org, pathways, '.xml', sep='') 
44:     # Create compact adjacency matrices for given pathways.
45:     types  <- getPathwayType(paste(xmlDir, paths, sep='//'))
46:     N <- length(paths)
56:                     funcName=pathwayToGraph,
59:                     N=length(paths),
61:                     xmlDir, paths, types, FALSE, edgeTypes, 
64:     names(cAdjMats) <- gsub('.xml', '', paths)
67:     eAdjMats <- .doSafeParallel(funcName=pathwayToGraph,
70:                                 N=length(paths),
72:                                 xmlDir, paths, types, TRUE, edgeTypes, 
75:     names(eAdjMats) <- gsub('.xml', '', paths)
143:     types <- vector(mode='numeric', length=length(filepath))
144:     for (i in 1:length(filepath))
146:         num <- tail(unlist(strsplit(filepath[i], '//')), 1)
156: # Graph from Metabolic Pathways
161:     xmlDoc <- tryCatch(xmlTreeParse(filepath,error=NULL),
344: # Graph from Mon Metabolic Pathways
350:     xmlDoc         <- tryCatch(xmlTreeParse(filepath,error=NULL),
49:         'nonMetabolicPathwayToGraph', 'expandMetabolicGraph', 
51:         'metabolicPathwayToGraph', 'expandNonMetabolicGraph',
609:                 # Set new interaction types to apathetic
732:     # apathetic  3
biodbKegg:R/KeggPathwayConn.R: [ ]
61:         path <- self$getEntry(path.id)
59:     for (path.id in id) {
127:     for (path.id in id) {
304:     path_idx <- sub('^[^0-9]+', '', id)
322:     path_idx <- sub('^[^0-9]+', '', id)
29: KeggPathwayConn <- R6::R6Class("KeggPathwayConn",
40:     super$initialize(db.name='pathway', db.abbrev='path', ...)
62:         if ( ! is.null(path) && path$hasField('kegg.module.id')) {
65:             for (mod.id in path$getFieldValue('kegg.module.id')) {
144:             graph[[path.id]]=list(vertices=vert, edges=edg)
147:             graph[[path.id]]=NULL
309:         params=c(org_name='map', mapno=path_idx,
325:     img_filename <- paste0('pathwaymap-', path_idx)
331:             biodb::error0('Impossible to find pathway image path inside',
335:         tmp_file <- file.path(cache$getTmpFolderPath(),
2: #' The connector class to KEGG Pathway database.
16: #' conn=mybiodb$getFactory()$createConn('kegg.pathway')
18: #' # Retrieve all reactions related to a mouse pathway:
21: #' # Get a pathway graph
44: #' Retrieves all reactions part of a KEGG pathway. Connects to
45: #'     KEGG databases, and walk through all pathways submitted, and
58:     # Loop on all Pathway IDs
89: #' Takes a list of pathways IDs and converts them to the specified organism,
92: #' @param org The organism in which to search for pathways, as a KEGG organism
113: #' Builds a pathway graph in the form of two tables of vertices and edges,
115: #' @param id A character vector of KEGG pathway entry IDs.
120: #' @return A named list whose names are the pathway IDs, and values are lists
126:     # Loop on all pathway IDs
158: #' Builds a pathway graph, as an igraph object, using KEGG database.
159: #' @param id A character vector of KEGG pathway entry IDs.
196: #' Create a pathway graph picture, with some of its elements colorized.
197: #' @param id A KEGG pathway ID.
227: #' Extracts shapes from a pathway map image.
228: #' @param id A KEGG pathway ID.
303:     # Extract pathway number
308:         'show_pathway'),
329:             'src="([^"]+)"(\\s+.*)?\\s+(name|id)="pathwayimage"')
332:                 ' HTML page for pathway ID ', id, '.')
342:     img_file <- cache$getFilePath(cid, img_filename, 'png')
22: #' graph=conn$buildPathwayGraph('mmu00260')
98: convertToOrgPathways=function(id, org) {
122: buildPathwayGraph=function(id, directed=FALSE, drop=TRUE) {
166: getPathwayIgraph=function(id, directed=FALSE, drop=TRUE) {
173:         g <- self$buildPathwayGraph(id=id, directed=directed, drop=FALSE)
210:         pix <- private$getPathwayImage(id)
213:         shapes <- self$extractPathwayMapShapes(id=id, color2ids=color2ids)
233: ,extractPathwayMapShapes=function(id, color2ids) {
237:     html <- private$getPathwayHtml(id)
301: ,getPathwayHtml=function(id) {
319: getPathwayImage=function(id) {
321:     html <- private$getPathwayHtml(id)
cisPath:inst/extdata/D3/cisPath.js: [ ]
127: var path = svg.append('svg:g').selectAll('path'),
2499: function ShowShortestPath(){
2: var basePath1="./PPIinfo/";
3: var basePath2="./PPIinfo/";
2630: function ShowShortestPath1(){
2711: var swiss2paths={};
2765: function showPath10(){
2778: function addResultPath10(p){
2784: function addFirstPath10(p){
2800: function addLastPath10(p){
2823: function addPreviousPath10(p){
2842: function addNextPath10(p){
2560: var resultPaths=[];
2561: function ShowPaths(allPaths){
2616: function ShowPathView(index){
2712: function generatePaths(swiss, root){
2729: function ShowShortestPathExp(){
2763: var showPathFirst=0;
2764: var showPathEnd=10;
3044: function getCisPathJS(){
3065: function getCisPathJSRetry(urltry){
3096: function getPathsForTargetProtein(swissID){
3118: function getPathsForTargetProteinRetry(urltry){
53:   .append('svg:path')
68:   .append('svg:path')
94:   .append('svg:path')
109:   .append('svg:path')
115: var drag_line = svg.append('svg:path')
166:   path.attr('d', function(d) {
360:   // path (link) group
381:   path = path.data(links);
384:   path.classed('selected', function(d) { return d === selected_link; })
393:   path.enter().append('svg:path')
414:   path.exit().remove();
2384: ///////////////////////////////////////////////////////////////////////shortest path
2504:        alert("No path between this two proteins!");
2508:        alert("No path between this two proteins!");
2568:     addTh(mycurrent_row, "Path");
2601:         mycurrent_button.id = "path"+x;
2614:     ShowPathView("path0");
2701:        alert("Sorry, detect no path from "+swiss1+" to "+ swiss2);
3097:    var url="./js/"+swissID+"_path.js";
954: 	 document.getElementById("detectPath").disabled=true;
1380:   idx=pathLen-1-idx;
1415: 	var url=basePath2+sourceSwiss+".js";
1679: 	var url=basePath2+swiss1+".js";
1684:            if(this.url==(basePath2+swiss1+".js")){
1820:   var url=basePath2+"A0A5B9.js";
1827:         debugObj("basePath1:"+basePath1);
1831:         basePath1="../geneset20140628/";
1832:         basePath2="../20140628PPI/";
1833:         debugObj("basePath1:"+basePath1);
1845:   var url=basePath2+"gene2swiss.js";
1899: 	var url=basePath2+"swiss2gene.js";
1954: 	var url=basePath2+"swiss2swiss.js";
2021: 	var url=basePath1+"PPI.js";
2411:     	 ShowShortestPath();
2421:           document.getElementById("detectPath").disabled=false;
2426:        document.getElementById("detectPath").disabled=true;
2438:           document.getElementById("detectPath").disabled=false;
2443:        document.getElementById("detectPath").disabled=true;
2515:     document.getElementById("detectPath").disabled=true;
2530:           document.getElementById("detectPath").disabled=false;
2543:            setTimeout("ShowShortestPath1()",1000);
2549:     setTimeout("ShowShortestPath1()",1000);
2574: 	    mycurrent_row.id="pathrow"+x;
2608: 	showPath10();
2611:     document.getElementById("detectPath").disabled=false;
2625: 	  	document.getElementById("pathrow"+x).className="rowNotSelected";
2627: 	document.getElementById("pathrow"+i).className="rowSelected";
2705:     swiss2paths={};
2707:     debugObj(swiss2paths[swiss2]);
2708:     debugObj(swiss2paths[swiss2].length);
2709:     ShowPaths(swiss2paths[swiss2]);
2713:     swiss2paths[swiss]=[];
2715:        swiss2paths[swiss].push(swiss);
2724:         for(var y in swiss2paths[nodes[x]]){
2725:             swiss2paths[swiss].push(swiss2paths[nodes[x]][y]+"#"+swiss);
2760:     ShowShortestPath();
2771:     addResultPath10(p);
2772:     addFirstPath10(p);
2773:     addPreviousPath10(p);
2774:     addNextPath10(p);
2775:     addLastPath10(p);
2796:     resultSPAN.addEventListener("click", function(e){showPathFirst=0;showPath10();}, false);
2819:     resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
2838:     resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
2857:     resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
2879: 	  	document.getElementById("pathrow"+i).style.display="none";
2882:         document.getElementById("pathrow"+i).style.display="";
3020: ...(31 bytes skipped)...ange", function(e){document.getElementById('targetPtxt').value=this.value;checkValid2();ShowShortestPath();}, false);
3108:         json.paths.pop();
3109:     	ShowPaths(json.paths);
3138:         json.paths.pop();
3139:     	ShowPaths(json.paths);
956: 	 document.getElementById("allShortestPaths").style.display="none";
957: 	 document.getElementById("allShortestPathsP").style.display="none";
1253: 	graphName = "cisPathValue#"+graphName;
1267:       if(key.substr(0,13)!="cisPathValue#"){
1285: 	graphName = "cisPathValue#"+graphName;
1306: 	graphName = "cisPathValue#"+graphName;
1327:       if(key.substr(0,13)!="cisPathValue#"){
1418:     callback: "cisPathCallBack",
1682:        callback: "cisPathCallBack",
1823:     callback: "cisPathCallBack",
1850:     callback: "cisPathCallBack",
1880:     callback: "cisPathCallBack",
1905:     callback: "cisPathCallBack",
1935:     callback: "cisPathCallBack",
1960:     callback: "cisPathCallBack",
1990:     callback: "cisPathCallBack",
2025:     callback: "cisPathCallBack",
2063:     callback: "cisPathCallBack",
2110: 	getCisPathJS();
2378:     var graphName="cisPathHTML";
2476: function ShowShortestPathWorker(){
2477:    var worker = new Worker("./D3/cisPathWorker.js");
2480:       var resultPaths=event.data.resultPaths;
2481:       ShowPaths(resultPaths);
2484:       var resultPaths=[];
2485:       ShowPaths(resultPaths);
2514:     document.getElementById("detectPathExp").disabled=true;
2521:     document.getElementById("allShortestPaths").style.display="none";
2522:     document.getElementById("allShortestPathsP").style.display="none";
2529:           document.getElementById("detectPathExp").disabled=false;
2533:           getPathsForTargetProtein(swiss2);
2539:            ShowShortestPathWorker();
2562:     resultPaths=allPaths;
2563:     var table=document.getElementById("allShortestPaths");
2571: 	for(var x in allPaths){
2572: 	    var nodes=allPaths[x].split("#");
2602:         mycurrent_button.addEventListener("click", function(e){ShowPathView(this.id);}, false);
2607: 	showPathFirst=0;
2610:     document.getElementById("detectPathExp").disabled=false;
2612:     document.getElementById("allShortestPaths").style.display="";
2613:     document.getElementById("allShortestPathsP").style.display="";
2617:     var total=resultPaths.length;
2619:     var nodes=resultPaths[i].split("#");
2706:     generatePaths(swiss2, swiss1);
2723:         generatePaths(nodes[x], root);
2767:     var p=document.getElementById("allShortestPathsP");
2780:     var content="Results: "+showPathFirst+"-"+showPathEnd+" of "+resultPaths.length+" ";
2790:     if(showPathFirst==0){
2805: 	var newFirst=(resultPaths.length-resultPaths.length%10);
2806: 	if(newFirst==resultPaths.length){
2807: 	   newFirst=resultPaths.length-10;
2813:     if(showPathEnd==resultPaths.length){
2828:     if(showPathFirst==0){
2834:     var newFirst=showPathFirst-10;
2847:     if(showPathEnd==resultPaths.length){
2853:     var newFirst=showPathFirst+10;
2854:     if(newFirst >= resultPaths.length){
2855:        newFirst = (resultPaths.length-resultPaths.length%10);
2863: 	var total=resultPaths.length;
2864: 	if(showPathFirst >= total){
2865: 	   showPathFirst=(total-total%10);
2867: 	if(showPathFirst == total){
2868: 	   showPathFirst = total-10;
2870:     if(showPathFirst < 0){
2871: 	   showPathFirst = 0;
2873: 	showPathFirst=showPathFirst-(showPathFirst%10);
2874: 	showPathEnd=showPathFirst+10;
2875: 	if(showPathEnd > total){
2876: 	   showPathEnd = total;
2881:     for(var i=showPathFirst;i<showPathEnd;i++){
3049:     callback: "cisPathCallBack",
3061:         getCisPathJSRetry(this.url);
3078:     callback: "cisPathCallBack",
3090:         getCisPathJSRetry(this.url);
3101:      callback: "cisPathCallBack",
3114:         getPathsForTargetProteinRetry(this.url);
3131:     callback: "cisPathCallBack",
3144:         getPathsForTargetProteinRetry(this.url);
3151: 	document.getElementById("detectPathExp").disabled=true;
3156: 	document.getElementById("detectPathExp").disabled=false;
cisPath:inst/extdata/D3/cisPathWeb.js: [ ]
127: var path = svg.append('svg:g').selectAll('path'),
2501: function ShowShortestPath(){
2: var basePath1="./PPIinfo/";
3: var basePath2="./PPIinfo/";
2632: function ShowShortestPath1(){
2719: var swiss2paths={};
2753: function showPath10(){
2766: function addResultPath10(p){
2772: function addFirstPath10(p){
2788: function addLastPath10(p){
2811: function addPreviousPath10(p){
2830: function addNextPath10(p){
2548: var resultPaths=[];
2549: function ShowPaths(allPaths){
2615: function ShowPathView(index){
2720: function generatePaths(swiss, root){
2737: function ShowShortestPathExp(){
2751: var showPathFirst=0;
2752: var showPathEnd=10;
53:   .append('svg:path')
68:   .append('svg:path')
94:   .append('svg:path')
109:   .append('svg:path')
115: var drag_line = svg.append('svg:path')
166:   path.attr('d', function(d) {
360:   // path (link) group
381:   path = path.data(links);
384:   path.classed('selected', function(d) { return d === selected_link; })
393:   path.enter().append('svg:path')
414:   path.exit().remove();
2386: ///////////////////////////////////////////////////////////////////////shortest path
2506:        alert("No path between this two proteins!");
2510:        alert("No path between this two proteins!");
2554:     	 alert("Sorry, detect no path from "+swiss1+" to "+ swiss2);
2566:     addTh(mycurrent_row, "Path");
2599:         mycurrent_button.id = "path"+x;
2613:       ShowPathView("path0");
2705:        alert("Sorry, detect no path from "+name1+" to "+ name2);
954: 	 document.getElementById("detectPath").disabled=true;
1380:   idx=pathLen-1-idx;
1415: 	var url=basePath2+sourceSwiss+".js";
1679: 	var url=basePath2+swiss1+".js";
1684:            if(this.url==(basePath2+swiss1+".js")){
1820:   var url=basePath2+"A0A5B9.js";
1827:         debugObj("basePath1:"+basePath1);
1831:         basePath1="../geneset20140628/";
1832:         basePath2="../20140628PPI/";
1833:         debugObj("basePath1:"+basePath1);
1845:   var url=basePath2+"gene2swiss.js";
1899: 	var url=basePath2+"swiss2gene.js";
1954: 	var url=basePath2+"swiss2swiss.js";
2021: 	var url=basePath1+"PPI.js";
2413:     	 ShowShortestPath();
2423:           document.getElementById("detectPath").disabled=false;
2428:        document.getElementById("detectPath").disabled=true;
2440:           document.getElementById("detectPath").disabled=false;
2445:        document.getElementById("detectPath").disabled=true;
2517:     document.getElementById("detectPath").disabled=true;
2531:            setTimeout("ShowShortestPath1()",1000);
2537:     setTimeout("ShowShortestPath1()",1000);
2558:        document.getElementById("detectPath").disabled=false;
2572: 	    mycurrent_row.id="pathrow"+x;
2606: 	showPath10();
2610:     document.getElementById("detectPath").disabled=false;
2627: 	  	document.getElementById("pathrow"+x).className="rowNotSelected";
2629: 	document.getElementById("pathrow"+i).className="rowSelected";
2709:        document.getElementById("detectPath").disabled=false;
2713:     swiss2paths={};
2715:     debugObj(swiss2paths[swiss2]);
2716:     debugObj(swiss2paths[swiss2].length);
2717:     ShowPaths(swiss2paths[swiss2]);
2721:     swiss2paths[swiss]=[];
2723:        swiss2paths[swiss].push(swiss);
2732:         for(var y in swiss2paths[nodes[x]]){
2733:             swiss2paths[swiss].push(swiss2paths[nodes[x]][y]+"#"+swiss);
2748:     ShowShortestPath();
2759:     addResultPath10(p);
2760:     addFirstPath10(p);
2761:     addPreviousPath10(p);
2762:     addNextPath10(p);
2763:     addLastPath10(p);
2784:     resultSPAN.addEventListener("click", function(e){showPathFirst=0;showPath10();}, false);
2807:     resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
2826:     resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
2845:     resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
2867: 	  	document.getElementById("pathrow"+i).style.display="none";
2870:         document.getElementById("pathrow"+i).style.display="";
956: 	 document.getElementById("allShortestPaths").style.display="none";
957: 	 document.getElementById("allShortestPathsP").style.display="none";
1253: 	graphName = "cisPathValue#"+graphName;
1267:       if(key.substr(0,13)!="cisPathValue#"){
1285: 	graphName = "cisPathValue#"+graphName;
1306: 	graphName = "cisPathValue#"+graphName;
1327:       if(key.substr(0,13)!="cisPathValue#"){
1418:     callback: "cisPathCallBack",
1682:        callback: "cisPathCallBack",
1823:     callback: "cisPathCallBack",
1850:     callback: "cisPathCallBack",
1880:     callback: "cisPathCallBack",
1905:     callback: "cisPathCallBack",
1935:     callback: "cisPathCallBack",
1960:     callback: "cisPathCallBack",
1990:     callback: "cisPathCallBack",
2025:     callback: "cisPathCallBack",
2063:     callback: "cisPathCallBack",
2380:     var graphName="cisPathHTML";
2478: function ShowShortestPathWorker(){
2479:    var worker = new Worker("./D3/cisPathWorker.js");
2482:       var resultPaths=event.data.resultPaths;
2483:       ShowPaths(resultPaths);
2486:       var resultPaths=[];
2487:       ShowPaths(resultPaths);
2516:     document.getElementById("detectPathExp").disabled=true;
2523:     document.getElementById("allShortestPaths").style.display="none";
2524:     document.getElementById("allShortestPathsP").style.display="none";
2527:            ShowShortestPathWorker();
2550:     resultPaths=allPaths;
2551:     if(resultPaths.length==0){
2557:        document.getElementById("detectPathExp").disabled=false;
2561:     var table=document.getElementById("allShortestPaths");
2569: 	for(var x in allPaths){
2570: 	    var nodes=allPaths[x].split("#");
2600:         mycurrent_button.addEventListener("click", function(e){ShowPathView(this.id);}, false);
2605: 	showPathFirst=0;
2609:     document.getElementById("detectPathExp").disabled=false;
2611:     document.getElementById("allShortestPaths").style.display="";
2612:     document.getElementById("allShortestPathsP").style.display="";
2616:     var total=resultPaths.length;
2618:     if(i>=resultPaths.length){
2621:     var nodes=resultPaths[i].split("#");
2708:        document.getElementById("detectPathExp").disabled=false;
2714:     generatePaths(swiss2, swiss1);
2731:         generatePaths(nodes[x], root);
2755:     var p=document.getElementById("allShortestPathsP");
2768:     var content="Results: "+showPathFirst+"-"+showPathEnd+" of "+resultPaths.length+" ";
2778:     if(showPathFirst==0){
2793: 	var newFirst=(resultPaths.length-resultPaths.length%10);
2794: 	if(newFirst==resultPaths.length){
2795: 	   newFirst=resultPaths.length-10;
2801:     if(showPathEnd==resultPaths.length){
2816:     if(showPathFirst==0){
2822:     var newFirst=showPathFirst-10;
2835:     if(showPathEnd==resultPaths.length){
2841:     var newFirst=showPathFirst+10;
2842:     if(newFirst >= resultPaths.length){
2843:        newFirst = (resultPaths.length-resultPaths.length%10);
2851: 	var total=resultPaths.length;
2852: 	if(showPathFirst >= total){
2853: 	   showPathFirst=(total-total%10);
2855: 	if(showPathFirst == total){
2856: 	   showPathFirst = total-10;
2858:     if(showPathFirst < 0){
2859: 	   showPathFirst = 0;
2861: 	showPathFirst=showPathFirst-(showPathFirst%10);
2862: 	showPathEnd=showPathFirst+10;
2863: 	if(showPathEnd > total){
2864: 	   showPathEnd = total;
2869:     for(var i=showPathFirst;i<showPathEnd;i++){
2942: 	document.getElementById("detectPathExp").disabled=true;
2947: 	document.getElementById("detectPathExp").disabled=false;
rtracklayer:R/ucsc.R: [ ]
1597:     path <- ucscURLTable[key]
364:   label_path <- "//select[@name = 'hgta_track']/option/text()"
366:   track_path <- "//select[@name = 'hgta_track']/option/@value"
365:   labels <- sub("\n.*$", "", sapply(getNodeSet(doc, label_path), xmlValue))
367:   tracks <- unlist(getNodeSet(doc, track_path))
1572:             upload <- fileUpload(path(object), "text/plain")
1598:     if (is.na(path))
1601:         path <- paste0(path, '?redirect="manual"')
1603:     paste(object@url, path, sep="")
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
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)...
6134:                        #x <- t.view.pathways(gsub("^#PC\\d+# ", "", pws), mat, matw, env = goenv, vhc = results$hvc, plot = FALSE, trim = lt...(14 bytes skipped)...
6174:                        ii <- which(results$ct == pathcl)
6251:                    '/pathways.json' = {
6252:                        lgt <- pathways
diffHic:src/report_hic_pairs.cpp: [ ]
313:     std::string path;
208:     Bamfile(const char * path) : holding(false) { 
209:         in = sam_open(path, "rb");
212:             out << "failed to open BAM file at '" << path << "'";
267:         path=converter.str();
292:             out=std::fopen(path.c_str(), "a");
294:             out=std::fopen(path.c_str(), "w"); // Overwrite any existing file, just to be safe.
298:             err << "failed to open output file at '" << path << "'"; 
326:     const Rcpp::String bampath=check_string(bamfile, "BAM file path");
512:                 outpaths[j]=collected[i][j].path;
595:     Rcpp::String bampath=check_string(incoming, "BAM file path");
334:     Bamfile input(bampath.get_cstring());
596:     Bamfile input(bampath.get_cstring());
507:     Rcpp::List filepaths(nc);
509:         Rcpp::StringVector outpaths(i+1);
515:         filepaths[i]=outpaths;
518:     return Rcpp::List::create(filepaths, 
GSCA:inst/shiny/server.R: [ ]
357:                         path <- system.file("extdata",package=paste0(input$Summarycompselect,"Expr"))
716:                                           path <- system.file("extdata",package=paste0(input$Summarycompselect,"Expr"))
358:                         load(paste0(path,"/geneid.rda"))
378:                                     load(paste0(path,"/quality.rda"))
388:                                     load(paste0(path,"/quality.rda"))
475:                               path <- system.file("extdata",package=paste0(input$Summarycompselect,"Expr"))
482:                                           tmpgeneexpr <- rbind(tmpgeneexpr,t(h5read(paste0(path,"/data",h5id,".h5"),"expr",index=list(NULL,match(currenth5gene,h5gene))))/1000)
485:                                     tmpgeneexpr <- t(h5read(paste0(path,"/data.h5"),"expr",index=list(NULL,match(currentgeneset[,2],Maindata$geneid))))/1000
724: ...(9 bytes skipped)...                                                   tmpgeneexpr <- rbind(tmpgeneexpr,t(h5read(paste0(path,"/data",h5id,".h5"),"expr",index=list(NULL,match(currenth5gene,h5gene))))/1000)
727:                                                 tmpgeneexpr <- t(h5read(paste0(path,"/data.h5"),"expr",index=list(NULL,match(currentgeneset[,2],Maindata$geneid))))/1000
118:                         tmpfile <- read.table(GenesetFileHandle$datapath,header=input$InputGenesetheader,sep=input$InputGenesetsep,quote=input$InputGenesetquote,stringsAsFa...(30 bytes skipped)...
401:                         tmptab <- read.table(input$Summaryuploadtabfile$datapath,stringsAsFactors=F,blank.lines.skip=TRUE)
405: ...(8 bytes skipped)...                Maindata$uploadgeneexpr <- as.matrix(read.table(input$Summaryuploadgeneexprfile$datapath,stringsAsFactors=F,blank.lines.skip=TRUE,row.names=1))
1917:                               tmp <- read.table(input$GSCAinteractiveload$datapath)
1946: ...(35 bytes skipped)... updateTextInput(session,"Formulainputtext","Input Formula",readLines(input$GSCAinteractiveload$datapath))
2412:                         Utidata$Maindata <- Utidata$rawdata <- read.table(UtiFileHandle$datapath,header=input$Utiheader,sep=input$Utisep,quote=input$Utiquote,stringsAsFactors=F,blank.lines.skip=TR...(15 bytes skipped)...
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")
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)
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, 
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)
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, 
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",
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)
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)})
Rqc:R/utils.R: [ ]
94:     path <- dirname(file)
99:     data.frame(filename, pair, format, group, reads, total.reads, path, 
MuData:R/write_h5mu.R: [ ]
313:     path <- paste(H5Iget_name(parent), key, sep="/")
315:                                      filepath=file, name=path, chunkdim=chunkdim, parent=parent, datasetname=key)
snapcount:R/basic_query_functions.R: [ ]
273:     path <- paste(compilation, paste0(endpoint, "?"), sep = "/")
319:     paste0(pkg_globals$snaptron_host, path, paste(query, collapse = "&"))
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("http://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 ####################
sevenbridges:inst/extdata/app/flow_star.json: [ ]
272:                 "path": "/demo/test-files/chr20.gtf"
280:               "path": "/sbgenomics/test-data/chr20.fa"
476:               "path": "/path/to/fastq.ext"
818:               "path": "/root/dir/example.tested.bam"
2894:               "path": "genome.ext"
2936:                 "path": "/test-data/mate_1.fastq.bz2"
2995:                 "path": "/demo/test-data/chr20.gtf"
349: ...(34 bytes skipped)...r sjFormat = \"False\"\n  var gtfgffFormat = \"False\"\n  var list = $job.inputs.sjdbGTFfile\n  var paths_list = []\n  var joined_paths = \"\"\n  \n  if (list) {\n    list.forEach(function(f){return paths_list.push(f.path)})\n    joined_paths = paths_list.join(\" \")\n\n\n    path...(355 bytes skipped)...ne\") {\n      if (sjFormat == \"True\") {\n        return \"--sjdbFileChrStartEnd \".concat(joined_path...(5 bytes skipped)...     }\n      else if (gtfgffFormat == \"True\") {\n        return \"--sjdbGTFfile \".concat(joined_paths)\n      }\n    }\n  }\n}",
445:         "sbg:cmdPreview": "python /opt/sbg_fastq_quality_scale_detector.py --fastq /path/to/fastq.ext /path/to/fastq.ext",
898:               "script": "{\n  filename = $job.inputs.input_bam.path\n  ext = $job.inputs.output_type\n\nif (ext === \"BAM\")\n{\n    return filename.split('.').slice(0...(372 bytes skipped)...
910:               "script": "{\n  filename = $job.inputs.input_bam.path\n  \n  /* figuring out output file type */\n  ext = $job.inputs.output_type\n  if (ext === \"BAM\")...(657 bytes skipped)...
992:             "script": "$job.inputs.genome.path",
1606: ...(48 bytes skipped)...concat($job.inputs.reads)\n  \n  var resp = []\n  \n  if (list.length == 1){\n    resp.push(list[0].path...(178 bytes skipped)...etadata != null){\n        if (list[index].metadata.paired_end == 1){\n          left = list[index].path\n        }else if (list[index].metadata.paired_end == 2){\n          right = list[index].path...(297 bytes skipped)...data != null){\n        if (list[index].metadata.paired_end == 1){\n          left.push(list[index].path)\n        }else if (list[index].metadata.paired_end == 2){\n          right.push(list[index].path)\n        }\n      }\n    }\n    left_join = left.join()\n    right_join = right.join()\n    if (le...(200 bytes skipped)...
3224:               "script": "{\n  file = [].concat($job.inputs.reads)[0].path\n  extension = /(?:\\.([^.]+))?$/.exec(file)[1]\n  if (extension == \"gz\") {\n    return \"--readF...(107 bytes skipped)...
3232: ...(34 bytes skipped)...r sjFormat = \"False\"\n  var gtfgffFormat = \"False\"\n  var list = $job.inputs.sjdbGTFfile\n  var paths_list = []\n  var joined_paths = \"\"\n  \n  if (list) {\n    list.forEach(function(f){return paths_list.push(f.path)})\n    joined_paths = paths_list.join(\" \")\n\n\n    path...(355 bytes skipped)...ne\") {\n      if (sjFormat == \"True\") {\n        return \"--sjdbFileChrStartEnd \".concat(joined_path...(5 bytes skipped)...     }\n      else if (gtfgffFormat == \"True\") {\n        return \"--sjdbGTFfile \".concat(joined_paths)\n      }\n    }\n  }\n}",
3280: ...(138 bytes skipped)...gth, i= 0;\n  while(i<L && a1.charAt(i)=== a2.charAt(i)) i++;\n  return a1.substring(0, i);\n  }\n  path_list = []\n  arr = [].concat($job.inputs.reads)\n  arr.forEach(function(f){return path_list.push(f.path.replace(/\\\\/g,'/').replace( /.*\\//, '' ))})\n  common_prefix = sharedStart(path_list)\n  intermediate = common_prefix.replace( /\\-$|\\_$|\\.$/, '' ).concat(\"._STARgenome\")\n  s...(262 bytes skipped)...
3289: ...(138 bytes skipped)...gth, i= 0;\n  while(i<L && a1.charAt(i)=== a2.charAt(i)) i++;\n  return a1.substring(0, i);\n  }\n  path_list = []\n  arr = [].concat($job.inputs.reads)\n  arr.forEach(function(f){return path_list.push(f.path.replace(/\\\\/g,'/').replace( /.*\\//, '' ))})\n  common_prefix = sharedStart(path_list)\n  return \"./\".concat(common_prefix.replace( /\\-$|\\_$|\\.$/, '' ), \".\")\n}",
3298: ...(138 bytes skipped)...gth, i= 0;\n  while(i<L && a1.charAt(i)=== a2.charAt(i)) i++;\n  return a1.substring(0, i);\n  }\n  path_list = []\n  arr = [].concat($job.inputs.reads)\n  arr.forEach(function(f){return path_list.push(f.path.replace(/\\\\/g,'/').replace( /.*\\//, '' ))})\n  common_prefix = sharedStart(path_list)\n  mate1 = common_prefix.replace( /\\-$|\\_$|\\.$/, '' ).concat(\".Unmapped.out.mate1\")\n  m...(463 bytes skipped)...
MesKit:R/phyloTreeAnno.R: [ ]
672:    path <- list()
570:    trunkPath <- rev(c(mainTrunk,rootNode))
674:       subPath <- c()
509:    ## label represents the common evolution path of samples
688:       path[[name]] <- subPath
695:    result <- path[[names(which.max(distanceTable))]]
571:    if(length(trunkPath) > 0){
572:      # subdat_list <- lapply(2:length(trunkPath), function(i){
573:      #   x1 <- treeData[treeData$end_num == trunkPath[i-1],]$x2
574:      #   y1 <- treeData[treeData$end_num == trunkPath[i-1],]$y2
577:      #   distance <- treeEdge[treeEdge$endNum == trunkPath[i],]$length
589:      #                                    'node' = trunkPath[i-1],'end_num' = trunkPath[i])
596:       for(i in 2:length(trunkPath)){
597:          x1 <- treeData[treeData$end_num == trunkPath[i-1],]$x2
598:          y1 <- treeData[treeData$end_num == trunkPath[i-1],]$y2
601:          distance <- treeEdge[treeEdge$endNum == trunkPath[i],]$length
613:                                           'node' = trunkPath[i-1],'end_num' = trunkPath[i])
682:          subPath <- append(subPath,end)
VarCon:inst/extdata/app.R: [ ]
235:   path <- reactiveValues(
240:   path2 <- reactiveValues(
244:   path3 <- reactiveValues(
24:   uploadReferenceDNA <- eventReactive(path$pth,{
26:     testFASTA <- strsplit(path$pth,"\\.")[[1]]
28:       referenceDnaStringSet2 <- readDNAStringSet(path$pth, format="fasta",use.names=TRUE)
34:       load(path$pth)
42:   uploadTranscriptTable <- eventReactive(path3$pth3,{
45:     testCSV <- strsplit(path3$pth3,"\\.")[[1]]
47:       transCoord <- read.csv(path3$pth3, sep=";")
48:     }else{ transCoord <- readRDS(path3$pth3)}
78:     gene2transcript <- read.csv(path2$pth2, sep=";", stringsAsFactors=FALSE)
99:     gene2transcript <- read.csv(path2$pth2, sep=";", stringsAsFactors=FALSE)
126:     gene2transcript <- read.csv(path2$pth2, sep=";", stringsAsFactors=FALSE)
145:     gene2transcript <- read.csv(path2$pth2, sep=";", stringsAsFactors=FALSE)
176:     gene2transcript <- read.csv(path2$pth2, sep=";", stringsAsFactors=FALSE)
205:     gene2transcript <- read.csv(path2$pth2, sep=";", stringsAsFactors=FALSE)
251:     path$pth <- file.choose()
255:     path2$pth2 <- file.choose()
259:     path3$pth3 <- file.choose()
234:   ## Define reactive paths
exomeCopy:R/main_functions.R: [ ]
199:   path <- viterbiPath(nm.fit$par,fx.par,data,nstates,stFn,trFn,emFn)
105:   V.path <- matrix(0,nrow=nstates,ncol=T)
90: viterbiPath <- function(par,fx.par,data,nstates,stFn,trFn,emFn) {
106: ...(78 bytes skipped)...start.probs=as.double(start.probs),A=as.double(A),emit.probs=as.double(emit.probs),V=as.double(V),V.path=as.integer(V.path),path=as.integer(numeric(T)),trans.prob=as.double(numeric(nstates^2)),trans.prob.max=as.double(numeric(ns...(57 bytes skipped)...
107:   return(viterbi.call$path + 1)
201:   log.odds <- log(emit.probs[cbind(path,seq(path))]+1e-6) - log(emit.probs[normal.state,]+1e-6)
203:   fit <- new("ExomeCopy",sample.name=sample.name,type=type,path=Rle(path),ranges=granges(gr),O.norm=as.numeric(O/mu.hat),log.odds=log.odds,fx.par=fx.par,init.par=init.par,f...(89 bytes skipped)...
Rgraphviz:src/graphviz/lib/common/types.h: [ ]
105:     typedef struct path {	/* internal specification for an edge spline */
110:     } path;
656: 	Ppolyline_t path;
685: #define ED_path(e) (((Agedgeinfo_t*)AGDATA(e))->path)
714: #define ED_path(e) (e)->u.path
97:     typedef struct pathend_t {
103:     } pathend_t;
158: 	/* we would have called it a path if that term wasn't already used */
174: 	 * It is used as the clipping path */
182: 	int (*pboxfn)(node_t* n, port* p, int side, boxf rv[], int *kptr); /* finds box path to reach port */
34: #include "pathgeom.h"
GBScleanR:R/Methods-GbsrGenotypeData.R: [ ]
424:               path <- paste0("annotation/info/", var)
449:                   path <- paste0("annotation/format/AD/", "norm")
525:               path <- ifelse(.existGdsNode(object, node),
1308:                   path <- .getNodeIndex(object, node)
1420:               path <- paste0("annotation/format/AD/", node)
1521:               path <- .getNodeIndex(object, "annotation/format/AD/norm")
18: ## Get the index of the specified path in the GDS file.
20: .getNodeIndex <- function(object, path){
21:     return(index.gdsn(.getGdsfmtObj(object), path))
83: .existGdsNode <- function(object, path){
84:     return(exist.gdsn(.getGdsfmtObj(object), path))
426:               if(!.existGdsNode(object, path)){
427:                   warning("No data at ", path)
430:               info_node <- .getNodeIndex(object, path)
451:                   path <- paste0("annotation/format/AD/", "filt.data")
453:                   path <- paste0("annotation/format/AD/", "data")
455:               if(!.existGdsNode(object, path)){
458:               ad_node <- .getNodeIndex(object, path)
529:               genotype_node <- .getNodeIndex(object, path)
1327:                                                path,
1336:                                               path,
1346: .countGenotypeScan <- function(object, path, sel, has_flipped, valid_flipped){
1347:     df <- apply.gdsn(path, 1, selection=sel, as.is="list",
1380: .countGenotypeSnp <- function(object, path, sel, has_flipped, valid_flipped){
1381:     df <- apply.gdsn(path, 2, selection=sel, as.is="list",
1421:               if(.existGdsNode(object, path)){
1422:                   path <- .getNodeIndex(object, path)
1437:                                            path,
1446:                                           path,
1454: .countReadScan <- function(object, path, sel, has_flipped, valid_flipped){
1455:     df <- apply.gdsn(path, 1, selection=sel, as.is="list",
1478: .countReadSnp <- function(object, path, sel, has_flipped, valid_flipped){
1479:     df <- apply.gdsn(path, 2, sum, sel, "list")
1526:                                                path,
1536:                                               path,
1570:                                path,
1575:     df <- apply.gdsn(path, 1, selection=sel, as.is="list",
1615:                               path,
1620:     df <- apply.gdsn(path, 2, selection=sel, as.is="list",
2509:                   stop("Failed to create a new file to the following path \n",
interactiveDisplay:inst/www/js/d3.v2.js: [ ]
6081:     function path(d, i) {
1451:   function d3_layout_bundlePath(link) {
1910:   function d3_path_circle(radius) {
1119:     var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ];
1120:     while (++i < n) path.push("V", (p = points[i])[1], "H", p[0]);
1121:     return path.join("");
1124:     var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ];
1125:     while (++i < n) path.push("H", (p = points[i])[0], "V", p[1]);
1126:     return path.join("");
1141:     var quad = points.length != tangents.length, path = "", p0 = points[0], p = points[1], t0 = tangents[0], t = t0, pi = 1;
1143:       path += "Q" + (p[0] - t0[0] * 2 / 3) + "," + (p[1] - t0[1] * 2 / 3) + "," + p[0] + "," + p[1];
1151:       path += "C" + (p0[0] + t0[0]) + "," + (p0[1] + t0[1]) + "," + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," ...(21 bytes skipped)...
1155:         path += "S" + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," + p[0] + "," + p[1];
1160:       path += "Q" + (p[0] + t[0] * 2 / 3) + "," + (p[1] + t[1] * 2 / 3) + "," + lp[0] + "," + lp[1];
1162:     return path;
1176: ...(44 bytes skipped)...s[0], x0 = pi[0], y0 = pi[1], px = [ x0, x0, x0, (pi = points[1])[0] ], py = [ y0, y0, y0, pi[1] ], path = [ x0, ",", y0 ];
1177:     d3_svg_lineBasisBezier(path, px, py);
1184:       d3_svg_lineBasisBezier(path, px, py);
1192:       d3_svg_lineBasisBezier(path, px, py);
1194:     return path.join("");
1198:     var path = [], i = -1, n = points.length, pi, px = [ 0 ], py = [ 0 ];
1204:     path.push(d3_svg_lineDot4(d3_svg_lineBasisBezier3, px) + "," + d3_svg_lineDot4(d3_svg_lineBasisBezier3, ...(6 bytes skipped)...
1212:       d3_svg_lineBasisBezier(path, px, py);
1214:     return path.join("");
1217:     var path, i = -1, n = points.length, m = n + 4, pi, px = [], py = [];
1223:     path = [ d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, py)...(4 bytes skipped)...
1231:       d3_svg_lineBasisBezier(path, px, py);
1233:     return path.join("");
1251:   function d3_svg_lineBasisBezier(path, x, y) {
1252:     path.push("C", d3_svg_lineDot4(d3_svg_lineBasisBezier1, x), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier1...(207 bytes skipped)...
4383:         var tick = g.selectAll("g").data(ticks, String), tickEnter = tick.enter().insert("g", "path").style("opacity", 1e-6), tickExit = d3.transition(tick.exit()).style("opacity", 1e-6).remove(), ti...(67 bytes skipped)...
4384:         var range = d3_scaleRange(scale), path = g.selectAll(".domain").data([ 0 ]), pathEnter = path.enter().append("path").attr("class", "domain"), pathUpdate = d3.transition(path);
6080:   d3.geo.path = function() {
6082:       if (typeof pointRadius === "function") pointCircle = d3_path_circle(pointRadius.apply(this, arguments));
6111:     var pointRadius = 4.5, pointCircle = d3_path_circle(pointRadius), projection = d3.geo.albersUsa(), buffer = [];
6178:     var areaType = path.area = d3_geo_type({
6201:     var centroidType = path.centroid = d3_geo_type({
6220:     path.projection = function(x) {
6222:       return path;
6224:     path.pointRadius = function(x) {
6227:         pointCircle = d3_path_circle(pointRadius);
6229:       return path;
6231:     return path;
4401:             pathUpdate.attr("d", "M" + range[0] + "," + tickEndSize + "V0H" + range[1] + "V" + tickEndSize);
4414:             pathUpdate.attr("d", "M" + range[0] + "," + -tickEndSize + "V0H" + range[1] + "V" + -tickEndSize);
4427:             pathUpdate.attr("d", "M" + -tickEndSize + "," + range[0] + "H0V" + range[1] + "H" + -tickEndSize);
4440:             pathUpdate.attr("d", "M" + tickEndSize + "," + range[0] + "H0V" + range[1] + "H" + tickEndSize);
4923:       var paths = [], i = -1, n = links.length;
4924:       while (++i < n) paths.push(d3_layout_bundlePath(links[i]));
4925:       return paths;
6083:       pathType(d);
6112:     var pathType = d3_geo_type({
6115:         while (++i < n) buffer.push(pathType(features[i].geometry));
6118:         pathType(o.geometry);
6175:         while (++i < n) buffer.push(pathType(geometries[i]));
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)
GeneGeneInteR:R/PLSPM.R: [ ]
207:     Path = inner_results[[2]]
1162:   Path = path_matrix
2176:     Path <- pathmod[[2]]
48:   my.path <- rbind(Gene1,Gene2)
209:     Path_effects = get_effects(Path)
363: check_path <- function(path_matrix)
1173:     path_lm = summary(lm(Y_lvs[,k1] ~ Y_lvs[,k2]))
1202:   path_effects = as.list(seq_len(lvs-1))
2177:     path.orig <- as.vector(Path[path_matrix==1])
2179:     Path.efs <- get_effects(Path)
2186:     path.labs <- NULL
2175:     pathmod <- get_paths(path_matrix, Y.lvs)
2194:     PATHS <- matrix(NA, bootnum, sum(path_matrix))
1805: get_path_scheme <- function(path_matrix, LV)
1156: get_paths <-  function(path_matrix, Y_lvs, full=TRUE)
1207:     indirect_paths = matrix(c(0,0,0,0), 2, 2)
1208:     total_paths = Path
55:   try(mod1 <- plspm(XCases,my.path,my.blocks, modes = my.modes), silent=TRUE)
59:   	res <- list(statistic=NA,p.value=NA,method="Partial Least Squares Path Modeling",parameter=list.param)
64:   try(mod0 <- plspm(XControls,my.path,my.blocks, modes = my.modes),silent=TRUE)
68:   	res <- list(statistic=NA,p.value=NA,method="Partial Least Squares Path Modeling",parameter=list.param)
91: 		try(mod1 <- plspm(XCases,my.path,my.blocks, modes = my.modes), silent=TRUE)
93: 		try(mod0 <- plspm(XControls,my.path,my.blocks, modes = my.modes),silent=TRUE)
110: #	res <- list(statistic=stat,p.value=pval,method="Partial Least Squares Path Modeling",parameter=list.param)
123: 		method="Gene-based interaction based on Partial Least Squares Path Modeling",
138:   function(Data, path_matrix, blocks, modes = NULL, scaling = NULL,  
146:     valid = check_args(Data=Data, path_matrix=path_matrix, blocks=blocks, 
153:     path_matrix = valid$path_matrix
167:     gens = get_generals(MV, path_matrix)
188:       weights = get_weights(X, path_matrix, blocks, specs)
194:       weights = get_weights_nonmetric(X, path_matrix, blocks, specs)
203:     # Path coefficients and total effects
205:     inner_results = get_paths(path_matrix, LV)
241:     inner_summary = get_inner_summary(path_matrix, blocks, specs$modes,
245:     gof = get_gof(communality, R2, blocks, path_matrix)
259:         bootstrap = get_boots(MV, path_matrix, blocks, specs, br)
264:     model = list(IDM=path_matrix, blocks=blocks, specs=specs,
270:                path_coefs = Path, 
274:                effects = Path_effects,
289:   cat("Partial Least Squares Path Modeling (PLS-PM)", "\n")
294:   cat("\n3  $path_coefs     ", "path coefficients matrix")
314:   function(Data, path_matrix, blocks, scaling, modes, scheme,
319:     path_matrix = check_path(path_matrix)
327:     good_model = check_model(path_matrix, blocks)
331:          path_matrix = path_matrix,
365:   if (is_not_matrix(path_matrix))
366:     stop("\n'path_matrix' must be a matrix.")
368:   if (!is_square_matrix(path_matrix))
369:     stop("\n'path_matrix' must be a square matrix.")
371:   if (nrow(path_matrix) == 1)
372:     stop("\n'path_matrix' must have more than one row")
374:   if (!is_lower_triangular(path_matrix))
375:     stop("\n'path_matrix' must be a lower triangular matrix")
378:   for (j in seq_len(ncol(path_matrix))) 
380:     for (i in seq_len(nrow(path_matrix)))
382:       if (length(intersect(path_matrix[i,j], c(1,0))) == 0)
383:         stop("\nElements in 'path_matrix' must be '1' or '0'")
387:   if (lacks_dimnames(path_matrix)) {
388:     LV_names = paste("LV", seq_len(ncol(path_matrix)), sep = "")
389:     dimnames(path_matrix) = list(LV_names, LV_names)
391:   if (has_rownames(path_matrix) && lacks_colnames(path_matrix)) {
392:     colnames(path_matrix) = rownames(path_matrix)
394:   if (has_colnames(path_matrix) && lacks_rownames(path_matrix)) {
395:     rownames(path_matrix) = colnames(path_matrix)
399:   path_matrix
458: check_model <- function(path_matrix, blocks)
460:   # compatibility between path_matrix and blocks
461:   if (length(blocks) != nrow(path_matrix))
462:     stop("\nNumber of rows in 'path_matrix' different from length of 'blocks'.")
687:   SCHEMES = c("centroid", "factorial", "path")
884: get_generals <- function(MV, path_matrix)
890:        lvs = nrow(path_matrix),
891:        lvs_names = rownames(path_matrix))
1035: get_weights <- function(X, path_matrix, blocks, specs)
1037:   lvs = nrow(path_matrix)
1055:                 "centroid" = sign(cor(Y) * (path_matrix + t(path_matrix))),
1056:                 "factorial" = cor(Y) * (path_matrix + t(path_matrix)),
1057:                 "path" = get_path_scheme(path_matrix, Y))
1083:     dimnames(W) = list(colnames(X), rownames(path_matrix))    
1158:   lvs_names = colnames(path_matrix)
1159:   endogenous = as.logical(rowSums(path_matrix))
1164:   R2 = rep(0, nrow(path_matrix))
1171:     k2 = which(path_matrix[k1,] == 1)
1174:     Path[k1,k2] = path_lm$coef[-1,1]
1175:     residuals[[aux]] = path_lm$residuals  
1176:     R2[k1] = path_lm$r.squared
1177:     inn_val = c(path_lm$r.squared, path_lm$coef[,1])
1180:     rownames(path_lm$coefficients) = inn_labels
1181:     results[[aux]] <- path_lm$coefficients
1184:     # paste(rep("path_",length(k2)),names(k2),sep=""))
1192:   list(results, Path, R2, residuals)
1195: get_effects <- function(Path)
1198:   lvs = nrow(Path)
1199:   lvs_names = rownames(Path)
1203:   path_effects[[1]] = Path
1212:       path_effects[[k]] = path_effects[[k-1]] %*% Path        
1215:     for (k in 2:length(path_effects)) {
1216:       indirect_paths = indirect_paths + path_effects[[k]]        
1218:     total_paths = Path + indirect_paths
1227:         direct = c(direct, Path[i,j])
1325:   function(path_matrix, blocks, modes, communality, redundancy, R2)
1328:     exo_endo = rep("Exogenous", nrow(path_matrix))
1329:     exo_endo[rowSums(path_matrix) != 0] = "Endogenous"
1330:     avg_comu = rep(0, nrow(path_matrix))
1331:     avg_redu = rep(0, nrow(path_matrix))
1332:     AVE = rep(0, nrow(path_matrix))
1334:     for (k in seq_len(nrow(path_matrix)))
1352:                row.names = rownames(path_matrix))
1355: get_gof <- function(comu, R2, blocks, path_matrix)
1357:   lvs = nrow(path_matrix)
1359:   endo = rowSums(path_matrix)
1392: get_weights <- function(X, path_matrix, blocks, specs)
1394:   lvs = nrow(path_matrix)
1412:                 "centroid" = sign(cor(Y) * (path_matrix + t(path_matrix))),
1413:                 "factorial" = cor(Y) * (path_matrix + t(path_matrix)),
1414:                 "path" = get_path_scheme(path_matrix, Y))
1440:     dimnames(W) = list(colnames(X), rownames(path_matrix))    
1478:   function(X, path_matrix, blocks, specs)
1480:     lvs = nrow(path_matrix)
1541:     link = t(path_matrix) + path_matrix
1556:                   "path" = get_path_scheme(path_matrix, Y))
1751:       dimnames(W) = list(colnames(X), rownames(path_matrix))
1752:       dimnames(Y) = list(rownames(X), rownames(path_matrix))
1808:   E = path_matrix
1810:   for (k in seq_len(ncol(path_matrix))) 
1813:     follow <- path_matrix[k,] == 1
1817:     predec <- path_matrix[,k] == 1
2141:   function(DM, path_matrix, blocks, specs, br)
2146:     lvs = nrow(path_matrix)
2147:     lvs.names = rownames(path_matrix)
2151:     endo = sign(rowSums(path_matrix))
2162:       out.ws = get_weights(X, path_matrix, blocks, specs)
2168:       out.ws = get_weights_nonmetric(X, path_matrix, blocks, specs)
2190:         if (path_matrix[i,j]==1) 
2191:           path.labs <- c(path.labs, paste(lvs.names[j],"->",lvs.names[i]))
2195:     TOEFS <- matrix(NA, bootnum, nrow(Path.efs))
2207:         w.boot = get_weights(X.boot, path_matrix, blocks, specs)
2215:         w.boot = get_weights_nonmetric(X.boot, path_matrix, blocks, specs)
2225:       pathmod <- get_paths(path_matrix, Y.boot)
2228:       PATHS[i,] <- as.vector(P.boot[path_matrix==1])
2249:     # Path coefficients
2250:     colnames(PATHS) = path.labs
2251:     PB = get_boot_stats(PATHS, path.orig)
2256:     colnames(TOEFS) = Path.efs[, 1]
2257:     TE = get_boot_stats(TOEFS, Path.efs[,4]) 
1214:     indirect_paths = matrix(0, lvs, lvs)
1228:         indirect = c(indirect, indirect_paths[i,j])
1229:         total = c(total, total_paths[i,j])
2178:     r2.orig <- pathmod[[3]][endo==1]
2226:       P.boot <- pathmod[[2]]
2230:       RSQRS[i,] <- pathmod[[3]][endo==1]
2262:          paths = PB, 
ChemmineR:R/AllClasses.R: [ ]
1734: 	path <- conMA(x, exclude="H")
1979:             path <- .linearCon(x=con)
1800: 	conpath <- t(sapply(path, function(x) x[c(1, length(x))]))
1812: 	pathlist <- cyclist$path
1813: 	conpath <- cyclist$conpath
1814: 	pathlistnew <- list() 
1735: 	noconnect <- rowSums(path) != 0 # Removes atoms with no connections
1736: 	path <- path[noconnect, noconnect]
1737: 	if(all(dim(path) == 0)) { return(path) } 
1738: 	term <- which(rowSums(path > 0)==1)
1740: 		path <- path[-term,-term]
1741: 		if(any(dim(path) == 0) | is.vector(path)) { break() }
1742: 		term <- which(rowSums(path > 0)==1)
1744: 	return(path)
1779: .update <- function(con, path) {
1780: 	## Remove non-terminal atoms in each path
1781: 	center_atoms <- unique(unlist(lapply(path, function(x) x[-c(1, length(x))])))
1791: 		path <- c(path, remainbonds)
1792: 		names(path) <- seq(along=path)
1794: 	## Collect complete rings and remove them from path object
1795: 	index <- unlist(lapply(path, function(y) any(duplicated(y))))
1796: 	rings <- path[index]
1797: 	path <- path[!index]
1798: 	names(path) <- seq(along=path)
1799: 	## Connection list for path component
1806: 	return(list(con=con, conpath=conpath, path=path, rings=rings))
1850: 		## Collect complete rings and remove them from path object
1980:             cyclist <- .update(con=con, path=path)
13: .sdfDownload <- function(mypath="ftp://ftp.ncbi.nih.gov/pubchem/Compound/CURRENT-Full/SDF/", myfile="Compound_00650001_00675000.sdf...(7 bytes skipped)...
14: 	system(paste("wget ", mypath, myfile, sep=""))
17: # .sdfDownload(mypath="ftp://ftp.ncbi.nih.gov/pubchem/Compound/CURRENT-Full/SDF/", myfile="Compound_00650001_00675000.sdf...(6 bytes skipped)...
1747: ## (b) Function to return the longest possible linear bond paths where:
1801: 	ends <- unique(as.vector(conpath))
1802: 	conpath <- lapply(ends, function(x) as.numeric(names(which(rowSums(conpath==x) > 0))))
1803: 	names(conpath) <- ends
1804: 	conpath <- conpath[sapply(conpath, length) > 1] # removes ends that occur only once 
1816: 	## Loop to join linear paths/fragments stored in pathlist
1817: 	for(i in names(conpath)) {
1818: 		if(length(conpath) == 0 | !any(names(conpath) == i)) { next() }
1819: 		pos <- t(combn(conpath[[i]], m=2))
1821: 			p1 <- pathlist[[pos[j,1]]]
1822: 			p2 <- pathlist[[pos[j,2]]]
1827: 				pathlistnew[[length(pathlistnew)+1]] <- c(rev(p2[-1]), p1)
1830: 				pathlistnew[[length(pathlistnew)+1]] <- c(p1, rev(p2[-length(p2)]))
1833: 				pathlistnew[[length(pathlistnew)+1]] <- c(p2, p1[-1])
1836: 				pathlistnew[[length(pathlistnew)+1]] <- c(p1, p2[-1])
1840: 		if(length(pathlistnew) == 0) { next() }
1842: 		dups <- duplicated(sapply(pathlistnew, function(x) paste(sort(unique(x)), collapse="_")))
1843: 		pathlistnew <- pathlistnew[!dups]
1846: 			l <- sapply(pathlistnew, length)
1847: 			pathlistnew <- pathlistnew[l <= upper]
1848: 			if(length(pathlistnew) == 0) { next() }
1851: 		index <- unlist(lapply(pathlistnew, function(y) any(duplicated(y[c(1, length(y))]))))
1852: 		rings[[length(rings)+1]] <- pathlistnew[index]
1853: 		pathlistnew <- pathlistnew[!index]
1854: 		## Remove paths with internal duplicates 
1855: 		if(length(pathlistnew) > 0) {
1856: 			index <- unlist(lapply(pathlistnew, function(y) any(duplicated(y))))
1857: 			pathlistnew <- pathlistnew[!index]
1859: 		## Update pathlist and conpath
1860: 		pathlist <- c(pathlist[-conpath[[i]]], pathlistnew)
1861: 		dups <- duplicated(sapply(pathlist, function(x) paste(sort(unique(x)), collapse="_")))
1862: 		pathlist <- pathlist[!dups]
1863: 		names(pathlist) <- seq(along=pathlist)
1864: 		conpath <- t(sapply(pathlist, function(x) x[c(1, length(x))]))
1865: 		ends <- unique(as.vector(conpath))
1866: 		conpath <- lapply(ends, function(x) as.numeric(names(which(rowSums(conpath==x) > 0))))
1867: 		names(conpath) <- ends
1868: 		conpath <- conpath[sapply(conpath, length) > 1] # removes ends that occur only once
1869: 		pathlistnew <- list()
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
DMRforPairs:R/functions.R: [ ]
457:     path = "figures"
450:     dir.create(file.path(getwd(), experiment.name))
452:     dir.create(file.path(paste(getwd(), experiment.name, sep = "/"), 
456:     setwd(file.path(getwd(), experiment.name))
485:                 path = path)
487:             tested$Figure[cr] = paste("<a href=\"./", path, "/", 
490:             tested$Statistics[cr] = paste("<a href=\"./", path, "/", 
493:             write.table(file = paste("./", path, "/", tested$regionID[cr], 
507:                 scores = FALSE, path = path)
509:             tested$Figure[cr] = paste("<a href=\"./", path, "/", 
512:             tested$Statistics[cr] = paste("<a href=\"./", path, "/", 
519:             write.table(file = paste("./", path, "/", tested$regionID[cr], 
533:                 scores = FALSE, path = path)
539:             tested$Figure[cr] = paste("<a href=\"./", path, "/", 
542:             tested$Statistics[cr] = paste("<a href=\"./", path, "/", 
545:             write.table(file = paste("./", path, "/", tested$regionID[cr], 
606:             path, "/", tested_4html_selected$ID, ".png\" height=\"63\" width=\"125\">", 
624:             path, "/", tested_4html_selected$ID, ".png\" height=\"63\" width=\"125\">", 
639:     regionID, clr = NA, annotate = TRUE, scores = TRUE, path) {
642:     path = paste("./", path, "/", sep = "")
651:         clr = clr, annotate = annotate, scores = scores, path = path)
656:     annotate = TRUE, path) {
659:     path = paste("./", path, "/", sep = "")
673:             annotate = annotate, scores = TRUE, path = path)
679:     ID = "CustomRegion", clr = NA, annotate = TRUE, path) {
680:     path = paste("./", path, "/", sep = "")
693:             annotate = annotate, scores = TRUE, path = path)
699:     ID = NA, clr = NA, annotate = TRUE, scores = NA, path) {
730:     png(paste(path, ID, ".png", sep = ""), width = 500, height = 250)
750:     pdf(paste(path, ID, ".pdf", sep = ""), width = 10, height = 10)
mAPKL:R/mAPKL.R: [ ]
64:     path <- as.integer(dataType)
54: ## path : 6-ratio data without normalization    or
69:     cluster_analysis <- cluster.Sim(ordIntensities_f[,start:end], path, min.clu,
PoTRA:R/PoTRA_corN.R: [ ]
76:             path<-data.frame(matrix(0,length.intersect,(Num.sample.normal+Num.sample.case)))
56:         graph.path<-igraph.from.graphNEL(g)    
42:     pathwaynames <- c()
40:     length.pathway<-c()
58:         #plot(graph.path, edge.arrow.size=.5, vertex.color="gold", vertex.size=5, 
66:         graph.path<-induced_subgraph(graph.path, as.character(intersect(unlist(nodelist),unlist(genelist_reformatted))))
67:         #plot(graph.path)   
82:                 path[j,]<-mydata[which(genelist_reformatted==a[j]),]  #collect expression data of genes for a specific pathway across normal and tumor samples.
87:             cor.normal <- apply(path[,1:Num.sample.normal], 1, function(x) { apply(path[,1:Num.sample.normal], 1, function(y) { cor.test(x,y)[[3]] })})
103:             cor.case <- apply(path[,(Num.sample.normal+1):(Num.sample.normal+Num.sample.case)], 1, function(x) { apply(path[,(Num.sample.normal+1):(Num.sample.normal+Num.sample.case)], 1, function(y) { cor.test(x,y)[[3]] })...(2 bytes skipped)...
1: #' The PoTRA analysis is based on topological ranks of genes in biological pathways. PoTRA can be used to detect pathways involved in disease. The PoTRA package contains one function for creating the PoTRA results obj...(4 bytes skipped)...
7: #' @param Pathway.database The pathway database, such as KEGG, Reactome, Biocarta and PharmGKB. 
14: ...(29 bytes skipped)...ta=mydata, genelist=genelist, Num.sample.normal=Num.sample.normal, Num.sample.case=Num.sample.case, Pathway.database=Pathway.database, PR.quantile=PR.quantile)
16: #' Pathway.database (options): 
17: #' humanKEGG=pathways('hsapiens','kegg')
18: #' humanReactome=pathways('hsapiens','reactome')
19: #' humanBiocarta=pathways('hsapiens','biocarta')
20: #' humanPharmGKB=pathways('hsapiens','pharmgkb')
22: #' Pathway.database=humanKEGG
23: #' Pathway.database=humanReactome
24: #' Pathway.database=humanBiocarta
25: #' Pathway.database=humanPharmGKB
28: PoTRA.corN <- function(mydata,genelist,Num.sample.normal,Num.sample.case,Pathway.database,PR.quantile) {
44:     #humanPharmGKB <- pathways("hsapiens", "pharmgkb")
45:     #humanBiocarta <- pathways("hsapiens", "biocarta")
46:     #humanKEGG <- pathways("hsapiens", "kegg")
48:     for (x in 1:length(Pathway.database[1:length(Pathway.database)])){
51:         p0 <-Pathway.database[[x]]
52:         pathwaynames[x] <- p0@title
54:         g<-pathwayGraph(p) 
65:         length.pathway[x]<-length.intersect
74:             #collect expression data of genes for a specific pathway across normal and tumor samples.
162:     return(list(Fishertest.p.value=Fishertest,KStest.p.value=kstest,LengthOfPathway=length.path...(62 bytes skipped)...rOfHubGenes.case=TheNumOfHubGene.case,TheNumberOfEdges.normal=E.normal,TheNumberOfEdges.case=E.case,PathwayName=pathwaynames))
NxtIRFcore:R/BuildRef.R: [ ]
984:         path <- tryCatch(BiocFileCache::bfcadd(bfc, url),
483: .validate_path <- function(reference_path, subdirs = NULL) {
577:     map_path <- file.path(normalizePath(reference_path), "Mappability")
836:     r_path <- file.path(reference_path, "resource")
837:     gtf_path <- file.path(r_path, "transcripts.gtf.gz")
10: #' subdirectory within the given `reference_path`. Resources are retrieved via
12: #' 1. User-supplied FASTA and GTF file. This can be a file path, or a web link
44: #' file, open the file specified in the path returned by
49: #' @param reference_path (REQUIRED) The directory path to store the generated
51: #' @param fasta The file path or web link to the user-supplied genome
54: #'   been run using the same `reference_path`.
55: #' @param gtf The file path or web link  to the user-supplied transcript
59: #'   `reference_path`.
65: #'   the file `IRFinder.ref.gz` is present inside `reference_path`.
112: #' * `reference_path/resource/genome.2bit`: Local copy of the genome sequences
114: #' * `reference_path/resource/transcripts.gtf.gz`: Local copy of the gene
117: #'   which is written to the given directory specified by `reference_path`.
119: #' * `reference_path/settings.Rds`: An RDS file containing parameters used
121: #' * `reference_path/IRFinder.ref.gz`: A gzipped text file containing collated
123: #' * `reference_path/fst/`: Contains fst files for subsequent easy access to
125: #' * `reference_path/cov_data.Rds`: An RDS file containing data required to
129: #'   subdirectory inside the designated `reference_path`
131: #' For `GetNonPolyARef`: Returns the file path to the BED file for
137: #' example_ref <- file.path(tempdir(), "Reference")
139: #'     reference_path = example_ref,
144: #'     reference_path = example_ref
149: #' example_ref <- file.path(tempdir(), "Reference")
151: #'     reference_path = example_ref,
156: #' # Get the path to the Non-PolyA BED file for hg19
167: #'     reference_path = "./Reference_user",
179: #'     reference_path = "./Reference_FTP",
214: #'     reference_path = "./Reference_AH",
226: #'     reference_path = "./Reference_UCSC",
236: #' #      inside the given `reference_path`.
241: #'     reference_path = "./Reference_with_STAR",
251: #'     reference_path = "./Reference_with_STAR",
255: #'     reference_path = reference_path,
260: #'     reference_path = "./Reference_with_STAR",
274: #' of the given reference path
277:         reference_path = "./Reference",
282:         reference_path = reference_path,
292: #' given reference path
295:         reference_path = "./Reference",
301:     .validate_path(reference_path)
303:             file.exists(file.path(reference_path, "IRFinder.ref.gz"))) {
307:     extra_files <- .fetch_genome_defaults(reference_path,
313:         reference_path = reference_path,
322:     .process_gtf(reference_data$gtf_gr, reference_path)
329:     .process_introns(reference_path, reference_data$genome,
333:     .gen_irf(reference_path, extra_files, reference_data$genome, chromosomes)
338:         .gen_nmd(reference_path, reference_data$genome))
341:     .gen_splice(reference_path)
342:     if (file.exists(file.path(reference_path, "fst", "Splice.fst"))) {
344:         .gen_splice_proteins(reference_path, reference_data$genome)
354:     cov_data <- .prepare_covplot_data(reference_path)
355:     saveRDS(cov_data, file.path(reference_path, "cov_data.Rds"))
358:     settings.list <- readRDS(file.path(reference_path, "settings.Rds"))
366:     saveRDS(settings.list, file.path(reference_path, "settings.Rds"))
369: #' @describeIn BuildReference Returns the path to the BED file containing
405:         reference_path,
417:             file.exists(file.path(reference_path, "IRFinder.ref.gz"))) {
424:     GetReferenceResource(reference_path = reference_path,
428:     STAR_buildRef(reference_path = reference_path,
432:     BuildReference(reference_path = reference_path,
440: Get_Genome <- function(reference_path, validate = TRUE,
442:     if (validate) .validate_reference(reference_path)
443:     twobit <- file.path(reference_path, "resource", "genome.2bit")
446:     } else if (file.exists(file.path(reference_path, "settings.Rds"))) {
447:         settings <- readRDS(file.path(reference_path, "settings.Rds"))
450:         .log("In Get_Genome, invalid reference_path supplied")
458: Get_GTF_file <- function(reference_path) {
459:     .validate_reference(reference_path)
460:     if (file.exists(file.path(reference_path,
462:         return(file.path(reference_path, "resource", "transcripts.gtf.gz"))
464:         .log("In Get_GTF_file, invalid reference_path supplied")
485:         reference_path != "" &&
487:             ifelse(normalizePath(dirname(reference_path)) != "", TRUE, TRUE),
493:         .log(paste("Error in 'reference_path',",
494:             paste0("base path of '", reference_path, "' does not exist")
498:     base <- normalizePath(dirname(reference_path))
499:     if (!dir.exists(file.path(base, basename(reference_path))))
500:         dir.create(file.path(base, basename(reference_path)))
504:             dir_to_make <- file.path(base, basename(reference_path), subdirs)
508:     return(file.path(base, basename(reference_path)))
511: .validate_reference_resource <- function(reference_path, from = "") {
512:     ref <- normalizePath(reference_path)
517:             "in reference_path =", reference_path,
518:             ": this path does not exist"))
520:     if (!file.exists(file.path(ref, "settings.Rds"))) {
522:             "in reference_path =", reference_path,
525:     settings.list <- readRDS(file.path(ref, "settings.Rds"))
529:             "in reference_path =", reference_path,
535: .validate_reference <- function(reference_path, from = "") {
536:     ref <- normalizePath(reference_path)
541:             "in reference_path =", reference_path,
542:             ": this path does not exist"))
544:     if (!file.exists(file.path(ref, "settings.Rds"))) {
546:             "in reference_path =", reference_path,
549:     if (!file.exists(file.path(ref, "IRFinder.ref.gz"))) {
551:             "in reference_path =", reference_path,
554:     settings.list <- readRDS(file.path(ref, "settings.Rds"))
558:             "in reference_path =", reference_path,
564: .fetch_genome_defaults <- function(reference_path, genome_type,
578:     map_file <- file.path(map_path, "MappabilityExclusion.bed.gz")
586:             genome_type, as_type = "bed.gz", path = map_path, overwrite = TRUE)
638: .get_reference_data <- function(reference_path, fasta, gtf,
644:     .validate_path(reference_path, subdirs = "resource")
646:         twobit <- file.path(reference_path, "resource", "genome.2bit")
650:         gtf <- file.path(reference_path, "resource", "transcripts.gtf.gz")
668:         reference_path = reference_path,
675:         reference_path = reference_path,
682:         reference_path = reference_path
685:     saveRDS(settings.list, file.path(reference_path, "settings.Rds"))
687:     settings.list <- readRDS(file.path(reference_path, "settings.Rds"))
720:         reference_path = "./Reference",
727:         .fetch_fasta_save_2bit(genome, reference_path, overwrite)
731:         twobit <- file.path(reference_path, "resource", "genome.2bit")
735:             genome <- Get_Genome(reference_path, validate = FALSE,
746:             twobit <- file.path(reference_path, "resource", "genome.2bit")
750:                 genome <- Get_Genome(reference_path, validate = FALSE,
764:         .fetch_fasta_save_2bit(genome, reference_path, overwrite)
769:         genome <- Get_Genome(reference_path, validate = FALSE,
794: .fetch_fasta_save_fasta <- function(genome, reference_path, overwrite) {
795:     genome.fa <- file.path(reference_path, "resource", "genome.fa")
806: .fetch_fasta_save_2bit <- function(genome, reference_path, overwrite) {
807:     genome.2bit <- file.path(reference_path, "resource", "genome.2bit")
809:             normalizePath(rtracklayer::path(genome)) ==
819:                 file.exists(rtracklayer::path(genome))) {
820:             file.copy(rtracklayer::path(genome), genome.2bit)
831:         reference_path = "./Reference",
841:         if (overwrite || !file.exists(gtf_path)) {
845:                 if (file.exists(gtf_path)) file.remove(gtf_path)
846:                 file.copy(cache_loc, gtf_path)
856:         if (!file.exists(gtf_path) ||
857:                 normalizePath(gtf_file) != normalizePath(gtf_path)) {
858:             if (overwrite || !file.exists(gtf_path)) {
863:                     if (file.exists(gtf_path)) file.remove(gtf_path)
864:                     file.copy(gtf_file, gtf_path)
866:                     gzip(filename = gtf_file, destname = gtf_path,
990:         if (identical(path, NA)) {
1059: .process_gtf <- function(gtf_gr, reference_path) {
1061:     .validate_path(reference_path, subdirs = "fst")
1064:     Genes_group <- .process_gtf_genes(gtf_gr, reference_path)
1066:     .process_gtf_transcripts(gtf_gr, reference_path)
1068:     .process_gtf_misc(gtf_gr, reference_path)
1070:     .process_gtf_exons(gtf_gr, reference_path, Genes_group)
1076: .process_gtf_genes <- function(gtf_gr, reference_path) {
1121:         file.path(reference_path, "fst", "Genes.fst")
1130: .process_gtf_transcripts <- function(gtf_gr, reference_path) {
1178:         file.path(reference_path, "fst", "Transcripts.fst")
1182: .process_gtf_misc <- function(gtf_gr, reference_path) {
1193:         file.path(reference_path, "fst", "Proteins.fst")
1204:         file.path(reference_path, "fst", "Misc.fst")
1208: .process_gtf_exons <- function(gtf_gr, reference_path, Genes_group) {
1251:         file.path(reference_path, "fst", "Exons.fst"))
1254:         file.path(reference_path, "fst", "Exons.Group.fst")
1316: .process_introns <- function(reference_path, genome,
1321:     data <- .process_introns_data(reference_path, genome, 
1335:         file.path(reference_path, "fst", "junctions.fst"))
1340: .process_introns_data <- function(reference_path, genome,
1343:         read.fst(file.path(reference_path, "fst", "Exons.fst")),
1346:         read.fst(file.path(reference_path, "fst", "Transcripts.fst")),
1349:         read.fst(file.path(reference_path, "fst", "Proteins.fst")),
1352:         read.fst(file.path(reference_path, "fst", "Exons.Group.fst")),
1665: .gen_irf <- function(reference_path, extra_files, genome, chromosome_aliases) {
1670:     data <- .gen_irf_prep_data(reference_path)
1682:         ), stranded = TRUE, reference_path, data2[["introns.unique"]]
1689:         ), stranded = FALSE, reference_path, data2[["introns.unique"]]
1692:     ref.cover <- .gen_irf_refcover(reference_path)
1694:     ref.ROI <- .gen_irf_ROI(reference_path, extra_files, genome,
1697:     readcons <- .gen_irf_readcons(reference_path,
1700:     ref.sj <- .gen_irf_sj(reference_path)
1712:     .gen_irf_final(reference_path, ref.cover, readcons, ref.ROI, ref.sj, chr)
1719: .gen_irf_prep_data <- function(reference_path) {
1721:         read.fst(file.path(reference_path, "fst", "Genes.fst")),
1734:         read.fst(file.path(reference_path, "fst", "junctions.fst"))
1737:         read.fst(file.path(reference_path, "fst", "Exons.fst")),
1741:         read.fst(file.path(reference_path, "fst", "Transcripts.fst")),
1948:         reference_path, introns.unique) {
2003:     rtracklayer::export(IntronCover, file.path(reference_path,
2006:     write.fst(IntronCover.summa, file.path(
2007:         reference_path, "fst",
2064: .gen_irf_refcover <- function(reference_path) {
2065:     tmpdir.IntronCover <- fread(file.path(
2066:         reference_path, "tmpdir.IntronCover.bed"
2069:     tmpnd.IntronCover <- fread(file.path(
2070:         reference_path, "tmpnd.IntronCover.bed"
2083: .gen_irf_ROI <- function(reference_path, extra_files, genome,
2145: .gen_irf_readcons <- function(reference_path,
2174: .gen_irf_sj <- function(reference_path) {
2178:         read.fst(file.path(reference_path, "fst", "junctions.fst"))
2199: .gen_irf_final <- function(reference_path,
2203:     IRF_file <- file.path(reference_path, "IRFinder.ref")
2242:     if (file.exists(file.path(reference_path, "tmpdir.IntronCover.bed"))) {
2243:         file.remove(file.path(reference_path, "tmpdir.IntronCover.bed"))
2245:     if (file.exists(file.path(reference_path, "tmpnd.IntronCover.bed"))) {
2246:         file.remove(file.path(reference_path, "tmpnd.IntronCover.bed"))
2253: .gen_nmd <- function(reference_path, genome) {
2255:     Exons.tr <- .gen_nmd_exons_trimmed(reference_path)
2256:     protein.introns <- .gen_nmd_protein_introns(reference_path, Exons.tr)
2264:     write.fst(NMD.Table, file.path(reference_path, "fst", "IR.NMD.fst"))
2269: .gen_nmd_exons_trimmed <- function(reference_path) {
2271:         read.fst(file.path(reference_path, "fst", "Exons.fst"))
2274:         read.fst(file.path(reference_path, "fst", "Misc.fst"))
2305: .gen_nmd_protein_introns <- function(reference_path, Exons.tr) {
2307:         read.fst(file.path(reference_path, "fst", "junctions.fst"))
2310:         read.fst(file.path(reference_path, "fst", "Misc.fst"))
2612: .gen_splice <- function(reference_path) {
2615:         read.fst(file.path(reference_path, "fst", "junctions.fst"))
2618:         reference_path, candidate.introns)
2649:     introns_found_RI <- .gen_splice_RI(candidate.introns, reference_path)
2665:         .gen_splice_save(AS_Table, candidate.introns, reference_path)
2676: .gen_splice_skipcoord <- function(reference_path, candidate.introns) {
2678:         read.fst(file.path(reference_path, "fst", "Genes.fst"))
3216: .gen_splice_RI <- function(candidate.introns, reference_path) {
3218:         read.fst(file.path(reference_path, "fst", "Exons.fst")),
3222:         read.fst(file.path(reference_path, "fst", "Introns.Dir.fst")))
3257: .gen_splice_save <- function(AS_Table, candidate.introns, reference_path) {
3269:         reference_path)
3270:     AS_Table <- .gen_splice_name_events(AS_Table, reference_path)
3300:         reference_path) {
3302:         read.fst(file.path(reference_path, "fst", "Exons.fst")),
3390:         file.path(reference_path, "fst", "Splice.options.fst"))
3396: .gen_splice_name_events <- function(AS_Table, reference_path) {
3444:         file.path(reference_path, "fst", "Splice.fst"))
3452: .gen_splice_proteins <- function(reference_path, genome) {
3457:         read.fst(file.path(reference_path, "fst", "Splice.fst"))
3460:         read.fst(file.path(reference_path, "fst", "Proteins.fst"))
3499:         file.path(reference_path, "fst", "Splice.Extended.fst"))
14: #'    to specify the files or web paths to use.
199: #' #   rdatapath, sourceurl, sourcetype
810:             normalizePath(genome.2bit)) {
981:         res <- BiocFileCache::bfcquery(bfc, url, "fpath", exact = TRUE)
982:         if (nrow(res) > 0 & !force_download) return(res$rpath[nrow(res)])
993:             return(res$rpath[nrow(res)]) # fetch local copy if available
996:         res <- BiocFileCache::bfcquery(bfc, url, "fpath", exact = TRUE)
998:         return(res$rpath[nrow(res)])
recountmethylation:inst/extdata/scripts/data_analyses.R: [ ]
26: path <- system.file("extdata", "metadata", package = "recountmethylation")
20: savepath <- paste(dfp, env.name, sep = "/")
27: mdpath <- paste(path, list.files(path)[1], sep = "/")
327:   save(ds, file = file.path("data_analyses", "df-l2med-signals.rda"))
28: md <- get(load(mdpath))
ORFik:R/experiment.R: [ ]
296:   for (path in filepaths) {
42:       cbu.path <- "/export/valenfs/data/processed_data/experiment_tables_for_R/"
240:     cbu.path <- "/export/valenfs/data/processed_data/experiment_tables_for_R/"
925:     cbu.path <- "/export/valenfs/data/processed_data/experiment_tables_for_R/"
287: findFromPath <- function(filepaths, candidates, slot = "auto") {
511: filepath <- function(df, type, basename = FALSE) {
515:   paths <- lapply(df$filepath, function(x, df, type) {
652:       paths <- filepath(df, type)
368:     reversePaths <- df$reverse[!(df$reverse %in% c("", "paired-end"))]
7: #' @param file relative path to a ORFik experiment. That is a .csv file following
10: #' also be full path to file, then in.dir argument is ignored.
13: #' it to disc. Does not apply if file is not a path, but a data.frame. Also
14: #' does not apply if file was given as full path.
30: #' # save.experiment(df, file = "path/to/save/experiment")
32: #' # read.experiment("path/to/save/experiment")
34: #' # read.experiment("experiment", in.dir = "path/to/save/")
43:       if (file.exists(pasteDir(cbu.path, basename(file))))
44:         file <- pasteDir(cbu.path, basename(file))
109: #' @param txdb A path to TxDb (prefered) or gff/gtf (not adviced, slower)
111: #' @param fa A path to fasta genome/sequences used for libraries, remember the
178: #' # Save with: save.experiment(df, file = "path/to/save/experiment.csv")
241:     if (dir.exists(cbu.path)) { # This will only trigger on CBU server @ UIB
243:       message(cbu.path)
244:       saveDir <- cbu.path
263: #' #save.experiment(df, file = "path/to/save/experiment.csv")
265: #' #save.experiment(df, file = "path/to/save/experiment")
280: #' @param filepaths path to all files
297:     hit <- names(unlist(sapply(candidates, grep, x = path)))
300:     hitRel <- names(unlist(sapply(candidates, grep, x = gsub(".*/", "", path))))
841:       fext[compressed] <-file_ext(file_path_sans_ext(files[compressed],
914: #' ## Path above is default path, so no dir argument needed
918: #' #list.experiments(dir = "MY/CUSTOM/PATH)
923:   experiments <- list.files(path = dir, pattern = "\\.csv")
926:     if (dir.exists(cbu.path)) { # If on UIB SERVER
927:       dir <- cbu.path
928:       experiments <- list.files(path = dir, pattern = "\\.csv")
24: #' df <- read.experiment(filepath) # <- valid ORFik .csv file
207:     df[4,] <- c("libtype", "stage", "rep", "condition", "fraction","filepath",
214:     df[4,] <- c("libtype", "stage", "rep", "condition", "fraction","filepath")
217:   # set file paths
220:   df[5:(5+length(files)-1), 1] <- findFromPath(files, libNames(), libtype)
223:   df[5:(5+length(files)-1), 2] <- findFromPath(files, stages, stage)
225:   df[5:(5+length(files)-1), 3] <- findFromPath(files, repNames(), rep)
227:   df[5:(5+length(files)-1), 4] <- findFromPath(files, conditionNames(), condition)
229:   df[5:(5+length(files)-1), 5] <- findFromPath(files, fractionNames(), fraction)
365:   files <- df$filepath
366:   if (length(df$filepath) == 0) stop("df have no filepaths!")
436: #' Get variable name per filepath in experiment
498: #' Get relative paths instead of full. Only use for inspection!
499: #' @return a character vector of paths, or a list of character with 2 paths per,
506: #' filepath(df, "default")
508: #' # filepath(df, "bedo")
510: #' # filepath(df, "pshifted")
516:     i <- which(df$filepath == x)
519:       out.dir <- paste0(dirname(df$filepath[1]), "/pshifted/")
549:       out.dir <- paste0(dirname(df$filepath[1]), "/",type,"/")
565:     if (is.null(input)) stop("filepath type not valid!")
569:   if (all(lengths(paths) == 1)) {
570:     paths <- unlist(paths)
572:   return(paths)
653:       libs <- bplapply(seq_along(paths),
654:                        function(i, paths, df, chrStyle, param, strandMode, varNames) {
656:         fimport(paths[i], chrStyle, param, strandMode)
657:       }, BPPARAM = BPPARAM, paths = paths, chrStyle = chrStyle, df = df,
698: #' dirname(df$filepath[1]),
726:                        out.dir = dirname(df$filepath[1]),
768:                        remove.file_ext(df$filepath[i], basename = TRUE),
284: #' else must be a character vector of length 1 or equal length as filepaths.
289:     if(length(slot) != 1 & length(slot) != length(filepaths)) {
369:     files <- c(files, reversePaths)
388:     stop("Duplicated filepaths in experiment!")
488: #' Get filepaths to ORFik experiment
492: #' default filepaths without warning. \cr
ArrayExpress:R/parseMAGE.r: [ ]
516: 	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 = "$", quote="\""))
105: readAEdata = function(path,files,dataCols,green.only){
108: 	source = getDataFormat(path,files)
121: 		rawdata = try(oligo::read.celfiles(filenames = file.path(path,unique(files))))
123: 			stop("Unable to read cel files in",path)
130: 			dataCols= try(getDataColsForAE1(path,files))
133: 		rawdata = try(read.maimages(files=files,path=path,source="generic",columns=dataCols,annotation=headers$ae1))
138: 		rawdata = try(read.maimages(files=files,path=path,source=source,columns=dataCols,green.only=green.only))
142: 		rawdata = try(read.maimages(files=files,path=path,source="generic",columns=dataCols,green.only=green.only))
149: 		stop("Unable to read data files in",path)
169: readFeatures<-function(adf,path,procADFref=NULL){
173: 	lines2skip = skipADFheader(adf,path,!is.null(procADFref))
174: 	features = try(read.table(file.path(path, adf), row.names = NULL, blank.lines.skip = TRUE, fill = TRUE, sep="\t", na.strings=c('?','NA'), sk...(40 bytes skipped)...
223: readExperimentData = function(idf, path){
224: 	idffile = scan(file.path(path,idf),character(),sep = "\n",encoding="UTF-8")
264: skipADFheader<-function(adf,path,proc=F){
270: 	con = file(file.path(path, adf), "r")	
319: getDataFormat=function(path,files){
326: 		allcnames = scan(file.path(path,files[1]),what = "",nlines = 200, sep = "\t",quiet=TRUE)
329: 			allcnames = scan(file.path(path,files[1]),what = "",nlines = 200, sep = "\t",quiet=TRUE,encoding="latin1")
345: 	allcnames = scan(file.path(path,files[1]),what = "",nlines = 1, sep = "\n",quiet=TRUE)
352: getDataColsForAE1 = function(path,files){
362: 						file.path(system.file("doc", package = "ArrayExpress"),"QT_list.txt"),
375: 	allcnames = scan(file.path(path,files[1]),what = "",nlines = 1, sep = "\t",quiet=TRUE)
430: 		if(!all(sapply(2:length(files), function(i) readLines(file.path(path,files[1]),1) == readLines(file.path(path,files[i]),1))))
518: 	try(file.remove(file.path(path, mageFiles$rawFiles)))
519: 	try(file.remove(file.path(path, mageFiles$processedFiles)))
521: 	try(file.remove(file.path(path, mageFiles$sdrf)))
522: 	try(file.remove(file.path(path, mageFiles$idf)))
523: 	try(file.remove(file.path(path, mageFiles$adf)))
524: 	try(file.remove(file.path(path, mageFiles$rawArchive)))
525: 	try(file.remove(file.path(path, mageFiles$processedArchive)))
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='')
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]
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), 
ribor:R/helper_functions.R: [ ]
163:     path  <- path(ribo.object)
66:     ribo.path <- ribo
11: #' file.path <- system.file("extdata", "HEK293_ingolia.ribo", package = "ribor")
12: #' sample <- Ribo(file.path)
18:     return(h5read(path(ribo.object),
25:     row.names <- h5read(path(ribo.object),
27:     lengths   <- h5read(path(ribo.object),
50: #' @param ribo a path to the ribo file or a 'Ribo' object
58: #' file.path <- system.file("extdata", "HEK293_ingolia.ribo", package = "ribor")
59: #' sample <- Ribo(file.path, rename = rename_default)
65:     #ensure that the ribo path is retrieved
68:         ribo.path <- path(ribo)
73:     original <- h5read(ribo.path, 
113: get_content_info <- function(ribo.path) {
114:     file_info     <- h5ls(ribo.path, recursive = TRUE, all = FALSE)
126:     ls <- h5ls(ribo.path)
135:         attribute      <- h5readAttributes(ribo.path, name)
164:     attribute <- h5readAttributes(path, "/")
264:                             path,
267:     experiment <- strsplit(path, split="/")[[1]][3]
268:     output <- t(h5read(file=file, index=index, name=path))
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, 
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())
CONFESS:R/internal_fluo_NBE.R: [ ]
1419:     path <- c(data$UpdatedPath, data$UpdatedPath[1])
2092:   path <- do.call(rbind, res)[, 1]
1493:     path.update <- path[1:(length(path) - 1)]
2432: path.initiator <- function(data, where) {
1381: fixPath <- function(data, groups) {
2060: estimatePath <- function(data, type, start) {
2116: pathUpdater <- function(data, path) {
2246: updateCentroidsPaths <- function(data, estimates, path.type) {
1370: #' It tests whether the path has been appropariately defined and produces an error if not.
1383:     stop("Insert the cell progression path using the labels of Fluo_inspection")
1387:       "Error in cell progression path: you have specified different number of groups than the one estimated"
1396: #' It sort the adjusted (and transfomed) fluorescence signals according to the path progression.
1399: #' @param path.start Integer. A cluster number indicating the starting cluster that algorithm should use to
1400: #'   build the path. The cluster numbers refer to the plot generated by Fluo_inspection(). Default is 1.
1401: #'   If path.type = "circular" the number does not matter. If path.type = "A2Z" the user should inspect the
1402: #'   Fluo_inspection() plot to detect the beginning of the path. If path.type = "other", the function will
1403: #'   not estimate a path. The user has to manually insert the path progression (the cluster numbers) in
1414: orderFluo <- function(data, path.type, updater = FALSE) {
1415:   if (path.type[1] != "circular" & path.type[1] != "A2Z") {
1416:     stop("The path.type is not correctly specified")
1424:     path <- c(1:max(data$Updated.groups), 1)
1435:   for (i in 1:(length(path) - 1)) {
1436:     center1 <- as.numeric(ms[which(ms[, 1] == path[i]), 2:3])
1437:     center2 <- as.numeric(ms[which(ms[, 1] == path[(i + 1)]), 2:3])
1438:     ww <- which(groups == path[(i + 1)])
1462:   for (i in 1:(length(path) - 1)) {
1464:       as.numeric(ms[which(ms[, 1] == path[(length(path) - i + 1)]), 2:3])
1466:       as.numeric(ms[which(ms[, 1] == path[(length(path) - i)]), 2:3])
1467:     ww <- which(groups == path[(length(path) - i)])
1492:   if (path.type[1] == "A2Z") {
1494:     ww <- which(as.numeric(all[, 4]) == path.update[1])
1497:       which(as.numeric(all[, 4]) == path.update[length(path.update)])
1505:   wh <- which(mydata[, 4] == path[length(path)])
1611: #' @param path.type Character vector. A user-defined vector that characterizes the cell progression dynamics.
1612: #'   The first element can be either "circular" or "A2Z" or "other". If "circular" the path progression is
1613: #'   assummed to exhibit a circle-like behavior. If "A2Z" the path is assumed to have a well-defined start
1629:                     path.type,
1643:                          path.type = path.type)
1644:   if (path.type[1] != "other") {
1657:                            path.type = path.type)
2027: #'   It can be either "clockwise" or "anticlockwise" depending on how the path is expected
2049: #' The main function for automatic path estimation .
2053: #'   It can be either "clockwise" or "anticlockwise" depending on how the path is expected
2055: #' @param start Integer. The cluster number that is assigned as the path starting point
2057: #' @return The sorted cluster indices (path)
2093:   path <- rep(path, 2)
2094:   if (length(which(path == start)) == 0) {
2097:   if (start != path[1]) {
2098:     w <- which(path == start)
2099:     path <- path[w[1]:(w[2] - 1)]
2101:     path <- path[1:(length(path) / 2)]
2103:   return(path)
2108: #' A helper that updates the path sorted clusters after re-estimation by change-point analysis.
2110: #' @param data Data matrix. A matrix of centroids with their path progression indices.
2111: #' @param path Numeric vector. The path progression indices.
2113: #' @return The sorted cluster indices (path)
2118:   for (i in 1:length(path)) {
2120:       matrix(rbind(res, data[which(data[, 4] == path[i]),]), ncol = ncol(res))
2142: #' @return The sorted transformed signal differences (path) and the associated change-points
2231: #' It updates the path sorted clusters after re-estimation by change-point analysis.
2236: #' @param path.type Character vector. A user-defined vector that characterizes the cell progression dynamics.
2237: #'   The first element can be either "circular" or "A2Z" or "other". If "circular" the path progression is
2238: #'   assummed to exhibit a circle-like behavior. If "A2Z" the path is assumed to have a well-defined start
2242: #' @return A list of adjusted fluorescence signals and the updated path after the change-point analysis
2258:   if (path.type[1] != "other" & length(estimates[[2]]) > 0) {
2268:       estimatePath(trigs, type = path.type[2], start =
2271:       pathUpdater(data = estimates[[1]], path = data$UpdatedPath)
2421: #' path.initiator
2423: #' It finds the cluster that initiates the progression path.
2427: #'   the starting point of the progression path.
2429: #' @return A starting point for the progression path
2455:     stop("Invalid starting point. Revise init.path parameter!")
2539: ...(7 bytes skipped)...stimates the average difference between the original and the CV estimated pseudotimes. For circular path
2541: #'   maximum pseudotime is 300) in a circular path, two pseudotimes 1 and 300 differ only by 1 and not by 299.
2544: #' @param path.type Character. The input of path.type parameter in pathEstimator().
2552: aveDiff <- function(data, path.type, maxPseudo) {
2553:   if (path.type != "circular") {
2557:   if (path.type == "circular") {
1368: #' FixPath
1390:   return(c(data, list(UpdatedPath = groups)))
1563:   nn <- data$UpdatedPath
2047: #' estimatePath
2106: #' pathUpdater
2267:     data$UpdatedPath <-
1641:     updateCentroidsPaths(data = data,
1655:       updateCentroidsPaths(data = res1[[1]],
2229: #' updateCentroidsPaths
oligoClasses:R/methods-GenomeAnnotatedDataFrame.R: [ ]
259:   path <- system.file("extdata", package=pkgname)
260:   if(path=="") stop("Are you sure ", pkgname, " is installed?")
262:   snpBuilds <- list.files(path, pattern="snpProbes_")
Director:inst/www/js/d3.v3.js: [ ]
4199:   d3.geo.path = function() {
4201:     function path(object) {
6082:   function d3_layout_bundlePath(link) {
3936:   var d3_geo_pathAreaSum, d3_geo_pathAreaPolygon, d3_geo_pathArea = {
3963:   var d3_geo_pathBoundsX0, d3_geo_pathBoundsY0, d3_geo_pathBoundsX1, d3_geo_pathBoundsY1;
3971:   function d3_geo_pathBoundsPoint(x, y) {
4022:   function d3_geo_pathBufferCircle(radius) {
4038:   function d3_geo_pathCentroidPoint(x, y) {
4057:   function d3_geo_pathCentroidLineEnd() {
4246:   function d3_geo_pathProjectStream(project) {
4209:     path.area = function(object) {
4214:     path.centroid = function(object) {
4219:     path.bounds = function(object) {
4224:     path.projection = function(_) {
4229:     path.context = function(_) {
4235:     path.pointRadius = function(_) {
4238:       return path;
4242:       return path;
4244:     return path.projection(d3.geo.albersUsa()).context(null);
7979:       var rc, cr, rp, ap, p0 = 0, p1 = 0, x0, y0, x1, y1, x2, y2, x3, y3, path = [];
8027:             path.push("M", t30[0], "A", rc1, ",", rc1, " 0 0,", cr, " ", t30[1], "A", r1, ",", r1, " 0 ", 1 - cw ^ d...(128 bytes skipped)...
8029:             path.push("M", t30[0], "A", rc1, ",", rc1, " 0 1,", cr, " ", t12[0]);
8032:           path.push("M", x0, ",", y0);
8037:             path.push("L", t21[0], "A", rc0, ",", rc0, " 0 0,", cr, " ", t21[1], "A", r0, ",", r0, " 0 ", cw ^ d3_sv...(128 bytes skipped)...
8039:             path.push("L", t21[0], "A", rc0, ",", rc0, " 0 0,", cr, " ", t03[0]);
8042:           path.push("L", x2, ",", y2);
8045:         path.push("M", x0, ",", y0);
8046:         if (x1 != null) path.push("A", r1, ",", r1, " 0 ", l1, ",", cw, " ", x1, ",", y1);
8047:         path.push("L", x2, ",", y2);
8048:         if (x3 != null) path.push("A", r0, ",", r0, " 0 ", l0, ",", 1 - cw, " ", x3, ",", y3);
8050:       path.push("Z");
8051:       return path.join("");
8195:     var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ];
8196:     while (++i < n) path.push("H", (p[0] + (p = points[i])[0]) / 2, "V", p[1]);
8197:     if (n > 1) path.push("H", p[0]);
8198:     return path.join("");
8201:     var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ];
8202:     while (++i < n) path.push("V", (p = points[i])[1], "H", p[0]);
8203:     return path.join("");
8206:     var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ];
8207:     while (++i < n) path.push("H", (p = points[i])[0], "V", p[1]);
8208:     return path.join("");
8224:     var quad = points.length != tangents.length, path = "", p0 = points[0], p = points[1], t0 = tangents[0], t = t0, pi = 1;
8226:       path += "Q" + (p[0] - t0[0] * 2 / 3) + "," + (p[1] - t0[1] * 2 / 3) + "," + p[0] + "," + p[1];
8234:       path += "C" + (p0[0] + t0[0]) + "," + (p0[1] + t0[1]) + "," + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," ...(20 bytes skipped)...
8238:         path += "S" + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," + p[0] + "," + p[1];
8243:       path += "Q" + (p[0] + t[0] * 2 / 3) + "," + (p[1] + t[1] * 2 / 3) + "," + lp[0] + "," + lp[1];
8245:     return path;
8259: ...(44 bytes skipped)...s[0], x0 = pi[0], y0 = pi[1], px = [ x0, x0, x0, (pi = points[1])[0] ], py = [ y0, y0, y0, pi[1] ], path = [ x0, ",", y0, "L", d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), ",", d3_svg_lineDot4(d3_svg_lin...(21 bytes skipped)...
8267:       d3_svg_lineBasisBezier(path, px, py);
8270:     path.push("L", pi);
8271:     return path.join("");
8275:     var path = [], i = -1, n = points.length, pi, px = [ 0 ], py = [ 0 ];
8281:     path.push(d3_svg_lineDot4(d3_svg_lineBasisBezier3, px) + "," + d3_svg_lineDot4(d3_svg_lineBasisBezier3, ...(5 bytes skipped)...
8289:       d3_svg_lineBasisBezier(path, px, py);
8291:     return path.join("");
8294:     var path, i = -1, n = points.length, m = n + 4, pi, px = [], py = [];
8300:     path = [ d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, py)...(3 bytes skipped)...
8308:       d3_svg_lineBasisBezier(path, px, py);
8310:     return path.join("");
8329:   function d3_svg_lineBasisBezier(path, x, y) {
8330:     path.push("C", d3_svg_lineDot4(d3_svg_lineBasisBezier1, x), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier1...(206 bytes skipped)...
8987:         var range = d3_scaleRange(scale1), path = g.selectAll(".domain").data([ 0 ]), pathUpdate = (path.enter().append("path").attr("class", "domain"), 
8988:         d3.transition(path));
3941:       d3_geo_pathAreaPolygon = 0;
3942:       d3_geo_pathArea.lineStart = d3_geo_pathAreaRingStart;
3945:       d3_geo_pathArea.lineStart = d3_geo_pathArea.lineEnd = d3_geo_pathArea.point = d3_noop;
3946:       d3_geo_pathAreaSum += abs(d3_geo_pathAreaPolygon / 2);
3949:   function d3_geo_pathAreaRingStart() {
3951:     d3_geo_pathArea.point = function(x, y) {
3952:       d3_geo_pathArea.point = nextPoint;
3956:       d3_geo_pathAreaPolygon += y0 * x - x0 * y;
3959:     d3_geo_pathArea.lineEnd = function() {
3964:   var d3_geo_pathBounds = {
3965:     point: d3_geo_pathBoundsPoint,
3972:     if (x < d3_geo_pathBoundsX0) d3_geo_pathBoundsX0 = x;
3973:     if (x > d3_geo_pathBoundsX1) d3_geo_pathBoundsX1 = x;
3974:     if (y < d3_geo_pathBoundsY0) d3_geo_pathBoundsY0 = y;
3975:     if (y > d3_geo_pathBoundsY1) d3_geo_pathBoundsY1 = y;
3977:   function d3_geo_pathBuffer() {
3978:     var pointCircle = d3_geo_pathBufferCircle(4.5), buffer = [];
3993:         pointCircle = d3_geo_pathBufferCircle(_);
4025:   var d3_geo_pathCentroid = {
4026:     point: d3_geo_pathCentroidPoint,
4027:     lineStart: d3_geo_pathCentroidLineStart,
4028:     lineEnd: d3_geo_pathCentroidLineEnd,
4030:       d3_geo_pathCentroid.lineStart = d3_geo_pathCentroidRingStart;
4033:       d3_geo_pathCentroid.point = d3_geo_pathCentroidPoint;
4034:       d3_geo_pathCentroid.lineStart = d3_geo_pathCentroidLineStart;
4035:       d3_geo_pathCentroid.lineEnd = d3_geo_pathCentroidLineEnd;
4043:   function d3_geo_pathCentroidLineStart() {
4045:     d3_geo_pathCentroid.point = function(x, y) {
4046:       d3_geo_pathCentroid.point = nextPoint;
4047:       d3_geo_pathCentroidPoint(x0 = x, y0 = y);
4054:       d3_geo_pathCentroidPoint(x0 = x, y0 = y);
4058:     d3_geo_pathCentroid.point = d3_geo_pathCentroidPoint;
4060:   function d3_geo_pathCentroidRingStart() {
4062:     d3_geo_pathCentroid.point = function(x, y) {
4063:       d3_geo_pathCentroid.point = nextPoint;
4064:       d3_geo_pathCentroidPoint(x00 = x0 = x, y00 = y0 = y);
4075:       d3_geo_pathCentroidPoint(x0 = x, y0 = y);
4077:     d3_geo_pathCentroid.lineEnd = function() {
4081:   function d3_geo_pathContext(context) {
4117:       context.closePath();
4210:       d3_geo_pathAreaSum = 0;
4211:       d3.geo.stream(object, projectStream(d3_geo_pathArea));
4212:       return d3_geo_pathAreaSum;
4216:       d3.geo.stream(object, projectStream(d3_geo_pathCentroid));
4220:       d3_geo_pathBoundsX1 = d3_geo_pathBoundsY1 = -(d3_geo_pathBoundsX0 = d3_geo_pathBoundsY0 = Infinity);
4221:       d3.geo.stream(object, projectStream(d3_geo_pathBounds));
4222:       return [ [ d3_geo_pathBoundsX0, d3_geo_pathBoundsY0 ], [ d3_geo_pathBoundsX1, d3_geo_pathBoundsY1 ] ];
4226:       projectStream = (projection = _) ? _.stream || d3_geo_pathProjectStream(_) : d3_identity;
4231:       contextStream = (context = _) == null ? new d3_geo_pathBuffer() : new d3_geo_pathContext(_);
6077:       var paths = [], i = -1, n = links.length;
6078:       while (++i < n) paths.push(d3_layout_bundlePath(links[i]));
6079:       return paths;
8995:           pathUpdate.attr("d", "M" + range[0] + "," + sign * outerTickSize + "V0H" + range[1] + "V" + sign * outer...(10 bytes skipped)...
8999:           pathUpdate.attr("d", "M" + sign * outerTickSize + "," + range[0] + "H0V" + range[1] + "H" + sign * outer...(10 bytes skipped)...