Found 88400 results in 8639 files, showing top 50 files (show more).
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)
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$names[genesIndx])
31:     # Choose valid pathways
32:     if (missing(pathways))  
37:     if (!missing(pathways)) 
39:         paths <- paste(org, pathways, '.xml', sep='') 
44:     # Create compact adjacency matrices for given pathways.
45:     types  <- getPathwayType(paste(xmlDir, paths, sep='//'))
46:     N <- length(paths)
56:                     funcName=pathwayToGraph,
59:                     N=length(paths),
61:                     xmlDir, paths, types, FALSE, edgeTypes, 
64:     names(cAdjMats) <- gsub('.xml', '', paths)
67:     eAdjMats <- .doSafeParallel(funcName=pathwayToGraph,
70:                                 N=length(paths),
72:                                 xmlDir, paths, types, TRUE, edgeTypes, 
75:     names(eAdjMats) <- gsub('.xml', '', paths)
143:     types <- vector(mode='numeric', length=length(filepath))
144:     for (i in 1:length(filepath))
146:         num <- tail(unlist(strsplit(filepath[i], '//')), 1)
156: # Graph from Metabolic Pathways
161:     xmlDoc <- tryCatch(xmlTreeParse(filepath,error=NULL),
344: # Graph from Mon Metabolic Pathways
350:     xmlDoc         <- tryCatch(xmlTreeParse(filepath,error=NULL),
49:         'nonMetabolicPathwayToGraph', 'expandMetabolicGraph', 
51:         'metabolicPathwayToGraph', 'expandNonMetabolicGraph',
609:                 # Set new interaction types to apathetic
732:     # apathetic  3
NoRCE:R/pathway.R: [ ]
353:   path <- merge(merge1, symb, by = "gene")
66:     pathTable <- unique(keggPathwayDB(org_assembly))
72:     pathfreq <- as.data.frame(table(annot$pathway))
100:     pathT <- as.character(freq$Var1[enrich])
119:     pathways <- data.frame(unique(pathT))
205:     pathTable <- unique(reactomePathwayDB(org_assembly))
211:     pathfreq <- as.data.frame(table(annot$pathway))
237:     pathT <- as.character(freq$Var1[enrich])
542:   pathTable <- unique(WikiPathwayDB(org_assembly))
547:   pathfreq <- as.data.frame(table(annot$pathID))
573:   pathT <- as.character(freq$Var1[enrich])
580:   pathTerms <- as.character(r$pathTerm[match(pathT, r$pathID)])
630: pathwayEnrichment <- function(genes,
679:   pathfreq <- as.data.frame(table(annot$pathTerm))
711:   pathT <- as.character(freq$Var1[enrich])
719:   pathTerms <- as.character(r$pathTerm[match(pathT, r$pathID)])
272: reactomePathwayDB <- function(org_assembly = c("hg19",
359: keggPathwayDB <- function(org_assembly = c("hg19",
435: WikiPathwayDB <- function(org_assembly = c("hg19",
15: #' @param gmtFile File path of the gmt file
92:         file.path(x[1], x[2]))
96:         file.path(x[1], x[2]))
156: #' @param gmtFile File path of the gmt file
230:       file.path(x[1], x[2]))
233:       file.path(x[1], x[2]))
355:   return(path)
501: #' @param gmtFile File path of the gmt file
565:       file.path(x[1], x[2]))
569:       file.path(x[1], x[2]))
610: #' @param gmtFile File path of the gmt file
704:     file.path(x[1], x[2]))
707:     file.path(x[1], x[2]))
1: #' KEGG pathway enrichment
22: #' @return KEGG pathway enrichment results
69:     annot <- pathTable[which(pathTable$symbol %in% genes$g),]
73:     pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
76:     geneSize = length(unique(pathTable$symbol))
78:     bckfreq <- as.data.frame(table(pathTable$pathway))
79:     notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
80:     freq <- merge(pathfreq, notGene, by = "Var1")
105:     r <- annot[annot$pathway %in% pathT,]
107:     for (i in seq_along(pathT))
109:       if (length(which(pathT[i] == r$pathway)) > 0)
114:               as.character(r[which(pathT[i] == r$pathway),]$symbol)),
115:                      paste(pathT[i])))
120:     tmp <- character(length(pathT))
121:     if (nrow(pathways) > 0) {
123:         unlist(lapply(seq_len(nrow(pathways)), function(x)
124:           tmp[x] <- try(KEGGREST::keggGet(pathT[x])[[1]]$NAME)
130:         ID = pathT,
142: #' Reactome pathway enrichment
164: #' @return Reactome pathway enrichment results
208:     annot <- pathTable[which(pathTable$symbol %in% genes$g),]
212:     pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
214:     geneSize = length(unique(pathTable$symbol))
216:     bckfreq <- as.data.frame(table(pathTable$pathway))
217:     notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
218:     freq <- merge(pathfreq, notGene, by = "Var1")
242:     r <- annot[annot$pathway %in% pathT,]
246:     for (i in seq_along(pathT))
248:       if (length(which(pathT[i] == r$pathway)) > 0)
253:               list(as.character(r[which(pathT[i] == r$pathway),]$symbol)),
254:                      paste(pathT[i])))
260:         ID = pathT,
261:         Term = as.character(rt[order(match(rt$pathway, pathT)), ]$name),
281:   table1 <- data.frame(pathway = rep(names(xx), lapply(xx, length)),
284:   pn <- data.frame(pathway = rep(names(pn), lapply(pn, length)),
290:     ty <- table1[grepl("^R-HSA", table1$pathway),]
291:     pn1 <- pn[grepl("^R-HSA", pn$pathway),]
298:     ty <- table1[grepl("^R-MMU", table1$pathway),]
299:     pn1 <- pn[grepl("^R-MMU", pn$pathway),]
306:     ty <- table1[grepl("^R-DRE", table1$pathway),]
307:     pn1 <- pn[grepl("^R-DRE", pn$pathway),]
314:     ty <- table1[grepl("^R-RNO", table1$pathway),]
315:     pn1 <- pn[grepl("^R-RNO", pn$pathway),]
322:     ty <- table1[grepl("^R-CEL", table1$pathway),]
323:     pn1 <- pn[grepl("^R-CEL", pn$pathway),]
330:     ty <- table1[grepl("^R-SCE", table1$pathway),]
331:     pn1 <- pn[grepl("^R-SCE", pn$pathway),]
344:     ty <- table1[grepl("^R-DME", table1$pathway),]
345:     pn1 <- pn[grepl("^R-DME", pn$pathway),]
351:                   by = "pathway",
371:     kegg <- org.Hs.eg.db::org.Hs.egPATH2EG
379:     kegg <- org.Mm.eg.db::org.Mm.egPATH2EG
387:     kegg <- org.Dr.eg.db::org.Dr.egPATH2EG
395:     kegg <- org.Rn.eg.db::org.Rn.egPATH2EG
403:     kegg <- org.Ce.eg.db::org.Ce.egPATH2EG
411:     kegg <- org.Sc.sgd.db::org.Sc.sgdPATH2ORF
419:     kegg <- org.Dm.eg.db::org.Dm.egPATH2EG
425:   pathTable <-
426:     data.frame(pathway = paste0(prefix, rep(names(kegg2),
431:   pathTable <- merge(pathTable, x, by = "gene")
432:   return(pathTable)
474:     do.call(rbind, strsplit(as.character(gmtFile$pathTerm), '%'))
480:         pathID = tmp[, 3],
481:         pathTerm = tmp[, 1]
508: #' @return Wiki Pathway Enrichment
545:   annot <- pathTable[which(pathTable$gene %in% genes$g),]
548:   pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
550:   geneSize = length(unique(pathTable$gene))
551:   bckfreq <- as.data.frame(table(pathTable$pathID))
552:   notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
553:   freq <- merge(pathfreq, notGene, by = "Var1")
578:   r <- annot[annot$pathID %in% pathT,]
581:   for (i in seq_along(pathT))
583:     if (length(which(pathT[i] == r$pathID)) > 0)
587:           list(as.character(r[which(pathT[i] == r$pathID),]$gene)),
588:                           paste(pathT[i])))
595:       ID = pathT,
596:       Term = pathTerms,
606: #' For a given gmt file of a specific pathway database, pathway enrichment
628: #' @return Pathway Enrichment
671:     pathTable <-
676:     pathTable <- geneListEnrich(f = gmtFile, isSymbol = isSymbol)
678:   annot <- pathTable[which(pathTable$symbol %in% genes$g),]
680:   pathfreq <- pathfreq[which(pathfreq$Freq > 0),]
684:     geneSize = length(unique(pathTable$symbol))
689:   bckfreq <- as.data.frame(table(pathTable$pathTerm))
691:   notGene <- bckfreq[bckfreq$Var1 %in% pathfreq$Var1,]
692:   freq <- merge(pathfreq, notGene, by = "Var1")
717:   r <- annot[annot$pathTerm %in% pathT,]
721:   for (i in seq_along(pathT))
723:     if (length(which(pathT[i] == r$pathTerm)) > 0)
726:           list(as.character(r[which(pathT[i] == r$pathTerm),]$symbol)),
727:                           paste(pathT[i])))
732:       ID = pathT,
733:       Term = pathTerms,
743: #' Convert gmt formatted pathway file to the Pathway ID, Entrez, symbol
746: #' @param gmtName Custom pathway gmt file
815:     colnames(f) <- c('pathTerm', 'Entrez', 'symbol')
830:     colnames(f) <- c('pathTerm', 'symbol', 'Entrez')
852:     colnames(f) <- c('pathTerm', 'Entrez', 'symbol')
863:     colnames(f) <- c('pathTerm', 'symbol', 'Entrez')
280:   xx <- as.list(reactome.db::reactomePATHID2EXTID)
283:   pn <- as.list(reactome.db::reactomePATHID2NAME)
445:       rWikiPathways::downloadPathwayArchive(organism = "Homo sapiens",
449:       rWikiPathways::downloadPathwayArchive(organism = "Mus musculus",
453:       rWikiPathways::downloadPathwayArchive(organism = "Danio rerio",
457:       rWikiPathways::downloadPathwayArchive(organism = "Rattus norvegicus",
461:       rWikiPathways::downloadPathwayArchive(
465:       rWikiPathways::downloadPathwayArchive(
469:       rWikiPathways::downloadPathwayArchive(
487: #' WikiPathways Enrichment
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;
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_")
ACE:R/ACE.R: [ ]
134: 		  readCounts <- QDNAseq::binReadCounts(bins, path = inputdir)
960:         if (dirname(filename)==".") {newpath <- file.path(outputdir,filename)}
131: 		  currentdir <- file.path(outputdir,paste0(b,"kbp"))
136: 		    saveRDS(readCounts, file = file.path(outputdir, paste0(b, "kbp-raw.rds")))
146: 		  saveRDS(copyNumbersSegmented, file = file.path(outputdir,paste0(b,"kbp.rds")))
157: 			currentdir <- file.path(outputdir,paste0(substr(files[f],0,nchar(files[f])-4)))
159: 			copyNumbersSegmented <- readRDS(file.path(inputdir,files[f]))
166: 	write.table(parameters, file=file.path(outputdir,"parameters.tsv"), quote = FALSE, sep = "\t", na = "", row.names = FALSE)
182: 	  qdir <- file.path(currentdir,paste0(q,"N"))
189:   	dir.create(file.path(qdir,"likelyfits"))  
258:   		fp <- file.path(qdir,pd$name[a])
263:   		dir.create(file.path(fp,"graphs"))
284:   		imagefunction(file.path(fp,paste0(pd$name[a],"_errorlist.",imagetype)))
320:   		  fn <- file.path(fp,"graphs",paste0(pd$name[a], " - ",q,"N fit ", m, ".",imagetype))
348:   		      imagefunction(file.path(qdir,"likelyfits",paste0(pd$name[a],"_bestfit_",q,"N.",imagetype)),width=10.5)
350:   		      imagefunction(file.path(qdir,"likelyfits",paste0(pd$name[a],"_bestfit_",q,"N.",imagetype)),width=720)
358:   		      imagefunction(file.path(qdir,"likelyfits",paste0(pd$name[a],"_lastminimum_",q,"N.",imagetype)),width=10.5)
360:   		      imagefunction(file.path(qdir,"likelyfits",paste0(pd$name[a],"_lastminimum_",q,"N.",imagetype)),width=720)
377:   		  pdf(file.path(fp,paste0("summary_",pd$name[a],".pdf")),width=10.5)
382:   		    imagefunction(file.path(fp,paste0("summary_",pd$name[a],".",imagetype)), width = 720)
386:     		  imagefunction(file.path(fp,paste0("summary_",pd$name[a],".",imagetype)), width = 2160, height = 480*ceiling(length(plots)/3...(3 bytes skipped)...
399:     	  pdf(file.path(qdir,"summary_likelyfits.pdf"),width=10.5)
402:       	pdf(file.path(qdir,"summary_errors.pdf"))
406:     	  imagefunction(file.path(qdir,paste0("summary_likelyfits.",imagetype)), width = 2160, height = 480*length(pd$name))
409:       	imagefunction(file.path(qdir,paste0("summary_errors.",imagetype)), width = 1920, height = 480*ceiling(length(pd$name)/4))
415:   	    pdf(file.path(qdir,"summary_errors.pdf"))
419:   	    imagefunction(file.path(qdir,paste0("summary_errors.",imagetype)), width = 1920, height = 480*ceiling(length(pd$name)/4))
425:   	write.table(fitpicker, file=file.path(qdir,paste0("fitpicker_",q,"N.tsv")), quote = FALSE, sep = "\t", na = "", row.names = FALSE)
830: # frequency in percentage). It can also be a file path to a tab-delimited
832: # by getadjustedsegments. Again, this can be either a data frame or a file path
1003:       copyNumbersSegmented <- readRDS(file.path(inputdir,files[1]))
1005:     if(missing(modelsfile)){models <- try(read.table(file.path(inputdir,"models.tsv"), header = TRUE, comment.char = "", sep = "\t"))
1009:       if (dir.exists(file.path(inputdir,"variantdata"))) {
1010:         variantdata <- file.path(inputdir,"variantdata")
1072:           if (!dir.exists(file.path(outputdir,"newplots"))) {dir.create(file.path(outputdir,"newplots"))}
1074:             imagefunction(file.path(outputdir,"newplots",paste0(pd$name[a],".",imagetype)),width=10.5)
1078:             imagefunction(file.path(outputdir,"newplots",paste0(pd$name[a],".",imagetype)), width=720)
1091:           variantfile <- file.path(variantdata,paste0(prefix,pd$name[a],postfix,varext))
1092:           folder <- file.path(outputdir,"variantdata")
1104:           if (!dir.exists(file.path(outputdir,"segmentfiles"))) {dir.create(file.path(outputdir,"segmentfiles"))}
1105:           fn <- file.path(outputdir,"segmentfiles",paste0(pd$name[a],"_segments.",segext))
961:         else {newpath <- sub(dirname(filename),outputdir,filename)}
962:         fn <- gsub(".csv","_ACE.csv",newpath)
megadepth:R/install.R: [ ]
146:         path <- Sys.getenv("APPDATA", "")
175:         path <- file.path(d, exec)
206:     path <- NULL # cache the path to megadepth
182:     path2 <- Sys.which(cmd)
143: bin_paths <- function(dir = "Megadepth",
15: #' found via the environment variable \code{PATH}.
17: #' If you want to install Megadepth to a custom path, you can set the global
35: #' @importFrom xfun is_windows is_macos same_path
75:     exec <- file.path(tempdir(), exec_name)
144:     extra_path = getOption("megadepth.dir")) {
147:         path <- if (fs::dir_exists(path)) {
148:             file.path(path, dir)
151:         path <- "~/Library/Application Support"
152:         path <- if (fs::dir_exists(path)) file.path(path, dir)
153:         path <- c("/usr/local/bin", path)
155:         path <- c("~/bin", "/snap/bin", "/var/lib/snapd/snap/bin")
157:     path <- c(extra_path, path, pkg_file(dir, mustWork = FALSE))
159:     path <- path[path != ""]
162:         path <- c(tempdir(), path)
164:     path
167: # find an executable from PATH, APPDATA, system.file(), ~/bin, etc
176:         if (utils::file_test("-x", path)) {
179:             path <- ""
183:     if (path == "" || xfun::same_path(path, path2)) {
184:         if (path2 == "") {
187:         return(cmd) # do not use the full path of the command
189:         if (path2 != "") {
194:                 path,
196:                 path2,
202:     normalizePath(path)
208:         if (is.null(path)) {
209:             path <<- find_exec(
215:         path
109:     dirs <- bin_paths()
139:     message("megadepth has been installed to ", normalizePath(destdir))
158:     # remove empty paths potentially created by pkgfile
169:     for (d in bin_paths(dir)) {
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)
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, 
flowGraph:R/04_flowgraph_plots.R: [ ]
1574:                         path <- paste0(
1536:             plot_path_ <- paste0(
2: #' @description Creates a cell hierarchy plot given a flowGraph object. If a path is not provided for \code{fg_plot} to save the plot, please use \code{plot_gr} to view plot given t...(28 bytes skipped)...
89: #' @param path A string indicating the path to where the function should save
104: #'  the plot by filling out the \code{path} parameter with a full path to the
117: #'    path=NULL) # set path to a full path to save plot as a PNG
142:     path=NULL, width=9, height=9
279:         if (!is.null(path) & !interactive)
281:                 grepl("[.]png$",path, ignore.case=TRUE),
282:                 path, paste0(path, ".png")),
285:         if (!is.null(path) & interactive & !visNet_plot)
287:                 gp, ifelse(grepl("[.]html$",path, ignore.case=TRUE),
288:                            path, paste0(path, ".html")))
289:         if (!is.null(path) & interactive & visNet_plot)
291:                 gp, file=ifelse(grepl("[.]html$",path, ignore.case=TRUE),
292:                                 path, paste0(path, ".html")),
295:     if (is.null(path)) message("use function plot_gr to plot fg_plot output")
407: #'    path=NULL) # set path to a full path to save plot as a PNG
719: #' @param path A string indicating the path to where the function should save
754:     main=NULL, interactive=FALSE, path=NULL
822:             if (!is.null(path))
824:                     qp, ifelse(grepl("[.]html$",path, ignore.case=TRUE),
825:                                path, paste0(path, ".html")))
829:         if (!is.null(path))
832:                     ifelse(grepl("[.]png$",path, ignore.case=TRUE),
833:                            path, paste0(path, ".png")),
903: #' @param path A string indicating the path to where the function should save
940:     main=NULL, path=NULL
1031:     if (!is.null(path))
1033:             ifelse(grepl("[.]png$",path, ignore.case=TRUE),
1034:                    path, paste0(path, ".png")),
1051:     main=NULL, path=NULL
1063:             main=main, path=path,
1202:     if (!is.null(path)) {
1205:                 "[.]png$",path, ignore.case=TRUE),
1206:                 path, paste0(path, ".png")),
1281: #' @param path A string indicating the path to where the function should save
1316:     main=NULL, interactive=FALSE, path=NULL
1386:         if (!is.null(path))
1389:                     "[.]png$",path, ignore.case=TRUE),
1390:                     path, paste0(path, ".png")), plot=gp)
1426:         if (!is.null(path))
1428:                 gp, ifelse(grepl("[.]html$", path, ignore.case=TRUE),
1429:                            path, paste0(path, ".html")))
1443: #' @param plot_path A string indicating the folder path to where the function
1520:     fg, plot_path, plot_types="node", interactive=FALSE,
1537:                 plot_path, "/", type, "/", paste0(sm, collapse="_"))
1538:             while (dir.exists(plot_path_) & !overwrite)
1539:                 plot_path_ <- paste0(plot_path_,"_")
1540:             dir.create(plot_path_, recursive=TRUE, showWarnings=FALSE)
1551:                     path=paste0(plot_path_, "/pVSdifference.png"))
1567:                     rdir_ <- paste0(plot_path_, "/boxplots")
1580:                             path=path, paired=paired,
1595:                     path=paste0(plot_path_,"/qq.png"),
1605:                         path=paste0(plot_path_,"/cell_hierarchy.png"),
1628:                             paste0(plot_path_,"/cell_hierarchy_",
ORFik:R/experiment.R: [ ]
22:   for (path in filepaths) {
616:     save_path <- file.path(out_dir, paste0(name, ".ofst"))
756:     cbu.path <- "/export/valenfs/data/processed_data/experiment_tables_for_R/"
13: findFromPath <- function(filepaths, candidates, slot = "auto") {
242: filepath <- function(df, type, basename = FALSE) {
246:   paths <- lapply(df$filepath, function(x, df, type) {
403:       paths <- filepath(df, type)
614:     specific_paths <- filepaths[libs[[name]]]
94:     reversePaths <- df$reverse[!(df$reverse %in% c("", "paired-end"))]
612:   filepaths <- filepath(df, type)
6: #' @param filepaths path to all files
23:     hit <- names(unlist(sapply(candidates, grep, x = path)))
26:     hitRel <- names(unlist(sapply(candidates, grep, x = gsub(".*/", "", path))))
238: #' # Other format path
574: #' @param out_dir Ouput directory, default \code{file.path(dirname(df$filepath[1]), "ofst_merged")},
588: #' #fimport(file.path(tempdir(), "all.ofst"))
590: #' #read_fst(file.path(tempdir(), "all.ofst"))
595: mergeLibs <- function(df, out_dir = file.path(libFolder(df), "ofst_merged"), mode = "all",
617:     write_fst(ofst_merge(specific_paths, specific_names, keep_all_scores), save_path)
671:       fext[compressed] <-file_ext(file_path_sans_ext(files[compressed],
745: #' ## Path above is default path, so no dir argument needed
749: #' #list.experiments(dir = "MY/CUSTOM/PATH)
754:   experiments <- list.files(path = dir, pattern = "\\.csv")
757:     if (dir.exists(cbu.path)) { # If on UIB SERVER
758:       dir <- cbu.path
759:       experiments <- list.files(path = dir, pattern = "\\.csv")
91:   files <- df$filepath
92:   if (length(df$filepath) == 0) stop("df have no filepaths!")
162: #' Get variable name per filepath in experiment
227: #' Get relative paths instead of full. Only use for inspection!
228: #' @return a character vector of paths, or a list of character with 2 paths per,
235: #' filepath(df, "default")
237: #' filepath(df[9,], "default")
239: #' filepath(df[9,], "ofst")
241: #' filepath(df[9,], "pshifted") # <- falls back to ofst
247:     i <- which(df$filepath == x)
306:     if (is.null(input)) stop("filepath type not valid!")
310:   if (all(lengths(paths) == 1)) {
311:     paths <- unlist(paths)
313:   return(paths)
405:         libs <- lapply(seq_along(paths),
406:                        function(i, paths, df, chrStyle, param, strandMode, varNames, verbose) {
408:                            fimport(paths[i], chrStyle, param, strandMode)
409:                          }, paths = paths, chrStyle = chrStyle, df = df,
413:         libs <- bplapply(seq_along(paths),
414:                          function(i, paths, df, chrStyle, param, strandMode, varNames, verbose) {
416:                            fimport(paths[i], chrStyle, param, strandMode)
417:                          }, paths = paths, chrStyle = chrStyle, df = df,
542:                        remove.file_ext(df$filepath[i], basename = TRUE),
10: #' else must be a character vector of length 1 or equal length as filepaths.
15:     if(length(slot) != 1 & length(slot) != length(filepaths)) {
95:     files <- c(files, reversePaths)
114:     stop("Duplicated filepaths in experiment!")
214: #' Get filepaths to ORFik experiment
218: #' default filepaths without warning. \cr
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, 
BiRewire:R/BiRewire.R: [ ]
635:     			PATH<-paste(path,'/',i,'/',sep='')
710:     			PATH<-paste(path,'/',i,'/',sep='')
916:     			PATH<-paste(path,'/',i,'/',sep='')
601: birewire.sampler.bipartite<-function(incidence,K,path,max.iter="n", accuracy=0.00005,verbose=TRUE,MAXITER_MUL=10,exact=FALSE,write.sparse=TRUE)
617: 		if(!file.exists(path))
619:     					dir.create(path) 
636: 				if(!file.exists(PATH))
638:       					dir.create(PATH)
647: ...(21 bytes skipped)...LUTO(as.simple_sparse_array(as.matrix(get.incidence(incidence,names=TRUE,sparse=FALSE))),file=paste(PATH,'network_',(i-1)*1000+j,sep=''))
650: 										write.table(get.incidence(incidence,names=TRUE,sparse=FALSE),file=paste(PATH,'network_',(i-1)*1000+j,sep=''),append=F)
656: ...(4 bytes skipped)...						write_stm_CLUTO(as.simple_sparse_array(as.matrix(incidence,names=TRUE,names=TRUE)),file=paste(PATH,'network_',(i-1)*1000+j,sep=''))
659: 										write.table(incidence,file=paste(PATH,'network_',(i-1)*1000+j,sep=''),append=FALSE)
676: birewire.sampler.undirected<-function(adjacency,K,path,max.iter="n", accuracy=0.00005,verbose=TRUE,MAXITER_MUL=10,exact=FALSE,write.sparse=TRUE)
692: 		if(!file.exists(path))
694:     					dir.create(path) 
711: 				if(!file.exists(PATH))
713:       					dir.create(PATH)
722: ...(21 bytes skipped)...LUTO(as.simple_sparse_array(as.matrix(get.adjacency(adjacency,names=TRUE,sparse=FALSE))),file=paste(PATH,'network_',(i-1)*1000+j,sep=''))
725: 										write.table(get.adjacency(adjacency,sparse=FALSE,names=TRUE),file=paste(PATH,'network_',(i-1)*1000+j,sep=''),append=F)
731: 										write_stm_CLUTO(as.simple_sparse_array(as.matrix(adjacency)),file=paste(PATH,'network_',(i-1)*1000+j,sep=''))
734: 										write.table(adjacency,file=paste(PATH,'network_',(i-1)*1000+j,sep=''),append=FALSE)
891: birewire.sampler.dsg<-function(dsg,K,path,delimitators=list(negative='-',positive='+'),exact=FALSE,verbose=TRUE, max.iter.pos='n',max.iter.ne...(59 bytes skipped)...
901: 		if(!file.exists(path))
903:     					dir.create(path) 
917: 				if(!file.exists(PATH))
919:       					dir.create(PATH)
925:     						dsg=birewire.rewire.dsg(dsg=dsg,delimitators=delimitators,exact=exact,path=paste(PATH,'network_',(i-1)*1000+j,'.sif',sep=''),
947: ...(22 bytes skipped)...unction(dsg,exact=FALSE,verbose=1,max.iter.pos='n',max.iter.neg='n',accuracy=0.00005,MAXITER_MUL=10,path=NULL,delimitators=list(positive='+',negative= '-'),check_pos_neg=FALSE,in_sampler=FALSE)
963: 	if(!is.null(path))
975: ...(144 bytes skipped)... return the DSG (in order to not interrupt the chain) but it will not be saved (if you pass a valid path")	
980: 			birewire.save.dsg(g=birewire.build.dsg(dsg,delimitators),file=path)
1105: birewire.load.dsg<-function(path)
1109: 		return(unique(read.table(path,stringsAsFactors=F)))
crlmm:R/cnrma-functions.R: [ ]
42: 	path <- system.file("extdata", package=pkgname)
1391: 	path <- system.file("extdata", package=pkgname)
43: 	##multiple.builds <- length(grep("hg19", list.files(path)) > 0)
44: 	snp.file <- list.files(path, pattern="snpProbes_hg")
47: 		snp.file <- list.files(path, pattern="snpProbes.rda")
51: 			snp.file <- list.files(path, pattern="snpProbes_hg")
61: ##		load(file.path(path, "snpProbes.rda"))
62: ##	} else load(file.path(path, paste("snpProbes_", genome, ".rda", sep="")))
63: 	load(file.path(path, snp.file))
71: 		load(file.path(path, cn.file))
73: 		##			load(file.path(path, "cnProbes.rda"))
74: 		##		} else load(file.path(path, paste("cnProbes_", genome, ".rda", sep="")))
1392: 	load(file.path(path, "cnProbes.rda"))
1393: 	load(file.path(path, "snpProbes.rda"))
1465: 				   path,
1468: 	load(file.path(path, "snpFile.rda"))
1470: 	load(file.path(path, "cnFile.rda"))
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$filepaths,
816:         Brick = Brick_filepaths[1], return.what = "group_handle")
SeqArray:R/Internal.R: [ ]
697:     path <- name.gdsn(node, TRUE)
175: .var_path <- function(var.name, prefix)
173: # Variable path
700:         varname <- .var_path(substring(varname, 2L), "@")
701:     fullvarname <- paste(path, varname, sep="/")
709:         if (path == "genotype")
712:             varname2 <- path
726:             if (path == "genotype")
ChemmineR:R/AllClasses.R: [ ]
1734: 	path <- conMA(x, exclude="H")
1979:             path <- .linearCon(x=con)
1800: 	conpath <- t(sapply(path, function(x) x[c(1, length(x))]))
1812: 	pathlist <- cyclist$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()
dir.expiry:R/clearDirectories.R: [ ]
99:     path <- file.path(dir, version)
111:     acc.path <- file.path(dir, expfile)
5: #' @param dir String containing the path to a package cache containing any number of versioned directories.
16: #' If the last access date is too old, the corresponding subdirectory in \code{path} is treated as expired and is deleted.
40: #' version.dir <- file.path(cache.dir, version)
70:     plock <- .plock_path(dir)
100:     vlock <- .vlock_path(path)
112:     last.used <- as.integer(read.dcf(acc.path)[,"AccessDate"])
116:         unlink(acc.path, force=TRUE)
117:         unlink(paste0(acc.path, lock.suffix), force=TRUE)
118:         unlink(path, recursive=TRUE, force=TRUE)
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)))
rtracklayer:R/ucsc.R: [ ]
1606:     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.*$", "", sapply(getNodeSet(doc, label_path), xmlValue))
367:   tracks <- unlist(getNodeSet(doc, track_path))
1581:             upload <- fileUpload(path(object), "text/plain")
1607:     if (is.na(path))
1610:         path <- paste0(path, '?redirect="manual"')
1612:     paste(object@url, path, sep="")
RBGL:R/interfaces.R: [ ]
308:     path <- f
305: extractPath <- function(s, f, pens) {
307: # linear path from node s to node f
311:     while (path[1] != s) {
312:         if (i > maxl)        # no path available
314:             path <- "NA" 
317:         path <- c(pens[f], path)
321:     as.numeric(path)
373:          # obtain weights in g for path of nodes in char vec nl
394:                           path_detail=as.vector(ans[[i]]), 
182:     ans <- .Call("BGL_dijkstra_shortest_paths_D", 
365:                    nG[extractPath(nodeind(thiss), nodeind(thisf[j]), curdi)]
410:     ans <- .Call("BGL_johnson_all_pairs_shortest_paths_D", 
428:     ans <- .Call("BGL_floyd_warshall_all_pairs_shortest_paths_D", 
450:     ans <- .Call("BGL_bellman_ford_shortest_paths", 
478:     ans <- .Call("BGL_dag_shortest_paths", 
VarCon:inst/extdata/app.R: [ ]
235:   path <- reactiveValues(
240:   path2 <- reactiveValues(
244:   path3 <- reactiveValues(
24:   uploadReferenceDNA <- eventReactive(path$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
EnMCB:R/utils.R: [ ]
219:           path = getwd(),dpi = 300,units = "in",width = 10, height = 5,
275:            path = getwd(),dpi = 300,units = "in",width = 5, height = 4.5,
388:                     path = getwd(),dpi = 300,units = "in",width = 5, height = 5,
18:   BiocFileCache::bfcadd(ca, rname="IlluminaHumanMethylation450kanno.ilmn12.hg19", fpath=tf,
191:          path = getwd(),dpi = 300,units = "in",width = 5, height = 4.5,
321:          path = getwd(),dpi = 300,units = "in",width = 5, height = 4.5,
8:   if (length(q$rpath)>0) return(readRDS(rev(q$rpath)[1])) # if multiple, use last
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
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$ID, ".png\" height=\"63\" width=\"125\">", 
639:     regionID, clr = NA, annotate = TRUE, scores = TRUE, path) {
642:     path = paste("./", path, "/", sep = "")
651:         clr = clr, annotate = annotate, scores = scores, path = path)
656:     annotate = TRUE, path) {
659:     path = paste("./", path, "/", sep = "")
673:             annotate = annotate, scores = TRUE, path = path)
679:     ID = "CustomRegion", clr = NA, annotate = TRUE, path) {
680:     path = paste("./", path, "/", sep = "")
693:             annotate = annotate, scores = TRUE, path = path)
699:     ID = NA, clr = NA, annotate = TRUE, scores = NA, path) {
730:     png(paste(path, ID, ".png", sep = ""), width = 500, height = 250)
750:     pdf(paste(path, ID, ".pdf", sep = ""), width = 10, height = 10)
IONiseR:R/fast5Readers.R: [ ]
375:         path <- paste0("/Analyses/Basecall_", d, "_000/Log")
376:         exists <- .groupExistsObj(fid, group = path)
378:             did <- H5Dopen(fid, path)
GSCA:inst/shiny/server.R: [ ]
357:                         path <- system.file("extdata",package=paste0(input$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)...
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)
peakPantheR:R/methods_peakPantheRAnnotation.R: [ ]
1724:             device = "png", path = saveFolder, dpi = 100, width = 21,
1853:     path_cpdMeta <- paste(saveFolder, "/", annotationName,
1871:     path_specMeta <- paste(saveFolder, "/", annotationName,
1883:         path_var <- paste(saveFolder, "/", annotationName, "_", i, ".csv",
1918:     path_summary <- paste(saveFolder, "/", annotationName, "_summary.csv",
1116:     .filepath <- x@filepath[i]
1863:     tmp_filepath <- filepath(object)
2045:     .spectraPaths <- resSpectra$spectraPaths
2182: resetAnnot_spectraPathMetadata <- function(previousAnnotation, spectraPaths,
2186:         .spectraPaths <- filepath(previousAnnotation)
1076:     return(tools::file_path_sans_ext(basename(object@filepath)))
1386: #' @param saveFolder (str) Path of folder where annotationParameters_summary.csv
1399: #' spectraPaths <- c('./path/file1', './path/file2', './path/file3')
1610: #' @param saveFolder (str) Path of folder where annotationParameters_summary.csv
1703:     # @param saveFolder (str) Path where plots will be saved
1727:         # output path
1758: #' @param saveFolder (str) Path of folder where the annotation result csv will
1855:     utils::write.csv(tmp_outCpdMeta, file = path_cpdMeta, row.names = FALSE,
1858:     if (verbose) { message("Compound metadata saved at ", path_cpdMeta) }
1873:     utils::write.csv(tmp_outSpecMeta, file = path_specMeta, row.names = FALSE,
1876:     if (verbose) { message("Spectra metadata saved at ", path_specMeta) }
1885:         utils::write.csv(tmp_var, file = path_var, row.names = TRUE,
1889:             message("Peak measurement \"", i, "\" saved at ", path_var)
1920:     utils::write.csv(tmp_summary, file = path_summary, row.names = TRUE,
1923:     if (verbose) { message("Summary saved at ", path_summary) }
24:             length(object@filepath), " samples. \n", sep = "")
53: # @filepath length. Slot type is not checked as \code{setClass} enforces it.
78: #' # Paths to spectra files
119: #' # Paths to spectra files
160: #' # Paths to spectra files
206: #' # Paths to spectra files
253: #' # Paths to spectra files
286: # filepath
287: setGeneric("filepath", function(object, ...) standardGeneric("filepath"))
288: #' filepath accessor
290: #' @return (str) A character vector of file paths, of length number of spectra
293: #' @aliases filepath
300: #' # Paths to spectra files
319: #' filepath(annotation)
324: setMethod("filepath", "peakPantheRAnnotation", function(object) {
325:     object@filepath
343: #' # Paths to spectra files
386: #' # Paths to spectra files
430: #' # Paths to spectra files
472: #' # Paths to spectra files
512: #' # Paths to spectra files
552: #' # Paths to spectra files
593: #' # Paths to spectra files
636: #' # Paths to spectra files
695: #' # Paths to spectra files
742: #' # Paths to spectra files
788: #' # Paths to spectra files
817: #' nbSamples accessor established on filepath
828: #' # Paths to spectra files
852:     return(length(object@filepath))
868: #' # Paths to spectra files
913: #' # Paths to spectra files
946:     nbSample <- length(object@filepath)
953:         rownames(tmpAnnotation) <- object@filepath
961:         rownames(tmpAnnotation) <- object@filepath
977:     rownames(tmpAnnotation) <- object@filepath
1000: #' # Paths to spectra files
1041: #' filename accessor by spliting filepath
1052: #' # Paths to spectra files
1134:         FIR = .FIR, uROI = .uROI, filepath = .filepath,
1214: #' # Paths to spectra files
1398: #' # Paths to spectra files
1416: #' savePath        <- tempdir()
1419: #' outputAnnotationParamsCSV(emptyAnnotation, saveFolder=savePath, verbose=TRUE)
1488: #' # Paths to spectra files
1627: #' # Paths to spectra files
1652: #' savePath1       <- tempdir()
1653: #' outputAnnotationDiagnostic(annotation, saveFolder=savePath1, savePlots=FALSE,
1771: #' # Paths to spectra files
1796: #' savePath1       <- tempdir()
1797: #' outputAnnotationResult(annotation, saveFolder=savePath1,
1867:     tmp_outSpecMeta <- data.frame(filepath = tmp_filepath,
1941: #' the slots (\code{filepath} (from \code{spectraPaths}), \code{ROI},
1947: #' @param spectraPaths NULL or a character vector of spectra file paths, to set
1972: #' (\code{cpdID}, \code{cpdName}, \code{ROI}, \code{filepath}, \code{TIC},
1984: #' # Paths to spectra files
2188:             message("  Previous \"filepath\" value kept")
2415: #' # Paths to spectra files
80: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
95: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
121: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
136: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
162: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
177: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
208: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
223: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
255: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
270: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
302: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
317: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
345: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
360: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
388: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
403: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
432: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
447: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
474: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
489: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
514: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
529: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
554: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
569: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
595: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
610: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
638: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
653: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
697: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
712: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
744: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
759: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
790: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
805: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
830: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
845: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
870: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
885: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
915: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
930: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
1002: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
1017: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
1054: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
1069: #' annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
1216: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
1231: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
1412: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
1490: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
1505: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
1629: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
1644: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
1773: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
1788: #' emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths,
1931:     function(previousAnnotation, spectraPaths = NULL, targetFeatTable = NULL,
1939: #' \code{spectraPaths}) or compounds (\code{targetFeatTable}) are passed, the
1986: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
2000: #' smallAnnotation  <- peakPantheRAnnotation(spectraPaths=spectraPaths, 
2011: #' newSpectraPaths  <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
2015: #'                                     spectraPaths=newSpectraPaths,
2026:     function(previousAnnotation, spectraPaths, targetFeatTable, uROI, FIR,
2042:     # spectraPaths, spectraMetadata
2043:     resSpectra <- resetAnnot_spectraPathMetadata(previousAnnotation,
2044:                                         spectraPaths, spectraMetadata, verbose)
2061:     # Create new object In all case (old or new value) spectraPaths and
2063:     peakPantheRAnnotation(spectraPaths = .spectraPaths,
2181: # resetAnnotation spectraPaths, spectraMetadata
2185:     if (all(is.null(spectraPaths))) {
2208:         .spectraPaths <- spectraPaths
2210:             message("  New \"spectraPaths\" value set")
2229:     return(list(spectraPaths = .spectraPaths,
2417: #' spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'),
2431: #' smallAnnotation  <- peakPantheRAnnotation(spectraPaths=spectraPaths,
pathVar:R/pipeline.final.R: [ ]
634:     path <- pvalue_results@pwayCounts[[pathway]]
780:     path1 <- pvalue_results@pwayCounts1[[pathway]]
781:     path2 <- pvalue_results@pwayCounts2[[pathway]]
99: pathVarOneSample <- function(dat.mat, pways, test = c("chisq", "exact"), varStat = c("sd", 
131:     pathwayCounts <- lapply(lapply(olap.pways, function(x) table(x, deparse.level = 0)), function(x) if (len...(10 bytes skipped)...
206: pathVarTwoSamplesCont <- function(dat.mat, pways, groups, boot = 1000, varStat = c("sd", "mean", 
290: pathVarTwoSamplesDisc <- function(dat.mat, pways, groups, perc = c(1/3, 2/3), test = c("chisq", 
344:     pathwayCounts1 <- lapply(lapply(olap.pways1, function(x) table(x, deparse.level = 0)), 
354:     pathwayCounts2 <- lapply(lapply(olap.pways2, function(x) table(x, deparse.level = 0)), 
853:     pathDat1 <- as.data.frame(table(mixDat1))
855:     pathDat2 <- as.data.frame(table(mixDat2))
943:     pathname <- sapply(listPath, function(x) if (length(unlist(strsplit(x, "/"))) > 1) {
796:     plotPath1 <- ggplot(path1, aes(x = Cluster, y = Number_of_genes, fill = Cluster)) + geom_bar(stat = "identity", 
800:     plotPath2 <- ggplot(path2, aes(x = Cluster, y = Number_of_genes, fill = Cluster)) + geom_bar(stat = "identity", 
659:         plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
738:             plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
806:         plotPathway1 <- plotPath1 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
809:         plotPathway2 <- plotPath2 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
871:     plotPathDat1 <- ggplot(path...(136 bytes skipped)...("Number of genes") + theme(legend.position = "none") + ggtitle("Group 1") + xlab("") + ylim(0, max(pathDat1[,2], pathDat2[,2]))
872:     plotPathDat2 <- ggplot(path...(136 bytes skipped)...("Number of genes") + theme(legend.position = "none") + ggtitle("Group 2") + xlab("") + ylim(0, max(pathDat1[,2], pathDat2[,2]))
26: ...(25 bytes skipped)...ptions are TRUE or FALSE. If TRUE then the first column of the tab delimited file is expected to be path IDs. If FALSE, then the first column is expected to be pathway names.
645:     path <- as.data.frame(path)
646:     colnames(path) <- c("Cluster", "Number_of_genes")
653:     d <- ggplot(path, aes(x = Cluster, y = Number_of_genes, fill = Cluster)) + geom_bar(stat = "identity", 
663:             plotPathway <- plotPathway + annotate("text", x = sigCat, y = path[sigCat + 0.1, 
789:     path1 <- as.data.frame(path1)
790:     colnames(path1) <- c("Cluster", "Number_of_genes")
792:     path2 <- as.data.frame(path2)
793:     colnames(path2) <- c("Cluster", "Number_of_genes")
794:     yLimMax <- max(path1[, 2], path2[, 2])
813:             plotPathway1 <- plotPathway1 + annotate("text", x = sigCat, y = path1[sigCat + 
815:             plotPathway2 <- plotPathway2 + annotate("text", x = sigCat, y = path2[sigCat + 
20: #makeDBList put your pathways text file into a list
21: #pway$PATHNAME is the pathway names from the file
22: #pway$PATHID is a vector of pathway ID numbers is there are any. Otherwise it will be a vector filled with NA
23: #pway$GENES is a list of vectors, where each vector are the genes for a single pathway
25: #file is a tab delimited text file, where first and second columns are pathwayID and pathway name. The third (or last column is the genes associated with each pathway, seperated by commas.
33:         pways$PATHNAME <- as.vector(pwayTable[, 2])
34:         pways$PATHID <- as.vector(pwayTable[, 1])
35:         pways$GENES <- list(length(pways$PATHID))
37:         for (i in 1:length(pways$PATHID)) {
43:                 pways$PATHID <- pways$PATHID[-i]
44:                 pways$PATHNAME <- pways$PATHNAME[-i]
49:         pways$PATHNAME <- as.vector(pwayTable[, 1])
50:         pways$PATHID <- rep("NA", length(pways$PATHNAME))
51:         pways$GENES <- list(length(pways$PATHID))
53:         for (i in 1:length(pways$PATHID)) {
59:                 pways$PATHID <- pways$PATHID[-i]
60:                 pways$PATHNAME <- pways$PATHNAME[-i]
64:     names(pways$GENES) <- pways$PATHNAME
69: #pathVarOneSample
73: # 3. For each pathway, we extract the gene in our dataset and in which cluster they belong.
74: # 4. For each pathway, we look how the gene counts in each category and compare it to the reference counts with all th...(59 bytes skipped)...
78: # Output 1: tablePway columns are :pathway name, path...(46 bytes skipped)...or exact test,the percentage of genes from our dataset related to the total number of genes in each pathway, the number of genes from our dataset inside the pathway and the total number of genes inside the pathway
79: #Output 2: NAPways corresponds to the pathway names of the pathway having less than 10 genes for the Chi-Squared or also more than 500 genes for the exact tes.
80: # Output 3: genesInPway correspond to each pathway with the genes from the datasets belonging to it and in which cluster they were classsify.
83: # Output 6: pwayCounts is the genes counts of the each pathway in each cluster.
91: #Input 2: pways contains the pathways of interest (KEGG, REACTOME, etc...) in the same format that makeDBList
103:     # check if any GENES are in the pathway.
106:         stop("None of the genes in the data set are found in the given gene set or pathway")
125:     # olap.pways contains the genes are in each pathway with their cluster number
127:     names(olap.pways) <- pways$PATHNAME
130:     # list of tables of the number of genes in each cluster per pathway
140:     # Chi-Square or Exact test to compare the reference and the pathway distribution
142:         # chisq test and ajustment of the pvalue for each pathway
143:         pvals.pways <- sapply(pathwayCounts, function(x) if (sum(x) >= 10) {
154:         # Exact test and ajustment of the pvalue for each pathway
157:         # We perform the multinomial test on the pathway containing between 10 and 500 genes because a bigger number will involve too many possibilities ...(11 bytes skipped)...
158:         pvals.pways <- sapply(pathwayCounts, function(x) if (sum(x) >= 10 & sum(x) < 500) {
170:     xtab <- data.table(PwayName = pways$PATHNAME[not_na], PwayID = pways$PATHID[not_na], APval = apvals.pways, 
172:         NumOfGenesFromDataSetInPathway = lengths(olap.pways[not_na]), PathwaySize = pways$SIZE[not_na])
176: ...(50 bytes skipped)...ab, NAPways=pval.NA, genesInPway=olap.pways, refProb=pexp, refCounts=pexp * length(mix), pwayCounts=pathwayCounts, numOfClus=nmix, varStat=varStat, genesInClus=mix, var=vs)
181: #pathVarTwoSamplesCont
184: # 2. For each pathway, we extract the gene in our dataset.
185: # 3. For each pathway, we look how its genes are distributed and compare the 2 groups using the bootstrap Kolmogorov-S...(12 bytes skipped)...
189: # Output 1: tablePway columns are :pathway name, pathway IDs, adjusted p-value ffrom the boot KS test, the number of genes from our dataset inside the pathway and the total number of genes inside the pathway.
190: #Output 2: NAPways corresponds to the pathway names of the pathway having no genes inside the dataset.
191: # Output 3: genesInPway correspond to the genes from the dataset belonging to each pathway
200: #Input 2: pways contains the pathways of interest (KEGG, REACTOME, etc...) in the same format that makeDBList
209:     # check if any GENES are in the pathway
212:         stop("None of the genes in the data set are found in the given gene set or pathway")
236:     # olap.pways contains the genes from the dataset in each pathway
238:     names(olap.pways) <- pways$PATHNAME
239:     # We compare the two densities (one for each group) of the genes of each pathway with the Kolmogorov-Smirnow test.       
251:     xtab <- data.table(PwayName = pways$PATHNAME[not_na], PwayID = pways$PATHID[not_na], APval = apvals, 
253:         NumOfGenesFromDataSetInPathway = lengths(olap.pways[not_na]), PathwaySize = pways$SIZE[not_na])
261: #pathVarTwoSamplesDisc
265: # 3. For each pathway, we extract the gene in our dataset and in which cluster they belong.
266: # 4. For each pathway, we look at the gene counts in each category and compare the 2 samples to each other with all th...(60 bytes skipped)...
269: # Output 1: tablePway columns are :pathway name, path...(11 bytes skipped)...justed p-value, the percentage of genes in our dataset related to the total number of genes in each pathway, the number of genes from our dataset inside the pathway and the total number of genes inside the pathway.
270: #Output 2: NAPways corresponds to the pathway names of the pathway having no genes inside the dataset.
271: # Output 3: genesInPway1 corresponds to the genes from the dataset belonging to each pathway in the first sample
272: # Output 4: genesInPway2 corresponds to the genes from the dataset belonging to each pathway in the second sample
273: # Output 5: pwayCounts1 corresponds to a list of tables of the number of genes in each cluster per pathway for group 1
274: # Output 6: pwayCounts2 corresponds to a list of tables of the number of genes in each cluster per pathway for group 2
283: #Input 2: pways contains the pathways of interest (KEGG, REACTOME, etc...) in the same format that makeDBList
294:     # check if any GENES are in the pathway
297:         stop("None of the genes in the data set are found in the given gene set or pathway")
338:     # olap.pways contains the genes from the dataset in each pathway
340:     names(olap.pways1) <- pways$PATHNAME
342:     names(olap.pways2) <- pways$PATHNAME
343:     # list of tables of the number of genes in each cluster per pathway
353:     # list of tables of the number of genes in each cluster per pathway
364:         # chisq test and ajustment of the pvalue for each pathway
365:         pvals.pways <- sapply(pways$PATHNAME, function(x) if (sum(pathwayCounts1[x][[1]]) >= 
367:             exp.val <- pathwayCounts1[x][[1]]  #forgot the.val
368:             chi <- sum((pathwayCounts2[x][[1]] - exp.val)^2/exp.val)
374:         pval.NA <- pways$PATHNAME[-not_na]
377:         # Exact test and ajustment of the pvalue for each pathway
380:         # We perform the multinomial test on the pathway containing between 10 and 500 genes because a bigger number will involve too many possibilities ...(11 bytes skipped)...
381:         pvals.pways <- sapply(pways$PATHNAME, function(x) if (sum(pathwayCounts1[x][[1]]) >= 
382:             10 & sum(pathwayCounts1[x][[1]]) < 500) {
383:             pexp <- pathwayCounts1[x][[1]]/sum(pathwayCounts1[x][[1]])
384:             multinomial.test(as.vector(pathwayCounts2[x][[1]]), as.vector(pexp), useChisq = FALSE)$p.value
391:         pval.NA <- pways$PATHNAME[-not_na]
395:     xtab <- data.table(PwayName = pways$PATHNAME[not_na], PwayID = pways$PATHID[not_na], APval = apvals.pways, 
397:         NumOfGenesFromDataSetInPway = lengths(olap.pways1[not_na]), PathwaySize = pways$SIZE[not_na])
402: ...(36 bytes skipped)...", tablePway=xtab, NAPways=pval.NA, genesInPway1=olap.pways1, genesInPway2=olap.pways2, pwayCounts1=pathwayCounts1, pwayCounts2=pathwayCounts2, groups=groups, groupNames=groupNames, var1=var_1, var2=var_2, varStat=varStat)
409: #It is a function that returns the significant pathway(s),which category(ies) from this pathway are significant and which gene(s) belongs to this(ese) category(ies).
413: # Output 1: genesInSigPways1 contains the genes per significant pathway belonging to the significant category.
414: #Output 2: sigCatPerPway contains the category(ies) per pathway that are significant.
418: #Input 1: pvalue_results is result from the pathVarOneSample function
430:         warning("There are no significant pathways. Quitting significant_category function and returning empty object")
434:     # PathName that were significant in xtab.
436:     # The list of table with the number of genes in each cluster from the significant pathways
442:     # results contain the p-value for each category in each pathway computed with the binomial test.
450:     # For each significant pathway we look which category(ies) is are significant and the genes
477: #It is a function that returns the significant pathways and which genes belongs to these #pathways.
481: # Output 1: genesInSigPways1 contains the genes belonging to each significant pathway
485: #Input 1: pvalue_results is result from the pathVarTwoSamplesCont function
493:         warning("There are no significant pathways. Quitting significant_category function and returning empty object")
497:     # Pathways that were significant in xtab.
499:     # Genes from the dataset inside each significant pathway
507: #It is a function that returns the significant pathways and which genes belong to these pathways
511: # Output 1: genesInSigPways1 contains the genes belonging to each significant pathway in significant categories in the first sample
512: # Output 2: genesInSigPways2 contains the genes belonging to each significant pathway in significant categories in the second sample
513: # Output 3: sigCatPerPway contains the significant categories in each pathway
517: #Input 1: pvalue_results is result from the pathVarTwoSamplesDisc function
527:         warning("There are no significant pathways. Quitting significant_category function and returning empty object")
531:     # PathName that were significant in xtab.
533:     # The list of table with the number of genes in each cluster from the significant pathways
540:     # results contain the p-value for each category in each pathway computed with the binomial
550:     # For each significant pathway we look which category(ies) are significant and the genes belonging to this(ese) category(ies). ...(81 bytes skipped)...
576: ...(77 bytes skipped)...es cases and then use sigOneSample, sigTwoSamplesCont, or sigTwoSamplesDisc to find the significant pathways.
582: #Input 1: pvalue_results is result from the pathVarOneSample, pathVarTwoSamplesCont, or pathVarTwoSamplesDisc function.
602: #It is a function that returns the plot of the reference counts along with the plot of a chosen #pathway. This function is made for output from pathVarOneSample.
605: # plot of the reference and a pathway counts
608: #Input 1: pvalue_results is result from the pathVarOneSample function
609: #Input 2: pathway is the chosen pathway you want to plot.
613: #If sig is not NULL, the function will check if the pathway is a significant one and if yes the
619: plotOneSample <- function(pvalue_results, pathway, sig) {
620:     mp <- pathway
621:     # If the name of the pathway is two long it will cut it into two lines in the plot.
644:     # data frame for the pathway distribution
656:     # If the pathway is one of the significant ones, the title will be in red. and the categories, if any, we be high...(24 bytes skipped)...
657:     if (pathway %in% names(category)) {
658:         sigCat <- category[[pathway]]
670:     # plot the reference and pathway counts side by side
676: ...(34 bytes skipped)... plot of the two densities (one for each group) of the statistics (sd, mad, cv or mean) of a chosen pathway. This function is made for output from pathVarTwoSamplesCont.
682: #Input 1: pvalue_results is result from the pathVarTwoSamplesCont function
683: #Input 2: pathway is the chosen pathway you want to plot.
687: #If sig is not NULL, the function will check if the pathway is a significant one and if yes the title will be printed in red.
690: plotTwoSamplesCont <- function(pvalue_results, pathway, sig) {
691:     mp <- pathway
692:     # If the name of the pathway is two long it will cut it into two lines in the plot.
705:     # If the number of genes of the pathway is less than 3, it is not possible to draw a density and it will return an empty plot with this ...(8 bytes skipped)...
706:     if (xtab[PwayName == pathway, NumOfGenesFromDataSetInPathway] < 3) {
717:         genes <- pvalue_results@genesInPway[[pathway]]
726: ...(0 bytes skipped)...        # Plot of the two densities (one for each group) of the variability of the genes inside the pathway.
736:         # If we included the results of sigTwoSamplesCont, it will verify if the pathway is one of them and if yes the title will be printed in red.
737:         if (pathway %in% significant) {
752: ##It is a function that returns 2 plots of the 2 samples for a chosen pathway. This function is made for output from pathVarTwoSamplesDisc.
755: # plot of the 2 samples for a significant pathway
758: #Input 1: pvalue_results is result from the pathVarTwoSamplesDisc function
759: #Input 2: pathway is the chosen pathway you want to plot.
763: #If sig is not NULL, the function will check if the pathway is a significant one and if yes the title will be printed in red.
766: plotTwoSamplesDisc <- function(pvalue_results, pathway, sig) {
767:     mp <- pathway
768:     # If the name of the pathway is two long it will cut it into two lines in the plot.
791:     # data frame for the pathway distribution
803:     # If the pathway is one of the significant ones, the title will be in red. and the categories, if any, we be high...(24 bytes skipped)...
804:     if (pathway %in% names(category)) {
805:         sigCat <- category[[pathway]]
819:         plotPathway1 <- plotPath1 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
821:         plotPathway2 <- plotPath2 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
824:     # plot the reference and pathway counts side by side
854:     colnames(pathDat1) <- c("Cluster", "Number_of_genes")
856:     colnames(pathDat2) <- c("Cluster", "Number_of_genes")
862:     results <- apply(rbind(pathDat2[,2],pathDat1[,2]/pathDat1[,2]/sum(pathDat1[,2])), 2, function(y) multinomial.test(c(y[1], sum(pathDat2[,2]) - y[1]), prob = c(y[2],1 - y[2]))$p.value)
874:         plotPathDat1 <- plotPathDat1 + annotate("text", x = category, y = pathDat1[category +
876:         plotPathDat2 <- plotPathDat2 + annotate("text", x = category, y = pathDat2[category +
895: ...(46 bytes skipped)...rom the one sample or two samples cases and then use plotOneSample or plotTwoSamples for the chosen pathway.
898: # plot of the results of the one or two samples case for a chosen pathway.
901: #Input 1: pvalue_results is the result from the pathVarOneSample, pathVarTwoSamplesCont, or pathVarTwoSamplesDisc function
902: #Input 2: pathway is the chosen pathway you want to plot.
906: #If sig is not NULL, the function will check if the pathway is a significant one. And they will be highlighted in the resulting plot (see plotOneSample or p...(14 bytes skipped)...
909: plotPway <- function(pvalue_results, pathway, sig = NULL) {
912:         plotOneSample(pvalue_results, pathway, sig)
914:         plotTwoSamplesCont(pvalue_results, pathway, sig)
916:         plotTwoSamplesDisc(pvalue_results, pathway, sig)
923: #Save as a pdf the plots for the one or two samples case of the significant pathway or a chosen list of pathway..
926: # Save as a pdf the plots of the significant pathway or a chosen list of pathway.
929: #Input 1: pvalue_results is the result from the pathVarOneSample, pathVarTwoSamplesCont, or pathVarTwoSamplesDisc function
931: #Input 3: listPath is "significant" if you want to save the plots of the significant pathways or can be a list of names of pathway of interest.
934: #If sig is not NULL, the function will check if the pathway is a significant one. And they will be highlighted in the resulting plot (see plotOneSample or p...(14 bytes skipped)...
937: saveAsPDF <- function(pvalue_results, sig, listPath = "significant") {
938:     # If listPath='significant' we will save as pdf all the plots corresponding to the significant pathway from sig. Other wise it will save the pathways given to listPath.
939:     if (listPath[1] == "significant") {
940:         listPath <- names(sig@genesInSigPways1)
942:     # The name of the file will be the pathname where we replace '/' by '_'
948:     # save as PDF all the pathways significant or given in listPath
949:     for (i in 1:length(pathname)) {
950:         pdf(file = paste(pathname[i], ".pdf", sep = ""), width = 10, height = 7)
951:         plotPway(pvalue_results, listPath[i], sig)
959: #It is a function that returns one list of genes for group 1 and one for group 2 of a chosen pathway having their statistics (sd, mad, cv or mean) inside a chosen interval.
962: # Output 1: genes1 contains the genes belonging to the pathway in the given window for group 1.
963: # Output 2: genes2 contains the genes belonging to the pathway in the given window for group 2.
964: # Output 3: genesAll contains the genes from the dataset belonging to the pathway
967: #Input 1: pvalue_results is result from the pathVarTwoSamplesCont function
968: #Input 2: pathway is the chosen pathway.
973: getGenes <- function(pvalue_results, pathway, window) {
978:     genes <- olap.pways[[pathway]]
981:     # Take the genes from group 1 from the pathway belonging to the window
983:     # Take the genes from group 3 from the pathway belonging to the window
985:     # Take all the genes from the pathway
431:         sig <- new("significantPathway", genesInSigPways1=list(), sigCatPerPway=list(), thresPValue=numeric())
471:     sig <- new("significantPathway", genesInSigPways1=genes, sigCatPerPway=category, thresPValue=pvalue)
494:         sig <- new("significantPathway2", genesInSigPways1=list(), thresPValue=numeric())
501:     sig <- new("significantPathway2", genesInSigPways1=genes, thresPValue=pvalue)
528:         sig <- new("significantPathway3", genesInSigPways1=list(), genesInSigPways2=list(), sigCatPerPway=list(), thresPValue=numeric()...(1 bytes skipped)...
570:     sig <- new("significantPathway3", genesInSigPways1=genes1, genesInSigPways2=genes2, sigCatPerPway=category, thresPValue=pvalue)...(0 bytes skipped)...
667:         plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
671:     grid.arrange(arrangeGrob(plotRef, plotPathway, nrow = 1))
742:             plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), 
746:     plot(plotPathway)
825:     grid.arrange(arrangeGrob(plotPathway1, plotPathway2, nrow = 1))
879:         plotPathDat1 <- plotPathDat1 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background...(63 bytes skipped)...
880:         plotPathDat2 <- plotPathDat2 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background...(63 bytes skipped)...
884:         grid.arrange(arrangeGrob(plotPathDat1, plotPathDat2, nrow = 1))
887:         grid.arrange(arrangeGrob(plotPathDat1, plotPathDat2, nrow = 1))
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( /\\-$|\\_$|\\.$/, '' ).concat(\".Unmapped.out.mate1\")\n  m...(463 bytes skipped)...
GeneGeneInteR:R/PLSPM.R: [ ]
207:     Path = inner_results[[2]]
1162:   Path = path_matrix
2176:     Path <- pathmod[[2]]
48:   my.path <- rbind(Gene1,Gene2)
209:     Path_effects = get_effects(Path)
363: check_path <- function(path_matrix)
1173:     path_lm = summary(lm(Y_lvs[,k1] ~ Y_lvs[,k2]))
1202:   path_effects = as.list(seq_len(lvs-1))
2177:     path.orig <- as.vector(Path[path_matrix==1])
2179:     Path.efs <- get_effects(Path)
2186:     path.labs <- NULL
2175:     pathmod <- get_paths(path_matrix, Y.lvs)
2194:     PATHS <- matrix(NA, bootnum, sum(path_matrix))
1805: get_path_scheme <- function(path_matrix, LV)
1156: get_paths <-  function(path_matrix, Y_lvs, full=TRUE)
1207:     indirect_paths = matrix(c(0,0,0,0), 2, 2)
1208:     total_paths = Path
55:   try(mod1 <- plspm(XCases,my.path,my.blocks, modes = my.modes), silent=TRUE)
59:   	res <- list(statistic=NA,p.value=NA,method="Partial Least Squares Path Modeling",parameter=list.param)
64:   try(mod0 <- plspm(XControls,my.path,my.blocks, modes = my.modes),silent=TRUE)
68:   	res <- list(statistic=NA,p.value=NA,method="Partial Least Squares Path Modeling",parameter=list.param)
91: 		try(mod1 <- plspm(XCases,my.path,my.blocks, modes = my.modes), silent=TRUE)
93: 		try(mod0 <- plspm(XControls,my.path,my.blocks, modes = my.modes),silent=TRUE)
110: #	res <- list(statistic=stat,p.value=pval,method="Partial Least Squares Path Modeling",parameter=list.param)
123: 		method="Gene-based interaction based on Partial Least Squares Path Modeling",
138:   function(Data, path_matrix, blocks, modes = NULL, scaling = NULL,  
146:     valid = check_args(Data=Data, path_matrix=path_matrix, blocks=blocks, 
153:     path_matrix = valid$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, 
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,
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)})
EpiMix:R/TCGA_Download_Preprocess.R: [ ]
1643:     path <- eh[[hub_id]]
176:         nameForDownloadedFileFullPath <- paste0(saveDir, nameForDownloadedFile)
41: #' @param saveDir path to directory to save downloaded files.
50: #' @return DownloadedFile path to directory with downloaded files.
405: #' @param METdirectory path to the 27K or 450K data
1497: #' @param TargetDirectory Path to save the sample.info. Default: ''.
1636: #' @return local file path where the lncRNA expression data are saved
1644:     return(path)
7: #' @return list with paths to downloaded files for both 27k and 450k methylation data.
116:         # warnMessage <- paste0('\nNot returning any viable url data paths
193:                 untar(nameForDownloadedFileFullPath, exdir = saveDir)
965: #' @return list with paths to downloaded files for gene expression.
onlineFDR:renv/activate.R: [ ]
167:       path <- tryCatch(method(version), error = identity)
591:     path <- Sys.getenv("RENV_PATHS_LIBRARY", unset = NA)
681:     path <- renv_bootstrap_paths_renv("profile", profile = FALSE)
122:     lockpath <- Sys.getenv("RENV_PATHS_LOCKFILE", unset = "renv.lock")
746:     descpath <- file.path(path, "DESCRIPTION")
909:   libpath <- file.path(root, prefix)
727:   renv_bootstrap_path_absolute <- function(path) {
736:   renv_bootstrap_paths_renv <- function(..., profile = TRUE, project = NULL) {
54:   # mask 'utils' packages, will come first on the search path
168:       if (is.character(path) && file.exists(path))
169:         return(path)
277:     urls <- file.path(repos, "src/contrib/Archive/renv", name)
278:     destfile <- file.path(tempdir(), name)
303:     # if the user has provided the path to a tarball via
313:       tarball <- file.path(tarball, name)
329:     fmt <- "* Bootstrapping with tarball at path '%s'."
361:     url <- file.path("https://api.github.com/repos/rstudio/renv/tarball", version)
363:     destfile <- file.path(tempdir(), name)
389:     r <- file.path(bin, exe)
393:       "-l", shQuote(path.expand(library)),
394:       shQuote(path.expand(tarball))
428:     # build list of path components
581:     # otherwise, disambiguate based on project's path
592:     if (!is.na(path))
593:       return(paste(c(path, prefix), collapse = "/"))
595:     path <- renv_bootstrap_library_root_impl(project)
596:     if (!is.null(path)) {
598:       return(paste(c(path, prefix, name), collapse = "/"))
614:       return(file.path(userdir, "library"))
682:     if (!file.exists(path))
686:     contents <- readLines(path, warn = FALSE)
702:       return(file.path("profiles", profile, "renv"))
729:     substr(path, 1L, 1L) %in% c("~", "/", "\\") || (
730:       substr(path, 1L, 1L) %in% c(letters, LETTERS) &&
731:       substr(path, 2L, 3L) %in% c(":/", ":\\")
738:     root <- if (renv_bootstrap_path_absolute(renv)) NULL else project
744:   renv_bootstrap_project_type <- function(path) {
772:     path.expand(chartr("\\", "/", dir))
792:         return(file.path(root, "R/renv"))
797:       file.path(Sys.getenv("LOCALAPPDATA"), "R/cache/R/renv")
902:   # construct path to library root
123:     if (!file.exists(lockpath))
126:     lockfile <- tryCatch(renv_json_read(lockpath), error = identity)
444:     prefix <- Sys.getenv("RENV_PATHS_PREFIX", unset = NA)
449:     auto <- Sys.getenv("RENV_PATHS_PREFIX_AUTO", unset = NA)
577:     asis <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT_ASIS", unset = "FALSE")
601:     renv_bootstrap_paths_renv("library", project = project)
607:     root <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT", unset = NA)
657:   renv_bootstrap_load <- function(project, libpath, version) {
660:     if (!requireNamespace("renv", lib.loc = libpath, quietly = TRUE))
737:     renv <- Sys.getenv("RENV_PATHS_RENV", unset = "renv")
747:     if (!file.exists(descpath))
751:       read.dcf(descpath, all = TRUE),
908:   # construct full libpath
912:   if (renv_bootstrap_load(project, libpath, version))
922:   bootstrap(version, libpath)
929:   if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) {
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"
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$UpdatedPath <-
1641:     updateCentroidsPaths(data = data,
1655:       updateCentroidsPaths(data = res1[[1]],
2229: #' updateCentroidsPaths
famat:R/compl_data.R: [ ]
568:         path<-h[2]
710:         path<-paste(stringr::str_sub(k, 1, 3),
858:             path<-stringr::str_split(s[1], "__")[[1]]
366:     notin_path<-vapply(elem_names, function(e){
369:         nb_path<-length(first_item[first_item %in% "X"])
387:     kegg_path<-pathways[stringr::str_sub(pathways, 1, 3) == "hsa"]
390:     path_walks_k<-vapply(kegg_path, function(x){
396:     wp_path<-pathways[stringr::str_sub(pathways, 1, 2) == "WP"]
397:     path_walks_w<-vapply(wp_path, function(x){
404:     path_walks_r<-vapply(first_walks_r, function(x){
425:     path_walks<-rbind(final_walks_r, path_walks_k,path_walks_w)
536:     cluster_elem<-save_cluster_elem<-listele[[1]];notin_path<-listele[[2]]
585:                 path_inter<-tagged[tagged$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
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))
SPIA:R/spia.R: [ ]
111:     path<-names(datp)[i]
62:   path.names<-NULL
51:   datpT=.myDataEnv[["path.info"]]
79:     path.names<-c(path.names,datpT[[jj]]$title)
84:   names(path.names)<-names(datpT)
86:   tor<-lapply(datp,function(d){sum(abs(d))})==0 | hasR | is.na(path.names)
88:   path.names<-path.names[!tor]
112:     M<-datp[[path]]
203:       cat(paste("Done pathway ",i," : ",substr(path.names[names(datp)[i]],1,30),"..",sep=""))
215:   Name=path.names[names(datp)]
1: spia<-function(de=NULL,all=NULL,organism="hsa",data.dir=NULL,pathids=NULL,nB=2000,plots=FALSE,verbose=TRUE,beta=NULL,combine="fisher"){
33:       cat("The KEGG pathway data for your organism is not present in the extdata folder of the SPIA package!!!")
53:   if (!is.null(pathids)){
54:     if( all(pathids%in%names(datpT))){
55:       datpT=datpT[pathids]
57:       stop( paste("pathids must be a subset of these pathway ids: ",paste(names(datpT),collapse=" "),sep=" "))
123:       KEGGLINK[i]<-paste("http://www.genome.jp/dbget-bin/show_pathway?",organism,names(datp)[i],"+",gnns,sep="")
133:         plot(X,pfs-X,main=paste("pathway ID=",names(datp)[i],sep=""),
186:           plot(density(pfstmp,bw=bwidth),cex.lab=1.2,col="black",lwd=2,main=paste("pathway ID=",names(datp)[i],"  P PERT=",round(pb[i],5),sep=""),
190:           plot(as.numeric(names(pfsTab)), as.numeric(pfsTab), cex.lab=1.2,col="black",main=paste("pathway ID=",names(datp)[i],"  P PERT=",round(pb[i],5),sep=""),
206:   }#end for each pathway
RProtoBufLib:inst/include/cytolib/GatingSet.pb.h: [ ]
6074: inline const std::string& BOOL_GATE_OP::path(int index) const {
6122: BOOL_GATE_OP::path() const {
6068: inline int BOOL_GATE_OP::path_size() const {
6071: inline void BOOL_GATE_OP::clear_path() {
6078: inline std::string* BOOL_GATE_OP::mutable_path(int index) {
6082: inline void BOOL_GATE_OP::set_path(int index, const std::string& value) {
6086: inline void BOOL_GATE_OP::set_path(int index, std::string&& value) {
6090: inline void BOOL_GATE_OP::set_path(int index, const char* value) {
6095: inline void BOOL_GATE_OP::set_path(int index, const char* value, size_t size) {
6100: inline std::string* BOOL_GATE_OP::add_path() {
6104: inline void BOOL_GATE_OP::add_path(const std::string& value) {
6108: inline void BOOL_GATE_OP::add_path(std::string&& value) {
6112: inline void BOOL_GATE_OP::add_path(const char* value) {
6117: inline void BOOL_GATE_OP::add_path(const char* value, size_t size) {
6127: BOOL_GATE_OP::mutable_path() {
1289:   static const int kPathFieldNumber = 1;
1286:   // repeated string path = 1;
1287:   int path_size() const;
1288:   void clear_path();
1290:   const std::string& path(int index) const;
1291:   std::string* mutable_path(int index);
1292:   void set_path(int index, const std::string& value);
1293:   void set_path(int index, std::string&& value);
1294:   void set_path(int index, const char* value);
1295:   void set_path(int index, const char* value, size_t size);
1296:   std::string* add_path();
1297:   void add_path(const std::string& value);
1298:   void add_path(std::string&& value);
1299:   void add_path(const char* value);
1300:   void add_path(const char* value, size_t size);
1301:   const ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string>& path() const;
1302:   ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string>* mutable_path();
1321:   ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string> path_;
6067: // repeated string path = 1;
6069:   return path_.size();
6072:   path_.Clear();
6075:   // @@protoc_insertion_point(field_get:pb.BOOL_GATE_OP.path)
6076:   return path_.Get(index);
6079:   // @@protoc_insertion_point(field_mutable:pb.BOOL_GATE_OP.path)
6080:   return path_.Mutable(index);
6083:   // @@protoc_insertion_point(field_set:pb.BOOL_GATE_OP.path)
6084:   path_.Mutable(index)->assign(value);
6087:   // @@protoc_insertion_point(field_set:pb.BOOL_GATE_OP.path)
6088:   path_.Mutable(index)->assign(std::move(value));
6092:   path_.Mutable(index)->assign(value);
6093:   // @@protoc_insertion_point(field_set_char:pb.BOOL_GATE_OP.path)
6096:   path_.Mutable(index)->assign(
6098:   // @@protoc_insertion_point(field_set_pointer:pb.BOOL_GATE_OP.path)
6101:   // @@protoc_insertion_point(field_add_mutable:pb.BOOL_GATE_OP.path)
6102:   return path_.Add();
6105:   path_.Add()->assign(value);
6106:   // @@protoc_insertion_point(field_add:pb.BOOL_GATE_OP.path)
6109:   path_.Add(std::move(value));
6110:   // @@protoc_insertion_point(field_add:pb.BOOL_GATE_OP.path)
6114:   path_.Add()->assign(value);
6115:   // @@protoc_insertion_point(field_add_char:pb.BOOL_GATE_OP.path)
6118:   path_.Add()->assign(reinterpret_cast<const char*>(value), size);
6119:   // @@protoc_insertion_point(field_add_pointer:pb.BOOL_GATE_OP.path)
6123:   // @@protoc_insertion_point(field_list:pb.BOOL_GATE_OP.path)
6124:   return path_;
6128:   // @@protoc_insertion_point(field_mutable_list:pb.BOOL_GATE_OP.path)
6129:   return &path_;
isobar:R/ProteinGroup-class.R: [ ]
424:                       host="www.ebi.ac.uk",path="/uniprot/biomart/martservice")
savR:R/savR-methods.R: [ ]
270:   path <- location(project)
373:   path <- getInterOpFilePath(project, format)
571:   path <- normalizePath(paste(project@location, "InterOp", format@filename, sep="/"))
611:   path <- getInterOpFilePath(project,format)
519:     filePath <- suppressWarnings(normalizePath(paste(project@location, "InterOp", format@filename, sep="/") ))
585: getInterOpFilePath <- function(project, format) {
271:   if (!file.exists(path))
272:     stop(paste("Project", path, "does not exist."))
285:   path <- normalizePath(paste(reports, "Intensity", sep="/"))
289:         Cairo::Cairo(file=paste(path, "/Chart_", cycle, "_", tolower(base), ".png", sep=""), width=300, height=800, dpi=72, type="png", ...(11 bytes skipped)...
304:   path <- normalizePath(paste(reports, "NumGT30", sep="/"))
307:       Cairo::Cairo(file=paste(path, "/Chart_", cycle, ".png", sep=""), width=300, height=800, dpi=72, type="png", bg="white")
323:   path <- normalizePath(paste(reports, "ByCycle", sep="/"))
326:       Cairo::Cairo(file=paste(path, "/QScore_L", lane, ".png", sep=""), width=800, height=400, dpi=72, type="png", bg="white")
341:   path <- normalizePath(paste(reports, "FWHM", sep="/"))
345:         Cairo::Cairo(file=paste(path, "/Chart_", cycle, "_", tolower(base), ".png", sep=""), width=300, height=800, dpi=72, type="png", ...(11 bytes skipped)...
374:   fh <- file(path, "rb")
572:   fh <- file(path, "rb")
612:   fh <- file(path, "rb")
7:   retval <- new("savProject", location=normalizePath(object))
10:   ri <- normalizePath(paste(object, "RunInfo.xml", sep="/"))
273:   reports <- normalizePath(destination, mustWork=F)
521:     if (file.exists(filePath)) {
586:   return(normalizePath(paste(project@location, "InterOp", format@filename, sep="/")))
12:   retval@runid <- XML::xmlAttrs(XML::xpathApply(runinfo, "/RunInfo/Run")[[1]])["Id"]
13:   retval@number <- as.integer(XML::xmlAttrs(xpathApply(runinfo, "/RunInfo/Run")[[1]])["Number"])
14:   retval@flowcell <- XML::xmlValue(XML::xpathApply(runinfo, "/RunInfo/Run/Flowcell")[[1]])
15:   retval@instrument <- XML::xmlValue(XML::xpathApply(runinfo, "/RunInfo/Run/Instrument")[[1]])
16:   retval@date <- XML::xmlValue(XML::xpathApply(runinfo, "/RunInfo/Run/Date")[[1]])
18:   for (x in XML::xpathApply(runinfo, "/RunInfo/Run/Reads/Read")) {
30:   layout <- XML::xpathApply(runinfo, "/RunInfo/Run/FlowcellLayout")[[1]]
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)
ISAnalytics:R/internal-functions.R: [ ]
1497:             path = project_folder, recurse = TRUE,
1947:                 path = association_file,
3801:                                 path = report_path,
1440:     path_cols <- .path_cols_names()
2356:     path_col_names <- .path_cols_names()
1736:     stats_paths <- purrr::pmap_dfr(temp, function(...) {
1824:     stats_paths <- .stats_report(association_file,
368:     corr_fold <- fs::path(dir, "fs")
372:         proj_fold <- fs::path(corr_fold, proj)
373:         quant_fold <- fs::path(proj_fold, "quantification")
378:             pool_fold <- fs::path(quant_fold, .y)
390:                 file = fs::path(pool_fold, paste(prefix,
397:                 file = fs::path(pool_fold, paste(prefix,
408:             proj_fold <- fs::path(corr_fold, proj)
409:             iss_fold <- fs::path(proj_fold, "iss")
412:                 pool_fold <- fs::path(iss_fold, .y)
420:                         file = fs::path(pool_fold, filename),
449:     err_fold <- fs::path(dir, "fserr")
457:         proj_fold <- fs::path(err_fold, proj)
458:         quant_fold <- fs::path(proj_fold, "quantification")
477:             pool_fold <- fs::path(quant_fold, .y)
490:                     file = fs::path(pool_fold, paste(prefix,
498:                 file = fs::path(pool_fold, paste(prefix,
509:             proj_fold <- fs::path(err_fold, proj)
510:             iss_fold <- fs::path(proj_fold, "iss")
519:                 pool_fold <- fs::path(iss_fold, .y)
528:                             file = fs::path(pool_fold, filename),
561: #' @importFrom fs path_ext
562: #' @importFrom tools file_path_sans_ext
564: .check_file_extension <- function(file_path) {
566:     last <- fs::path_ext(file_path)
570:         file_path[compressed] <- tools::file_path_sans_ext(
571:             file_path[compressed]
573:         last <- fs::path_ext(file_path)
981: .read_with_fread <- function(path, additional_cols, annotated, sep) {
1010:         file = path,
1047: .read_with_readr <- function(path, additional_cols, annotated, sep) {
1072:         file = path,
1107: .import_single_matrix <- function(path,
1139:     is_compressed <- fs::path_ext(path) %in% .compressed_formats()
1142:         compression_type <- fs::path_ext(path)
1155:     peek_headers <- readr::read_delim(path,
1177:             path = path, additional_cols = additional_cols,
1182:             path = path, additional_cols = additional_cols,
1296: .read_af <- function(path, date_format, delimiter) {
1299:     file_ext <- .check_file_extension(path)
1313:         headers_peek <- readr::read_delim(path,
1321:         headers_peek <- readxl::read_excel(path, n_max = 0)
1342:             df <- readr::read_delim(path,
1352:             df <- readxl::read_excel(path,
1420: # - root_folder: Path to the root folder
1423: # ProjectID - ConcatenatePoolIDSeqRun - PathToFolderProjectID - Found - Path -
1424: # Path_quant - Path_iss (NOTE: headers are dynamic!)
1448:                 fs::path(
1449:                     fs::path(root_folder),
1460:             !!path_cols$project := NA_character_,
1461:             !!path_cols$quant := NA_character_,
1462:             !!path_cols$iss := NA_character_
1466:         project_folder <- fs::path(
1467:             fs::path(root_folder),
1471:             paste0(fs::path(
1473:                 fs::path(cur[[concat_pool_col]])
1489:             paste0(fs::path(
1491:                 fs::path(cur[[concat_pool_col]])
1505:             path = project_folder, recurse = TRUE,
1515:                     !!path_cols$project := project_folder,
1516:                     !!path_cols$quant := quant_found,
1517:                     !!path_cols$iss := iss_found
1548: .manage_association_file <- function(af_path,
1558:         path = af_path,
1689: # Finds automatically the path on disk to each stats file.
1696: # Path_iss (or designated dynamic name), stats_files, info
1701:     path_iss_col) {
1704:             dplyr::all_of(c(proj_col, pool_col, path_iss_col))
1712:     if (all(is.na(temp[[path_iss_col]]))) {
1719:         if (is.na(temp_row[[path_iss_col]])) {
1727:         files <- fs::dir_ls(temp_row[[path_iss_col]],
1809: # - path_iss_col: name of the column that contains the path
1818:     path_iss_col,
1828:         path_iss_col = path_iss_col
1939: .pre_manage_af <- function(association_file, import_af_args, report_path) {
1940:     if (!is.null(report_path) && !fs::is_dir(report_path)) {
1941:         report_path <- fs::path_dir(report_path)
1943:     ## Import association file if provided a path
1948:                 report_path = report_path,
1954:     if (!.path_cols_names()$quant %in% colnames(association_file)) {
1955:         rlang::abort(.af_missing_path_error(.path_cols_names()$quant),
1956:             class = "missing_path_col"
1960:         dplyr::filter(!is.na(.data[[.path_cols_names()$quant]]))
2345: #' @importFrom fs dir_ls as_fs_path
2359:             dplyr::all_of(c(proj_col, pool_col, path_col_names$quant))
2382:             matches <- fs::dir_ls(temp_row[[path_col_names$quant]],
2433: # @param dupl The tibble containing quantification types and path to the files
2481: # * Removing files not found (files for which Files_count$Found == 0 and Path
2656: #' @importFrom fs as_fs_path
2699:                     dplyr::mutate(Files_found = fs::as_fs_path(
2723:                     dplyr::mutate(Files_found = fs::as_fs_path(
2812: # * Removing files not found (files for which Files_count$Found == 0 and Path
2992:             list(path = x),
3740:     report_path) {
3787:                         fs::dir_create(report_path)
3817:                                 path = report_path,
4217: # @param file_path The file path as a string
4218: #' @importFrom fs dir_create path_wd path
4223: .write_recalibr_map <- function(map, file_path) {
4224:     if (!fs::file_exists(file_path)) {
4225:         ext <- fs::path_ext(file_path)
4228:             fs::dir_create(file_path)
4230:             tmp_filename <- fs::path(file_path, gen_filename)
4232:             tmp_filename <- fs::path_ext_remove(file_path)
4234:                 ext <- paste(fs::path_ext(tmp_filename), ext, sep = ".")
4235:                 tmp_filename <- fs::path_ext_remove(tmp_filename)
4253:     } else if (fs::is_dir(file_path)) {
4255:         tmp_filename <- fs::path(file_path, gen_filename)
4257:         tmp_filename <- file_path
5051:                 x = "Did you provide the correct path?"
5068:                 x = "Did you provide the correct path?"
560: # Returns the file format for each of the file paths passed as a parameter.
1431:         rlang::abort(.af_missing_pathfolder_error(proj_fold_col))
1531: # containing paths to project folder, quant folders and iss folders
1711:     # If paths are all NA return
1791:     stats_paths
1823:     # Obtain paths
1830:     stats_paths <- stats_paths %>%
1832:     if (all(is.na(stats_paths$stats_files))) {
1833:         stats_paths <- stats_paths %>%
1839:         return(list(stats = NULL, report = stats_paths))
1870:         data_list = stats_paths$stats_files,
1886:     stats_paths <- stats_paths %>%
1888:     stats_paths <- purrr::pmap_dfr(stats_paths, function(...) {
1905:     stats_paths <- stats_paths %>%
1907:     stats_dfs <- stats_dfs$res[stats_paths$Imported]
1910:         return(list(stats = NULL, report = stats_paths))
1917:     list(stats = stats_dfs, report = stats_paths)
2972: # @param files Files_found table where absolute paths of chosen files
ELMER:R/plots.R: [ ]
924:   readr::write_delim(pairs, path = filename, append = TRUE)
614:         write_tsv(cbind(as.data.frame(pairs)[,c(1:3,6)],corretlation.tab),path = file.name.table)
628:         write_tsv(cbind(as.data.frame(pairs)[,c(1:3,6)],corretlation.tab),path = file.name.table)
974:       filename <- file.path(dir,track.names[idx])
976:       filename <- file.path(dir,paste0(sample,".bw"))
994: #' dir(path = "analysis",
1068:                   ret <- summarizeTF(path = x,
1103:                         function(path){
1104:                           TF <- readr::read_csv(dir(path = path, pattern = ".significant.TFs.with.motif.summary.csv",
1106:                           motif <- readr::read_csv(dir(path = path, pattern = ".motif.enrichment.csv",
1129:                           function(path){
1130:                             TF <- readr::read_csv(dir(path = path, 
1133:                             motif <- readr::read_csv(dir(path = path, 
1141:                             TF.meth.cor <- get(load(dir(path = path, pattern = ".TFs.with.motif.pvalue.rda", recursive = T, full.names = T)))
1184:                              function(path){
1185:                                TF <- readr::read_csv(dir(path = path, 
1189:                                motif <- readr::read_csv(dir(path = path, 
1199:                                TF.meth.cor <- get(load(dir(path = path, 
1213:                                TF.meth.cor$analysis <- path
GeneTonic:R/GeneTonic.R: [ ]
2294:       path <- system.file("doc", "GeneTonic_manual.html", package = "GeneTonic")
2295:       if (path == "") {
2298:         browseURL(path)
2352:           reactive_values$in_gtl <- readRDS(input$uploadgtl$datapath)