Found 93028 results in 9025 files, showing top 50 files (show more).
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),]$symbol)),
|
727: paste(pathT[i])))
|
732: ID = pathT,
|
733: Term = pathTerms,
|
743: #' Convert gmt formatted pathway file to the Pathway ID, Entrez, symbol
|
746: #' @param gmtName Custom pathway gmt file
|
815: colnames(f) <- c('pathTerm', 'Entrez', 'symbol')
|
830: colnames(f) <- c('pathTerm', 'symbol', 'Entrez')
|
852: colnames(f) <- c('pathTerm', 'Entrez', 'symbol')
|
863: colnames(f) <- c('pathTerm', 'symbol', 'Entrez')
|
280: xx <- as.list(reactome.db::reactomePATHID2EXTID)
|
283: pn <- as.list(reactome.db::reactomePATHID2NAME)
|
445: rWikiPathways::downloadPathwayArchive(organism = "Homo sapiens",
|
449: rWikiPathways::downloadPathwayArchive(organism = "Mus musculus",
|
453: rWikiPathways::downloadPathwayArchive(organism = "Danio rerio",
|
457: rWikiPathways::downloadPathwayArchive(organism = "Rattus norvegicus",
|
461: rWikiPathways::downloadPathwayArchive(
|
465: rWikiPathways::downloadPathwayArchive(
|
469: rWikiPathways::downloadPathwayArchive(
|
487: #' WikiPathways Enrichment
|
biodbKegg:R/KeggPathwayConn.R: [ ] |
---|
61: path <- self$getEntry(path.id)
|
59: for (path.id in id) {
|
127: for (path.id in id) {
|
304: path_idx <- sub('^[^0-9]+', '', id)
|
322: path_idx <- sub('^[^0-9]+', '', id)
|
29: KeggPathwayConn <- R6::R6Class("KeggPathwayConn",
|
40: super$initialize(db.name='pathway', db.abbrev='path', ...)
|
62: if ( ! is.null(path) && path$hasField('kegg.module.id')) {
|
65: for (mod.id in path$getFieldValue('kegg.module.id')) {
|
144: graph[[path.id]]=list(vertices=vert, edges=edg)
|
147: graph[[path.id]]=NULL
|
309: params=c(org_name='map', mapno=path_idx,
|
325: img_filename <- paste0('pathwaymap-', path_idx)
|
331: biodb::error0('Impossible to find pathway image path inside',
|
335: tmp_file <- file.path(cache$getTmpFolderPath(),
|
2: #' The connector class to KEGG Pathway database.
|
16: #' conn=mybiodb$getFactory()$createConn('kegg.pathway')
|
18: #' # Retrieve all reactions related to a mouse pathway:
|
21: #' # Get a pathway graph
|
44: #' Retrieves all reactions part of a KEGG pathway. Connects to
|
45: #' KEGG databases, and walk through all pathways submitted, and
|
58: # Loop on all Pathway IDs
|
89: #' Takes a list of pathways IDs and converts them to the specified organism,
|
92: #' @param org The organism in which to search for pathways, as a KEGG organism
|
113: #' Builds a pathway graph in the form of two tables of vertices and edges,
|
115: #' @param id A character vector of KEGG pathway entry IDs.
|
120: #' @return A named list whose names are the pathway IDs, and values are lists
|
126: # Loop on all pathway IDs
|
158: #' Builds a pathway graph, as an igraph object, using KEGG database.
|
159: #' @param id A character vector of KEGG pathway entry IDs.
|
196: #' Create a pathway graph picture, with some of its elements colorized.
|
197: #' @param id A KEGG pathway ID.
|
227: #' Extracts shapes from a pathway map image.
|
228: #' @param id A KEGG pathway ID.
|
303: # Extract pathway number
|
308: 'show_pathway'),
|
329: 'src="([^"]+)"(\\s+.*)?\\s+(name|id)="pathwayimage"')
|
332: ' HTML page for pathway ID ', id, '.')
|
342: img_file <- cache$getFilePath(cid, img_filename, 'png')
|
22: #' graph=conn$buildPathwayGraph('mmu00260')
|
98: convertToOrgPathways=function(id, org) {
|
122: buildPathwayGraph=function(id, directed=FALSE, drop=TRUE) {
|
166: getPathwayIgraph=function(id, directed=FALSE, drop=TRUE) {
|
173: g <- self$buildPathwayGraph(id=id, directed=directed, drop=FALSE)
|
210: pix <- private$getPathwayImage(id)
|
213: shapes <- self$extractPathwayMapShapes(id=id, color2ids=color2ids)
|
233: ,extractPathwayMapShapes=function(id, color2ids) {
|
237: html <- private$getPathwayHtml(id)
|
301: ,getPathwayHtml=function(id) {
|
319: getPathwayImage=function(id) {
|
321: html <- private$getPathwayHtml(id)
|
cisPath:inst/extdata/D3/cisPath.js: [ ] |
---|
127: var path = svg.append('svg:g').selectAll('path'),
|
2499: function ShowShortestPath(){
|
2: var basePath1="./PPIinfo/";
|
3: var basePath2="./PPIinfo/";
|
2630: function ShowShortestPath1(){
|
2711: var swiss2paths={};
|
2765: function showPath10(){
|
2778: function addResultPath10(p){
|
2784: function addFirstPath10(p){
|
2800: function addLastPath10(p){
|
2823: function addPreviousPath10(p){
|
2842: function addNextPath10(p){
|
2560: var resultPaths=[];
|
2561: function ShowPaths(allPaths){
|
2616: function ShowPathView(index){
|
2712: function generatePaths(swiss, root){
|
2729: function ShowShortestPathExp(){
|
2763: var showPathFirst=0;
|
2764: var showPathEnd=10;
|
3044: function getCisPathJS(){
|
3065: function getCisPathJSRetry(urltry){
|
3096: function getPathsForTargetProtein(swissID){
|
3118: function getPathsForTargetProteinRetry(urltry){
|
53: .append('svg:path')
|
68: .append('svg:path')
|
94: .append('svg:path')
|
109: .append('svg:path')
|
115: var drag_line = svg.append('svg:path')
|
166: path.attr('d', function(d) {
|
360: // path (link) group
|
381: path = path.data(links);
|
384: path.classed('selected', function(d) { return d === selected_link; })
|
393: path.enter().append('svg:path')
|
414: path.exit().remove();
|
2384: ///////////////////////////////////////////////////////////////////////shortest path
|
2504: alert("No path between this two proteins!");
|
2508: alert("No path between this two proteins!");
|
2568: addTh(mycurrent_row, "Path");
|
2601: mycurrent_button.id = "path"+x;
|
2614: ShowPathView("path0");
|
2701: alert("Sorry, detect no path from "+swiss1+" to "+ swiss2);
|
3097: var url="./js/"+swissID+"_path.js";
|
954: document.getElementById("detectPath").disabled=true;
|
1380: idx=pathLen-1-idx;
|
1415: var url=basePath2+sourceSwiss+".js";
|
1679: var url=basePath2+swiss1+".js";
|
1684: if(this.url==(basePath2+swiss1+".js")){
|
1820: var url=basePath2+"A0A5B9.js";
|
1827: debugObj("basePath1:"+basePath1);
|
1831: basePath1="../geneset20140628/";
|
1832: basePath2="../20140628PPI/";
|
1833: debugObj("basePath1:"+basePath1);
|
1845: var url=basePath2+"gene2swiss.js";
|
1899: var url=basePath2+"swiss2gene.js";
|
1954: var url=basePath2+"swiss2swiss.js";
|
2021: var url=basePath1+"PPI.js";
|
2411: ShowShortestPath();
|
2421: document.getElementById("detectPath").disabled=false;
|
2426: document.getElementById("detectPath").disabled=true;
|
2438: document.getElementById("detectPath").disabled=false;
|
2443: document.getElementById("detectPath").disabled=true;
|
2515: document.getElementById("detectPath").disabled=true;
|
2530: document.getElementById("detectPath").disabled=false;
|
2543: setTimeout("ShowShortestPath1()",1000);
|
2549: setTimeout("ShowShortestPath1()",1000);
|
2574: mycurrent_row.id="pathrow"+x;
|
2608: showPath10();
|
2611: document.getElementById("detectPath").disabled=false;
|
2625: document.getElementById("pathrow"+x).className="rowNotSelected";
|
2627: document.getElementById("pathrow"+i).className="rowSelected";
|
2705: swiss2paths={};
|
2707: debugObj(swiss2paths[swiss2]);
|
2708: debugObj(swiss2paths[swiss2].length);
|
2709: ShowPaths(swiss2paths[swiss2]);
|
2713: swiss2paths[swiss]=[];
|
2715: swiss2paths[swiss].push(swiss);
|
2724: for(var y in swiss2paths[nodes[x]]){
|
2725: swiss2paths[swiss].push(swiss2paths[nodes[x]][y]+"#"+swiss);
|
2760: ShowShortestPath();
|
2771: addResultPath10(p);
|
2772: addFirstPath10(p);
|
2773: addPreviousPath10(p);
|
2774: addNextPath10(p);
|
2775: addLastPath10(p);
|
2796: resultSPAN.addEventListener("click", function(e){showPathFirst=0;showPath10();}, false);
|
2819: resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
|
2838: resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
|
2857: resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
|
2879: document.getElementById("pathrow"+i).style.display="none";
|
2882: document.getElementById("pathrow"+i).style.display="";
|
3020: ...(31 bytes skipped)...ange", function(e){document.getElementById('targetPtxt').value=this.value;checkValid2();ShowShortestPath();}, false);
|
3108: json.paths.pop();
|
3109: ShowPaths(json.paths);
|
3138: json.paths.pop();
|
3139: ShowPaths(json.paths);
|
956: document.getElementById("allShortestPaths").style.display="none";
|
957: document.getElementById("allShortestPathsP").style.display="none";
|
1253: graphName = "cisPathValue#"+graphName;
|
1267: if(key.substr(0,13)!="cisPathValue#"){
|
1285: graphName = "cisPathValue#"+graphName;
|
1306: graphName = "cisPathValue#"+graphName;
|
1327: if(key.substr(0,13)!="cisPathValue#"){
|
1418: callback: "cisPathCallBack",
|
1682: callback: "cisPathCallBack",
|
1823: callback: "cisPathCallBack",
|
1850: callback: "cisPathCallBack",
|
1880: callback: "cisPathCallBack",
|
1905: callback: "cisPathCallBack",
|
1935: callback: "cisPathCallBack",
|
1960: callback: "cisPathCallBack",
|
1990: callback: "cisPathCallBack",
|
2025: callback: "cisPathCallBack",
|
2063: callback: "cisPathCallBack",
|
2110: getCisPathJS();
|
2378: var graphName="cisPathHTML";
|
2476: function ShowShortestPathWorker(){
|
2477: var worker = new Worker("./D3/cisPathWorker.js");
|
2480: var resultPaths=event.data.resultPaths;
|
2481: ShowPaths(resultPaths);
|
2484: var resultPaths=[];
|
2485: ShowPaths(resultPaths);
|
2514: document.getElementById("detectPathExp").disabled=true;
|
2521: document.getElementById("allShortestPaths").style.display="none";
|
2522: document.getElementById("allShortestPathsP").style.display="none";
|
2529: document.getElementById("detectPathExp").disabled=false;
|
2533: getPathsForTargetProtein(swiss2);
|
2539: ShowShortestPathWorker();
|
2562: resultPaths=allPaths;
|
2563: var table=document.getElementById("allShortestPaths");
|
2571: for(var x in allPaths){
|
2572: var nodes=allPaths[x].split("#");
|
2602: mycurrent_button.addEventListener("click", function(e){ShowPathView(this.id);}, false);
|
2607: showPathFirst=0;
|
2610: document.getElementById("detectPathExp").disabled=false;
|
2612: document.getElementById("allShortestPaths").style.display="";
|
2613: document.getElementById("allShortestPathsP").style.display="";
|
2617: var total=resultPaths.length;
|
2619: var nodes=resultPaths[i].split("#");
|
2706: generatePaths(swiss2, swiss1);
|
2723: generatePaths(nodes[x], root);
|
2767: var p=document.getElementById("allShortestPathsP");
|
2780: var content="Results: "+showPathFirst+"-"+showPathEnd+" of "+resultPaths.length+" ";
|
2790: if(showPathFirst==0){
|
2805: var newFirst=(resultPaths.length-resultPaths.length%10);
|
2806: if(newFirst==resultPaths.length){
|
2807: newFirst=resultPaths.length-10;
|
2813: if(showPathEnd==resultPaths.length){
|
2828: if(showPathFirst==0){
|
2834: var newFirst=showPathFirst-10;
|
2847: if(showPathEnd==resultPaths.length){
|
2853: var newFirst=showPathFirst+10;
|
2854: if(newFirst >= resultPaths.length){
|
2855: newFirst = (resultPaths.length-resultPaths.length%10);
|
2863: var total=resultPaths.length;
|
2864: if(showPathFirst >= total){
|
2865: showPathFirst=(total-total%10);
|
2867: if(showPathFirst == total){
|
2868: showPathFirst = total-10;
|
2870: if(showPathFirst < 0){
|
2871: showPathFirst = 0;
|
2873: showPathFirst=showPathFirst-(showPathFirst%10);
|
2874: showPathEnd=showPathFirst+10;
|
2875: if(showPathEnd > total){
|
2876: showPathEnd = total;
|
2881: for(var i=showPathFirst;i<showPathEnd;i++){
|
3049: callback: "cisPathCallBack",
|
3061: getCisPathJSRetry(this.url);
|
3078: callback: "cisPathCallBack",
|
3090: getCisPathJSRetry(this.url);
|
3101: callback: "cisPathCallBack",
|
3114: getPathsForTargetProteinRetry(this.url);
|
3131: callback: "cisPathCallBack",
|
3144: getPathsForTargetProteinRetry(this.url);
|
3151: document.getElementById("detectPathExp").disabled=true;
|
3156: document.getElementById("detectPathExp").disabled=false;
|
cisPath:inst/extdata/D3/cisPathWeb.js: [ ] |
---|
127: var path = svg.append('svg:g').selectAll('path'),
|
2501: function ShowShortestPath(){
|
2: var basePath1="./PPIinfo/";
|
3: var basePath2="./PPIinfo/";
|
2632: function ShowShortestPath1(){
|
2719: var swiss2paths={};
|
2753: function showPath10(){
|
2766: function addResultPath10(p){
|
2772: function addFirstPath10(p){
|
2788: function addLastPath10(p){
|
2811: function addPreviousPath10(p){
|
2830: function addNextPath10(p){
|
2548: var resultPaths=[];
|
2549: function ShowPaths(allPaths){
|
2615: function ShowPathView(index){
|
2720: function generatePaths(swiss, root){
|
2737: function ShowShortestPathExp(){
|
2751: var showPathFirst=0;
|
2752: var showPathEnd=10;
|
53: .append('svg:path')
|
68: .append('svg:path')
|
94: .append('svg:path')
|
109: .append('svg:path')
|
115: var drag_line = svg.append('svg:path')
|
166: path.attr('d', function(d) {
|
360: // path (link) group
|
381: path = path.data(links);
|
384: path.classed('selected', function(d) { return d === selected_link; })
|
393: path.enter().append('svg:path')
|
414: path.exit().remove();
|
2386: ///////////////////////////////////////////////////////////////////////shortest path
|
2506: alert("No path between this two proteins!");
|
2510: alert("No path between this two proteins!");
|
2554: alert("Sorry, detect no path from "+swiss1+" to "+ swiss2);
|
2566: addTh(mycurrent_row, "Path");
|
2599: mycurrent_button.id = "path"+x;
|
2613: ShowPathView("path0");
|
2705: alert("Sorry, detect no path from "+name1+" to "+ name2);
|
954: document.getElementById("detectPath").disabled=true;
|
1380: idx=pathLen-1-idx;
|
1415: var url=basePath2+sourceSwiss+".js";
|
1679: var url=basePath2+swiss1+".js";
|
1684: if(this.url==(basePath2+swiss1+".js")){
|
1820: var url=basePath2+"A0A5B9.js";
|
1827: debugObj("basePath1:"+basePath1);
|
1831: basePath1="../geneset20140628/";
|
1832: basePath2="../20140628PPI/";
|
1833: debugObj("basePath1:"+basePath1);
|
1845: var url=basePath2+"gene2swiss.js";
|
1899: var url=basePath2+"swiss2gene.js";
|
1954: var url=basePath2+"swiss2swiss.js";
|
2021: var url=basePath1+"PPI.js";
|
2413: ShowShortestPath();
|
2423: document.getElementById("detectPath").disabled=false;
|
2428: document.getElementById("detectPath").disabled=true;
|
2440: document.getElementById("detectPath").disabled=false;
|
2445: document.getElementById("detectPath").disabled=true;
|
2517: document.getElementById("detectPath").disabled=true;
|
2531: setTimeout("ShowShortestPath1()",1000);
|
2537: setTimeout("ShowShortestPath1()",1000);
|
2558: document.getElementById("detectPath").disabled=false;
|
2572: mycurrent_row.id="pathrow"+x;
|
2606: showPath10();
|
2610: document.getElementById("detectPath").disabled=false;
|
2627: document.getElementById("pathrow"+x).className="rowNotSelected";
|
2629: document.getElementById("pathrow"+i).className="rowSelected";
|
2709: document.getElementById("detectPath").disabled=false;
|
2713: swiss2paths={};
|
2715: debugObj(swiss2paths[swiss2]);
|
2716: debugObj(swiss2paths[swiss2].length);
|
2717: ShowPaths(swiss2paths[swiss2]);
|
2721: swiss2paths[swiss]=[];
|
2723: swiss2paths[swiss].push(swiss);
|
2732: for(var y in swiss2paths[nodes[x]]){
|
2733: swiss2paths[swiss].push(swiss2paths[nodes[x]][y]+"#"+swiss);
|
2748: ShowShortestPath();
|
2759: addResultPath10(p);
|
2760: addFirstPath10(p);
|
2761: addPreviousPath10(p);
|
2762: addNextPath10(p);
|
2763: addLastPath10(p);
|
2784: resultSPAN.addEventListener("click", function(e){showPathFirst=0;showPath10();}, false);
|
2807: resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
|
2826: resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
|
2845: resultSPAN.addEventListener("click", function(e){showPathFirst=newFirst;showPath10();}, false);
|
2867: document.getElementById("pathrow"+i).style.display="none";
|
2870: document.getElementById("pathrow"+i).style.display="";
|
956: document.getElementById("allShortestPaths").style.display="none";
|
957: document.getElementById("allShortestPathsP").style.display="none";
|
1253: graphName = "cisPathValue#"+graphName;
|
1267: if(key.substr(0,13)!="cisPathValue#"){
|
1285: graphName = "cisPathValue#"+graphName;
|
1306: graphName = "cisPathValue#"+graphName;
|
1327: if(key.substr(0,13)!="cisPathValue#"){
|
1418: callback: "cisPathCallBack",
|
1682: callback: "cisPathCallBack",
|
1823: callback: "cisPathCallBack",
|
1850: callback: "cisPathCallBack",
|
1880: callback: "cisPathCallBack",
|
1905: callback: "cisPathCallBack",
|
1935: callback: "cisPathCallBack",
|
1960: callback: "cisPathCallBack",
|
1990: callback: "cisPathCallBack",
|
2025: callback: "cisPathCallBack",
|
2063: callback: "cisPathCallBack",
|
2380: var graphName="cisPathHTML";
|
2478: function ShowShortestPathWorker(){
|
2479: var worker = new Worker("./D3/cisPathWorker.js");
|
2482: var resultPaths=event.data.resultPaths;
|
2483: ShowPaths(resultPaths);
|
2486: var resultPaths=[];
|
2487: ShowPaths(resultPaths);
|
2516: document.getElementById("detectPathExp").disabled=true;
|
2523: document.getElementById("allShortestPaths").style.display="none";
|
2524: document.getElementById("allShortestPathsP").style.display="none";
|
2527: ShowShortestPathWorker();
|
2550: resultPaths=allPaths;
|
2551: if(resultPaths.length==0){
|
2557: document.getElementById("detectPathExp").disabled=false;
|
2561: var table=document.getElementById("allShortestPaths");
|
2569: for(var x in allPaths){
|
2570: var nodes=allPaths[x].split("#");
|
2600: mycurrent_button.addEventListener("click", function(e){ShowPathView(this.id);}, false);
|
2605: showPathFirst=0;
|
2609: document.getElementById("detectPathExp").disabled=false;
|
2611: document.getElementById("allShortestPaths").style.display="";
|
2612: document.getElementById("allShortestPathsP").style.display="";
|
2616: var total=resultPaths.length;
|
2618: if(i>=resultPaths.length){
|
2621: var nodes=resultPaths[i].split("#");
|
2708: document.getElementById("detectPathExp").disabled=false;
|
2714: generatePaths(swiss2, swiss1);
|
2731: generatePaths(nodes[x], root);
|
2755: var p=document.getElementById("allShortestPathsP");
|
2768: var content="Results: "+showPathFirst+"-"+showPathEnd+" of "+resultPaths.length+" ";
|
2778: if(showPathFirst==0){
|
2793: var newFirst=(resultPaths.length-resultPaths.length%10);
|
2794: if(newFirst==resultPaths.length){
|
2795: newFirst=resultPaths.length-10;
|
2801: if(showPathEnd==resultPaths.length){
|
2816: if(showPathFirst==0){
|
2822: var newFirst=showPathFirst-10;
|
2835: if(showPathEnd==resultPaths.length){
|
2841: var newFirst=showPathFirst+10;
|
2842: if(newFirst >= resultPaths.length){
|
2843: newFirst = (resultPaths.length-resultPaths.length%10);
|
2851: var total=resultPaths.length;
|
2852: if(showPathFirst >= total){
|
2853: showPathFirst=(total-total%10);
|
2855: if(showPathFirst == total){
|
2856: showPathFirst = total-10;
|
2858: if(showPathFirst < 0){
|
2859: showPathFirst = 0;
|
2861: showPathFirst=showPathFirst-(showPathFirst%10);
|
2862: showPathEnd=showPathFirst+10;
|
2863: if(showPathEnd > total){
|
2864: showPathEnd = total;
|
2869: for(var i=showPathFirst;i<showPathEnd;i++){
|
2942: document.getElementById("detectPathExp").disabled=true;
|
2947: document.getElementById("detectPathExp").disabled=false;
|
megadepth:R/install.R: [ ] |
---|
147: path <- Sys.getenv("APPDATA", "")
|
176: path <- file.path(d, exec)
|
207: path <- NULL # cache the path to megadepth
|
183: path2 <- Sys.which(cmd)
|
143: bin_paths <- function(
|
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)
|
145: extra_path = getOption("megadepth.dir")) {
|
148: path <- if (fs::dir_exists(path)) {
|
149: file.path(path, dir)
|
152: path <- "~/Library/Application Support"
|
153: path <- if (fs::dir_exists(path)) file.path(path, dir)
|
154: path <- c("/usr/local/bin", path)
|
156: path <- c("~/bin", "/snap/bin", "/var/lib/snapd/snap/bin")
|
158: path <- c(extra_path, path, pkg_file(dir, mustWork = FALSE))
|
160: path <- path[path != ""]
|
163: path <- c(tempdir(), path)
|
165: path
|
168: # find an executable from PATH, APPDATA, system.file(), ~/bin, etc
|
177: if (utils::file_test("-x", path)) {
|
180: path <- ""
|
184: if (path == "" || xfun::same_path(path, path2)) {
|
185: if (path2 == "") {
|
188: return(cmd) # do not use the full path of the command
|
190: if (path2 != "") {
|
195: path,
|
197: path2,
|
203: normalizePath(path)
|
209: if (is.null(path)) {
|
210: path <<- find_exec(
|
216: path
|
109: dirs <- bin_paths()
|
139: message("megadepth has been installed to ", normalizePath(destdir))
|
159: # remove empty paths potentially created by pkgfile
|
170: for (d in bin_paths(dir)) {
|
BiocBook:R/init.R: [ ] |
---|
217: path <- file.path("inst", "assets", "_book.yml")
|
246: gert::git_init(path = repo)
|
207: file.path(tmpdir, 'BiocBook.template'),
|
218: .fix_placeholders(file.path(repo, path), pkg = repo, usr = user)
|
219: cli::cli_alert_success(cli::col_grey("Filled out `{cli::col_cyan(path)}` fields"))
|
222: path <- "README.md"
|
223: .fix_placeholders(file.path(repo, path), pkg = repo, usr = user)
|
224: cli::cli_alert_success(cli::col_grey("Filled out `{cli::col_cyan(path)}` fields"))
|
227: path <- "DESCRIPTION"
|
228: .fix_placeholders(file.path(repo, path), pkg = repo, usr = user)
|
229: cli::cli_alert_success(cli::col_grey("Filled out `{cli::col_cyan(path)}` fields"))
|
230: cli::cli_alert_info(cli::col_grey("Please finish editing the `{cli::col_cyan(path)}` fields, including:"))
|
236: path <- file.path("inst", "index.qmd")
|
237: .fix_placeholders(file.path(repo, path), pkg = repo, usr = user)
|
238: cli::cli_alert_success(cli::col_grey("Filled out `{cli::col_cyan(path)}` fields"))
|
239: cli::cli_alert_info(cli::col_grey("Please finish editing the `{cli::col_cyan(path)}` fields, including the `Welcome` section"))
|
255: version <- read.dcf(file.path(repo, "DESCRIPTION"))[1,"BiocBookTemplate"]
|
325: path = "_temp",
|
362: charToRaw('{ "source": { "branch": "gh-pages", "path": "/docs" } }'),
|
386: file.path("inst", "assets", "cover.png")
|
PloGO2:inst/script/WGCNA_proteomics.R: [ ] |
---|
106: path <- system.file("files", package="PloGO2")
|
107: allDat = read.csv(file.path(path,"rice.csv") )
|
108: Group = read.csv(file.path(path, "group_rice.csv") ) [,2]
|
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)...
|
ISAnalytics:R/internal-functions.R: [ ] |
---|
1626: path = project_folder, recurse = TRUE,
|
3357: path = report_path,
|
1569: path_cols <- .path_cols_names()
|
2142: path_col_names <- .path_cols_names()
|
1870: stats_paths <- purrr::pmap(temp, function(...) {
|
1961: stats_paths <- .stats_report(association_file,
|
409: corr_fold <- fs::path(dir, "fs")
|
413: proj_fold <- fs::path(corr_fold, proj)
|
414: quant_fold <- fs::path(proj_fold, "quantification")
|
419: pool_fold <- fs::path(quant_fold, .y)
|
431: file = fs::path(pool_fold, paste(prefix,
|
438: file = fs::path(pool_fold, paste(prefix,
|
449: proj_fold <- fs::path(corr_fold, proj)
|
450: iss_fold <- fs::path(proj_fold, "iss")
|
453: pool_fold <- fs::path(iss_fold, .y)
|
461: file = fs::path(pool_fold, filename),
|
490: err_fold <- fs::path(dir, "fserr")
|
498: proj_fold <- fs::path(err_fold, proj)
|
499: quant_fold <- fs::path(proj_fold, "quantification")
|
518: pool_fold <- fs::path(quant_fold, .y)
|
531: file = fs::path(pool_fold, paste(prefix,
|
539: file = fs::path(pool_fold, paste(prefix,
|
550: proj_fold <- fs::path(err_fold, proj)
|
551: iss_fold <- fs::path(proj_fold, "iss")
|
560: pool_fold <- fs::path(iss_fold, .y)
|
569: file = fs::path(pool_fold, filename),
|
602: #' @importFrom fs path_ext
|
603: #' @importFrom tools file_path_sans_ext
|
605: .check_file_extension <- function(file_path) {
|
607: last <- fs::path_ext(file_path)
|
611: file_path[compressed] <- tools::file_path_sans_ext(
|
612: file_path[compressed]
|
614: last <- fs::path_ext(file_path)
|
1063: .read_with_fread <- function(path, additional_cols, annotated, sep) {
|
1092: file = path,
|
1130: .read_with_readr <- function(path, additional_cols, annotated, sep) {
|
1155: file = path,
|
1251: path,
|
1282: peek_headers <- readr::read_delim(path,
|
1303: is_compressed <- fs::path_ext(path) %in% .compressed_formats()
|
1306: compression_type <- fs::path_ext(path)
|
1326: path = path, additional_cols = additional_cols,
|
1331: path = path, additional_cols = additional_cols,
|
1423: .read_af <- function(path, date_format, delimiter) {
|
1426: file_ext <- .check_file_extension(path)
|
1440: headers_peek <- readr::read_delim(path,
|
1448: headers_peek <- readxl::read_excel(path, n_max = 0)
|
1469: df <- readr::read_delim(path,
|
1479: df <- readxl::read_excel(path,
|
1547: # - root_folder: Path to the root folder
|
1549: #' @importFrom fs path dir_ls
|
1551: # ProjectID - ConcatenatePoolIDSeqRun - PathToFolderProjectID - Found - Path -
|
1552: # Path_quant - Path_iss (NOTE: headers are dynamic!)
|
1577: fs::path(
|
1578: fs::path(root_folder),
|
1589: !!path_cols$project := NA_character_,
|
1590: !!path_cols$quant := NA_character_,
|
1591: !!path_cols$iss := NA_character_
|
1595: project_folder <- fs::path(
|
1596: fs::path(root_folder),
|
1600: paste0(fs::path(
|
1602: fs::path(cur[[concat_pool_col]])
|
1618: paste0(fs::path(
|
1620: fs::path(cur[[concat_pool_col]])
|
1634: path = project_folder, recurse = TRUE,
|
1644: !!path_cols$project := project_folder,
|
1645: !!path_cols$quant := quant_found,
|
1646: !!path_cols$iss := iss_found
|
1681: af_path,
|
1691: path = af_path,
|
1822: # Finds automatically the path on disk to each stats file.
|
1829: # Path_iss (or designated dynamic name), stats_files, info
|
1835: path_iss_col) {
|
1838: dplyr::all_of(c(proj_col, pool_col, path_iss_col))
|
1846: if (all(is.na(temp[[path_iss_col]]))) {
|
1853: if (is.na(temp_row[[path_iss_col]])) {
|
1861: files <- fs::dir_ls(temp_row[[path_iss_col]],
|
1945: # - path_iss_col: name of the column that contains the path
|
1955: path_iss_col,
|
1965: path_iss_col = path_iss_col
|
2076: if (!.path_cols_names()$quant %in% colnames(association_file)) {
|
2077: rlang::abort(.af_missing_path_error(.path_cols_names()$quant),
|
2078: class = "missing_path_col"
|
2082: dplyr::filter(!is.na(.data[[.path_cols_names()$quant]]))
|
2131: #' @importFrom fs dir_ls as_fs_path
|
2145: dplyr::all_of(c(proj_col, pool_col, path_col_names$quant))
|
2168: matches <- fs::dir_ls(temp_row[[path_col_names$quant]],
|
2269: #' @importFrom fs as_fs_path
|
2312: dplyr::mutate(Files_found = fs::as_fs_path(
|
2336: dplyr::mutate(Files_found = fs::as_fs_path(
|
2426: # * Removing files not found (files for which Files_count$Found == 0 and Path
|
2589: wrapper <- function(path, import_matrix_args, progress) {
|
2591: path = path,
|
3296: report_path) {
|
3343: fs::dir_create(report_path)
|
3373: path = report_path,
|
4457: x = "Did you provide the correct path?"
|
4474: x = "Did you provide the correct path?"
|
601: # Returns the file format for each of the file paths passed as a parameter.
|
1560: rlang::abort(.af_missing_pathfolder_error(proj_fold_col))
|
1661: # containing paths to project folder, quant folders and iss folders
|
1845: # If paths are all NA return
|
1927: stats_paths
|
1960: # Obtain paths
|
1967: stats_paths <- stats_paths |>
|
1969: if (all(is.na(stats_paths$stats_files))) {
|
1970: stats_paths <- stats_paths |>
|
1976: return(list(stats = NULL, report = stats_paths))
|
2006: data_list = stats_paths$stats_files,
|
2022: stats_paths <- stats_paths |>
|
2024: stats_paths <- purrr::pmap(stats_paths, function(...) {
|
2042: stats_paths <- stats_paths |>
|
2044: stats_dfs <- stats_dfs$res[stats_paths$Imported]
|
2047: return(list(stats = NULL, report = stats_paths))
|
2054: list(stats = stats_dfs, report = stats_paths)
|
pathVar:R/pipeline.final.R: [ ] |
---|
634: path <- pvalue_results@pwayCounts[[pathway]]
|
780: path1 <- pvalue_results@pwayCounts1[[pathway]]
|
781: path2 <- pvalue_results@pwayCounts2[[pathway]]
|
99: pathVarOneSample <- function(dat.mat, pways, test = c("chisq", "exact"), varStat = c("sd",
|
131: pathwayCounts <- lapply(lapply(olap.pways, function(x) table(x, deparse.level = 0)), function(x) if (len...(10 bytes skipped)...
|
206: pathVarTwoSamplesCont <- function(dat.mat, pways, groups, boot = 1000, varStat = c("sd", "mean",
|
290: pathVarTwoSamplesDisc <- function(dat.mat, pways, groups, perc = c(1/3, 2/3), test = c("chisq",
|
344: pathwayCounts1 <- lapply(lapply(olap.pways1, function(x) table(x, deparse.level = 0)),
|
354: pathwayCounts2 <- lapply(lapply(olap.pways2, function(x) table(x, deparse.level = 0)),
|
853: pathDat1 <- as.data.frame(table(mixDat1))
|
855: pathDat2 <- as.data.frame(table(mixDat2))
|
943: pathname <- sapply(listPath, function(x) if (length(unlist(strsplit(x, "/"))) > 1) {
|
796: plotPath1 <- ggplot(path1, aes(x = Cluster, y = Number_of_genes, fill = Cluster)) + geom_bar(stat = "identity",
|
800: plotPath2 <- ggplot(path2, aes(x = Cluster, y = Number_of_genes, fill = Cluster)) + geom_bar(stat = "identity",
|
659: plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
738: plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
806: plotPathway1 <- plotPath1 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
809: plotPathway2 <- plotPath2 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
871: plotPathDat1 <- ggplot(path...(136 bytes skipped)...("Number of genes") + theme(legend.position = "none") + ggtitle("Group 1") + xlab("") + ylim(0, max(pathDat1[,2], pathDat2[,2]))
|
872: plotPathDat2 <- ggplot(path...(136 bytes skipped)...("Number of genes") + theme(legend.position = "none") + ggtitle("Group 2") + xlab("") + ylim(0, max(pathDat1[,2], pathDat2[,2]))
|
26: ...(25 bytes skipped)...ptions are TRUE or FALSE. If TRUE then the first column of the tab delimited file is expected to be path IDs. If FALSE, then the first column is expected to be pathway names.
|
645: path <- as.data.frame(path)
|
646: colnames(path) <- c("Cluster", "Number_of_genes")
|
653: d <- ggplot(path, aes(x = Cluster, y = Number_of_genes, fill = Cluster)) + geom_bar(stat = "identity",
|
663: plotPathway <- plotPathway + annotate("text", x = sigCat, y = path[sigCat + 0.1,
|
789: path1 <- as.data.frame(path1)
|
790: colnames(path1) <- c("Cluster", "Number_of_genes")
|
792: path2 <- as.data.frame(path2)
|
793: colnames(path2) <- c("Cluster", "Number_of_genes")
|
794: yLimMax <- max(path1[, 2], path2[, 2])
|
813: plotPathway1 <- plotPathway1 + annotate("text", x = sigCat, y = path1[sigCat +
|
815: plotPathway2 <- plotPathway2 + annotate("text", x = sigCat, y = path2[sigCat +
|
20: #makeDBList put your pathways text file into a list
|
21: #pway$PATHNAME is the pathway names from the file
|
22: #pway$PATHID is a vector of pathway ID numbers is there are any. Otherwise it will be a vector filled with NA
|
23: #pway$GENES is a list of vectors, where each vector are the genes for a single pathway
|
25: #file is a tab delimited text file, where first and second columns are pathwayID and pathway name. The third (or last column is the genes associated with each pathway, seperated by commas.
|
33: pways$PATHNAME <- as.vector(pwayTable[, 2])
|
34: pways$PATHID <- as.vector(pwayTable[, 1])
|
35: pways$GENES <- list(length(pways$PATHID))
|
37: for (i in 1:length(pways$PATHID)) {
|
43: pways$PATHID <- pways$PATHID[-i]
|
44: pways$PATHNAME <- pways$PATHNAME[-i]
|
49: pways$PATHNAME <- as.vector(pwayTable[, 1])
|
50: pways$PATHID <- rep("NA", length(pways$PATHNAME))
|
51: pways$GENES <- list(length(pways$PATHID))
|
53: for (i in 1:length(pways$PATHID)) {
|
59: pways$PATHID <- pways$PATHID[-i]
|
60: pways$PATHNAME <- pways$PATHNAME[-i]
|
64: names(pways$GENES) <- pways$PATHNAME
|
69: #pathVarOneSample
|
73: # 3. For each pathway, we extract the gene in our dataset and in which cluster they belong.
|
74: # 4. For each pathway, we look how the gene counts in each category and compare it to the reference counts with all th...(59 bytes skipped)...
|
78: # Output 1: tablePway columns are :pathway name, path...(46 bytes skipped)...or exact test,the percentage of genes from our dataset related to the total number of genes in each pathway, the number of genes from our dataset inside the pathway and the total number of genes inside the pathway
|
79: #Output 2: NAPways corresponds to the pathway names of the pathway having less than 10 genes for the Chi-Squared or also more than 500 genes for the exact tes.
|
80: # Output 3: genesInPway correspond to each pathway with the genes from the datasets belonging to it and in which cluster they were classsify.
|
83: # Output 6: pwayCounts is the genes counts of the each pathway in each cluster.
|
91: #Input 2: pways contains the pathways of interest (KEGG, REACTOME, etc...) in the same format that makeDBList
|
103: # check if any GENES are in the pathway.
|
106: stop("None of the genes in the data set are found in the given gene set or pathway")
|
125: # olap.pways contains the genes are in each pathway with their cluster number
|
127: names(olap.pways) <- pways$PATHNAME
|
130: # list of tables of the number of genes in each cluster per pathway
|
140: # Chi-Square or Exact test to compare the reference and the pathway distribution
|
142: # chisq test and ajustment of the pvalue for each pathway
|
143: pvals.pways <- sapply(pathwayCounts, function(x) if (sum(x) >= 10) {
|
154: # Exact test and ajustment of the pvalue for each pathway
|
157: # We perform the multinomial test on the pathway containing between 10 and 500 genes because a bigger number will involve too many possibilities ...(11 bytes skipped)...
|
158: pvals.pways <- sapply(pathwayCounts, function(x) if (sum(x) >= 10 & sum(x) < 500) {
|
170: xtab <- data.table(PwayName = pways$PATHNAME[not_na], PwayID = pways$PATHID[not_na], APval = apvals.pways,
|
172: NumOfGenesFromDataSetInPathway = lengths(olap.pways[not_na]), PathwaySize = pways$SIZE[not_na])
|
176: ...(50 bytes skipped)...ab, NAPways=pval.NA, genesInPway=olap.pways, refProb=pexp, refCounts=pexp * length(mix), pwayCounts=pathwayCounts, numOfClus=nmix, varStat=varStat, genesInClus=mix, var=vs)
|
181: #pathVarTwoSamplesCont
|
184: # 2. For each pathway, we extract the gene in our dataset.
|
185: # 3. For each pathway, we look how its genes are distributed and compare the 2 groups using the bootstrap Kolmogorov-S...(12 bytes skipped)...
|
189: # Output 1: tablePway columns are :pathway name, pathway IDs, adjusted p-value ffrom the boot KS test, the number of genes from our dataset inside the pathway and the total number of genes inside the pathway.
|
190: #Output 2: NAPways corresponds to the pathway names of the pathway having no genes inside the dataset.
|
191: # Output 3: genesInPway correspond to the genes from the dataset belonging to each pathway
|
200: #Input 2: pways contains the pathways of interest (KEGG, REACTOME, etc...) in the same format that makeDBList
|
209: # check if any GENES are in the pathway
|
212: stop("None of the genes in the data set are found in the given gene set or pathway")
|
236: # olap.pways contains the genes from the dataset in each pathway
|
238: names(olap.pways) <- pways$PATHNAME
|
239: # We compare the two densities (one for each group) of the genes of each pathway with the Kolmogorov-Smirnow test. Â Â Â Â
|
251: xtab <- data.table(PwayName = pways$PATHNAME[not_na], PwayID = pways$PATHID[not_na], APval = apvals,
|
253: NumOfGenesFromDataSetInPathway = lengths(olap.pways[not_na]), PathwaySize = pways$SIZE[not_na])
|
261: #pathVarTwoSamplesDisc
|
265: # 3. For each pathway, we extract the gene in our dataset and in which cluster they belong.
|
266: # 4. For each pathway, we look at the gene counts in each category and compare the 2 samples to each other with all th...(60 bytes skipped)...
|
269: # Output 1: tablePway columns are :pathway name, path...(11 bytes skipped)...justed p-value, the percentage of genes in our dataset related to the total number of genes in each pathway, the number of genes from our dataset inside the pathway and the total number of genes inside the pathway.
|
270: #Output 2: NAPways corresponds to the pathway names of the pathway having no genes inside the dataset.
|
271: # Output 3: genesInPway1 corresponds to the genes from the dataset belonging to each pathway in the first sample
|
272: # Output 4: genesInPway2 corresponds to the genes from the dataset belonging to each pathway in the second sample
|
273: # Output 5: pwayCounts1 corresponds to a list of tables of the number of genes in each cluster per pathway for group 1
|
274: # Output 6: pwayCounts2 corresponds to a list of tables of the number of genes in each cluster per pathway for group 2
|
283: #Input 2: pways contains the pathways of interest (KEGG, REACTOME, etc...) in the same format that makeDBList
|
294: # check if any GENES are in the pathway
|
297: stop("None of the genes in the data set are found in the given gene set or pathway")
|
338: # olap.pways contains the genes from the dataset in each pathway
|
340: names(olap.pways1) <- pways$PATHNAME
|
342: names(olap.pways2) <- pways$PATHNAME
|
343: # list of tables of the number of genes in each cluster per pathway
|
353: # list of tables of the number of genes in each cluster per pathway
|
364: # chisq test and ajustment of the pvalue for each pathway
|
365: pvals.pways <- sapply(pways$PATHNAME, function(x) if (sum(pathwayCounts1[x][[1]]) >=
|
367: exp.val <- pathwayCounts1[x][[1]] #forgot the.val
|
368: chi <- sum((pathwayCounts2[x][[1]] - exp.val)^2/exp.val)
|
374: pval.NA <- pways$PATHNAME[-not_na]
|
377: # Exact test and ajustment of the pvalue for each pathway
|
380: # We perform the multinomial test on the pathway containing between 10 and 500 genes because a bigger number will involve too many possibilities ...(11 bytes skipped)...
|
381: pvals.pways <- sapply(pways$PATHNAME, function(x) if (sum(pathwayCounts1[x][[1]]) >=
|
382: 10 & sum(pathwayCounts1[x][[1]]) < 500) {
|
383: pexp <- pathwayCounts1[x][[1]]/sum(pathwayCounts1[x][[1]])
|
384: multinomial.test(as.vector(pathwayCounts2[x][[1]]), as.vector(pexp), useChisq = FALSE)$p.value
|
391: pval.NA <- pways$PATHNAME[-not_na]
|
395: xtab <- data.table(PwayName = pways$PATHNAME[not_na], PwayID = pways$PATHID[not_na], APval = apvals.pways,
|
397: NumOfGenesFromDataSetInPway = lengths(olap.pways1[not_na]), PathwaySize = pways$SIZE[not_na])
|
402: ...(36 bytes skipped)...", tablePway=xtab, NAPways=pval.NA, genesInPway1=olap.pways1, genesInPway2=olap.pways2, pwayCounts1=pathwayCounts1, pwayCounts2=pathwayCounts2, groups=groups, groupNames=groupNames, var1=var_1, var2=var_2, varStat=varStat)
|
409: #It is a function that returns the significant pathway(s),which category(ies) from this pathway are significant and which gene(s) belongs to this(ese) category(ies).
|
413: # Output 1: genesInSigPways1 contains the genes per significant pathway belonging to the significant category.
|
414: #Output 2: sigCatPerPway contains the category(ies) per pathway that are significant.
|
418: #Input 1: pvalue_results is result from the pathVarOneSample function
|
430: warning("There are no significant pathways. Quitting significant_category function and returning empty object")
|
434: # PathName that were significant in xtab.
|
436: # The list of table with the number of genes in each cluster from the significant pathways
|
442: # results contain the p-value for each category in each pathway computed with the binomial test.
|
450: # For each significant pathway we look which category(ies) is are significant and the genes
|
477: #It is a function that returns the significant pathways and which genes belongs to these #pathways.
|
481: # Output 1: genesInSigPways1 contains the genes belonging to each significant pathway
|
485: #Input 1: pvalue_results is result from the pathVarTwoSamplesCont function
|
493: warning("There are no significant pathways. Quitting significant_category function and returning empty object")
|
497: # Pathways that were significant in xtab.
|
499: # Genes from the dataset inside each significant pathway
|
507: #It is a function that returns the significant pathways and which genes belong to these pathways
|
511: # Output 1: genesInSigPways1 contains the genes belonging to each significant pathway in significant categories in the first sample
|
512: # Output 2: genesInSigPways2 contains the genes belonging to each significant pathway in significant categories in the second sample
|
513: # Output 3: sigCatPerPway contains the significant categories in each pathway
|
517: #Input 1: pvalue_results is result from the pathVarTwoSamplesDisc function
|
527: warning("There are no significant pathways. Quitting significant_category function and returning empty object")
|
531: # PathName that were significant in xtab.
|
533: # The list of table with the number of genes in each cluster from the significant pathways
|
540: # results contain the p-value for each category in each pathway computed with the binomial
|
550: # For each significant pathway we look which category(ies) are significant and the genes belonging to this(ese) category(ies). ...(81 bytes skipped)...
|
576: ...(77 bytes skipped)...es cases and then use sigOneSample, sigTwoSamplesCont, or sigTwoSamplesDisc to find the significant pathways.
|
582: #Input 1: pvalue_results is result from the pathVarOneSample, pathVarTwoSamplesCont, or pathVarTwoSamplesDisc function.
|
602: #It is a function that returns the plot of the reference counts along with the plot of a chosen #pathway. This function is made for output from pathVarOneSample.
|
605: # plot of the reference and a pathway counts
|
608: #Input 1: pvalue_results is result from the pathVarOneSample function
|
609: #Input 2: pathway is the chosen pathway you want to plot.
|
613: #If sig is not NULL, the function will check if the pathway is a significant one and if yes the
|
619: plotOneSample <- function(pvalue_results, pathway, sig) {
|
620: mp <- pathway
|
621: # If the name of the pathway is two long it will cut it into two lines in the plot.
|
644: # data frame for the pathway distribution
|
656: # If the pathway is one of the significant ones, the title will be in red. and the categories, if any, we be high...(24 bytes skipped)...
|
657: if (pathway %in% names(category)) {
|
658: sigCat <- category[[pathway]]
|
670: # plot the reference and pathway counts side by side
|
676: ...(34 bytes skipped)... plot of the two densities (one for each group) of the statistics (sd, mad, cv or mean) of a chosen pathway. This function is made for output from pathVarTwoSamplesCont.
|
682: #Input 1: pvalue_results is result from the pathVarTwoSamplesCont function
|
683: #Input 2: pathway is the chosen pathway you want to plot.
|
687: #If sig is not NULL, the function will check if the pathway is a significant one and if yes the title will be printed in red.
|
690: plotTwoSamplesCont <- function(pvalue_results, pathway, sig) {
|
691: mp <- pathway
|
692: # If the name of the pathway is two long it will cut it into two lines in the plot.
|
705: # If the number of genes of the pathway is less than 3, it is not possible to draw a density and it will return an empty plot with this ...(8 bytes skipped)...
|
706: if (xtab[PwayName == pathway, NumOfGenesFromDataSetInPathway] < 3) {
|
717: genes <- pvalue_results@genesInPway[[pathway]]
|
726: ...(0 bytes skipped)... # Plot of the two densities (one for each group) of the variability of the genes inside the pathway.
|
736: # If we included the results of sigTwoSamplesCont, it will verify if the pathway is one of them and if yes the title will be printed in red.
|
737: if (pathway %in% significant) {
|
752: ##It is a function that returns 2 plots of the 2 samples for a chosen pathway. This function is made for output from pathVarTwoSamplesDisc.
|
755: # plot of the 2 samples for a significant pathway
|
758: #Input 1: pvalue_results is result from the pathVarTwoSamplesDisc function
|
759: #Input 2: pathway is the chosen pathway you want to plot.
|
763: #If sig is not NULL, the function will check if the pathway is a significant one and if yes the title will be printed in red.
|
766: plotTwoSamplesDisc <- function(pvalue_results, pathway, sig) {
|
767: mp <- pathway
|
768: # If the name of the pathway is two long it will cut it into two lines in the plot.
|
791: # data frame for the pathway distribution
|
803: # If the pathway is one of the significant ones, the title will be in red. and the categories, if any, we be high...(24 bytes skipped)...
|
804: if (pathway %in% names(category)) {
|
805: sigCat <- category[[pathway]]
|
819: plotPathway1 <- plotPath1 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
821: plotPathway2 <- plotPath2 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
824: # plot the reference and pathway counts side by side
|
854: colnames(pathDat1) <- c("Cluster", "Number_of_genes")
|
856: colnames(pathDat2) <- c("Cluster", "Number_of_genes")
|
862: results <- apply(rbind(pathDat2[,2],pathDat1[,2]/pathDat1[,2]/sum(pathDat1[,2])), 2, function(y) multinomial.test(c(y[1], sum(pathDat2[,2]) - y[1]), prob = c(y[2],1 - y[2]))$p.value)
|
874: plotPathDat1 <- plotPathDat1 + annotate("text", x = category, y = pathDat1[category +
|
876: plotPathDat2 <- plotPathDat2 + annotate("text", x = category, y = pathDat2[category +
|
895: ...(46 bytes skipped)...rom the one sample or two samples cases and then use plotOneSample or plotTwoSamples for the chosen pathway.
|
898: # plot of the results of the one or two samples case for a chosen pathway.
|
901: #Input 1: pvalue_results is the result from the pathVarOneSample, pathVarTwoSamplesCont, or pathVarTwoSamplesDisc function
|
902: #Input 2: pathway is the chosen pathway you want to plot.
|
906: #If sig is not NULL, the function will check if the pathway is a significant one. And they will be highlighted in the resulting plot (see plotOneSample or p...(14 bytes skipped)...
|
909: plotPway <- function(pvalue_results, pathway, sig = NULL) {
|
912: plotOneSample(pvalue_results, pathway, sig)
|
914: plotTwoSamplesCont(pvalue_results, pathway, sig)
|
916: plotTwoSamplesDisc(pvalue_results, pathway, sig)
|
923: #Save as a pdf the plots for the one or two samples case of the significant pathway or a chosen list of pathway..
|
926: # Save as a pdf the plots of the significant pathway or a chosen list of pathway.
|
929: #Input 1: pvalue_results is the result from the pathVarOneSample, pathVarTwoSamplesCont, or pathVarTwoSamplesDisc function
|
931: #Input 3: listPath is "significant" if you want to save the plots of the significant pathways or can be a list of names of pathway of interest.
|
934: #If sig is not NULL, the function will check if the pathway is a significant one. And they will be highlighted in the resulting plot (see plotOneSample or p...(14 bytes skipped)...
|
937: saveAsPDF <- function(pvalue_results, sig, listPath = "significant") {
|
938: # If listPath='significant' we will save as pdf all the plots corresponding to the significant pathway from sig. Other wise it will save the pathways given to listPath.
|
939: if (listPath[1] == "significant") {
|
940: listPath <- names(sig@genesInSigPways1)
|
942: # The name of the file will be the pathname where we replace '/' by '_'
|
948: # save as PDF all the pathways significant or given in listPath
|
949: for (i in 1:length(pathname)) {
|
950: pdf(file = paste(pathname[i], ".pdf", sep = ""), width = 10, height = 7)
|
951: plotPway(pvalue_results, listPath[i], sig)
|
959: #It is a function that returns one list of genes for group 1 and one for group 2 of a chosen pathway having their statistics (sd, mad, cv or mean) inside a chosen interval.
|
962: # Output 1: genes1 contains the genes belonging to the pathway in the given window for group 1.
|
963: # Output 2: genes2 contains the genes belonging to the pathway in the given window for group 2.
|
964: # Output 3: genesAll contains the genes from the dataset belonging to the pathway
|
967: #Input 1: pvalue_results is result from the pathVarTwoSamplesCont function
|
968: #Input 2: pathway is the chosen pathway.
|
973: getGenes <- function(pvalue_results, pathway, window) {
|
978: genes <- olap.pways[[pathway]]
|
981: # Take the genes from group 1 from the pathway belonging to the window
|
983: # Take the genes from group 3 from the pathway belonging to the window
|
985: # Take all the genes from the pathway
|
431: sig <- new("significantPathway", genesInSigPways1=list(), sigCatPerPway=list(), thresPValue=numeric())
|
471: sig <- new("significantPathway", genesInSigPways1=genes, sigCatPerPway=category, thresPValue=pvalue)
|
494: sig <- new("significantPathway2", genesInSigPways1=list(), thresPValue=numeric())
|
501: sig <- new("significantPathway2", genesInSigPways1=genes, thresPValue=pvalue)
|
528: sig <- new("significantPathway3", genesInSigPways1=list(), genesInSigPways2=list(), sigCatPerPway=list(), thresPValue=numeric()...(1 bytes skipped)...
|
570: sig <- new("significantPathway3", genesInSigPways1=genes1, genesInSigPways2=genes2, sigCatPerPway=category, thresPValue=pvalue)...(0 bytes skipped)...
|
667: plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
671: grid.arrange(arrangeGrob(plotRef, plotPathway, nrow = 1))
|
742: plotPathway <- d + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
|
746: plot(plotPathway)
|
825: grid.arrange(arrangeGrob(plotPathway1, plotPathway2, nrow = 1))
|
879: plotPathDat1 <- plotPathDat1 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background...(63 bytes skipped)...
|
880: plotPathDat2 <- plotPathDat2 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background...(63 bytes skipped)...
|
884: grid.arrange(arrangeGrob(plotPathDat1, plotPathDat2, nrow = 1))
|
887: grid.arrange(arrangeGrob(plotPathDat1, plotPathDat2, nrow = 1))
|
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$name[a],"_segments.",segext))
|
961: else {newpath <- sub(dirname(filename),outputdir,filename)}
|
962: fn <- gsub(".csv","_ACE.csv",newpath)
|
MuData:R/write_h5mu.R: [ ] |
---|
313: path <- paste(H5Iget_name(parent), key, sep="/")
|
315: filepath=file, name=path, chunkdim=chunkdim, parent=parent, datasetname=key)
|
gDRstyle:R/build_tools.R: [ ] |
---|
105: remotes::install_local(path = repo_path)
|
15: #' @param base_dir String, path to dir with token file
|
24: gh_access_token_file <- file.path(base_dir, filename)
|
89: #' @param repo_path String of repository directory.
|
100: installLocalPackage <- function(repo_path,
|
133: deps_yaml <- file.path(base_dir, "/dependencies.yaml")
|
snapcount:R/basic_query_functions.R: [ ] |
---|
273: path <- paste(compilation, paste0(endpoint, "?"), sep = "/")
|
319: paste0(pkg_globals$snaptron_host, path, paste(query, collapse = "&"))
|
BiocFileCache:R/BiocFileCache-class.R: [ ] |
---|
497: path <- .sql_get_rpath(x, rids)
|
533: update_time_and_path <- function(x, i) {
|
542: locfile_path <- file.path(bfccache(x), id)
|
404: rpath <- .sql_add_resource(x, rname, rtype, fpath, ext, fname)
|
1009: fpath <- .sql_get_fpath(x, rid)
|
1193: paths <- .sql_get_rpath(x, bfcrid(x))
|
1320: newpath <- file.path(dir, basename(orig))
|
1393: exportPath <- file.path(exdir, "BiocFileCacheExport")
|
1139: rpaths <- .sql_get_rpath(x, rids)
|
1441: rpaths <- .sql_get_rpath(x, rids)
|
34: #' \item{'cache': }{character(1) on-disk location (directory path) of the
|
53: #' \item{'rpath': }{resource path. This is the path to the local
|
74: #' @param cache character(1) On-disk location (directory path) of
|
105: cache <- file.path(tempdir(), "BiocFileCache")
|
213: #' @describeIn BiocFileCache Get a file path for select resources from
|
228: #' @describeIn BiocFileCache Set the file path of selected resources
|
230: #' @param value character(1) Replacement file path.
|
279: #' @return For 'bfcnew': named character(1), the path to save your
|
283: #' path <- bfcnew(bfc0, "NewResource")
|
284: #' path
|
329: #' @param fpath For bfcadd(), character(1) path to current file
|
331: #' assumed to also be the path location. For bfcupdate()
|
334: #' if the resource is a local file, a relative path in the cache,
|
337: #' relative or web paths, based on the path prefix.
|
341: #' in current location but save the path in the cache. If 'rtype
|
357: #' @return For 'bfcadd': named character(1), the path to save your
|
484: #' @return For 'bfcpath': the file path location to load
|
498: path
|
517: #' in the cache the path is returned, if it is not it will try to
|
522: #' @return For 'bfcrpath': The local file path location to load.
|
543: locfile <- .lock2(locfile_path, exclusive = TRUE)
|
552: names(update_time_and_path(x, res))
|
561: .unlock2(locfile_path)
|
564: names(update_time_and_path(x, res))
|
591: update_time_and_path(x, rids)
|
678: "Setting a new remote path results in immediate\n",
|
1077: #' @return For 'bfcdownload': character(1) path to downloaded resource
|
1192: files <- file.path(bfccache(x), setdiff(dir(bfccache(x)),c(.CACHE_FILE, .CACHE_FILE_LOCK)))
|
1261: #' @return character(1) The outputFile path.
|
1283: dir <- file.path(tempdir(), "BiocFileCacheExport")
|
1324: newpath <- file.path(dir, filename)
|
1351: outputFile = file.path(origdir, outputFile)
|
1361: .util_unlink(file.path(dir, .CACHE_FILE_LOCK))
|
57: #' \item{'fpath': }{If rtype is "web", this is the link to the
|
217: #' @return For '[[': named character(1) rpath for the given resource
|
225: .sql_get_rpath(x, i)
|
240: .sql_set_rpath(x, i, value)
|
243: warning("updating rpath, changing rtype to 'local'")
|
304: x, rname, fpath = rname, rtype=c("auto", "relative", "local", "web"),
|
317: x, rname, fpath = rname, rtype=c("auto", "relative", "local", "web"),
|
323: bfcadd(x=BiocFileCache(), rname=rname, fpath=fpath, rtype=rtype,
|
339: #' \code{copy} of \code{fpath} in the cache directory; \code{move}
|
376: #' bfcadd(bfc0, "TestWeb", fpath=url)
|
381: x, rname, fpath = rname,
|
389: is.character(fpath), length(fpath) > 0L, !any(is.na(fpath))
|
395: stopifnot((length(action) == 1) || (length(action) == length(fpath)))
|
396: stopifnot((length(rtype) == 1) || (length(rtype) == length(fpath)))
|
397: if (length(action) == 1) action = rep(action, length(fpath))
|
398: if (length(rtype) == 1) rtype = rep(rtype, length(fpath))
|
400: rtype <- .util_standardize_rtype(rtype, fpath, action)
|
401: stopifnot(all(rtype == "web" | file.exists(fpath)))
|
405: rid <- names(rpath)
|
407: for(i in seq_along(rpath)){
|
411: copy = file.copy(fpath[i], rpath[i]),
|
412: move = file.rename(fpath[i], rpath[i]),
|
414: .sql_set_rpath(x, rid[i], fpath[i])
|
415: rpath[i] <- bfcrpath(x, rids = rid[i])
|
423: rpath
|
457: tbl <- mutate(tbl, rpath = unname(bfcrpath(x, rids=rids)))
|
469: setGeneric("bfcpath",
|
470: function(x, rids) standardGeneric("bfcpath"),
|
475: #' @aliases bfcpath,missing-method
|
476: #' @exportMethod bfcpath
|
477: setMethod("bfcpath", "missing",
|
480: bfcpath(x=BiocFileCache(), rids=rids)
|
486: #' bfcpath(bfc0, rid3)
|
487: #' @aliases bfcpath
|
488: #' @exportMethod bfcpath
|
489: setMethod("bfcpath", "BiocFileCacheBase",
|
502: setGeneric("bfcrpath",
|
503: function(x, rnames, ..., rids, exact = TRUE) standardGeneric("bfcrpath"),
|
508: #' @aliases bfcrpath,missing-method
|
509: #' @exportMethod bfcrpath
|
510: setMethod("bfcrpath", "missing",
|
513: bfcrpath(x=BiocFileCache(), rnames=rnames, ..., rids=rids, exact=exact)
|
516: #' @describeIn BiocFileCache display rpath of resource. If 'rnames' is
|
524: #' bfcrpath(bfc0, rids = rid3)
|
525: #' @aliases bfcrpath
|
526: #' @exportMethod bfcrpath
|
527: setMethod("bfcrpath", "BiocFileCacheBase",
|
534: .sql_get_rpath(x, i)
|
588: bfcrpath(x, rids = rids0)
|
611: #' @param rpath character() vector of replacement rpaths.
|
615: #' bfcupdate(bfc0, rid3, rpath=fl3, rname="NewRname")
|
617: #' bfcupdate(bfc0, "BFC5", fpath="http://google.com")
|
621: function(x, rids, ..., rname=NULL, rpath=NULL, fpath=NULL,
|
627: is.null(rpath) || (length(rids) == length(rpath)),
|
628: is.null(fpath) || (length(rids) == length(fpath))
|
632: is.null(rpath) || is.character(rpath),
|
633: is.null(fpath) || is.character(fpath)
|
636: if(is.null(rname) && is.null(rpath) && is.null(fpath)) {
|
638: "\n Please set rname, rpath, or fpath",
|
652: if (!is.null(rpath)) {
|
653: if (!file.exists(rpath[i]))
|
657: "\n rpath: ", sQuote(rpath[i]),
|
658: "\n reason: rpath does not exist.",
|
661: .sql_set_rpath(x, rids[i], rpath[i])
|
664: warning("updating rpath, changing rtype to 'local'")
|
669: if (!is.null(fpath)) {
|
687: x, rids[i], proxy, config, "bfcupdate()", fpath[i], ...
|
689: .sql_set_fpath(x, rids[i], fpath[i])
|
871: function(x, query, field=c("rname", "rpath", "fpath"), ..., exact = FALSE)
|
880: function(x, query, field=c("rname", "rpath", "fpath"), ..., exact = FALSE)
|
893: #' matches pattern agains rname, rpath, and fpath. If exact
|
898: #' \code{bfcrpath}, the default is \code{TRUE} (exact matching).
|
910: function(x, query, field=c("rname", "rpath", "fpath"), ..., exact = FALSE)
|
986: #' 'rid'. \code{TRUE}: fpath \code{etag} or \code{modified} time of
|
987: #' web resource more recent than in BiocFileCache; \code{FALSE}: fpath
|
1012: cache_info <- .httr_get_cache_info(fpath)
|
1094: if (ask && any(file.exists(.sql_get_rpath(x, rid)))) {
|
1105: bfcrpath(x, rids=rid)
|
1194: # normalizePath on windows
|
1197: files = normalizePath(files)
|
1198: paths = normalizePath(paths)
|
1200: untracked <- setdiff(files, paths)
|
1255: #' @param outputFile character(1) The <filepath>/basename for the
|
1319: orig <- .sql_get_rpath(x, i)
|
1321: if (file.exists(newpath)) {
|
1326: file.copy(orig, newpath)
|
1394: stopifnot(!dir.exists(exportPath))
|
1404: bfc = BiocFileCache(exportPath)
|
349: #' \code{httr::GET}. For 'bfcrpaths': Additional arguments passed
|
483: #' @describeIn BiocFileCache display rpaths of resource.
|
1140: cached <- startsWith(rpaths, bfccache(x))
|
1143: status <- .util_unlink(rpaths[cached])
|
1442: cached <- startsWith(rpaths, bfccache(x))
|
1445: txt0 <- paste("file ", sQuote(rpaths))
|
1454: .util_unlink(rpaths[cached])
|
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)})
|
mdp:R/mdp.R: [ ] |
---|
71: path = directory
|
497: path = directory
|
193: pathway_results <- pathway_summary(sample_results,
|
705: pathway_summary <- function(sample_results, path, file_name,
|
720: pathway_scores <- data.frame(Geneset = names(sample_results),
|
726: top_pathway <- pathway_scores[1:3, "Geneset"]
|
76: path = "."
|
178: directory = path,
|
186: directory = path,
|
194: path, file_name,
|
205: file = file.path(path, paste0(file_name, "zscore.tsv")),
|
208: file = file.path(path, paste0(file_name, "gene_scores.tsv")),
|
211: file = file.path(path, paste0(file_name, "sample_scores.tsv")),
|
503: path = "."
|
628: grDevices::pdf(file.path(path, sample_name))
|
699: #' @param path directory to save images
|
734: directory = path, title = top_pathway,
|
15: #' @param pathways (optional) \code{list} whose names are pathways and elements are
|
16: #' genes in the pathway. see details section for more information
|
41: #' \item Pathways - if genesets are provided, they are ranked according to the
|
48: #' # run with pathways
|
49: #' pathway_file <- system.file('extdata', 'ReactomePathways.gmt',
|
51: #' mypathway <- fgsea::gmtPathways(pathway_file) # load a gmt file
|
53: #' pathways=mypathway)
|
56: #' @section Loading pathways:
|
57: #' a \code{list} of pathways can be loaded from a .gmt file using the
|
63: mdp <- function(data, pdata, control_lab, directory = "", pathways,
|
118: if (!missing(pathways)) {
|
119: if (!is.list(pathways)) {
|
120: stop("Please provide pathways in a list format (see help for more details")
|
167: pathways, pdata)
|
192: if (!missing(pathways)) {
|
217: if (missing(pathways)) {
|
237: pathway_results)
|
244: "pathways")
|
427: #' Compute sample scores for each pathway
|
432: #' @param pathways list of pathways
|
436: test_samples, pathways, pdata) {
|
443: if (!missing(pathways)) {
|
444: genesets <- c(genesets, pathways)
|
696: #' print pathways
|
697: #' generates a summary plot for pathways and sample score plot of best gene set
|
704: #' for each pathway
|
723: pathway_scores <- pathway_scores[order(-pathway_scores$Sig2noise), ]
|
725: # find best pathway
|
727: top_pathway <- top_pathway[top_pathway != "allgenes" &
|
728: top_pathway != "perturbedgenes"]
|
729: top_pathway <- top_pathway[1]
|
732: sample_plot(sample_results[[top_pathway]],
|
738: return(pathway_scores)
|
58: #' \code{fgsea} function using \code{fgsea::gmtPathways('gmt.file.location')}
|
733: filename = paste0(file_name, "bestPathway"),
|
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")
|
BEclear:R/imputeMissingDataForBlock.R: [ ] |
---|
83: path <- paste(dir, filename, sep = "/")
|
84: save(D1, file = path)
|
spikeLI:R/collapse.R: [ ] |
---|
18: path <- system("pwd",TRUE);
|
20: {postscript(paste(path,paste("/",probe_set[1],sep=""),sep=""));}
|
21: else {postscript(paste(path,paste("/",filename,sep=""),sep=""));}
|
TrajectoryGeometry:R/TrajectoryGeometry.R: [ ] |
---|
796: path = samplePath(attributes, pseudotime, nWindows = nWindows)
|
302: pathToSphericalData = function(path,from,to,d,statistic)
|
431: randomPath = path
|
492: randomPath = matrix(0,nrow=n,ncol=d)
|
628: pathProgression = function(path,from=1,to=nrow(path),d=ncol(path),
|
670: samplePath = function(attributes, pseudotime, nWindows = 10){
|
678: pathLength = end - start
|
681: sampledPath = matrix(, nrow = 0, ncol = ncol(attributes))
|
1135: pathLineWidth = 3
|
45: testPathForDirectionality = function(path,from=1,to=nrow(path),d=ncol(path),
|
61: randomPaths = generateRandomPaths(path,
|
106: projectPathToSphere = function(path,from=1,to=nrow(path),d=ncol(path))
|
374: generateRandomPaths = function(path,from=1,to=nrow(path),d=ncol(path),
|
421: randomPathList = list()
|
475: generateRandomPathsBySteps = function(path,randomizationParams,N)
|
489: randomPathList = list()
|
564: getDistanceDataForPaths = function(paths,statistic)
|
1114: plotPathProjectionCenterAndCircle = function(path,
|
7: #' Test a path for directionality
|
9: #' This is the core function of this package. It takes a path, and a
|
11: #' for the directionality of that path.
|
13: #' @param path - An n x m matrix representing a series of n points in
|
15: #' @param from - The starting place along the path which will be
|
17: #' @param to - The end point of the path. This defaults to
|
18: #' nrow(path).
|
20: #' ncol(path)
|
25: #' comparison to the given path.
|
27: #' pValue - the p-value for the path and statistic in question;
|
28: #' sphericalData - a list containing the projections of the path to
|
39: #' p = testPathForDirectionality(path=straight_path,
|
42: #' q = testPathForDirectionality(path=crooked_path,from=6,
|
48: testPathForDirectionalityTest(path,from,to,d,
|
53: path = path[from:to,seq_len(d)]
|
57: sphericalData = getSphericalData(path,statistic)
|
86: #' Project a path onto the unit sphere
|
88: #' This function takes a path in d dimensional space and projects it onto
|
92: #' @param path - This is an mxn dimensional matrix. Each row is
|
94: #' @param from - The starting place along the path which will be
|
96: #' @param to - The end point of the path. This defaults to
|
97: #' nrow(path).
|
99: #' ncol(path)
|
100: #' @return This returns a projection of the path onto the d-1 sphere
|
104: #' projection1 = projectPathToSphere(straight_path)
|
105: #' projection2 = projectPathToSphere(crooked_path,from=6)
|
108: projectPathToSphereTest(path,from,to,d)
|
112: path = path[from:to,seq_len(d)]
|
113: n = nrow(path)
|
121: v = path[i,] - path[1,]
|
150: #' projection = projectPathToSphere(straight_path)
|
221: #' distances = findSphericalDistance(straight_path_center,
|
222: #' straight_path_projection)
|
253: #' given by the dimensions of the path
|
255: #' @param path - an m x n matrix. Each row is considered a point
|
258: #' projections of the path to the sphere, the center for those
|
263: #' sphericalData = getSphericalData(straight_path,'max')
|
264: getSphericalData = function(path,statistic)
|
266: getSphericalDataTest(path,statistic)
|
269: to = nrow(path)
|
270: d = ncol(path)
|
272: return(pathToSphericalData(path,from,to,d,statistic))
|
277: #' Find the spherical data for a given path
|
279: #' This function takes a path and returns a list containing
|
284: #' @param path - This is an mxn dimensional matrix. Each row is
|
286: #' @param from - The starting place along the path which will be
|
288: #' @param to - The end point of the path. This defaults to
|
289: #' nrow(path).
|
291: #' ncol(path)
|
294: #' projections of the path to the sphere, the center for those
|
299: #' sphericalData = pathToSphericalData(straight_path,from=1,
|
300: #' to=nrow(straight_path), d=3,
|
304: pathToSphericalDataTest(path,from,to,d,statistic)
|
309: path = path[from:to,seq_len(d)]
|
310: n = nrow(path)
|
313: ## Get the projections of the path to the sphere
|
314: projections = projectPathToSphere(path)
|
345: #' Produce random paths modeled on a given path
|
347: #' This function takes a path and produces N random paths of the same
|
349: #' permuting the entries in path or by taking steps from the initial
|
350: #' point of path. Exact behaviour is controlled by
|
353: #' @param path - This is an mxn dimensional matrix. Each row is
|
355: #' @param from - The starting place along the path which will be
|
357: #' @param to - The end point of the path. This defaults to
|
358: #' nrow(path).
|
360: #' ncol(path)
|
366: #' @return This function returns a list of random paths. Each path is
|
371: #' randomPaths = generateRandomPaths(crooked_path,from=6,to=nrow(crooked_path),
|
372: #' d=ncol(crooked_path),randomizationParams=randomizationParams,
|
377: generateRandomPathsTest(path,from,to,d,randomizationParams,N)
|
388: path = path[from:to,seq_len(d)]
|
391: return(generateRandomPathsByPermutation(path,
|
396: return(generateRandomPathsBySteps(path,
|
407: ## ' This function produces randomized paths from a given path via
|
413: ## ' @param path - An n x d matrix.
|
419: function(path,randomizationParams,N)
|
422: n = nrow(path)
|
423: d = ncol(path)
|
447: a = as.numeric(path)
|
450: b = as.numeric(path)
|
463: ## ' This function produces randomized paths from a given path by taking
|
465: ## ' have the same Euclidean length as those of the original path or
|
467: ## ' requiring the path to stay in the non-negative orthant or allowing
|
470: ## ' @param path - An n x d matrix.
|
477: n = nrow(path)
|
478: d = ncol(path)
|
484: stepLengths = getStepLengths(path)
|
493: randomPath[1,] = path[1,]
|
515: #' This finds the lengths of the steps along a path
|
517: #' @param path - This is an mxn dimensional matrix. Each row is
|
519: #' @param from - The starting place along the path which will be
|
521: #' @param to - The end point of the path. This defaults to
|
522: #' nrow(path).
|
524: #' ncol(path)
|
525: #' @return This function returns the length of each step in a path.
|
528: #' stepLengths = getStepLengths(path=crooked_path)
|
529: #' stepLengths = getStepLengths(path=crooked_path,from=4)
|
530: getStepLengths = function(path,from=1,to=nrow(path),d=ncol(path))
|
532: getStepLengthsTest(path,from,to,d)
|
536: path = path[from:to,seq_len(d)]
|
537: n = nrow(path)
|
541: stepLengths[i] = Norm(path[i+1,] - path[i,])
|
551: #' for the appropriate center for each path. Each path
|
562: #' generateRandomPaths(path=straight_path,randomizationParam='bySteps',N=5)
|
604: #' Measure a path's progression
|
606: #' This function measures the progress of a path in a specified
|
611: #' @param path - An n x d matrix
|
612: #' @param from - The point along the path to be taken as the starting
|
614: #' @param to - The point along the path to be used as the end point.
|
615: #' This defaults to nrow(path).
|
616: #' @param d - The dimension to be used. This defaults to ncol(path).
|
620: #' of the path along the line through its starting point in the
|
625: #' pathProgression(straight_path,direction=straight_path_center)
|
627: #' pathProgression(crooked_path,from=6,direction=crooked_path_center)
|
631: pathProgressionTest(path,from,to,d,direction)
|
633: path = path[from:to,seq_len(d)]
|
635: distance = numeric(nrow(path)-1)
|
636: for(i in 2:nrow(path))
|
638: delta = path[i,] - path[1,]
|
646: #' Sample a path from single cell data
|
652: #' coordinates of the sampled path. The matrix of attribute values should
|
657: #' the sampled path give the window number a cell was sampled from.
|
664: #' @return sampledPath - A path consisting of a matrix of attributes of sampled
|
675: ## Set parameters for path.
|
726: #' and comparing each path to random paths.
|
731: #' The function returns a list of Answers for each comparison of a sampled path
|
732: #' to a random path.
|
746: #' comparison to the given path (defaults to 1000).
|
748: #' information comparing a sampled path to random paths.
|
750: #' pValue - the p-value for the path and statistic in question;
|
751: #' sphericalData - a list containing the projections of the path to
|
797: answers[[i]] = testPathForDirectionality(path, randomizationParams =
|
835: #' comparison to the given path (defaults to 1000).
|
839: #' entry for each sampled path.
|
841: #' sampled path in question
|
843: #' pValue - the p-value for the path and statistic in question;
|
844: #' sphericalData - a list containing the projections of the path to
|
1070: #' Plot a path, its projection, its center and its circle
|
1072: #' This function assumes you have a path in dimension 3 and you have
|
1075: #' appropriate statistic. Scales the path to keep it comparable to
|
1079: #' @param path - A path of dimension 3 in the form of an N x 3 matrix.
|
1083: #' @param to - Likewise. It defaults to nrow(path).
|
1085: #' path.
|
1088: #' @param color - The color to use for this path and its associated
|
1093: #' path. Defaults to 8.
|
1095: #' projected path. Defaults to 8.
|
1096: #' @param scale - The path will be start (its actual start) at 0 and
|
1108: #' plotPathProjectionCenterAndCircle(path=straight_path,
|
1109: #' projection=straight_path_projection,
|
1110: #' center=straight_path_center,
|
1111: #' radius=straight_path_radius,
|
1116: to=nrow(path),
|
1127: plotPathProjectionCenterAndCircleTest(path,from,to,projection,
|
1143: ## Translate the path to begin at the origin and scale:
|
1144: N = nrow(path)
|
1146: offset = path[1,]
|
1149: path[i,] = path[i,] - offset
|
1150: distances[i] = Norm(path[i,])
|
1152: path = (scale / max(distances)) * path
|
1163: ## Plot the path and mark the relevant portion:
|
1164: points3d(path,size=pathPointSize,color=color)
|
1165: lines3d(path,lwd=pathLineWidth,color=color)
|
1167: points3d(path[from:to,],size=pathPointSize+relevantPortionPointHump,
|
1169: lines3d(path[from:to,],lwd=pathLineWidth+relevantPortionLineHump,
|
4: ## Code for testing directionality in paths:
|
23: #' control the production of randomized paths for comparison.
|
24: #' @param N - The number of random paths to generated for statistical
|
32: #' paths;
|
60: ## Generate random paths:
|
66: ## Compute the distance statistics for random paths:
|
250: #' This is a simplified wrapper for pathToSphericalData
|
365: #' @param N - The number of random paths required.
|
404: ## ' Produce randomized paths by permutation
|
416: ## ' @param N - The number of paths required.
|
417: ## ' @return This function returns a list of random paths.
|
435: randomPath[,j] = randomPath[perm,j]
|
437: randomPathList[[i]] = randomPath
|
460: ## ' Produce randomized paths by steps
|
473: ## ' @param N - The number of paths required.
|
474: ## ' @return This function returns a list of random paths.
|
488: ## Generate the random paths:
|
496: randomPath[j+1,] = randomPath[j,] +
|
501: idx = randomPath < 0
|
502: randomPath[idx] = - randomPath[idx]
|
505: randomPathList[[i]] = randomPath
|
547: #' Produce distance statistics for random paths
|
549: #' This function takes a list of paths and a choice of
|
553: #' will be the randomized paths. It is therefore assumed
|
556: #' @param paths - A list of paths. Each of these is an n x d matrix.
|
561: #' paths =
|
563: #' distance = getDistanceDataForPaths(paths=paths,statistic='max')
|
566: getDistanceDataForPathsTest(paths,statistic)
|
568: n = nrow(paths[[1]])
|
569: N = length(paths)
|
573: ## Iterate over paths:
|
576: sphericalData = getSphericalData(paths[[i]],statistic)
|
668: #' samplePath(chol_attributes, chol_pseudo_time_normalised)
|
669: #' samplePath(hep_attributes, hep_pseudo_time_normalised)
|
679: windowSize = pathLength/nWindows
|
710: sampledPath = rbind(sampledPath, chosenAttributes)
|
717: rownames(sampledPath) = windowNumber
|
718: return(sampledPath)
|
725: #' This function analyses a single cell trajectory by sampling multiple paths
|
738: #' control the production of randomized paths for comparison.
|
740: #' @param nSamples - The number of sampled paths to generate (default 1000).
|
745: #' @param N - The number of random paths to generated for statistical
|
755: #' paths;
|
794: ## Sample paths and test each one for directionality
|
800: print(paste(i, "sampled paths analysed"))
|
818: #' control the production of randomized paths for comparison.
|
829: #' @param nSamples - The number of sampled paths to generate (defaults to 1000).
|
834: #' @param N - The number of random paths to generated for statistical
|
842: #' to random paths. The entries consist of:
|
848: #' paths;
|
967: ## Code for plotting paths and their spherical data:
|
1077: #' called repeatedly to add additional paths in different colors.
|
1092: #' @param pathPointSize - Sets the size of points which represent the
|
1100: #' here when plotting multiple paths.
|
1104: #' additional paths to the same figure.
|
1122: pathPointSize = 8,
|
1129: pathPointSize,projectionPointSize,
|
1194: #' metrics for sampled paths to random paths. It can also create plots for
|
1195: #' comparing two sets of sampled paths by providing the traj2Data argument.
|
1205: #' stats - output of wilcox test (paired if comparing sampled to random paths,
|
1206: #' unpaired if comparing sampled paths for two different trajectories)
|
1270: ## Code for comparing sampled pathways to random pathways
|
1286: ## Use paired wilcox test to compare values sampled and random pathways,
|
1287: ## as random trajectories are parametised based on the sampled pathways
|
1317: #' paths for different trajectory different starting points
|
1319: #' comparing sampled paths to random paths for different trajectory starting
|
67: distances = getDistanceDataForPaths(randomPaths,statistic)
|
418: generateRandomPathsByPermutation =
|
439: return(randomPathList)
|
453: randomPathList[[i]] = matrix(b,nrow=n)
|
455: return(randomPathList)
|
508: return(randomPathList)
|
672: samplePathTest(attributes, pseudotime, nWindows)
|
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.
|
biodb:R/BiodbConn.R: [ ] |
---|
734: path <- cch$getFilePath(self$getCacheId(), name='download', ext=ext)
|
147: #' Get the path to the persistent cache file.
|
727: #' Gets the path where the downloaded content is written.
|
728: #' @return The path where the downloaded database is written.
|
736: logDebug0('Download path of ', self$getId(), ' is "', path, '".')
|
738: return(path)
|
743: #' @param src Path to the downloaded file.
|
151: #' containing the paths to the cache files corresponding to the requested
|
156: fp <- c$getFilePath(self$getCacheId(), entry.id, self$getEntryFileExt())
|
729: getDownloadPath=function() {
|
793: if ( ! file.exists(self$getDownloadPath()))
|
795: self$getDownloadPath())
|
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,
|
alabaster.ranges:R/readAtomicVectorList.R: [ ] |
---|
69: path <- acquireFile(project, info$path)
|
33: fpath <- file.path(path, "partitions.h5")
|
5: #' @param path String containing a path to a directory, itself created with the \code{\link{saveObject}} method for \linkS4class{Compressed...(13 bytes skipped)...
|
25: readAtomicVectorList <- function(path, ...) {
|
26: .read_compressed_list(path, "atomic_vector_list", ...)
|
30: .read_compressed_list <- function(path, name, ...) {
|
31: concat <- altReadObject(file.path(path, "concatenated"), ...)
|
47: mcols.path=file.path(path, "element_annotations"),
|
48: metadata.path=file.path(path, "other_annotations"),
|
59: concat.info <- acquireMetadata(project, info$atomic_vector_list$concatenated$resource$path)
|
72: groups <- .quickReadCsv(path,
|
34: fhandle <- H5Fopen(fpath)
|
TEQC:R/multiTEQCreport.R: [ ] |
---|
42: Path <- getwd()
|
44: print(paste("results and report are saved in folder", Path))
|
47: imgDir <- file.path(destDir, "image")
|
82: write.table(speci, file=file.path(destDir, "fractionReadsOnTarget.txt"), sep="\t", quote=FALSE)
|
86: write.table(targcov, file=file.path(destDir, "targetCoverageStats.txt"), sep="\t", quote=FALSE)
|
100: write.table(sensi, file=file.path(destDir, "sensitivity.txt"), sep="\t", row.names=FALSE, quote=FALSE)
|
103: write.table(perTargCov, file=file.path(destDir, "targetCoverage.txt"), sep="\t", quote=FALSE)
|
136: htmlFile <- file.path(destDir, "index.html")
|
150: file.copy(cssFile, file.path(destDir, names(cssFile)))
|
151: file.copy(system.file("template", "image", biocFile, package="TEQC"), file.path(destDir, "image", biocFile))
|
167: imgDir <- file.path(dir, "image")
|
170: jpeg(file.path(imgDir, figFile), ...)
|
172: png(file.path(imgDir, figFile), ...)
|
174: tiff(file.path(imgDir, figFile), ...)
|
178: pdf(file.path(imgDir, pdfFile), ...)
|
183: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
207: imgDir <- file.path(dir, "image")
|
210: jpeg(file.path(imgDir, figFile), ...)
|
212: png(file.path(imgDir, figFile), ...)
|
214: tiff(file.path(imgDir, figFile), ...)
|
218: pdf(file.path(imgDir, pdfFile), ...)
|
223: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
246: imgDir <- file.path(dir, "image")
|
249: jpeg(file.path(imgDir, figFile), ...)
|
251: png(file.path(imgDir, figFile), ...)
|
253: tiff(file.path(imgDir, figFile), ...)
|
257: pdf(file.path(imgDir, pdfFile), ...)
|
262: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
289: imgDir <- file.path(dir, "image")
|
292: jpeg(file.path(imgDir, figFile), width=1000, ...)
|
294: png(file.path(imgDir, figFile), width=1000, ...)
|
296: tiff(file.path(imgDir, figFile),width=1000, ...)
|
300: pdf(file.path(imgDir, pdfFile), width=14, ...)
|
305: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
335: imgDir <- file.path(dir, "image")
|
338: jpeg(file.path(imgDir, figFile), width=800, height=800, ...)
|
340: png(file.path(imgDir, figFile), width=800, height=800, ...)
|
342: tiff(file.path(imgDir, figFile), width=800, height=800, ...)
|
346: pdf(file.path(imgDir, pdfFile), width=12, height=12, ...)
|
351: hwriteImage(file.path(".", "image", figFile), link=file.path(".", "image", pdfFile))
|
ORFik:R/experiment.R: [ ] |
---|
22: for (path in filepaths) {
|
663: save_path <- file.path(out_dir, paste0(name, ".ofst"))
|
803: cbu.path <- "/export/valenfs/data/processed_data/experiment_tables_for_R/"
|
13: findFromPath <- function(filepaths, candidates, slot = "auto") {
|
253: filepath <- function(df, type, basename = FALSE,
|
263: paths <- lapply(df$filepath, function(x, df, type) {
|
335: filepath_errors <- function(format) {
|
661: specific_paths <- filepaths[libs[[name]]]
|
94: reversePaths <- df$reverse[!(df$reverse %in% c("", "paired-end"))]
|
659: 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))))
|
234: #' path to base folder to search for library variant directories.
|
235: #' If single path (length == 1), it will apply to all libraries in df.
|
249: #' # Other format path
|
271: out.dir.type <- file.path(base_folder, rel_folder["pshifted"])
|
296: out.dir.type <- file.path(base_folder, rel_folder[t])
|
621: #' @param out_dir Ouput directory, default \code{file.path(dirname(df$filepath[1]), "ofst_merged")},
|
635: #' #fimport(file.path(tempdir(), "all.ofst"))
|
637: #' #read_fst(file.path(tempdir(), "all.ofst"))
|
642: mergeLibs <- function(df, out_dir = file.path(libFolder(df), "ofst_merged"), mode = "all",
|
664: write_fst(ofst_merge(specific_paths, specific_names, keep_all_scores), save_path)
|
718: fext[compressed] <-file_ext(file_path_sans_ext(files[compressed],
|
792: #' ## Path above is default path, so no dir argument needed
|
796: #' #list.experiments(dir = "MY/CUSTOM/PATH)
|
801: experiments <- list.files(path = dir, pattern = "\\.csv")
|
804: if (dir.exists(cbu.path)) { # If on UIB SERVER
|
805: dir <- cbu.path
|
806: experiments <- list.files(path = dir, pattern = "\\.csv")
|
91: files <- df$filepath
|
92: if (length(df$filepath) == 0) stop("df have no filepaths!")
|
154: #' Get variable name per filepath in experiment
|
226: #' Get relative paths instead of full. Only use for inspection!
|
239: #' @return a character vector of paths, or a list of character with 2 paths per,
|
246: #' filepath(df, "default")
|
248: #' filepath(df[9,], "default")
|
250: #' filepath(df[9,], "ofst")
|
252: #' filepath(df[9,], "pshifted") # <- falls back to ofst
|
264: i <- which(df$filepath == x)
|
288: } else stop(filepath_errors(type))
|
307: } else stop(filepath_errors(t))
|
325: if (is.null(input)) stop("filepath type not valid!")
|
329: if (all(lengths(paths) == 1)) {
|
330: paths <- unlist(paths)
|
332: return(paths)
|
371: #' @param paths character vector, the filpaths to use,
|
372: #' default \code{filepath(df, type)}. Change type argument if not correct.
|
419: outputLibs <- function(df, type = "default", paths = filepath(df, type),
|
444: libs <- lapply(seq_along(paths),
|
445: function(i, paths, df, chrStyle, param, strandMode, varNames, verbose) {
|
447: fimport(paths[i], chrStyle, param, strandMode)
|
448: }, paths = paths, chrStyle = chrStyle, df = df,
|
452: libs <- bplapply(seq_along(paths),
|
453: function(i, paths, df, chrStyle, param, strandMode, varNames, verbose) {
|
455: fimport(paths[i], chrStyle, param, strandMode)
|
456: }, paths = paths, chrStyle = chrStyle, df = df,
|
589: remove.file_ext(df$filepath[i], basename = TRUE),
|
10: #' else must be a character vector of length 1 or equal length as filepaths.
|
15: if(length(slot) != 1 & length(slot) != length(filepaths)) {
|
95: files <- c(files, reversePaths)
|
114: stop("Duplicated filepaths in experiment!")
|
213: #' Get filepaths to ORFik experiment
|
217: #' then default filepaths without warning. \cr
|
epivizrServer:R/middleware-plus-supporting.R: [ ] |
---|
438: path <- req$PATH_INFO
|
217: abs.path <- file.path(dir, relpath)
|
446: abs.path <- resolve(root, path)
|
367: pathPattern <- paste("^\\Q", prefix, "\\E/", sep = "")
|
371: origPath <- req$PATH_INFO
|
376: pathInfo <- substr(req$PATH_INFO, nchar(prefix)+1, nchar(req$PATH_INFO))
|
402: pathPattern <- paste("^\\Q", prefix, "\\E/", sep = "")
|
407: origPath <- req$PATH_INFO
|
412: pathInfo <- substr(req$PATH_INFO, nchar(prefix)+1, nchar(req$PATH_INFO))
|
205: # Attempt to join a path and relative path, and turn the result into a
|
206: # (normalized) absolute path. The result will only be returned if it is an
|
218: if (!file.exists(abs.path))
|
220: abs.path <- normalizePath(abs.path, winslash='/', mustWork=TRUE)
|
224: if (nchar(abs.path) <= nchar(dir) + 1)
|
226: if (substr(abs.path, 1, nchar(dir)) != dir ||
|
227: substr(abs.path, nchar(dir)+1, nchar(dir)+1) != '/') {
|
230: return(abs.path)
|
344: # `PATH_INFO` field, but since it's such a common need, let's make it simple by
|
349: # the route off of the `PATH_INFO` (and add it to the end of `SCRIPT_NAME`).
|
351: # path has already been matched via routing.
|
369: if (isTRUE(grepl(pathPattern, req$PATH_INFO))) {
|
374: req$PATH_INFO <- origPath
|
378: req$PATH_INFO <- pathInfo
|
405: if (isTRUE(grepl(pathPattern, req$PATH_INFO))) {
|
410: req$PATH_INFO <- origPath
|
414: req$PATH_INFO <- pathInfo
|
440: if (is.null(path))
|
443: if (path == '/')
|
444: path <- '/index.html'
|
447: if (is.null(abs.path))
|
450: content.type <- mime::guess_type(abs.path)
|
451: response.content <- readBin(abs.path, 'raw', n=file.info(abs.path)$size)
|
216: resolve <- function(dir, relpath) {
|
221: dir <- normalizePath(dir, winslash='/', mustWork=TRUE)
|
295: # representing paths to be used instead of handlers; any such strings we
|
TPP2D:R/import_funcs.R: [ ] |
---|
348: Experiment <- Path <- Compound <- NULL
|
570: Path <- label <- conc <- Compound <- Experiment <-
|
371: givenPaths <- NULL
|
253: "Experiment", "Path", "Path",
|
254: "Path", "Condition", "Replicate",
|
334: #' @param infoTable character string of a file path to
|
372: if (any("Path" %in% colnames(infoTable))) {
|
373: if (all(infoTable$Path == "") || all(is.na(infoTable$Path))) {
|
374: message("Removing empty 'Path' column from config table")
|
375: infoTable <- infoTable %>% select(-Path)
|
378: givenPaths <- infoTable$Path
|
443: #' @param configTable character string of a file path to a config table
|
485: files <- configTable$Path
|
511: "RefCol", "Path", "Condition")
|
573: if(any(grepl("Path", colnames(configWide)))){
|
575: dplyr::select(-Path) %>%
|
756: #' @param configTable character string of a file path to a config table
|
433: expCond = infoTable$Condition, files = givenPaths,
|
RProtoBufLib:inst/include/cytolib/GatingSet.pb.h: [ ] |
---|
6488: inline const std::string& BOOL_GATE_OP::path(int index) const {
|
6536: BOOL_GATE_OP::path() const {
|
6482: inline int BOOL_GATE_OP::path_size() const {
|
6485: inline void BOOL_GATE_OP::clear_path() {
|
6492: inline std::string* BOOL_GATE_OP::mutable_path(int index) {
|
6496: inline void BOOL_GATE_OP::set_path(int index, const std::string& value) {
|
6500: inline void BOOL_GATE_OP::set_path(int index, std::string&& value) {
|
6504: inline void BOOL_GATE_OP::set_path(int index, const char* value) {
|
6509: inline void BOOL_GATE_OP::set_path(int index, const char* value, size_t size) {
|
6514: inline std::string* BOOL_GATE_OP::add_path() {
|
6518: inline void BOOL_GATE_OP::add_path(const std::string& value) {
|
6522: inline void BOOL_GATE_OP::add_path(std::string&& value) {
|
6526: inline void BOOL_GATE_OP::add_path(const char* value) {
|
6531: inline void BOOL_GATE_OP::add_path(const char* value, size_t size) {
|
6541: BOOL_GATE_OP::mutable_path() {
|
1576: static const int kPathFieldNumber = 1;
|
1573: // repeated string path = 1;
|
1574: int path_size() const;
|
1575: void clear_path();
|
1577: const std::string& path(int index) const;
|
1578: std::string* mutable_path(int index);
|
1579: void set_path(int index, const std::string& value);
|
1580: void set_path(int index, std::string&& value);
|
1581: void set_path(int index, const char* value);
|
1582: void set_path(int index, const char* value, size_t size);
|
1583: std::string* add_path();
|
1584: void add_path(const std::string& value);
|
1585: void add_path(std::string&& value);
|
1586: void add_path(const char* value);
|
1587: void add_path(const char* value, size_t size);
|
1588: const ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string>& path() const;
|
1589: ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string>* mutable_path();
|
1608: ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string> path_;
|
6481: // repeated string path = 1;
|
6483: return path_.size();
|
6486: path_.Clear();
|
6489: // @@protoc_insertion_point(field_get:pb.BOOL_GATE_OP.path)
|
6490: return path_.Get(index);
|
6493: // @@protoc_insertion_point(field_mutable:pb.BOOL_GATE_OP.path)
|
6494: return path_.Mutable(index);
|
6497: // @@protoc_insertion_point(field_set:pb.BOOL_GATE_OP.path)
|
6498: path_.Mutable(index)->assign(value);
|
6501: // @@protoc_insertion_point(field_set:pb.BOOL_GATE_OP.path)
|
6502: path_.Mutable(index)->assign(std::move(value));
|
6506: path_.Mutable(index)->assign(value);
|
6507: // @@protoc_insertion_point(field_set_char:pb.BOOL_GATE_OP.path)
|
6510: path_.Mutable(index)->assign(
|
6512: // @@protoc_insertion_point(field_set_pointer:pb.BOOL_GATE_OP.path)
|
6515: // @@protoc_insertion_point(field_add_mutable:pb.BOOL_GATE_OP.path)
|
6516: return path_.Add();
|
6519: path_.Add()->assign(value);
|
6520: // @@protoc_insertion_point(field_add:pb.BOOL_GATE_OP.path)
|
6523: path_.Add(std::move(value));
|
6524: // @@protoc_insertion_point(field_add:pb.BOOL_GATE_OP.path)
|
6528: path_.Add()->assign(value);
|
6529: // @@protoc_insertion_point(field_add_char:pb.BOOL_GATE_OP.path)
|
6532: path_.Add()->assign(reinterpret_cast<const char*>(value), size);
|
6533: // @@protoc_insertion_point(field_add_pointer:pb.BOOL_GATE_OP.path)
|
6537: // @@protoc_insertion_point(field_list:pb.BOOL_GATE_OP.path)
|
6538: return path_;
|
6542: // @@protoc_insertion_point(field_mutable_list:pb.BOOL_GATE_OP.path)
|
6543: return &path_;
|
CBNplot:R/utilities.R: [ ] |
---|
310: path <- BiocFileCache::bfcrpath(bfc, url)
|
311: pathRel <- read.csv(path, sep="\t", header=FALSE)
|
312: pathRelG <- graph_from_edgelist(as.matrix(pathRel), directed = FALSE)
|
313: incPath <- V(pathRelG)[names(V(pathRelG)) %in% res$ID]
|
351: obtainPath <- function(res, geneSymbol) {
|
464: pathwayMatrix <- exp[ intersect(rownames(exp), genesInPathway),
|
469: pathwayMatrixPca <- prcomp(t(pathwayMatrix), scale. = FALSE)$x[,1]
|
314: incPathG <- igraph::subgraph(pathRelG, incPath)
|
423: bnpathtest <- function (results, exp, expSample=NULL, algo="hc",
|
455: genesInPathway <- strsplit(res[i, ]$geneID, "/")[[1]]
|
563: genesInPathway <- unlist(strsplit(res[pathNum, ]$geneID, "/"))
|
19: #' pathNum=1, returnNet=TRUE)
|
138: #' pathNum=1, returnNet=TRUE)
|
337: #' obtainPath
|
348: #' obtainPath(res = exampleEaRes, geneSymbol="ERCC7")
|
371: #' exp = exampleGeneExp, pathNum = 1, R = 10, returnNet=TRUE)
|
373: #' exp = exampleGeneExp, pathNum = 1, R = 10, returnNet=TRUE)
|
395: #' Testing various R for bayesian network between pathways
|
409: #' @param nCategory the number of pathways to be included
|
466: if (dim(pathwayMatrix)[1]==0) {
|
467: message("no gene in the pathway present in expression data")
|
470: avExp <- apply(pathwayMatrix, 2, mean)
|
471: corFlag <- cor(pathwayMatrixPca, avExp)
|
472: if (corFlag < 0){pathwayMatrixPca <- pathwayMatrixPca*-1}
|
473: # pathwayMatrixSum <- apply(pathwayMatrix, 2, sum)
|
475: pcs <- cbind(pcs, pathwayMatrixPca)
|
530: #' @param pathNum the pathway number (the number of row of the original result,
|
540: #' algo="hc", Rrange=seq(10, 30, 10), pathNum=1, scoreType="bge")
|
545: pathNum=NULL, convertSymbol=TRUE, expRow="ENSEMBL",
|
308: url <- "https://reactome.org/download/current/ReactomePathwaysRelation.txt"
|
315: incPathG <- set.vertex.attribute(incPathG, "name",
|
316: value=res[names(V(incPathG)), "Description"])
|
318: ovlG <- as_edgelist(igraph::intersection(incPathG, undirG))
|
393: #' bnpathtest
|
418: #' res <- bnpathtest(results = exampleEaRes, exp = exampleGeneExp,
|
458: genesInPathway <- clusterProfiler::bitr(genesInPathway,
|
566: genesInPathway <- clusterProfiler::bitr(genesInPathway,
|
573: pcs <- exp[ intersect(rownames(exp), genesInPathway), expSample ]
|
interactiveDisplay:inst/www/js/d3.v2.js: [ ] |
---|
6081: function path(d, i) {
|
1451: function d3_layout_bundlePath(link) {
|
1910: function d3_path_circle(radius) {
|
1119: var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ];
|
1120: while (++i < n) path.push("V", (p = points[i])[1], "H", p[0]);
|
1121: return path.join("");
|
1124: var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ];
|
1125: while (++i < n) path.push("H", (p = points[i])[0], "V", p[1]);
|
1126: return path.join("");
|
1141: var quad = points.length != tangents.length, path = "", p0 = points[0], p = points[1], t0 = tangents[0], t = t0, pi = 1;
|
1143: path += "Q" + (p[0] - t0[0] * 2 / 3) + "," + (p[1] - t0[1] * 2 / 3) + "," + p[0] + "," + p[1];
|
1151: path += "C" + (p0[0] + t0[0]) + "," + (p0[1] + t0[1]) + "," + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," ...(21 bytes skipped)...
|
1155: path += "S" + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," + p[0] + "," + p[1];
|
1160: path += "Q" + (p[0] + t[0] * 2 / 3) + "," + (p[1] + t[1] * 2 / 3) + "," + lp[0] + "," + lp[1];
|
1162: return path;
|
1176: ...(44 bytes skipped)...s[0], x0 = pi[0], y0 = pi[1], px = [ x0, x0, x0, (pi = points[1])[0] ], py = [ y0, y0, y0, pi[1] ], path = [ x0, ",", y0 ];
|
1177: d3_svg_lineBasisBezier(path, px, py);
|
1184: d3_svg_lineBasisBezier(path, px, py);
|
1192: d3_svg_lineBasisBezier(path, px, py);
|
1194: return path.join("");
|
1198: var path = [], i = -1, n = points.length, pi, px = [ 0 ], py = [ 0 ];
|
1204: path.push(d3_svg_lineDot4(d3_svg_lineBasisBezier3, px) + "," + d3_svg_lineDot4(d3_svg_lineBasisBezier3, ...(6 bytes skipped)...
|
1212: d3_svg_lineBasisBezier(path, px, py);
|
1214: return path.join("");
|
1217: var path, i = -1, n = points.length, m = n + 4, pi, px = [], py = [];
|
1223: path = [ d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, py)...(4 bytes skipped)...
|
1231: d3_svg_lineBasisBezier(path, px, py);
|
1233: return path.join("");
|
1251: function d3_svg_lineBasisBezier(path, x, y) {
|
1252: path.push("C", d3_svg_lineDot4(d3_svg_lineBasisBezier1, x), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier1...(207 bytes skipped)...
|
4383: var tick = g.selectAll("g").data(ticks, String), tickEnter = tick.enter().insert("g", "path").style("opacity", 1e-6), tickExit = d3.transition(tick.exit()).style("opacity", 1e-6).remove(), ti...(67 bytes skipped)...
|
4384: var range = d3_scaleRange(scale), path = g.selectAll(".domain").data([ 0 ]), pathEnter = path.enter().append("path").attr("class", "domain"), pathUpdate = d3.transition(path);
|
6080: d3.geo.path = function() {
|
6082: if (typeof pointRadius === "function") pointCircle = d3_path_circle(pointRadius.apply(this, arguments));
|
6111: var pointRadius = 4.5, pointCircle = d3_path_circle(pointRadius), projection = d3.geo.albersUsa(), buffer = [];
|
6178: var areaType = path.area = d3_geo_type({
|
6201: var centroidType = path.centroid = d3_geo_type({
|
6220: path.projection = function(x) {
|
6222: return path;
|
6224: path.pointRadius = function(x) {
|
6227: pointCircle = d3_path_circle(pointRadius);
|
6229: return path;
|
6231: return path;
|
4401: pathUpdate.attr("d", "M" + range[0] + "," + tickEndSize + "V0H" + range[1] + "V" + tickEndSize);
|
4414: pathUpdate.attr("d", "M" + range[0] + "," + -tickEndSize + "V0H" + range[1] + "V" + -tickEndSize);
|
4427: pathUpdate.attr("d", "M" + -tickEndSize + "," + range[0] + "H0V" + range[1] + "H" + -tickEndSize);
|
4440: pathUpdate.attr("d", "M" + tickEndSize + "," + range[0] + "H0V" + range[1] + "H" + tickEndSize);
|
4923: var paths = [], i = -1, n = links.length;
|
4924: while (++i < n) paths.push(d3_layout_bundlePath(links[i]));
|
4925: return paths;
|
6083: pathType(d);
|
6112: var pathType = d3_geo_type({
|
6115: while (++i < n) buffer.push(pathType(features[i].geometry));
|
6118: pathType(o.geometry);
|
6175: while (++i < n) buffer.push(pathType(geometries[i]));
|
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$", path, ignore.case=TRUE),
|
1429: path, paste0(path, ".html")))
|
1443: #' @param plot_path A string indicating the folder path to where the function
|
1520: fg, plot_path, plot_types="node", interactive=FALSE,
|
1537: plot_path, "/", type, "/", paste0(sm, collapse="_"))
|
1538: while (dir.exists(plot_path_) & !overwrite)
|
1539: plot_path_ <- paste0(plot_path_,"_")
|
1540: dir.create(plot_path_, recursive=TRUE, showWarnings=FALSE)
|
1551: path=paste0(plot_path_, "/pVSdifference.png"))
|
1567: rdir_ <- paste0(plot_path_, "/boxplots")
|
1580: path=path, paired=paired,
|
1595: path=paste0(plot_path_,"/qq.png"),
|
1605: path=paste0(plot_path_,"/cell_hierarchy.png"),
|
1628: paste0(plot_path_,"/cell_hierarchy_",
|
Rqc:R/utils.R: [ ] |
---|
94: path <- dirname(file)
|
99: data.frame(filename, pair, format, group, reads, total.reads, path,
|
MSstatsBig:R/backends.R: [ ] |
---|
11: sparklyr::spark_read_csv(connection, "mstinput", path = input_file,
|
ChemmineR:R/AllClasses.R: [ ] |
---|
1741: path <- conMA(x, exclude="H")
|
1986: path <- .linearCon(x=con)
|
1807: conpath <- t(sapply(path, function(x) x[c(1, length(x))]))
|
1819: pathlist <- cyclist$path
|
1820: conpath <- cyclist$conpath
|
1821: pathlistnew <- list()
|
1742: noconnect <- rowSums(path) != 0 # Removes atoms with no connections
|
1743: path <- path[noconnect, noconnect]
|
1744: if(all(dim(path) == 0)) { return(path) }
|
1745: term <- which(rowSums(path > 0)==1)
|
1747: path <- path[-term,-term]
|
1748: if(any(dim(path) == 0) | is.vector(path)) { break() }
|
1749: term <- which(rowSums(path > 0)==1)
|
1751: return(path)
|
1786: .update <- function(con, path) {
|
1787: ## Remove non-terminal atoms in each path
|
1788: center_atoms <- unique(unlist(lapply(path, function(x) x[-c(1, length(x))])))
|
1798: path <- c(path, remainbonds)
|
1799: names(path) <- seq(along=path)
|
1801: ## Collect complete rings and remove them from path object
|
1802: index <- unlist(lapply(path, function(y) any(duplicated(y))))
|
1803: rings <- path[index]
|
1804: path <- path[!index]
|
1805: names(path) <- seq(along=path)
|
1806: ## Connection list for path component
|
1813: return(list(con=con, conpath=conpath, path=path, rings=rings))
|
1857: ## Collect complete rings and remove them from path object
|
1987: 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)...
|
1754: ## (b) Function to return the longest possible linear bond paths where:
|
1808: ends <- unique(as.vector(conpath))
|
1809: conpath <- lapply(ends, function(x) as.numeric(names(which(rowSums(conpath==x) > 0))))
|
1810: names(conpath) <- ends
|
1811: conpath <- conpath[sapply(conpath, length) > 1] # removes ends that occur only once
|
1823: ## Loop to join linear paths/fragments stored in pathlist
|
1824: for(i in names(conpath)) {
|
1825: if(length(conpath) == 0 | !any(names(conpath) == i)) { next() }
|
1826: pos <- t(combn(conpath[[i]], m=2))
|
1828: p1 <- pathlist[[pos[j,1]]]
|
1829: p2 <- pathlist[[pos[j,2]]]
|
1834: pathlistnew[[length(pathlistnew)+1]] <- c(rev(p2[-1]), p1)
|
1837: pathlistnew[[length(pathlistnew)+1]] <- c(p1, rev(p2[-length(p2)]))
|
1840: pathlistnew[[length(pathlistnew)+1]] <- c(p2, p1[-1])
|
1843: pathlistnew[[length(pathlistnew)+1]] <- c(p1, p2[-1])
|
1847: if(length(pathlistnew) == 0) { next() }
|
1849: dups <- duplicated(sapply(pathlistnew, function(x) paste(sort(unique(x)), collapse="_")))
|
1850: pathlistnew <- pathlistnew[!dups]
|
1853: l <- sapply(pathlistnew, length)
|
1854: pathlistnew <- pathlistnew[l <= upper]
|
1855: if(length(pathlistnew) == 0) { next() }
|
1858: index <- unlist(lapply(pathlistnew, function(y) any(duplicated(y[c(1, length(y))]))))
|
1859: rings[[length(rings)+1]] <- pathlistnew[index]
|
1860: pathlistnew <- pathlistnew[!index]
|
1861: ## Remove paths with internal duplicates
|
1862: if(length(pathlistnew) > 0) {
|
1863: index <- unlist(lapply(pathlistnew, function(y) any(duplicated(y))))
|
1864: pathlistnew <- pathlistnew[!index]
|
1866: ## Update pathlist and conpath
|
1867: pathlist <- c(pathlist[-conpath[[i]]], pathlistnew)
|
1868: dups <- duplicated(sapply(pathlist, function(x) paste(sort(unique(x)), collapse="_")))
|
1869: pathlist <- pathlist[!dups]
|
1870: names(pathlist) <- seq(along=pathlist)
|
1871: conpath <- t(sapply(pathlist, function(x) x[c(1, length(x))]))
|
1872: ends <- unique(as.vector(conpath))
|
1873: conpath <- lapply(ends, function(x) as.numeric(names(which(rowSums(conpath==x) > 0))))
|
1874: names(conpath) <- ends
|
1875: conpath <- conpath[sapply(conpath, length) > 1] # removes ends that occur only once
|
1876: pathlistnew <- list()
|
TIN:R/correlationPlot.R: [ ] |
---|
104: path<-getwd()
|
105: cat("Plot was saved in ",paste(path,"/",fileName,sep=""),"\n")
|
isobar:R/ProteinGroup-class.R: [ ] |
---|
424: host="www.ebi.ac.uk",path="/uniprot/biomart/martservice")
|
biocViews:R/repository.R: [ ] |
---|
14: path <- packagesPaths[[type]]
|
181: DESCRIPTION_path <- file.path(tmp_pkgdir, "DESCRIPTION")
|
182: CITATION_path <- file.path(tmp_pkgdir, "inst", "CITATION")
|
183: paths <- c(DESCRIPTION_path, CITATION_path)
|
193: DESCRIPTION_tpath <- paste0(pkgname, "/DESCRIPTION")
|
194: CITATION_tpath <- paste0(pkgname, "/inst/CITATION")
|
648: cPath <- reposInfo[, ctype]
|
649: buildPkgPath <- function(pkgs, vers) {
|
900: cssPath <- system.file(file.path("css", paste(cssName, ".in", sep="")),
|
919: cssPath <- system.file(file.path("css", paste(cssName, ".in", sep="")),
|
11: packagesPaths <- file.path(reposRoot, contribPaths)
|
195: tpaths <- c(DESCRIPTION_tpath, CITATION_tpath)
|
20: message("- write_PACKAGES() to ", path, " ... ", appendLF=FALSE)
|
21: t <- system.time(write_PACKAGES(path, type=type))[["elapsed"]]
|
55: pkgDir <- file.path(unpackDir, pkg, subDir)
|
65: ## reposRoot - Top level path for CRAN-style repos
|
75: destDir <- file.path(reposRoot, "manuals")
|
88: pkgDir <- file.path(unpackDir, pkg, "man")
|
89: RCmd <- file.path(Sys.getenv("R_HOME"), "bin", "R")
|
104: tarballs <- list.files(file.path(reposRoot, srcContrib),
|
130: refmanDir <- file.path(reposRootPath, refman.dir, pkg, refmanSubDir)
|
147: unpack(tarball, unpackDir, file.path(pkg, fileName))
|
150: tarballs <- list.files(file.path(reposRoot, srcContrib),
|
180: tmp_pkgdir <- file.path(tmpdir, pkgname)
|
191: ## Note that the path separator is **always** / in a tarball, even
|
192: ## on Windows, so do NOT use file.path() here.
|
210: if (!file.exists(DESCRIPTION_path)) # should never happen
|
216: if (file.exists(CITATION_path)) {
|
218: citation <- try(readCitationFile(CITATION_path, meta=description),
|
233: destfile <- file.path(destdir, "citation.html")
|
280: file.path(reposRoot, srcContrib),
|
292: .write_citation_as_HTML(pkgname, citation, file.path(destDir, pkgname))
|
300: ## reposRoot - Top level path for CRAN-style repos
|
314: destDir <- file.path(reposRoot, "news")
|
324: srcDir <- file.path(srcDir, pkg)
|
325: destDir <- file.path(destDir, pkg)
|
328: destFile <- file.path(destDir, "NEWS")
|
332: tarballs <- list.files(file.path(reposRoot, srcContrib),
|
354: ## reposRoot - Top level path for CRAN-style repos
|
364: destDir <- file.path(reposRoot, "vignettes")
|
367: cleanUnpackDir(tarball, unpackDir, subDir=file.path("inst", "doc"))
|
374: tarballs <- list.files(file.path(reposRoot, srcContrib),
|
388: ## reposRoot - Top level path for CRAN-style repos
|
398: destDir <- file.path(reposRoot, "vignettes")
|
420: tarballs <- list.files(file.path(reposRoot, srcContrib),
|
444: filedir <- file.path(reposRootPath, dir, pkg)
|
445: file <- file.path(filedir, filename)
|
458: vigDir <- file.path(reposRootPath, vignette.dir, pkg, vigSubDir)
|
481: files <- file.path(reposRootPath, files)
|
535: fn <- file.path(reposRootPath, "REPOSITORY")
|
541: reposInfo <- read.dcf(file.path(reposRootPath, "REPOSITORY"))
|
562: out <- file(file.path(dir, fname), "wt")
|
564: ##outgz <- gzfile(file.path(dir, gzname), "wt")
|
601: ## Read REPOSITORY file for contrib path info
|
612: pkg.dir <- file.path(reposRootPath, reposInfo[, os])
|
641: ## Integrate version and archive file path info for the different contrib
|
642: ## paths in this repos. We duplicate the source path info here, but that
|
661: packagesFile <- file.path(reposRootPath, cPath, "PACKAGES")
|
664: file.path(reposRootPath, cPath),
|
665: "\nSkipping this contrib path.")
|
676: file.path(reposRootPath, cPath),
|
677: "\nSkipping this contrib path.")
|
693: ## Add vignette path info
|
784: desc <- tools:::.read_description(file.path(meatPath, missing_pkgs[i], "DESCRIPTION"))
|
859: viewsFile <- file.path(reposRoot, "VIEWS")
|
894: writePackageDetailHtml(pkgList, file.path(reposRoot, "html"),
|
902: res <- try(copySubstitute(cssPath, file.path(reposRoot, cssName),
|
913: f <- file.path(htmlDir, htmlFilename(pkg))
|
921: res <- try(copySubstitute(cssPath, file.path(htmlDir, cssName),
|
936: f <- file.path(reposRoot, htmlFilename(repos))
|
941: con <- file(file.path(dir, "SYMBOLS"), open="w")
|
965: tarballs <- list.files(file.path(dir, "src/contrib"),
|
1: genReposControlFiles <- function(reposRoot, contribPaths, manifestFile=NA, meatPath=NA)
|
10: ## Write PACKAGES files for all contrib paths
|
28: write_VIEWS(reposRoot, manifestFile=manifestFile, meatPath=meatPath)
|
127: getRefmanLinks <- function(pkgList, reposRootPath, refman.dir) {
|
184: status <- unlink(paths)
|
441: getFileExistsAttr <- function(pkgList, reposRootPath, dir, filename) {
|
452: getFileLinks <- function(pkgList, reposRootPath, vignette.dir, ext,
|
472: getDocumentTitles <- function(docs, ext="pdf", src=c("Rnw", "Rmd"), reposRootPath, fun) {
|
530: write_REPOSITORY <- function(reposRootPath, contribPaths)
|
539: read_REPOSITORY <- function(reposRootPath)
|
585: ## meatPath <- "~biocbuild/bbs-3.14-bioc/MEAT0"
|
587: ## write_VIEWS(reposRoot, manifestFile=manifestFile, meatPath=meatPath)
|
588: write_VIEWS <- function(reposRootPath, fields = NULL,
|
590: manifestFile=NA, meatPath=NA
|
602: reposInfo <- read_REPOSITORY(reposRootPath)
|
609: convertToMat <- function(reposRootPath, reposInfo, os, fields, verbose){
|
628: dbMat = convertToMat(reposRootPath, reposInfo, os, fields, verbose)
|
632: dbMat2 = convertToMat(reposRootPath, reposInfo, os, fields, verbose)
|
659: paste(cPath, "/", pkgs, "_", vers, ext, sep="")
|
684: dbMat[dbMatIdx, col] <- buildPkgPath(cDat[cDatGood, "Package"],
|
694: vigs <- getFileLinks(dbMat[, "Package"], reposRootPath, vignette.dir, "pdf")
|
695: vtitles <- getDocumentTitles(vigs, reposRootPath=reposRootPath, fun=getPdfTitle)
|
697: rfiles <- getFileLinks(dbMat[, "Package"], reposRootPath, vignette.dir, "R")
|
699: htmlDocs <- getFileLinks(dbMat[, "Package"], reposRootPath, vignette.dir, "html", TRUE)
|
701: htmlTitles <- getDocumentTitles(htmlDocs, ext="html", src=c("Rmd", "Rhtml"), reposRootPath, getHtmlTitle)
|
718: readmes <- getFileExistsAttr(dbMat[, "Package"], reposRootPath, "readmes", "README")
|
719: news <- getFileExistsAttr(dbMat[, "Package"], reposRootPath, "news", "NEWS")
|
720: install <- getFileExistsAttr(dbMat[, "Package"], reposRootPath, "install", "INSTALL")
|
721: license <- getFileExistsAttr(dbMat[, "Package"], reposRootPath, "licenses",
|
781: if (!is.na(meatPath)){
|
824: .write_repository_db(dbMat, reposRootPath, "VIEWS")
|
8: t <- system.time(write_REPOSITORY(reposRoot, contribPaths))[["elapsed"]]
|
12: names(packagesPaths) <- names(contribPaths)
|
13: for (type in names(packagesPaths)) {
|
196: status <- untar(tarball, tpaths, exdir=tmpdir)
|
523: res <- xpathApply(doc, "//title", xmlValue)
|
532: contrib <- as.list(contribPaths)
|
533: names(contrib) <- gsub("-", ".", names(contribPaths))
|
534: contrib[["provides"]] <- paste(names(contribPaths), collapse=", ")
|
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)
|
BSgenome:R/OnDiskLongTable-class.R: [ ] |
---|
159: path <- rowids_paths[[i]]
|
62: .valid_OnDiskLongTable_dirpath <- function(dirpath)
|
129: .check_OnDiskLongTable_dirpath <- function(dirpath)
|
244: .make_filepath <- function(dirpath, opath)
|
249: filepath <- .make_filepath(dirpath, opath)
|
256: filepath <- .make_filepath(dirpath, opath)
|
315: paths_in_1string <- paste0(paths[fail_idx], collapse=", ")
|
324: paths_in_1string <- paste0(paths[fail_idx], collapse=", ")
|
341: colpath <- colpaths[[c]]
|
401: colpath <- colpaths[[c]]
|
496: opath <- paste0("rowids", i)
|
179: rowids_paths <- .get_rowids_filepaths(x@dirpath)
|
332: rowids_paths <- .get_rowids_filepaths(dirpath)
|
377: rowids_paths <- .get_rowids_filepaths(dirpath)
|
623: rowids_paths <- character(0)
|
151: .get_rowids_filepaths <- function(dirpath)
|
334: colpaths <- file.path(dirpath, .col_physname(seq_len(ncol(df))))
|
392: colpaths <- file.path(dirpath, .col_physname(seq_len(ncol(df))))
|
160: rowids <- readRDS(path)
|
242: ### 'opath' is the relative path (with respect to 'dirpath') to the file that
|
245: file.path(dirpath, paste0(opath, ".rds"))
|
284: # block_physname <- file.path(batch_physname, col_physname)
|
286: block_physname <- file.path(col_physname, batch_physname)
|
386: file.path(dirpath, "header.rds")))
|
540: "Please make sure that '", dirpath, "' is the path to ",
|
12: dirpath="character",
|
45: dirpath=NA_character_,
|
64: if (!is.character(dirpath) || length(dirpath) != 1L)
|
65: return(wmsg("'dirpath' must be a single string"))
|
66: if (is.na(dirpath))
|
68: if (dirpath == "")
|
69: return(wmsg("'dirpath' must be a non-empty string"))
|
70: if (!dir.exists(dirpath))
|
71: return(wmsg("directory not found: ", dirpath))
|
121: c(.valid_OnDiskLongTable_dirpath(x@dirpath),
|
131: errmsg <- .valid_OnDiskLongTable_dirpath(dirpath)
|
134: if (is.na(dirpath))
|
135: stop("'dirpath' must be a single string")
|
152: dir(dirpath, pattern="rowids[1-9]?\\.rds", full.names=TRUE)
|
155: .load_rowids <- function(rowids_paths, envir, total_nb_rowids)
|
157: objnames <- sub("\\.rds$", "", basename(rowids_paths))
|
176: if (!is.na(x@dirpath)) {
|
180: if (length(rowids_paths) != 0L)
|
181: .load_rowids(rowids_paths, ans, nrow(x))
|
247: .read_object <- function(dirpath, opath)
|
250: suppressWarnings(readRDS(filepath))
|
253: .write_object <- function(object, dirpath, opath,
|
257: if (!overwrite && file.exists(filepath))
|
258: stop(wmsg("file already exists: ", filepath))
|
259: saveRDS(object, file=filepath, compress=compress)
|
291: .read_OnDiskLongTable_block <- function(dirpath, b, c, bybatch=FALSE)
|
294: .read_object(dirpath, block_physname)
|
298: dirpath, b, c, bybatch=FALSE,
|
302: .write_object(block_data, dirpath, block_physname, compress=compress)
|
310: .remove_files <- function(paths)
|
312: fail_idx <- which(!suppressWarnings(file.remove(paths)))
|
316: stop(wmsg("failed to remove file(s): ", paths_in_1string))
|
320: .remove_dirs <- function(paths)
|
322: fail_idx <- which(as.logical(unlink(paths, recursive=TRUE)))
|
325: stop(wmsg("failed to remove dir(s): ", paths_in_1string))
|
329: .write_zero_row_OnDiskLongTable <- function(df, dirpath, spatial_index)
|
333: .remove_files(rowids_paths)
|
339: .write_object(header, dirpath, "header", overwrite=TRUE)
|
342: if (!dir.create(colpath, showWarnings=TRUE, mode="0775"))
|
343: stop(wmsg("failed to create directory: ", colpath))
|
345: .write_object(integer(0), dirpath, "breakpoints", overwrite=TRUE)
|
347: .write_object(spatial_index[0], dirpath, "spatial_index",
|
359: .write_OnDiskLongTable_column <- function(df_col, dirpath,
|
367: .write_OnDiskLongTable_block(block_data, dirpath, b, c,
|
372: .append_df_to_OnDiskLongTable <- function(df, dirpath,
|
378: if (length(rowids_paths) != 0L)
|
383: old_colnames <- names(.read_object(dirpath, "header"))
|
389: breakpoints0 <- .read_object(dirpath, "breakpoints")
|
397: spatial_index0 <- .read_object(dirpath, "spatial_index")
|
402: if (!dir.exists(colpath))
|
403: stop(wmsg("directory not found: ", colpath))
|
404: .write_OnDiskLongTable_column(df[[c]], dirpath,
|
412: .write_object(breakpoints, dirpath, "breakpoints", overwrite=TRUE)
|
423: .write_object(spatial_index, dirpath, "spatial_index", overwrite=TRUE)
|
449: writeOnDiskLongTable <- function(df, dirpath=".",
|
455: .check_OnDiskLongTable_dirpath(dirpath)
|
484: .write_zero_row_OnDiskLongTable(df, dirpath, spatial_index)
|
485: .append_df_to_OnDiskLongTable(df, dirpath, batchsize, spatial_index,
|
490: dirpath=".", compress=FALSE)
|
497: .write_object(object, dirpath, opath, compress=compress)
|
505: dirpath=".", compress=FALSE)
|
514: .check_OnDiskLongTable_dirpath(dirpath)
|
516: breakpoints <- .read_object(dirpath, "breakpoints")
|
520: "with the OnDiskLongTable object in ", dirpath))
|
523: .write_object(rowids, dirpath, "rowids", compress=compress)
|
526: dirpath=dirpath, compress=compress)
|
535: .friendly_read_object <- function(dirpath, opath)
|
537: object <- try(.read_object(dirpath, opath), silent=TRUE)
|
539: stop(wmsg("Cannot open ", .make_filepath(dirpath, opath), ". ",
|
546: OnDiskLongTable <- function(dirpath=".")
|
548: .check_OnDiskLongTable_dirpath(dirpath)
|
549: header <- .friendly_read_object(dirpath, "header")
|
550: breakpoints <- .friendly_read_object(dirpath, "breakpoints")
|
551: spatial_index <- try(.read_object(dirpath, "spatial_index"), silent=TRUE)
|
558: new2("OnDiskLongTable", dirpath=dirpath,
|
622: if (is.na(object@dirpath)) {
|
625: rowids_paths <- .get_rowids_filepaths(object@dirpath)
|
628: if (length(rowids_paths) != 0L) {
|
683: function(b) .read_OnDiskLongTable_block(x@dirpath, b, c))
|
871: block_data <- .read_OnDiskLongTable_block(x@dirpath, b, c)
|
335: .remove_dirs(colpaths)
|
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"))
|
GeneTonic:R/GeneTonic.R: [ ] |
---|
2302: path <- system.file("doc", "GeneTonic_manual.html", package = "GeneTonic")
|
2303: if (path == "") {
|
2306: browseURL(path)
|
2360: reactive_values$in_gtl <- readRDS(input$uploadgtl$datapath)
|
maigesPack:R/plot-methods.R: [ ] |
---|
190: Path <- list(Type1=new("graphNEL", vertices, edgeL=arestas1),
|
326: Path <- new("graphNEL", vertices, edgeL=arestas)
|
167: graphPath <- function(data=NULL, cuttoffPvalue) {
|
309: graphPath <- function(data=NULL, cuttoffCor=NULL, cuttoffP=NULL) {
|
194: return(Path)
|
327: return(Path)
|
287: graph <- graphPath(x, cutPval)
|
426: graph <- graphPath(x, cutCor, NULL)
|
431: graph <- graphPath(x, NULL, cutPval)
|
MOGAMUN:R/MOGAMUN_FUN.R: [ ] |
---|
1232: for (Path in Dirs) {
|
1233: PathForPlots <- ExperimentsPath <- paste0(Path, "/") # path for plots
|
1659: GeneralPath <- ExperimentDir
|
1666: ExpPath <- paste0(GeneralPath, "/", d, "/")
|
1289: # get all files in the path that match the pattern
|
1763: # ExperimentsPath - path to the results
|
1234: Population <- GetIndividualsAllRuns(ExperimentsPath) # get results
|
1249: SimilarityBetweenRunsBoxplot(PathForPlots, Nodes1stRank_AllRuns)
|
1253: AccPF <- ObtainAccParetoFront(PathForPlots, Inds1stRank_AllRuns,
|
1258: FilterNetworks(ExperimentsPath, LoadedData, AccPF)
|
1265: SaveFilteredAccPF(DiversePopulation, ExperimentsPath, Threshold)
|
1278: # INPUT: ExperimentsPath - folder containing the results to be processed
|
1280: GetIndividualsAllRuns <- function(ExperimentsPath) {
|
1290: ResFiles <- list.files(ExperimentsPath, pattern = PatResFiles)
|
1295: con <- base::file(paste0(ExperimentsPath, ResFiles[counter]), open="r")
|
1328: # INPUTS: PathForPlots - folder to save the plot
|
1331: SimilarityBetweenRunsBoxplot <- function(PathForPlots, Nodes1stRank_AllRuns) {
|
1362: svg(paste0(PathForPlots, "A_Boxplot_similarities_between_runs.svg"))
|
1372: # INPUTS: PathForPlots - folder to save the plot
|
1378: ObtainAccParetoFront <- function(PathForPlots, Inds1stRank_AllRuns,
|
1389: svg(paste0(PathForPlots, "A_ScatterPlot_AccPF_ALL_INDS.svg"))
|
1397: svg(paste0(PathForPlots, "A_ScatterPlot_AccPF_LEGEND_ALL_INDS.svg"))
|
1409: svg(paste0(PathForPlots, "A_ScatterPlot_AccPF.svg"))
|
1466: # INPUTS: ExperimentsPath - folder to save the filtered networks
|
1471: FilterNetworks <- function(ExperimentsPath, LoadedData, AccPF) {
|
1495: write.table(MyFilteredNetwork, file = paste0(ExperimentsPath,
|
1501: myDataFiles <- list.files(ExperimentsPath, pattern = '_FILTERED.csv')
|
1508: data <- read.table(paste0(ExperimentsPath, myDataFiles[i]),
|
1516: write.table(myFullListOfInteractions, file = paste0(ExperimentsPath,
|
1614: # ExperimentsPath - folder to save the filtered networks
|
1617: SaveFilteredAccPF <- function(DiversePopulation, ExperimentsPath, Threshold) {
|
1621: write(paste(Ind, collapse=" ", sep=""), file = paste0(ExperimentsPath,
|
1643: write.csv(AllNodesDF_Acc, paste0(ExperimentsPath,
|
1662: Dirs <- list.dirs(GeneralPath, recursive = FALSE, full.names = FALSE)
|
1669: Network <- read.csv( paste0(ExpPath,
|
1698: CreateActiveModules(d, ExpPath)
|
1703: filename = paste0(ExpPath, "A_Acc_PF_", d))
|
1704: } else { saveSession(filename = paste0(ExpPath, "A_Acc_PF_", d)) }
|
1765: CreateActiveModules <- function(d, ExperimentsPath) {
|
1772: read.csv(paste0(ExperimentsPath, list.files(ExperimentsPath,
|
1799: MogamunBody <- function(RunNumber, LoadedData, BestIndsPath) {
|
1800: BestIndsFile <- paste0(BestIndsPath, "_Run_", RunNumber, ".txt")
|
1829: file = paste0(BestIndsPath,"StatisticsPerGeneration_Run", RunNumber,
|
hca:R/lol.R: [ ] |
---|
132: path <- ls(x, all.names = FALSE)
|
208: path <- lol_path(x)
|
290: path <- lol_path(x)
|
272: lol_path <- function(x) x[["path"]]
|
172: paths <- lol_path(x)
|
1: .lol_visit_impl <- function(x, path, index, dict)
|
5: .lol_visit_impl.default <- function(x, path, index, dict) {
|
6: dict[[path]] <- append(dict[[path]], list(index))
|
7: attr(dict[[path]], "leaf") <- TRUE
|
15: .lol_visit_impl.list <- function(x, path, index, dict) {
|
18: dict[[path]] <- append(dict[[path]], list(index))
|
19: attr(dict[[path]], "leaf") <- FALSE
|
29: ## logic for building out path names
|
30: ## starting with current node of the nested list and extending the path by
|
32: if (identical(path, ".")) {
|
33: path <- nms
|
36: path <- paste0(path, nms)
|
38: path <- paste0(path, ".", nms)
|
44: .lol_visit_impl(x[[i]], path[[i]], append(index, i), dict)
|
48: function(lol, dict, path = .lol_path(dict), class = "lol")
|
53: list(lol = lol, dict = dict, path = path),
|
75: #' path, number of occurrences, and leaf status of each unique
|
76: #' path.
|
129: .lol_path <-
|
133: is_leaf <- .lol_is_leaf(x)[path]
|
135: path = path,
|
136: n = unname(.lol_lengths(x)[path]),
|
139: arrange(tbl, .data$path)
|
142: .lol_valid_path <-
|
143: function(x, path)
|
145: ok <- .is_character_0(path) || path %in% lol_path(x)$path
|
146: ok || stop("'path' not in 'x':\n", " path: '", path, "'")
|
153: #' @param path character(1) from the tibble returned by `lol_path(x)`.
|
156: #' to contain just the elements matching `path` as 'top-level'
|
164: function(x, path = character())
|
168: .is_character_0(path) || .is_scalar_character(path),
|
169: .lol_valid_path(x, path)
|
173: idx <- paths$path[startsWith(paths$path, path)]
|
174: paths <- paths[paths$path %in% idx,]
|
175: dict <- .lol_dict(x)[paths$path]
|
190: #' of rows in `lol_path()`.
|
209: ## FIXME: don't allow filtering on 'path$path'
|
210: path <- filter(path, ...)
|
211: dict <- .lol_dict(x)[path$path]
|
213: .lol(.lol_lol(x), dict, path, class(x))
|
219: #' corresponding to a single `path`.
|
222: #' corresponds to an element found at `path` in the list-of-lists
|
227: function(x, path)
|
231: .is_scalar_character(path),
|
232: .lol_valid_path(x, path)
|
235: value <- lapply(.lol_dict(x)[[path]], function(idx) lol[[idx]])
|
236: names(value) <- rep(path, length(value))
|
257: function(x, path)
|
259: value <- lol_lpull(x, path)
|
265: #' @description `lol_path()` returns a tibble representing the paths
|
269: #' plol |> lol_path()
|
293: "# number of distinct paths: ", NROW(path), "\n",
|
297: "# lol_path():\n",
|
300: print(path, ...)
|
17: ## building out the various paths
|
67: #' individual paths from across the list-of-lists.
|
74: #' paths through the list, as well as a tibble summarizing the
|
177: .lol(.lol_lol(x), dict, paths, class(x))
|
183: #' @description `lol_filter()` filters available paths based on
|
295: "# number of leaf paths: ", sum(is_leaf), "\n",
|
matter:R/signal.R: [ ] |
---|
213: path <- data.frame(x=lx, y=ly)
|
264: path <- .Call(C_warpDTW, x, y, tx, ty,
|
315: path <- .Call(C_warpCOW, x, y, tx, ty,
|
231: path <- NULL
|
236: attr(xout, "path") <- path
|
266: i <- rev(path[!is.na(path[,1L]),1L]) + 1L
|
267: j <- rev(path[!is.na(path[,2L]),2L]) + 1L
|
268: path <- data.frame(x=tx[i], y=ty[j])
|
270: tout <- approx(ty[path$y], tx[path$x],
|
273: attr(xout, "path") <- path
|
318: i <- path[,1L] + 1L
|
319: j <- path[,2L] + 1L
|
320: path <- data.frame(x=tx[i], y=ty[j])
|
326: attr(xout, "path") <- path
|
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_")
|