In [1]:
library(GO.db)
library(topGO)
library(GOSim)
library(org.Sc.sgd.db)
library(igraph)
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, cbind, colnames, do.call,
duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect,
is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
Reduce, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit, which, which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: ‘S4Vectors’
The following objects are masked from ‘package:base’:
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: graph
Loading required package: SparseM
Attaching package: ‘SparseM’
The following object is masked from ‘package:base’:
backsolve
groupGOTerms: GOBPTerm, GOMFTerm, GOCCTerm environments built.
Attaching package: ‘topGO’
The following object is masked from ‘package:IRanges’:
members
Loading required package: annotate
Loading required package: XML
Attaching package: ‘XML’
The following object is masked from ‘package:graph’:
addNode
Attaching package: ‘igraph’
The following objects are masked from ‘package:topGO’:
algorithm, graph
The following objects are masked from ‘package:graph’:
degree, edges, intersection, union
The following objects are masked from ‘package:IRanges’:
simplify, union
The following objects are masked from ‘package:S4Vectors’:
compare, union
The following objects are masked from ‘package:BiocGenerics’:
normalize, union
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
In [2]:
file <- "yeast_uetz"
ont <- "BP"
p <- 0.1
init <- 1
db <- org.Sc.sgd.db
mapping <- "org.Sc.sgd.db"
ID <- "ENSEMBL"
##load all community gene lists
setwd(sprintf("/home/david/Documents/ghsom/%s_hierarchy_communities_%s_%s", file, p, init))
setOntology(ont, loadIC=TRUE)
setEvidenceLevel(evidences="all", organism=org.Sc.sgdORGANISM, gomap=org.Sc.sgdGO)
initializing GOSim package ...
-> retrieving GO information for all available genes for organism 'human' in GO database
-> filtering GO terms according to evidence levels 'all'
-> loading files with information content for corresponding GO category (human)
finished.
-> loading files with information content for corresponding GO category (human)
-> retrieving GO information for all available genes for organism 'Saccharomyces cerevisiae' in GO database
-> filtering GO terms according to evidence levels 'all'
In [3]:
generateMap <- function(filename){
map <- as.matrix(read.csv(filename, sep=",", header = F))
communities <- map[,1]
map <- map[,2:ncol(map)]
rownames(map) <- communities
colnames(map) <- communities
return (map)
}
In [4]:
#background gene list
backgroundFilename <- "all_genes.txt"
allGenes <- scan(backgroundFilename, character())
#shortest path files
shortestPathFiles <- list.files(pattern="*shortest_path*")
#shortest paths list
shortestPaths <- sapply(shortestPathFiles, generateMap)
names(shortestPaths) <- sapply(names(shortestPaths), function(name) strsplit(name, "_")[[1]][[1]])
#communitiy assignemtns
assignments <- as.matrix(read.csv("assignment_matrix.csv", sep=",", header=F))
rownames(assignments) <- allGenes
colnames <- sapply(1:ncol(assignments), function(i) as.character(i-1))
colnames(assignments) <- colnames
In [5]:
getDepth <- function(com) {
return(which(apply(assignments, 2, function(i) any(i == com))))
}
getGenes <- function(com){
return(names(which(assignments[,getDepth(com)] == com)))
}
getSubCommunities <- function(com){
return(try(as.character(unique(assignments[getGenes(com), getDepth(com) + 1]))))
}
getSuperCommunity <- function(com){
return(try(as.character(unique(assignments[getGenes(com), getDepth(com) - 1]))))
}
getShortestPath <- function(com){
return (try(shortestPaths[[com]]))
}
In [6]:
allGenesInDB <- keys(db)
allGenes <- allGenes[allGenes %in% allGenesInDB]
enrichmentResults <- sapply(1:max(assignments), function(i) {
genesOfInterest <- getGenes(i)
genesOfInterest <- genesOfInterest[genesOfInterest %in% allGenesInDB]
GOenrichment(genesOfInterest, allGenesInDB, cutoff=0.05, method="weight01")
}
)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 1848 nontrivial nodes
parameters:
test statistic: fisher
Level 16: 3 nodes to be scored (0 eliminated genes)
Level 15: 12 nodes to be scored (0 eliminated genes)
Level 14: 33 nodes to be scored (13 eliminated genes)
Level 13: 63 nodes to be scored (116 eliminated genes)
Level 12: 93 nodes to be scored (406 eliminated genes)
Level 11: 131 nodes to be scored (804 eliminated genes)
Level 10: 190 nodes to be scored (1400 eliminated genes)
Level 9: 232 nodes to be scored (2023 eliminated genes)
Level 8: 234 nodes to be scored (2704 eliminated genes)
Level 7: 245 nodes to be scored (3775 eliminated genes)
Level 6: 254 nodes to be scored (4476 eliminated genes)
Level 5: 195 nodes to be scored (4884 eliminated genes)
Level 4: 110 nodes to be scored (5233 eliminated genes)
Level 3: 39 nodes to be scored (5412 eliminated genes)
Level 2: 13 nodes to be scored (5489 eliminated genes)
Level 1: 1 nodes to be scored (5602 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 87 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 2 nodes to be scored (0 eliminated genes)
Level 10: 4 nodes to be scored (119 eliminated genes)
Level 9: 6 nodes to be scored (132 eliminated genes)
Level 8: 7 nodes to be scored (158 eliminated genes)
Level 7: 9 nodes to be scored (288 eliminated genes)
Level 6: 12 nodes to be scored (808 eliminated genes)
Level 5: 16 nodes to be scored (1897 eliminated genes)
Level 4: 16 nodes to be scored (2368 eliminated genes)
Level 3: 8 nodes to be scored (3947 eliminated genes)
Level 2: 5 nodes to be scored (4428 eliminated genes)
Level 1: 1 nodes to be scored (5113 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 160 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 3 nodes to be scored (0 eliminated genes)
Level 10: 3 nodes to be scored (11 eliminated genes)
Level 9: 7 nodes to be scored (31 eliminated genes)
Level 8: 8 nodes to be scored (127 eliminated genes)
Level 7: 11 nodes to be scored (240 eliminated genes)
Level 6: 22 nodes to be scored (504 eliminated genes)
Level 5: 30 nodes to be scored (918 eliminated genes)
Level 4: 40 nodes to be scored (2931 eliminated genes)
Level 3: 23 nodes to be scored (4783 eliminated genes)
Level 2: 11 nodes to be scored (5283 eliminated genes)
Level 1: 1 nodes to be scored (5540 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 189 nontrivial nodes
parameters:
test statistic: fisher
Level 14: 1 nodes to be scored (0 eliminated genes)
Level 13: 3 nodes to be scored (0 eliminated genes)
Level 12: 8 nodes to be scored (45 eliminated genes)
Level 11: 7 nodes to be scored (279 eliminated genes)
Level 10: 7 nodes to be scored (545 eliminated genes)
Level 9: 8 nodes to be scored (842 eliminated genes)
Level 8: 12 nodes to be scored (1274 eliminated genes)
Level 7: 19 nodes to be scored (1464 eliminated genes)
Level 6: 29 nodes to be scored (2246 eliminated genes)
Level 5: 38 nodes to be scored (2743 eliminated genes)
Level 4: 28 nodes to be scored (3476 eliminated genes)
Level 3: 18 nodes to be scored (4204 eliminated genes)
Level 2: 10 nodes to be scored (4832 eliminated genes)
Level 1: 1 nodes to be scored (5216 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 175 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 2 nodes to be scored (0 eliminated genes)
Level 11: 3 nodes to be scored (33 eliminated genes)
Level 10: 7 nodes to be scored (46 eliminated genes)
Level 9: 15 nodes to be scored (121 eliminated genes)
Level 8: 15 nodes to be scored (238 eliminated genes)
Level 7: 17 nodes to be scored (457 eliminated genes)
Level 6: 24 nodes to be scored (887 eliminated genes)
Level 5: 33 nodes to be scored (1544 eliminated genes)
Level 4: 32 nodes to be scored (2457 eliminated genes)
Level 3: 17 nodes to be scored (4205 eliminated genes)
Level 2: 8 nodes to be scored (5246 eliminated genes)
Level 1: 1 nodes to be scored (5526 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 149 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 3 nodes to be scored (0 eliminated genes)
Level 11: 5 nodes to be scored (0 eliminated genes)
Level 10: 6 nodes to be scored (450 eliminated genes)
Level 9: 7 nodes to be scored (815 eliminated genes)
Level 8: 9 nodes to be scored (913 eliminated genes)
Level 7: 7 nodes to be scored (1022 eliminated genes)
Level 6: 25 nodes to be scored (1365 eliminated genes)
Level 5: 37 nodes to be scored (2337 eliminated genes)
Level 4: 25 nodes to be scored (3388 eliminated genes)
Level 3: 16 nodes to be scored (4390 eliminated genes)
Level 2: 8 nodes to be scored (5294 eliminated genes)
Level 1: 1 nodes to be scored (5540 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 194 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 2 nodes to be scored (0 eliminated genes)
Level 11: 4 nodes to be scored (0 eliminated genes)
Level 10: 8 nodes to be scored (2 eliminated genes)
Level 9: 11 nodes to be scored (5 eliminated genes)
Level 8: 18 nodes to be scored (47 eliminated genes)
Level 7: 23 nodes to be scored (186 eliminated genes)
Level 6: 34 nodes to be scored (599 eliminated genes)
Level 5: 38 nodes to be scored (1627 eliminated genes)
Level 4: 29 nodes to be scored (3787 eliminated genes)
Level 3: 17 nodes to be scored (4832 eliminated genes)
Level 2: 9 nodes to be scored (5262 eliminated genes)
Level 1: 1 nodes to be scored (5563 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 372 nontrivial nodes
parameters:
test statistic: fisher
Level 15: 1 nodes to be scored (0 eliminated genes)
Level 14: 4 nodes to be scored (0 eliminated genes)
Level 13: 7 nodes to be scored (28 eliminated genes)
Level 12: 10 nodes to be scored (176 eliminated genes)
Level 11: 17 nodes to be scored (451 eliminated genes)
Level 10: 24 nodes to be scored (645 eliminated genes)
Level 9: 35 nodes to be scored (936 eliminated genes)
Level 8: 41 nodes to be scored (1187 eliminated genes)
Level 7: 45 nodes to be scored (1584 eliminated genes)
Level 6: 62 nodes to be scored (2823 eliminated genes)
Level 5: 63 nodes to be scored (3659 eliminated genes)
Level 4: 36 nodes to be scored (4187 eliminated genes)
Level 3: 19 nodes to be scored (4993 eliminated genes)
Level 2: 7 nodes to be scored (5362 eliminated genes)
Level 1: 1 nodes to be scored (5563 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 324 nontrivial nodes
parameters:
test statistic: fisher
Level 16: 1 nodes to be scored (0 eliminated genes)
Level 15: 1 nodes to be scored (0 eliminated genes)
Level 14: 3 nodes to be scored (4 eliminated genes)
Level 13: 6 nodes to be scored (4 eliminated genes)
Level 12: 10 nodes to be scored (14 eliminated genes)
Level 11: 12 nodes to be scored (371 eliminated genes)
Level 10: 17 nodes to be scored (748 eliminated genes)
Level 9: 29 nodes to be scored (1068 eliminated genes)
Level 8: 31 nodes to be scored (1361 eliminated genes)
Level 7: 34 nodes to be scored (1704 eliminated genes)
Level 6: 51 nodes to be scored (2661 eliminated genes)
Level 5: 57 nodes to be scored (3205 eliminated genes)
Level 4: 41 nodes to be scored (3840 eliminated genes)
Level 3: 20 nodes to be scored (4876 eliminated genes)
Level 2: 10 nodes to be scored (5298 eliminated genes)
Level 1: 1 nodes to be scored (5569 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 466 nontrivial nodes
parameters:
test statistic: fisher
Level 15: 1 nodes to be scored (0 eliminated genes)
Level 14: 1 nodes to be scored (0 eliminated genes)
Level 13: 7 nodes to be scored (26 eliminated genes)
Level 12: 14 nodes to be scored (40 eliminated genes)
Level 11: 24 nodes to be scored (161 eliminated genes)
Level 10: 44 nodes to be scored (574 eliminated genes)
Level 9: 48 nodes to be scored (1068 eliminated genes)
Level 8: 45 nodes to be scored (1382 eliminated genes)
Level 7: 53 nodes to be scored (1885 eliminated genes)
Level 6: 70 nodes to be scored (2429 eliminated genes)
Level 5: 74 nodes to be scored (3443 eliminated genes)
Level 4: 53 nodes to be scored (4319 eliminated genes)
Level 3: 22 nodes to be scored (4715 eliminated genes)
Level 2: 9 nodes to be scored (5069 eliminated genes)
Level 1: 1 nodes to be scored (5282 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 147 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 2 nodes to be scored (0 eliminated genes)
Level 10: 5 nodes to be scored (6 eliminated genes)
Level 9: 8 nodes to be scored (75 eliminated genes)
Level 8: 11 nodes to be scored (141 eliminated genes)
Level 7: 14 nodes to be scored (384 eliminated genes)
Level 6: 24 nodes to be scored (870 eliminated genes)
Level 5: 31 nodes to be scored (1994 eliminated genes)
Level 4: 28 nodes to be scored (3783 eliminated genes)
Level 3: 15 nodes to be scored (4628 eliminated genes)
Level 2: 7 nodes to be scored (5355 eliminated genes)
Level 1: 1 nodes to be scored (5553 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 308 nontrivial nodes
parameters:
test statistic: fisher
Level 15: 1 nodes to be scored (0 eliminated genes)
Level 14: 2 nodes to be scored (0 eliminated genes)
Level 13: 5 nodes to be scored (7 eliminated genes)
Level 12: 7 nodes to be scored (50 eliminated genes)
Level 11: 9 nodes to be scored (339 eliminated genes)
Level 10: 10 nodes to be scored (574 eliminated genes)
Level 9: 14 nodes to be scored (821 eliminated genes)
Level 8: 20 nodes to be scored (915 eliminated genes)
Level 7: 38 nodes to be scored (1323 eliminated genes)
Level 6: 53 nodes to be scored (2480 eliminated genes)
Level 5: 62 nodes to be scored (3114 eliminated genes)
Level 4: 48 nodes to be scored (3939 eliminated genes)
Level 3: 26 nodes to be scored (4603 eliminated genes)
Level 2: 12 nodes to be scored (4945 eliminated genes)
Level 1: 1 nodes to be scored (5271 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 172 nontrivial nodes
parameters:
test statistic: fisher
Level 14: 1 nodes to be scored (0 eliminated genes)
Level 13: 4 nodes to be scored (0 eliminated genes)
Level 12: 7 nodes to be scored (15 eliminated genes)
Level 11: 9 nodes to be scored (280 eliminated genes)
Level 10: 8 nodes to be scored (500 eliminated genes)
Level 9: 9 nodes to be scored (805 eliminated genes)
Level 8: 13 nodes to be scored (913 eliminated genes)
Level 7: 19 nodes to be scored (1474 eliminated genes)
Level 6: 28 nodes to be scored (2186 eliminated genes)
Level 5: 35 nodes to be scored (2401 eliminated genes)
Level 4: 22 nodes to be scored (3503 eliminated genes)
Level 3: 11 nodes to be scored (4460 eliminated genes)
Level 2: 5 nodes to be scored (5216 eliminated genes)
Level 1: 1 nodes to be scored (5352 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 254 nontrivial nodes
parameters:
test statistic: fisher
Level 15: 1 nodes to be scored (0 eliminated genes)
Level 14: 2 nodes to be scored (0 eliminated genes)
Level 13: 1 nodes to be scored (27 eliminated genes)
Level 12: 2 nodes to be scored (98 eliminated genes)
Level 11: 7 nodes to be scored (154 eliminated genes)
Level 10: 13 nodes to be scored (286 eliminated genes)
Level 9: 21 nodes to be scored (866 eliminated genes)
Level 8: 27 nodes to be scored (1237 eliminated genes)
Level 7: 31 nodes to be scored (1548 eliminated genes)
Level 6: 43 nodes to be scored (2261 eliminated genes)
Level 5: 43 nodes to be scored (2860 eliminated genes)
Level 4: 33 nodes to be scored (4157 eliminated genes)
Level 3: 20 nodes to be scored (4909 eliminated genes)
Level 2: 9 nodes to be scored (5325 eliminated genes)
Level 1: 1 nodes to be scored (5559 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 16 nontrivial nodes
parameters:
test statistic: fisher
Level 8: 1 nodes to be scored (0 eliminated genes)
Level 7: 1 nodes to be scored (0 eliminated genes)
Level 6: 1 nodes to be scored (4 eliminated genes)
Level 5: 1 nodes to be scored (36 eliminated genes)
Level 4: 2 nodes to be scored (224 eliminated genes)
Level 3: 4 nodes to be scored (342 eliminated genes)
Level 2: 5 nodes to be scored (1628 eliminated genes)
Level 1: 1 nodes to be scored (3624 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 226 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 4 nodes to be scored (0 eliminated genes)
Level 12: 7 nodes to be scored (0 eliminated genes)
Level 11: 12 nodes to be scored (16 eliminated genes)
Level 10: 15 nodes to be scored (373 eliminated genes)
Level 9: 18 nodes to be scored (858 eliminated genes)
Level 8: 16 nodes to be scored (970 eliminated genes)
Level 7: 22 nodes to be scored (1375 eliminated genes)
Level 6: 36 nodes to be scored (2358 eliminated genes)
Level 5: 41 nodes to be scored (2709 eliminated genes)
Level 4: 28 nodes to be scored (3638 eliminated genes)
Level 3: 19 nodes to be scored (4572 eliminated genes)
Level 2: 7 nodes to be scored (5067 eliminated genes)
Level 1: 1 nodes to be scored (5505 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 178 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 2 nodes to be scored (0 eliminated genes)
Level 11: 5 nodes to be scored (0 eliminated genes)
Level 10: 6 nodes to be scored (327 eliminated genes)
Level 9: 13 nodes to be scored (767 eliminated genes)
Level 8: 13 nodes to be scored (850 eliminated genes)
Level 7: 19 nodes to be scored (1028 eliminated genes)
Level 6: 30 nodes to be scored (1610 eliminated genes)
Level 5: 36 nodes to be scored (2652 eliminated genes)
Level 4: 28 nodes to be scored (3995 eliminated genes)
Level 3: 18 nodes to be scored (4726 eliminated genes)
Level 2: 7 nodes to be scored (5240 eliminated genes)
Level 1: 1 nodes to be scored (5523 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 199 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 3 nodes to be scored (0 eliminated genes)
Level 11: 6 nodes to be scored (4 eliminated genes)
Level 10: 15 nodes to be scored (27 eliminated genes)
Level 9: 18 nodes to be scored (200 eliminated genes)
Level 8: 17 nodes to be scored (441 eliminated genes)
Level 7: 19 nodes to be scored (641 eliminated genes)
Level 6: 23 nodes to be scored (1082 eliminated genes)
Level 5: 37 nodes to be scored (1692 eliminated genes)
Level 4: 33 nodes to be scored (3282 eliminated genes)
Level 3: 19 nodes to be scored (4698 eliminated genes)
Level 2: 7 nodes to be scored (5301 eliminated genes)
Level 1: 1 nodes to be scored (5555 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 297 nontrivial nodes
parameters:
test statistic: fisher
Level 14: 2 nodes to be scored (0 eliminated genes)
Level 13: 8 nodes to be scored (0 eliminated genes)
Level 12: 13 nodes to be scored (20 eliminated genes)
Level 11: 18 nodes to be scored (93 eliminated genes)
Level 10: 24 nodes to be scored (267 eliminated genes)
Level 9: 36 nodes to be scored (437 eliminated genes)
Level 8: 26 nodes to be scored (923 eliminated genes)
Level 7: 21 nodes to be scored (1324 eliminated genes)
Level 6: 31 nodes to be scored (2032 eliminated genes)
Level 5: 52 nodes to be scored (3031 eliminated genes)
Level 4: 38 nodes to be scored (4274 eliminated genes)
Level 3: 19 nodes to be scored (4973 eliminated genes)
Level 2: 8 nodes to be scored (5414 eliminated genes)
Level 1: 1 nodes to be scored (5558 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 84 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 3 nodes to be scored (0 eliminated genes)
Level 11: 4 nodes to be scored (0 eliminated genes)
Level 10: 6 nodes to be scored (56 eliminated genes)
Level 9: 7 nodes to be scored (115 eliminated genes)
Level 8: 5 nodes to be scored (163 eliminated genes)
Level 7: 7 nodes to be scored (242 eliminated genes)
Level 6: 9 nodes to be scored (453 eliminated genes)
Level 5: 14 nodes to be scored (568 eliminated genes)
Level 4: 15 nodes to be scored (834 eliminated genes)
Level 3: 8 nodes to be scored (3352 eliminated genes)
Level 2: 5 nodes to be scored (4552 eliminated genes)
Level 1: 1 nodes to be scored (5440 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 80 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 4 nodes to be scored (0 eliminated genes)
Level 11: 4 nodes to be scored (48 eliminated genes)
Level 10: 4 nodes to be scored (118 eliminated genes)
Level 9: 4 nodes to be scored (763 eliminated genes)
Level 8: 5 nodes to be scored (793 eliminated genes)
Level 7: 4 nodes to be scored (838 eliminated genes)
Level 6: 11 nodes to be scored (930 eliminated genes)
Level 5: 18 nodes to be scored (2139 eliminated genes)
Level 4: 11 nodes to be scored (3348 eliminated genes)
Level 3: 9 nodes to be scored (3707 eliminated genes)
Level 2: 4 nodes to be scored (4297 eliminated genes)
Level 1: 1 nodes to be scored (4918 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 102 nontrivial nodes
parameters:
test statistic: fisher
Level 14: 1 nodes to be scored (0 eliminated genes)
Level 13: 2 nodes to be scored (0 eliminated genes)
Level 12: 2 nodes to be scored (86 eliminated genes)
Level 11: 4 nodes to be scored (155 eliminated genes)
Level 10: 5 nodes to be scored (286 eliminated genes)
Level 9: 5 nodes to be scored (734 eliminated genes)
Level 8: 9 nodes to be scored (800 eliminated genes)
Level 7: 9 nodes to be scored (842 eliminated genes)
Level 6: 16 nodes to be scored (1016 eliminated genes)
Level 5: 20 nodes to be scored (2353 eliminated genes)
Level 4: 13 nodes to be scored (3165 eliminated genes)
Level 3: 10 nodes to be scored (3612 eliminated genes)
Level 2: 5 nodes to be scored (4618 eliminated genes)
Level 1: 1 nodes to be scored (5125 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 42 nontrivial nodes
parameters:
test statistic: fisher
Level 11: 1 nodes to be scored (0 eliminated genes)
Level 10: 1 nodes to be scored (0 eliminated genes)
Level 9: 4 nodes to be scored (1 eliminated genes)
Level 8: 4 nodes to be scored (4 eliminated genes)
Level 7: 4 nodes to be scored (25 eliminated genes)
Level 6: 4 nodes to be scored (278 eliminated genes)
Level 5: 5 nodes to be scored (473 eliminated genes)
Level 4: 8 nodes to be scored (516 eliminated genes)
Level 3: 7 nodes to be scored (1176 eliminated genes)
Level 2: 3 nodes to be scored (2543 eliminated genes)
Level 1: 1 nodes to be scored (4939 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 248 nontrivial nodes
parameters:
test statistic: fisher
Level 15: 2 nodes to be scored (0 eliminated genes)
Level 14: 3 nodes to be scored (0 eliminated genes)
Level 13: 4 nodes to be scored (28 eliminated genes)
Level 12: 7 nodes to be scored (45 eliminated genes)
Level 11: 11 nodes to be scored (49 eliminated genes)
Level 10: 10 nodes to be scored (131 eliminated genes)
Level 9: 21 nodes to be scored (967 eliminated genes)
Level 8: 29 nodes to be scored (1140 eliminated genes)
Level 7: 28 nodes to be scored (1238 eliminated genes)
Level 6: 42 nodes to be scored (1585 eliminated genes)
Level 5: 40 nodes to be scored (2683 eliminated genes)
Level 4: 27 nodes to be scored (3668 eliminated genes)
Level 3: 16 nodes to be scored (4235 eliminated genes)
Level 2: 7 nodes to be scored (4829 eliminated genes)
Level 1: 1 nodes to be scored (5226 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 43 nontrivial nodes
parameters:
test statistic: fisher
Level 9: 1 nodes to be scored (0 eliminated genes)
Level 8: 1 nodes to be scored (0 eliminated genes)
Level 7: 1 nodes to be scored (171 eliminated genes)
Level 6: 5 nodes to be scored (762 eliminated genes)
Level 5: 10 nodes to be scored (766 eliminated genes)
Level 4: 10 nodes to be scored (2419 eliminated genes)
Level 3: 9 nodes to be scored (3706 eliminated genes)
Level 2: 5 nodes to be scored (4972 eliminated genes)
Level 1: 1 nodes to be scored (5346 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 103 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 1 nodes to be scored (12 eliminated genes)
Level 10: 3 nodes to be scored (13 eliminated genes)
Level 9: 6 nodes to be scored (14 eliminated genes)
Level 8: 8 nodes to be scored (63 eliminated genes)
Level 7: 14 nodes to be scored (320 eliminated genes)
Level 6: 18 nodes to be scored (1118 eliminated genes)
Level 5: 21 nodes to be scored (2250 eliminated genes)
Level 4: 13 nodes to be scored (3306 eliminated genes)
Level 3: 11 nodes to be scored (4578 eliminated genes)
Level 2: 5 nodes to be scored (4964 eliminated genes)
Level 1: 1 nodes to be scored (5371 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 151 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 2 nodes to be scored (0 eliminated genes)
Level 12: 4 nodes to be scored (0 eliminated genes)
Level 11: 5 nodes to be scored (27 eliminated genes)
Level 10: 5 nodes to be scored (462 eliminated genes)
Level 9: 8 nodes to be scored (786 eliminated genes)
Level 8: 8 nodes to be scored (845 eliminated genes)
Level 7: 11 nodes to be scored (894 eliminated genes)
Level 6: 23 nodes to be scored (1208 eliminated genes)
Level 5: 32 nodes to be scored (2394 eliminated genes)
Level 4: 23 nodes to be scored (3527 eliminated genes)
Level 3: 19 nodes to be scored (4087 eliminated genes)
Level 2: 10 nodes to be scored (4604 eliminated genes)
Level 1: 1 nodes to be scored (5219 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 155 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 3 nodes to be scored (5 eliminated genes)
Level 10: 4 nodes to be scored (10 eliminated genes)
Level 9: 7 nodes to be scored (72 eliminated genes)
Level 8: 8 nodes to be scored (178 eliminated genes)
Level 7: 10 nodes to be scored (687 eliminated genes)
Level 6: 23 nodes to be scored (1948 eliminated genes)
Level 5: 36 nodes to be scored (2798 eliminated genes)
Level 4: 31 nodes to be scored (3672 eliminated genes)
Level 3: 20 nodes to be scored (4744 eliminated genes)
Level 2: 10 nodes to be scored (5218 eliminated genes)
Level 1: 1 nodes to be scored (5522 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 230 nontrivial nodes
parameters:
test statistic: fisher
Level 15: 1 nodes to be scored (0 eliminated genes)
Level 14: 1 nodes to be scored (0 eliminated genes)
Level 13: 2 nodes to be scored (14 eliminated genes)
Level 12: 3 nodes to be scored (42 eliminated genes)
Level 11: 7 nodes to be scored (313 eliminated genes)
Level 10: 15 nodes to be scored (509 eliminated genes)
Level 9: 21 nodes to be scored (810 eliminated genes)
Level 8: 25 nodes to be scored (1032 eliminated genes)
Level 7: 32 nodes to be scored (1434 eliminated genes)
Level 6: 36 nodes to be scored (1865 eliminated genes)
Level 5: 36 nodes to be scored (2351 eliminated genes)
Level 4: 29 nodes to be scored (3459 eliminated genes)
Level 3: 15 nodes to be scored (4273 eliminated genes)
Level 2: 6 nodes to be scored (4916 eliminated genes)
Level 1: 1 nodes to be scored (5463 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 162 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 2 nodes to be scored (0 eliminated genes)
Level 12: 3 nodes to be scored (0 eliminated genes)
Level 11: 6 nodes to be scored (272 eliminated genes)
Level 10: 8 nodes to be scored (462 eliminated genes)
Level 9: 11 nodes to be scored (754 eliminated genes)
Level 8: 13 nodes to be scored (794 eliminated genes)
Level 7: 16 nodes to be scored (856 eliminated genes)
Level 6: 27 nodes to be scored (1107 eliminated genes)
Level 5: 30 nodes to be scored (2222 eliminated genes)
Level 4: 24 nodes to be scored (3484 eliminated genes)
Level 3: 14 nodes to be scored (4184 eliminated genes)
Level 2: 7 nodes to be scored (4730 eliminated genes)
Level 1: 1 nodes to be scored (5198 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 119 nontrivial nodes
parameters:
test statistic: fisher
Level 14: 3 nodes to be scored (0 eliminated genes)
Level 13: 2 nodes to be scored (0 eliminated genes)
Level 12: 4 nodes to be scored (56 eliminated genes)
Level 11: 7 nodes to be scored (82 eliminated genes)
Level 10: 5 nodes to be scored (268 eliminated genes)
Level 9: 8 nodes to be scored (291 eliminated genes)
Level 8: 8 nodes to be scored (544 eliminated genes)
Level 7: 9 nodes to be scored (712 eliminated genes)
Level 6: 14 nodes to be scored (837 eliminated genes)
Level 5: 20 nodes to be scored (2152 eliminated genes)
Level 4: 18 nodes to be scored (3597 eliminated genes)
Level 3: 13 nodes to be scored (4091 eliminated genes)
Level 2: 7 nodes to be scored (4395 eliminated genes)
Level 1: 1 nodes to be scored (5199 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 261 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 2 nodes to be scored (0 eliminated genes)
Level 11: 5 nodes to be scored (2 eliminated genes)
Level 10: 10 nodes to be scored (10 eliminated genes)
Level 9: 17 nodes to be scored (753 eliminated genes)
Level 8: 24 nodes to be scored (1000 eliminated genes)
Level 7: 34 nodes to be scored (1527 eliminated genes)
Level 6: 50 nodes to be scored (2305 eliminated genes)
Level 5: 53 nodes to be scored (3633 eliminated genes)
Level 4: 36 nodes to be scored (4243 eliminated genes)
Level 3: 20 nodes to be scored (4940 eliminated genes)
Level 2: 8 nodes to be scored (5240 eliminated genes)
Level 1: 1 nodes to be scored (5539 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 113 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 3 nodes to be scored (0 eliminated genes)
Level 12: 6 nodes to be scored (0 eliminated genes)
Level 11: 5 nodes to be scored (61 eliminated genes)
Level 10: 3 nodes to be scored (491 eliminated genes)
Level 9: 3 nodes to be scored (765 eliminated genes)
Level 8: 4 nodes to be scored (784 eliminated genes)
Level 7: 10 nodes to be scored (791 eliminated genes)
Level 6: 18 nodes to be scored (822 eliminated genes)
Level 5: 23 nodes to be scored (1698 eliminated genes)
Level 4: 17 nodes to be scored (3040 eliminated genes)
Level 3: 13 nodes to be scored (4435 eliminated genes)
Level 2: 7 nodes to be scored (5154 eliminated genes)
Level 1: 1 nodes to be scored (5406 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 255 nontrivial nodes
parameters:
test statistic: fisher
Level 15: 1 nodes to be scored (0 eliminated genes)
Level 14: 1 nodes to be scored (0 eliminated genes)
Level 13: 4 nodes to be scored (13 eliminated genes)
Level 12: 8 nodes to be scored (32 eliminated genes)
Level 11: 11 nodes to be scored (391 eliminated genes)
Level 10: 13 nodes to be scored (745 eliminated genes)
Level 9: 24 nodes to be scored (1119 eliminated genes)
Level 8: 24 nodes to be scored (1332 eliminated genes)
Level 7: 33 nodes to be scored (1474 eliminated genes)
Level 6: 38 nodes to be scored (2408 eliminated genes)
Level 5: 43 nodes to be scored (3336 eliminated genes)
Level 4: 33 nodes to be scored (4192 eliminated genes)
Level 3: 15 nodes to be scored (4800 eliminated genes)
Level 2: 6 nodes to be scored (5361 eliminated genes)
Level 1: 1 nodes to be scored (5500 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 1 nontrivial nodes
parameters:
test statistic: fisher
Nothing to do:
Nothing to do:
Level 1: 1 nodes to be scored (0 eliminated genes)
Warning message in .genesInNode(graph(object), whichGO):
“Nodes not present in the graph:NA”
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 87 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 2 nodes to be scored (0 eliminated genes)
Level 10: 4 nodes to be scored (426 eliminated genes)
Level 9: 5 nodes to be scored (752 eliminated genes)
Level 8: 5 nodes to be scored (785 eliminated genes)
Level 7: 4 nodes to be scored (786 eliminated genes)
Level 6: 11 nodes to be scored (920 eliminated genes)
Level 5: 20 nodes to be scored (1776 eliminated genes)
Level 4: 16 nodes to be scored (2843 eliminated genes)
Level 3: 12 nodes to be scored (3831 eliminated genes)
Level 2: 6 nodes to be scored (4270 eliminated genes)
Level 1: 1 nodes to be scored (5040 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 261 nontrivial nodes
parameters:
test statistic: fisher
Level 14: 2 nodes to be scored (0 eliminated genes)
Level 13: 2 nodes to be scored (0 eliminated genes)
Level 12: 5 nodes to be scored (36 eliminated genes)
Level 11: 8 nodes to be scored (389 eliminated genes)
Level 10: 12 nodes to be scored (557 eliminated genes)
Level 9: 22 nodes to be scored (775 eliminated genes)
Level 8: 28 nodes to be scored (850 eliminated genes)
Level 7: 31 nodes to be scored (1096 eliminated genes)
Level 6: 41 nodes to be scored (1682 eliminated genes)
Level 5: 48 nodes to be scored (2820 eliminated genes)
Level 4: 32 nodes to be scored (4098 eliminated genes)
Level 3: 20 nodes to be scored (4905 eliminated genes)
Level 2: 9 nodes to be scored (5348 eliminated genes)
Level 1: 1 nodes to be scored (5555 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 270 nontrivial nodes
parameters:
test statistic: fisher
Level 16: 1 nodes to be scored (0 eliminated genes)
Level 15: 3 nodes to be scored (0 eliminated genes)
Level 14: 4 nodes to be scored (4 eliminated genes)
Level 13: 3 nodes to be scored (14 eliminated genes)
Level 12: 5 nodes to be scored (49 eliminated genes)
Level 11: 6 nodes to be scored (295 eliminated genes)
Level 10: 12 nodes to be scored (479 eliminated genes)
Level 9: 15 nodes to be scored (799 eliminated genes)
Level 8: 17 nodes to be scored (910 eliminated genes)
Level 7: 26 nodes to be scored (1110 eliminated genes)
Level 6: 43 nodes to be scored (1954 eliminated genes)
Level 5: 56 nodes to be scored (3392 eliminated genes)
Level 4: 45 nodes to be scored (4124 eliminated genes)
Level 3: 23 nodes to be scored (4855 eliminated genes)
Level 2: 10 nodes to be scored (5331 eliminated genes)
Level 1: 1 nodes to be scored (5542 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 139 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 5 nodes to be scored (0 eliminated genes)
Level 11: 10 nodes to be scored (48 eliminated genes)
Level 10: 15 nodes to be scored (119 eliminated genes)
Level 9: 16 nodes to be scored (821 eliminated genes)
Level 8: 12 nodes to be scored (948 eliminated genes)
Level 7: 7 nodes to be scored (1030 eliminated genes)
Level 6: 16 nodes to be scored (1160 eliminated genes)
Level 5: 24 nodes to be scored (2365 eliminated genes)
Level 4: 17 nodes to be scored (3512 eliminated genes)
Level 3: 10 nodes to be scored (3917 eliminated genes)
Level 2: 5 nodes to be scored (4531 eliminated genes)
Level 1: 1 nodes to be scored (5159 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 185 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 4 nodes to be scored (0 eliminated genes)
Level 12: 6 nodes to be scored (0 eliminated genes)
Level 11: 8 nodes to be scored (361 eliminated genes)
Level 10: 9 nodes to be scored (579 eliminated genes)
Level 9: 9 nodes to be scored (756 eliminated genes)
Level 8: 12 nodes to be scored (857 eliminated genes)
Level 7: 20 nodes to be scored (902 eliminated genes)
Level 6: 30 nodes to be scored (1344 eliminated genes)
Level 5: 33 nodes to be scored (2677 eliminated genes)
Level 4: 27 nodes to be scored (3577 eliminated genes)
Level 3: 17 nodes to be scored (4020 eliminated genes)
Level 2: 9 nodes to be scored (4800 eliminated genes)
Level 1: 1 nodes to be scored (5198 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 272 nontrivial nodes
parameters:
test statistic: fisher
Level 16: 1 nodes to be scored (0 eliminated genes)
Level 15: 2 nodes to be scored (0 eliminated genes)
Level 14: 2 nodes to be scored (5 eliminated genes)
Level 13: 6 nodes to be scored (37 eliminated genes)
Level 12: 8 nodes to be scored (98 eliminated genes)
Level 11: 10 nodes to be scored (440 eliminated genes)
Level 10: 13 nodes to be scored (578 eliminated genes)
Level 9: 20 nodes to be scored (929 eliminated genes)
Level 8: 23 nodes to be scored (1087 eliminated genes)
Level 7: 28 nodes to be scored (1241 eliminated genes)
Level 6: 41 nodes to be scored (1811 eliminated genes)
Level 5: 46 nodes to be scored (2899 eliminated genes)
Level 4: 40 nodes to be scored (4041 eliminated genes)
Level 3: 23 nodes to be scored (4568 eliminated genes)
Level 2: 8 nodes to be scored (5130 eliminated genes)
Level 1: 1 nodes to be scored (5487 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 107 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 2 nodes to be scored (26 eliminated genes)
Level 10: 5 nodes to be scored (26 eliminated genes)
Level 9: 5 nodes to be scored (35 eliminated genes)
Level 8: 9 nodes to be scored (91 eliminated genes)
Level 7: 14 nodes to be scored (181 eliminated genes)
Level 6: 15 nodes to be scored (499 eliminated genes)
Level 5: 21 nodes to be scored (888 eliminated genes)
Level 4: 15 nodes to be scored (1865 eliminated genes)
Level 3: 12 nodes to be scored (2638 eliminated genes)
Level 2: 6 nodes to be scored (3710 eliminated genes)
Level 1: 1 nodes to be scored (5501 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 106 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 1 nodes to be scored (0 eliminated genes)
Level 10: 4 nodes to be scored (17 eliminated genes)
Level 9: 5 nodes to be scored (28 eliminated genes)
Level 8: 9 nodes to be scored (133 eliminated genes)
Level 7: 12 nodes to be scored (238 eliminated genes)
Level 6: 14 nodes to be scored (658 eliminated genes)
Level 5: 24 nodes to be scored (1384 eliminated genes)
Level 4: 17 nodes to be scored (2594 eliminated genes)
Level 3: 12 nodes to be scored (4507 eliminated genes)
Level 2: 6 nodes to be scored (4870 eliminated genes)
Level 1: 1 nodes to be scored (5456 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 102 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 1 nodes to be scored (0 eliminated genes)
Level 10: 3 nodes to be scored (9 eliminated genes)
Level 9: 4 nodes to be scored (14 eliminated genes)
Level 8: 8 nodes to be scored (34 eliminated genes)
Level 7: 12 nodes to be scored (96 eliminated genes)
Level 6: 14 nodes to be scored (204 eliminated genes)
Level 5: 17 nodes to be scored (458 eliminated genes)
Level 4: 19 nodes to be scored (845 eliminated genes)
Level 3: 13 nodes to be scored (1849 eliminated genes)
Level 2: 9 nodes to be scored (4470 eliminated genes)
Level 1: 1 nodes to be scored (5420 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 99 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 1 nodes to be scored (0 eliminated genes)
Level 10: 2 nodes to be scored (54 eliminated genes)
Level 9: 4 nodes to be scored (159 eliminated genes)
Level 8: 5 nodes to be scored (276 eliminated genes)
Level 7: 10 nodes to be scored (340 eliminated genes)
Level 6: 13 nodes to be scored (786 eliminated genes)
Level 5: 19 nodes to be scored (1383 eliminated genes)
Level 4: 17 nodes to be scored (2788 eliminated genes)
Level 3: 18 nodes to be scored (4530 eliminated genes)
Level 2: 8 nodes to be scored (5068 eliminated genes)
Level 1: 1 nodes to be scored (5558 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 27 nontrivial nodes
parameters:
test statistic: fisher
Level 8: 1 nodes to be scored (0 eliminated genes)
Level 7: 1 nodes to be scored (0 eliminated genes)
Level 6: 5 nodes to be scored (9 eliminated genes)
Level 5: 5 nodes to be scored (259 eliminated genes)
Level 4: 5 nodes to be scored (2304 eliminated genes)
Level 3: 6 nodes to be scored (3762 eliminated genes)
Level 2: 3 nodes to be scored (4385 eliminated genes)
Level 1: 1 nodes to be scored (4945 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 242 nontrivial nodes
parameters:
test statistic: fisher
Level 15: 1 nodes to be scored (0 eliminated genes)
Level 14: 9 nodes to be scored (0 eliminated genes)
Level 13: 12 nodes to be scored (2 eliminated genes)
Level 12: 7 nodes to be scored (11 eliminated genes)
Level 11: 5 nodes to be scored (344 eliminated genes)
Level 10: 7 nodes to be scored (556 eliminated genes)
Level 9: 9 nodes to be scored (752 eliminated genes)
Level 8: 14 nodes to be scored (789 eliminated genes)
Level 7: 32 nodes to be scored (806 eliminated genes)
Level 6: 40 nodes to be scored (994 eliminated genes)
Level 5: 45 nodes to be scored (2144 eliminated genes)
Level 4: 34 nodes to be scored (3377 eliminated genes)
Level 3: 17 nodes to be scored (4292 eliminated genes)
Level 2: 9 nodes to be scored (4872 eliminated genes)
Level 1: 1 nodes to be scored (5205 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 94 nontrivial nodes
parameters:
test statistic: fisher
Level 11: 1 nodes to be scored (0 eliminated genes)
Level 10: 2 nodes to be scored (0 eliminated genes)
Level 9: 6 nodes to be scored (6 eliminated genes)
Level 8: 6 nodes to be scored (39 eliminated genes)
Level 7: 10 nodes to be scored (165 eliminated genes)
Level 6: 9 nodes to be scored (395 eliminated genes)
Level 5: 20 nodes to be scored (749 eliminated genes)
Level 4: 17 nodes to be scored (1301 eliminated genes)
Level 3: 14 nodes to be scored (2288 eliminated genes)
Level 2: 8 nodes to be scored (3423 eliminated genes)
Level 1: 1 nodes to be scored (4616 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 132 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 2 nodes to be scored (0 eliminated genes)
Level 11: 6 nodes to be scored (0 eliminated genes)
Level 10: 8 nodes to be scored (10 eliminated genes)
Level 9: 10 nodes to be scored (763 eliminated genes)
Level 8: 10 nodes to be scored (894 eliminated genes)
Level 7: 8 nodes to be scored (931 eliminated genes)
Level 6: 17 nodes to be scored (1259 eliminated genes)
Level 5: 25 nodes to be scored (2028 eliminated genes)
Level 4: 21 nodes to be scored (3034 eliminated genes)
Level 3: 15 nodes to be scored (3829 eliminated genes)
Level 2: 9 nodes to be scored (4757 eliminated genes)
Level 1: 1 nodes to be scored (5194 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 67 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 2 nodes to be scored (0 eliminated genes)
Level 11: 3 nodes to be scored (149 eliminated genes)
Level 10: 3 nodes to be scored (529 eliminated genes)
Level 9: 3 nodes to be scored (752 eliminated genes)
Level 8: 3 nodes to be scored (784 eliminated genes)
Level 7: 6 nodes to be scored (785 eliminated genes)
Level 6: 11 nodes to be scored (845 eliminated genes)
Level 5: 15 nodes to be scored (1625 eliminated genes)
Level 4: 10 nodes to be scored (2722 eliminated genes)
Level 3: 6 nodes to be scored (3525 eliminated genes)
Level 2: 3 nodes to be scored (4075 eliminated genes)
Level 1: 1 nodes to be scored (4344 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 204 nontrivial nodes
parameters:
test statistic: fisher
Level 13: 1 nodes to be scored (0 eliminated genes)
Level 12: 1 nodes to be scored (0 eliminated genes)
Level 11: 6 nodes to be scored (19 eliminated genes)
Level 10: 14 nodes to be scored (21 eliminated genes)
Level 9: 19 nodes to be scored (311 eliminated genes)
Level 8: 15 nodes to be scored (447 eliminated genes)
Level 7: 20 nodes to be scored (606 eliminated genes)
Level 6: 27 nodes to be scored (933 eliminated genes)
Level 5: 35 nodes to be scored (1168 eliminated genes)
Level 4: 34 nodes to be scored (3708 eliminated genes)
Level 3: 21 nodes to be scored (5012 eliminated genes)
Level 2: 10 nodes to be scored (5297 eliminated genes)
Level 1: 1 nodes to be scored (5526 eliminated genes)
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 75 nontrivial nodes
parameters:
test statistic: fisher
Level 12: 2 nodes to be scored (0 eliminated genes)
Level 11: 2 nodes to be scored (0 eliminated genes)
Level 10: 3 nodes to be scored (47 eliminated genes)
Level 9: 5 nodes to be scored (99 eliminated genes)
Level 8: 5 nodes to be scored (152 eliminated genes)
Level 7: 8 nodes to be scored (186 eliminated genes)
Level 6: 9 nodes to be scored (456 eliminated genes)
Level 5: 14 nodes to be scored (752 eliminated genes)
Level 4: 14 nodes to be scored (1291 eliminated genes)
Level 3: 7 nodes to be scored (3335 eliminated genes)
Level 2: 5 nodes to be scored (4503 eliminated genes)
Level 1: 1 nodes to be scored (5252 eliminated genes)
In [16]:
rownames(enrichmentResults) <- c("terms","p-values","genes")
colnames(enrichmentResults) <- 2:max(assignments)
In [17]:
communitySimilarity <- function(community) {
termSims <- getTermSim(termlist = names(community), method = "Lin", verbose = F)
if (length(termSims) > 1) {
return(mean(termSims[upper.tri(termSims)]))
} else {
return (NaN)
}
}
In [19]:
communitySimilarity(enrichmentResults[["p-values", 27]])
0.17377976391584
In [20]:
getGenes(27)
- 'YHR102W'
- 'YOR353C'
- 'YKR062W'
- 'YKL028W'
- 'YDR311W'
In [21]:
layerSimilarity <- function(layer) {
pvalueList <- enrichmentResults["p-values", unique(assignments[,layer][assignments[,layer] != -1]) - 1]
communitiesSimilarity <- sapply(pvalueList, communitySimilarity)
communitiesSimilarity <- communitiesSimilarity[!is.na(communitiesSimilarity)]
return(mean(communitiesSimilarity))
}
In [22]:
layerMeanSimilarities <- sapply(colnames, layerSimilarity)
Warning message in mean.default(communitiesSimilarity):
“argument is not numeric or logical: returning NA”
Error in if (term1 == term2) {: missing value where TRUE/FALSE needed
Traceback:
1. sapply(colnames, layerSimilarity)
2. sapply(colnames, layerSimilarity)
3. lapply(X = X, FUN = FUN, ...)
4. FUN(X[[i]], ...)
5. sapply(pvalueList, communitySimilarity) # at line 3 of file <text>
6. sapply(pvalueList, communitySimilarity)
7. lapply(X = X, FUN = FUN, ...)
8. FUN(X[[i]], ...)
9. getTermSim(termlist = names(community), method = "Lin", verbose = F) # at line 2 of file <text>
10. calcTermSim(termlist[i], termlist[i], method, verbose)
11. getMinimumSubsumer(term1, term2)
In [ ]:
layerMeanSimilarities
In [12]:
geneCommunities <- sapply(1:max(assignments), function (i) getGenes(i)[getGenes(i) %in% allGenesInDB])
In [15]:
getSubCommunities(6)
'-1'
In [14]:
as.list(org.Sc.sgdPATH[geneCommunities[[6]]])
- $YCL046W
- NA
- $YJL030W
- '04111'
- '04113'
- $YBR057C
- NA
- $YPL211W
- NA
- $YCR050C
- NA
- $YBR196C
- '00010'
- '00030'
- '00500'
- '00520'
- '01100'
- '01110'
- $YJL178C
- NA
- $YGL208W
- NA
- $YJL211C
- NA
- $YNR048W
- NA
- $YKL015W
- NA
- $YGL192W
- NA
- $YGR014W
- NA
- $YEL023C
- NA
- $YJR122W
- NA
- $YEL015W
- '03018'
- $YDL110C
- NA
- $YCL055W
- NA
- $YLR323C
- NA
- $YDL011C
- NA
- $YCR022C
- NA
- $YFR057W
- NA
- $YDL160C
- '03018'
- $YPR040W
- NA
- $YGR099W
- NA
- $YDR214W
- NA
- $YOR062C
- NA
- $YGR057C
- NA
- $YBR094W
- NA
- $YLR264W
- '03010'
- $YGL229C
- NA
- $YER105C
- NA
- $YDR315C
- NA
- $YMR102C
- NA
- $YJL088W
- '00330'
- '01100'
- '01110'
- $YNL118C
- '03018'
- $YGL116W
- '04111'
- '04113'
- '04120'
- $YJR117W
- NA
- $YKL039W
- NA
- $YDL165W
- '03018'
- $YHR158C
- NA
- $YDL017W
- '04111'
- '04113'
- $YDR477W
- '04113'
- $YGL115W
- NA
- $YOR047C
- NA
- $YER027C
- NA
- $YOR006C
- NA
- $YDR206W
- NA
- $YOL149W
- '03018'
- $YNL218W
- NA
- $YOR078W
- NA
In [23]:
as.list(org.Sc.sgdPATH2ORF[["00330"]]) %in% allGenes
- FALSE
- TRUE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- TRUE
- FALSE
- FALSE
- FALSE
- FALSE
- TRUE
- FALSE
- FALSE
- TRUE
- FALSE
- FALSE
- TRUE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
- FALSE
In [21]:
length(allGenes)
263
In [104]:
geneCommunities[[1]]
- 'YGR046W'
- 'YCL046W'
- 'YJL030W'
- 'YBR057C'
- 'YKR026C'
- 'YFL056C'
- 'YFL061W'
- 'YDR174W'
- 'YLR303W'
- 'YPL211W'
- 'YOL020W'
- 'YLR432W'
- 'YPL229W'
- 'YMR236W'
- 'YLR015W'
- 'YCR050C'
- 'YOR372C'
- 'YLR046C'
- 'YGR136W'
- 'YJR072C'
- 'YDR469W'
- 'YBR196C'
- 'YHR140W'
- 'YDL088C'
- 'YEL062W'
- 'YJL178C'
- 'YKR037C'
- 'YNL047C'
- 'YLR113W'
- 'YGR268C'
- 'YPR048W'
- 'YJL184W'
- 'YGL112C'
- 'YAL040C'
- 'YCR011C'
- 'YGL208W'
- 'YOR355W'
- 'YBR133C'
- 'YDR439W'
- 'YMR267W'
- 'YGL175C'
- 'YOR262W'
- 'YJL211C'
- 'YDR078C'
- 'YKL103C'
- 'YOL123W'
- 'YNR048W'
- 'YKL015W'
- 'YDL154W'
- 'YOL082W'
- 'YPR115W'
- 'YJR091C'
- 'YGL192W'
- 'YDL246C'
- 'YNL122C'
- 'YGR014W'
- 'YNL244C'
- 'YER179W'
- 'YKL075C'
- 'YPR062W'
- 'YHR060W'
- 'YOR331C'
- 'YNR029C'
- 'YIL105C'
- 'YJL013C'
- 'YEL023C'
- 'MEL1'
- 'YOR284W'
- 'YOL105C'
- 'YNL201C'
- 'YJL064W'
- 'YHR102W'
- 'YGL170C'
- 'YML028W'
- 'YJR122W'
- 'YEL015W'
- 'YDR201W'
- 'YGL180W'
- 'YLR315W'
- 'YGR155W'
- 'YDL110C'
- 'YCL055W'
- 'YKL090W'
- 'YLR323C'
- 'YER092W'
- 'YFR052W'
- 'YPL111W'
- 'YNL288W'
- 'YDL011C'
- 'YLR243W'
- 'YNL189W'
- 'YLR423C'
- 'YJL065C'
- 'YCR022C'
- 'YAL032C'
- 'YIL013C'
- 'YLR433C'
- 'YMR068W'
- 'YFR057W'
- 'YFR047C'
- 'YGL145W'
- 'YMR153W'
- 'YDL160C'
- 'YBL007C'
- 'YPR040W'
- 'YBR252W'
- 'YOR353C'
- 'YKL061W'
- 'YHL009C'
- 'YDR503C'
- 'YPL260W'
- 'YDR273W'
- 'YDL111C'
- 'YGR099W'
- 'YGL238W'
- 'YLR392C'
- 'YDR214W'
- 'YDR326C'
- 'YJL048C'
- 'YJR133W'
- 'YOR062C'
- 'YNL086W'
- 'YGR057C'
- 'YGR058W'
- 'YBR094W'
- 'YCL054W'
- 'YKR062W'
- 'YMR224C'
- 'YIL144W'
- 'YOR138C'
- 'YIL172C'
- 'YJL041W'
- 'YGL024W'
- 'YMR181C'
- 'YDR061W'
- 'YGL221C'
- 'YMR129W'
- 'YFR033C'
- 'YOL130W'
- 'YLR150W'
- 'YDR106W'
- 'YMR269W'
- 'YDR207C'
- 'YOR026W'
- 'YLR264W'
- 'YOL058W'
- 'YOR115C'
- 'YLL046C'
- 'YGL172W'
- 'YGL229C'
- 'YMR314W'
- 'YKL204W'
- 'YGR117C'
- 'YBR221C'
- 'YGL122C'
- 'YNL236W'
- 'YDR151C'
- 'YPR020W'
- 'YER105C'
- 'YDR383C'
- 'YDR315C'
- 'YPL214C'
- 'YMR102C'
- 'YHR032W'
- 'YJL088W'
- 'YIL132C'
- 'YNL118C'
- 'YLR345W'
- 'YGL116W'
- 'YGR158C'
- 'YBR270C'
- 'YJR117W'
- 'YGR119C'
- 'YDL203C'
- 'YKL039W'
- 'YLR447C'
- 'YPL110C'
- 'YGR253C'
- 'YDR510W'
- 'YPR105C'
- 'YPL174C'
- 'YGR120C'
- 'YKL130C'
- 'YDL012C'
- 'YPL059W'
- 'YIL082W'
- 'YNL021W'
- 'YLR258W'
- 'YHL006C'
- 'YNL091W'
- 'YGL158W'
- 'YPL128C'
- 'YMR226C'
- 'YML015C'
- 'YDR054C'
- 'YDL165W'
- 'YHR158C'
- 'YML031W'
- 'YLR321C'
- 'YHR016C'
- 'YER079W'
- 'YLR305C'
- 'YJR034W'
- 'YPR185W'
- 'YCR086W'
- 'YOR036W'
- 'YDL017W'
- 'YKL028W'
- 'YDR477W'
- 'YDR148C'
- 'YGL150C'
- 'YDL215C'
- 'YGR024C'
- 'YNL154C'
- 'YKL012W'
- 'YJR159W'
- 'YGL115W'
- 'YPL070W'
- 'YHR128W'
- 'YJL057C'
- 'YJL092W'
- 'YPR070W'
- 'YLR322W'
- 'YDL236W'
- 'YDR122W'
- 'YBR274W'
- 'YMR309C'
- 'YHL046C'
- 'YMR255W'
- 'YOR047C'
- 'YIL065C'
- 'YOR264W'
- 'YER116C'
- 'YPL151C'
- 'YNL164C'
- 'YER027C'
- 'YOR006C'
- 'YDL013W'
- 'YDR167W'
- 'YNL023C'
- 'YMR180C'
- 'YJL110C'
- 'YNL199C'
- 'YHR057C'
- 'YFR002W'
- 'YOR164C'
- 'YLR291C'
- 'YNR025C'
- 'YGL025C'
- 'YML114C'
- 'YHR129C'
- 'YLR376C'
- 'YDR311W'
- 'YDR206W'
- 'YOL149W'
- 'YER065C'
- 'YNL218W'
- 'YOR078W'
- 'YPL019C'
- 'YGL153W'
- 'YDL002C'
In [27]:
allGenes <- allGenes[allGenes%in% allGenesInDB]
In [28]:
GOenrichment(allGenes, allGenesInDB, cutoff = 0.01, method = "weight01")
Building most specific GOs .....
( 2909 GO terms found. )
Build GO DAG topology ..........
( 5064 GO terms and 11404 relations. )
Annotating nodes ...............
( 6419 genes annotated to the GO terms. )
-- Weight01 Algorithm --
the algorithm is scoring 1848 nontrivial nodes
parameters:
test statistic: fisher
Level 16: 3 nodes to be scored (0 eliminated genes)
Level 15: 12 nodes to be scored (0 eliminated genes)
Level 14: 33 nodes to be scored (13 eliminated genes)
Level 13: 63 nodes to be scored (116 eliminated genes)
Level 12: 93 nodes to be scored (406 eliminated genes)
Level 11: 131 nodes to be scored (804 eliminated genes)
Level 10: 190 nodes to be scored (1400 eliminated genes)
Level 9: 232 nodes to be scored (2023 eliminated genes)
Level 8: 234 nodes to be scored (2704 eliminated genes)
Level 7: 245 nodes to be scored (3775 eliminated genes)
Level 6: 254 nodes to be scored (4476 eliminated genes)
Level 5: 195 nodes to be scored (4884 eliminated genes)
Level 4: 110 nodes to be scored (5233 eliminated genes)
Level 3: 39 nodes to be scored (5412 eliminated genes)
Level 2: 13 nodes to be scored (5489 eliminated genes)
Level 1: 1 nodes to be scored (5602 eliminated genes)
- $GOTerms
go_id Term Definition 131 GO:0000050 urea cycle The sequence of reactions by which arginine is synthesized from ornithine, then cleaved to yield urea and regenerate ornithine. The overall reaction equation is NH3 + CO2 + aspartate + 3 ATP + 2 H2O = urea + fumarate + 2 ADP + 2 phosphate + AMP + diphosphate. 459 GO:0000184 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay The nonsense-mediated decay pathway for nuclear-transcribed mRNAs degrades mRNAs in which an amino-acid codon has changed to a nonsense codon; this prevents the translation of such mRNAs into truncated, and potentially harmful, proteins. 845 GO:0000290 deadenylation-dependent decapping of nuclear-transcribed mRNA Cleavage of the 5'-cap of a nuclear mRNA triggered by shortening of the poly(A) tail to below a minimum functional length. 1283 GO:0000723 telomere maintenance Any process that contributes to the maintenance of proper telomeric length and structure by affecting and monitoring the activity of telomeric proteins, the length of telomeric DNA and the replication and repair of the DNA. These processes includes those that shorten, lengthen, replicate and repair the telomeric DNA sequences. 1296 GO:0000730 DNA recombinase assembly The aggregation, arrangement and bonding together of strand exchange proteins (recombinases) into higher order oligomers on single-stranded DNA. 2134 GO:0001558 regulation of cell growth Any process that modulates the frequency, rate, extent or direction of cell growth. 6237 GO:0051017 actin filament bundle assembly The assembly of actin filament bundles; actin filaments are on the same axis but may be oriented with the same or opposite polarities and may be packed with different levels of tightness. 15538 GO:0006468 protein phosphorylation The process of introducing a phosphate group on to a protein. 15892 GO:0006607 NLS-bearing protein import into nucleus The directed movement of a protein bearing a nuclear localization signal (NLS) from the cytoplasm into the nucleus, across the nuclear membrane. 16867 GO:0006999 nuclear pore organization A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the nuclear pore. 16968 GO:0007067 mitotic nuclear division A cell cycle process comprising the steps by which the nucleus of a eukaryotic cell divides; the process involves condensation of chromosomal DNA into a highly compacted form. Canonically, mitosis produces two daughter nuclei whose chromosome complement is identical to that of the mother cell. 17027 GO:0007094 mitotic spindle assembly checkpoint A cell cycle checkpoint that delays the metaphase/anaphase transition of a mitotic nuclear division until the spindle is correctly assembled and chromosomes are attached to the spindle. 24566 GO:0010791 DNA double-strand break processing involved in repair via synthesis-dependent strand annealing The 5' to 3' exonucleolytic resection of the DNA at the site of the break to form a 3' single-strand DNA overhang that results in the repair of a double strand break via synthesis-dependent strand annealing. 28775 GO:0017148 negative regulation of translation Any process that stops, prevents, or reduces the frequency, rate or extent of the chemical reactions and pathways resulting in the formation of proteins by the translation of mRNA. 34090 GO:0030242 pexophagy The process in which peroxisomes are delivered to the vacuole and degraded in response to changing nutrient conditions. 36000 GO:0031087 deadenylation-independent decapping of nuclear-transcribed mRNA Cleavage of the 5'-cap of a nuclear-transcribed mRNA that is independent of poly(A) tail shortening. 38427 GO:0032258 CVT pathway A constitutive biosynthetic process that occurs under nutrient-rich conditions, in which two resident vacuolar hydrolases, aminopeptidase I and alpha-mannosidase, are sequestered into vesicles; these vesicles are transported to, and then fuse with, the vacuole. This pathway is mostly observed in yeast. 47807 GO:0038203 TORC2 signaling A series of intracellular molecular signals mediated by TORC2; TOR (rapamycin-insensitive companion of TOR) in complex with at least Rictor (regulatory-associated protein of TOR), or orthologs of, and other signaling components. 50814 GO:0043007 maintenance of rDNA Any process involved in sustaining the fidelity and copy number of rDNA repeats. 53373 GO:0044206 UMP salvage Any process which produces UMP, uridine monophosphate, from derivatives of it (e.g. cytidine, uridine, cytosine) without de novo synthesis. 53678 GO:0044376 RNA polymerase II complex import to nucleus The directed movement of the DNA-directed RNA polymerase II core complex from the cytoplasm into the nucleus. 54154 GO:0044774 mitotic DNA integrity checkpoint A mitotic cell cycle process that controls cell cycle progression in response to changes in DNA structure by monitoring the integrity of the DNA. The DNA integrity checkpoint begins with detection of DNA damage, defects in DNA structure or DNA replication, and ends with signal transduction. 54302 GO:0070987 error-free translesion synthesis The conversion of DNA-damage induced single-stranded gaps into large molecular weight DNA after replication by using a specialized DNA polymerase or replication complex to insert a defined nucleotide across the lesion. This process does not remove the replication-blocking lesions but does not causes an increase in the endogenous mutation level. For S. cerevisiae, RAD30 encodes DNA polymerase eta, which incorporates two adenines. When incorporated across a thymine-thymine dimer, it does not increase the endogenous mutation level. 69682 GO:0051028 mRNA transport The directed movement of mRNA, messenger ribonucleic acid, into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. 69919 GO:0051123 RNA polymerase II transcriptional preinitiation complex assembly The aggregation, arrangement and bonding together of proteins on an RNA polymerase II promoter DNA to form the transcriptional preinitiation complex (PIC), the formation of which is a prerequisite for transcription by RNA polymerase. 70542 GO:0051301 cell division The process resulting in division and partitioning of components of a cell to form more cells; may or may not be accompanied by the physical separation of a cell into distinct, individually membrane-bounded daughter cells. 82920 GO:0080009 mRNA methylation The posttranscriptional addition of methyl groups to specific residues in an mRNA molecule. 129962 GO:1990022 RNA polymerase III complex localization to nucleus The directed movement of an RNA polymerase III complex from the cytoplasm to the nucleus.
- $p.values
- GO:0032258
- 0.00535663564789109
- GO:0044206
- 0.00874053399737461
- GO:0030242
- 0.00168606124730799
- GO:0006999
- 1.86115383174225e-05
- GO:0051301
- 0.000247378002819304
- GO:0044774
- 0.00184954302497703
- GO:0000184
- 0.00166700786727578
- GO:1990022
- 0.00448641628469648
- GO:0000290
- 0.000134094048551179
- GO:0051123
- 0.00245289974545606
- GO:0070987
- 0.00780484511090381
- GO:0017148
- 0.00483779050551011
- GO:0038203
- 0.00023229857201835
- GO:0007094
- 0.00419574127412856
- GO:0000050
- 0.00109504705730622
- GO:0001558
- 0.0099743869179752
- GO:0044376
- 0.000563874387503784
- GO:0007067
- 0.00391938573977879
- GO:0010791
- 0.00153534922398258
- GO:0000723
- 0.00125144136972518
- GO:0000730
- 3.22898345221797e-05
- GO:0051028
- 0.000113257699617022
- GO:0051017
- 0.00419432094777694
- GO:0006607
- 0.00290553028161677
- GO:0006468
- 0.00937238530764923
- GO:0031087
- 7.39498058912123e-05
- GO:0080009
- 0.00448641628469648
- GO:0043007
- 0.0042120427737538
- $genes
- $`GO:0032258`
- 'YBL078C'
- 'YBR128C'
- 'YBR131W'
- 'YBR217W'
- 'YDL029W'
- 'YDL113C'
- 'YDL149W'
- 'YDR080W'
- 'YDR108W'
- 'YDR425W'
- 'YEL013W'
- 'YER157W'
- 'YFL038C'
- 'YFR021W'
- 'YGL005C'
- 'YGL095C'
- 'YGL124C'
- 'YGL180W'
- 'YGL212W'
- 'YGL223C'
- 'YGR120C'
- 'YHR105W'
- 'YHR171W'
- 'YJL036W'
- 'YJL083W'
- 'YJL178C'
- 'YKR019C'
- 'YKR078W'
- 'YLL042C'
- 'YLR417W'
- 'YLR431C'
- 'YML001W'
- 'YML071C'
- 'YMR004W'
- 'YMR159C'
- 'YMR218C'
- 'YNL041C'
- 'YNL051W'
- 'YNL098C'
- 'YNL223W'
- 'YNL242W'
- 'YNR007C'
- 'YOL018C'
- 'YOL082W'
- 'YOR036W'
- 'YOR357C'
- 'YPL100W'
- 'YPL120W'
- 'YPL149W'
- 'YPL154C'
- 'YPL204W'
- 'YPL250C'
- 'YPR049C'
- 'YPR105C'
- 'YPR185W'
- $`GO:0044206`
- 'YDR020C'
- 'YHR128W'
- 'YNR012W'
- 'YPR062W'
- $`GO:0030242`
- 'YBL105C'
- 'YBR097W'
- 'YBR128C'
- 'YDR108W'
- 'YER157W'
- 'YFR021W'
- 'YGR120C'
- 'YHR030C'
- 'YJL095W'
- 'YJL178C'
- 'YJL185C'
- 'YLR240W'
- 'YLR332W'
- 'YLR423C'
- 'YNL242W'
- 'YOL105C'
- 'YOR008C'
- 'YOR231W'
- 'YPL120W'
- 'YPL140C'
- 'YPL204W'
- 'YPR049C'
- 'YPR105C'
- $`GO:0006999`
- 'YBL079W'
- 'YDL088C'
- 'YDR233C'
- 'YDR424C'
- 'YER105C'
- 'YFR002W'
- 'YGR119C'
- 'YIL016W'
- 'YJL039C'
- 'YLL023C'
- 'YLR018C'
- 'YLR064W'
- 'YLR347C'
- 'YML031W'
- 'YML103C'
- 'YMR047C'
- 'YMR129W'
- 'YMR153W'
- 'YPR028W'
- $`GO:0051301`
- 'YAL020C'
- 'YAL024C'
- 'YAL034W-A'
- 'YAL040C'
- 'YAL041W'
- 'YAL047C'
- 'YAR014C'
- 'YAR019C'
- 'YBL016W'
- 'YBL031W'
- 'YBL034C'
- 'YBL047C'
- 'YBL063W'
- 'YBL084C'
- 'YBL085W'
- 'YBL097W'
- 'YBL105C'
- 'YBR038W'
- 'YBR107C'
- 'YBR109C'
- 'YBR133C'
- 'YBR135W'
- 'YBR156C'
- 'YBR158W'
- 'YBR160W'
- 'YBR210W'
- 'YBR211C'
- 'YBR214W'
- 'YBR233W-A'
- 'YBR267W'
- 'YCL014W'
- 'YCL024W'
- 'YCL029C'
- 'YCR002C'
- 'YCR038C'
- 'YCR047C'
- 'YCR057C'
- 'YCR063W'
- 'YCR077C'
- 'YDL003W'
- 'YDL008W'
- 'YDL017W'
- 'YDL047W'
- 'YDL064W'
- 'YDL088C'
- 'YDL108W'
- 'YDL117W'
- 'YDL127W'
- 'YDL132W'
- 'YDL134C'
- 'YDL155W'
- 'YDL164C'
- 'YDL179W'
- 'YDL188C'
- 'YDL220C'
- 'YDL225W'
- 'YDL239C'
- 'YDR016C'
- 'YDR052C'
- 'YDR054C'
- 'YDR113C'
- 'YDR118W'
- 'YDR137W'
- 'YDR168W'
- 'YDR182W'
- 'YDR184C'
- 'YDR201W'
- 'YDR218C'
- 'YDR245W'
- 'YDR254W'
- 'YDR260C'
- 'YDR273W'
- 'YDR293C'
- 'YDR318W'
- 'YDR320C-A'
- 'YDR325W'
- 'YDR383C'
- 'YDR420W'
- 'YDR507C'
- 'YEL029C'
- 'YEL061C'
- 'YER014C-A'
- 'YER016W'
- 'YER018C'
- 'YER114C'
- 'YER124C'
- 'YER133W'
- 'YER147C'
- 'YER149C'
- 'YFL005W'
- 'YFL008W'
- 'YFL009W'
- 'YFL034C-B'
- 'YFL039C'
- 'YFR028C'
- 'YFR031C'
- 'YFR036W'
- 'YFR046C'
- 'YGL003C'
- 'YGL054C'
- 'YGL056C'
- 'YGL061C'
- 'YGL086W'
- 'YGL097W'
- 'YGL106W'
- 'YGL116W'
- 'YGL174W'
- 'YGL238W'
- 'YGL240W'
- 'YGR041W'
- 'YGR044C'
- 'YGR058W'
- 'YGR092W'
- 'YGR108W'
- 'YGR109C'
- 'YGR113W'
- 'YGR152C'
- 'YGR179C'
- 'YGR221C'
- 'YGR225W'
- 'YGR238C'
- 'YGR262C'
- 'YHL007C'
- 'YHL027W'
- 'YHR023W'
- 'YHR030C'
- 'YHR071W'
- 'YHR102W'
- 'YHR107C'
- 'YHR111W'
- 'YHR115C'
- 'YHR124W'
- 'YHR143W'
- 'YHR158C'
- 'YHR166C'
- 'YHR184W'
- 'YIL008W'
- 'YIL026C'
- 'YIL050W'
- 'YIL106W'
- 'YIL122W'
- 'YIL129C'
- 'YIL135C'
- 'YIL138C'
- 'YIL140W'
- 'YIL144W'
- 'YIR006C'
- 'YIR010W'
- 'YIR025W'
- 'YJL013C'
- 'YJL030W'
- 'YJL047C'
- 'YJL074C'
- 'YJL076W'
- 'YJL157C'
- 'YJL164C'
- 'YJL187C'
- 'YJL194W'
- 'YJR046W'
- 'YJR053W'
- 'YJR076C'
- 'YJR089W'
- 'YJR092W'
- 'YJR112W'
- 'YJR135C'
- 'YKL022C'
- 'YKL046C'
- 'YKL048C'
- 'YKL052C'
- 'YKL089W'
- 'YKL092C'
- 'YKL129C'
- 'YKL138C-A'
- 'YKL189W'
- 'YKL193C'
- 'YKR001C'
- 'YKR010C'
- 'YKR020W'
- 'YKR037C'
- 'YKR042W'
- 'YKR048C'
- 'YKR083C'
- 'YKR089C'
- 'YLL003W'
- 'YLL016W'
- 'YLL017W'
- 'YLL021W'
- 'YLR002C'
- 'YLR074C'
- 'YLR084C'
- 'YLR086W'
- 'YLR102C'
- 'YLR127C'
- 'YLR131C'
- 'YLR175W'
- 'YLR190W'
- 'YLR210W'
- 'YLR215C'
- 'YLR227C'
- 'YLR229C'
- 'YLR254C'
- 'YLR272C'
- 'YLR286C'
- 'YLR310C'
- 'YLR313C'
- 'YLR314C'
- 'YLR315W'
- 'YLR319C'
- 'YLR320W'
- 'YLR330W'
- 'YLR337C'
- 'YLR353W'
- 'YLR381W'
- 'YLR387C'
- 'YLR459W'
- 'YML064C'
- 'YMR001C'
- 'YMR032W'
- 'YMR036C'
- 'YMR055C'
- 'YMR076C'
- 'YMR092C'
- 'YMR109W'
- 'YMR117C'
- 'YMR153W'
- 'YMR199W'
- 'YMR200W'
- 'YMR238W'
- 'YMR313C'
- 'YNL001W'
- 'YNL066W'
- 'YNL079C'
- 'YNL084C'
- 'YNL110C'
- 'YNL116W'
- 'YNL152W'
- 'YNL161W'
- 'YNL164C'
- 'YNL172W'
- 'YNL188W'
- 'YNL192W'
- 'YNL225C'
- 'YNL233W'
- 'YNL243W'
- 'YNL271C'
- 'YNL289W'
- 'YNL299W'
- 'YNL327W'
- 'YNR027W'
- 'YNR067C'
- 'YOL004W'
- 'YOL069W'
- 'YOL072W'
- 'YOL091W'
- 'YOL113W'
- 'YOL115W'
- 'YOL133W'
- 'YOR005C'
- 'YOR014W'
- 'YOR058C'
- 'YOR073W'
- 'YOR094W'
- 'YOR119C'
- 'YOR127W'
- 'YOR129C'
- 'YOR177C'
- 'YOR195W'
- 'YOR217W'
- 'YOR249C'
- 'YOR257W'
- 'YOR269W'
- 'YOR299W'
- 'YOR301W'
- 'YOR326W'
- 'YOR353C'
- 'YOR373W'
- 'YPL018W'
- 'YPL024W'
- 'YPL155C'
- 'YPL158C'
- 'YPL209C'
- 'YPL233W'
- 'YPL242C'
- 'YPL256C'
- 'YPL267W'
- 'YPR046W'
- 'YPR082C'
- 'YPR111W'
- 'YPR119W'
- 'YPR120C'
- 'YPR122W'
- 'YPR141C'
- 'YPR164W'
- 'YPR165W'
- 'YPR188C'
- $`GO:0044774`
- 'YCL061C'
- 'YDL074C'
- 'YDR217C'
- 'YDR363W'
- 'YDR440W'
- 'YGL058W'
- 'YGL086W'
- 'YJL013C'
- 'YJL030W'
- 'YJL090C'
- 'YKL108W'
- 'YMR190C'
- 'YMR224C'
- 'YNL088W'
- 'YNL127W'
- 'YNL262W'
- 'YOR026W'
- 'YPL194W'
- 'YPL209C'
- $`GO:0000184`
- 'YCR035C'
- 'YDL111C'
- 'YDR206W'
- 'YDR280W'
- 'YER035W'
- 'YGL173C'
- 'YGL213C'
- 'YGL222C'
- 'YGR072W'
- 'YGR095C'
- 'YGR158C'
- 'YGR195W'
- 'YHR069C'
- 'YHR077C'
- 'YLR363C'
- 'YLR398C'
- 'YMR080C'
- 'YMR125W'
- 'YNL016W'
- 'YNL112W'
- 'YNL118C'
- 'YNL232W'
- 'YOL021C'
- 'YOL123W'
- 'YOL139C'
- 'YOL142W'
- 'YOL149W'
- 'YOR076C'
- 'YOR173W'
- 'YPL178W'
- 'YPR189W'
- $`GO:1990022`
- 'YDL115C'
- 'YLR243W'
- 'YOR262W'
- $`GO:0000290`
- 'YCR077C'
- 'YDL160C'
- 'YDL165W'
- 'YDR370C'
- 'YER035W'
- 'YGL222C'
- 'YIL038C'
- 'YJL124C'
- 'YKL204W'
- 'YLR270W'
- 'YNL118C'
- 'YOL149W'
- 'YOR173W'
- 'YPR072W'
- $`GO:0051123`
- 'YBL093C'
- 'YBR198C'
- 'YCR042C'
- 'YDL005C'
- 'YDL007W'
- 'YDR145W'
- 'YDR167W'
- 'YDR394W'
- 'YDR397C'
- 'YEL009C'
- 'YER148W'
- 'YER159C'
- 'YGL025C'
- 'YGL043W'
- 'YGL048C'
- 'YGL066W'
- 'YGL112C'
- 'YGL207W'
- 'YGR005C'
- 'YGR104C'
- 'YGR186W'
- 'YGR274C'
- 'YHR041C'
- 'YHR058C'
- 'YIL041W'
- 'YJR017C'
- 'YKL058W'
- 'YKL145W'
- 'YML015C'
- 'YML069W'
- 'YML098W'
- 'YML114C'
- 'YMR005W'
- 'YMR112C'
- 'YMR172W'
- 'YMR227C'
- 'YMR236W'
- 'YNL236W'
- 'YOL051W'
- 'YOR117W'
- 'YOR194C'
- 'YOR259C'
- 'YPL011C'
- 'YPL082C'
- 'YPL129W'
- 'YPR052C'
- 'YPR086W'
- 'YPR148C'
- $`GO:0070987`
- 'YBR088C'
- 'YCR066W'
- 'YDR078C'
- 'YDR419W'
- 'YGL058W'
- 'YIL132C'
- 'YIL139C'
- 'YLR376C'
- 'YML060W'
- 'YOR346W'
- 'YPL167C'
- $`GO:0017148`
- 'YBR212W'
- 'YCR077C'
- 'YCR093W'
- 'YDL160C'
- 'YDL165W'
- 'YDL224C'
- 'YDR206W'
- 'YDR293C'
- 'YDR328C'
- 'YDR432W'
- 'YDR477W'
- 'YDR496C'
- 'YGL014W'
- 'YGL178W'
- 'YGR083C'
- 'YHL034C'
- 'YIL038C'
- 'YJL062W-A'
- 'YKL204W'
- 'YML129C'
- 'YMR024W'
- 'YMR116C'
- 'YMR239C'
- 'YNL197C'
- 'YNL288W'
- 'YNR052C'
- 'YOL045W'
- 'YOR276W'
- 'YPL052W'
- 'YPR041W'
- 'YPR072W'
- 'YPR129W'
- $`GO:0038203`
- 'YBR270C'
- 'YIL105C'
- 'YJL058C'
- 'YNL047C'
- $`GO:0007094`
- 'YAL016W'
- 'YDL028C'
- 'YDL134C'
- 'YDL188C'
- 'YDR130C'
- 'YDR254W'
- 'YER133W'
- 'YFR003C'
- 'YGL003C'
- 'YGL086W'
- 'YGL093W'
- 'YGL116W'
- 'YGL190C'
- 'YGR188C'
- 'YIL031W'
- 'YJL013C'
- 'YJL030W'
- 'YJR089W'
- 'YKR095W'
- 'YLR381W'
- 'YMR168C'
- 'YNL031C'
- 'YNL164C'
- 'YOR026W'
- 'YOR073W'
- 'YOR178C'
- 'YPL018W'
- 'YPL209C'
- $`GO:0000050`
- 'YJL088W'
- 'YJL130C'
- 'YJR109C'
- 'YOL058W'
- 'YOR303W'
- 'YPL111W'
- $`GO:0001558`
- 'YBR057C'
- 'YBR083W'
- 'YDR480W'
- 'YER093C'
- 'YER109C'
- 'YER110C'
- 'YGL192W'
- 'YHR082C'
- 'YHR111W'
- 'YHR186C'
- 'YIL105C'
- 'YIL131C'
- 'YJL058C'
- 'YJR066W'
- 'YKL043W'
- 'YKL185W'
- 'YKL203C'
- 'YLR229C'
- 'YMR068W'
- 'YMR164C'
- 'YNL006W'
- 'YNL047C'
- 'YNL068C'
- 'YNL076W'
- 'YNL098C'
- 'YNL196C'
- 'YOL078W'
- 'YOL116W'
- 'YOR140W'
- 'YPL049C'
- 'YPL180W'
- 'YPR194C'
- $`GO:0044376`
- 'YDL115C'
- 'YJR072C'
- 'YLR243W'
- 'YMR185W'
- 'YOR262W'
- $`GO:0007067`
- 'YAL016W'
- 'YAL024C'
- 'YAL034W-A'
- 'YAL041W'
- 'YAL047C'
- 'YAR019C'
- 'YBL009W'
- 'YBL016W'
- 'YBL031W'
- 'YBL034C'
- 'YBL063W'
- 'YBL084C'
- 'YBL097W'
- 'YBR017C'
- 'YBR088C'
- 'YBR107C'
- 'YBR112C'
- 'YBR133C'
- 'YBR158W'
- 'YBR160W'
- 'YBR211C'
- 'YBR214W'
- 'YBR233W-A'
- 'YCL016C'
- 'YCL029C'
- 'YCL061C'
- 'YCR002C'
- 'YCR084C'
- 'YDL003W'
- 'YDL008W'
- 'YDL017W'
- 'YDL028C'
- 'YDL047W'
- 'YDL064W'
- 'YDL088C'
- 'YDL089W'
- 'YDL134C'
- 'YDL155W'
- 'YDL188C'
- 'YDL225W'
- 'YDR014W'
- 'YDR016C'
- 'YDR052C'
- 'YDR106W'
- 'YDR113C'
- 'YDR118W'
- 'YDR130C'
- 'YDR137W'
- 'YDR146C'
- 'YDR159W'
- 'YDR180W'
- 'YDR201W'
- 'YDR207C'
- 'YDR254W'
- 'YDR260C'
- 'YDR293C'
- 'YDR309C'
- 'YDR310C'
- 'YDR318W'
- 'YDR320C-A'
- 'YDR325W'
- 'YDR328C'
- 'YDR356W'
- 'YDR363W'
- 'YDR383C'
- 'YDR419W'
- 'YDR457W'
- 'YDR510W'
- 'YEL061C'
- 'YER016W'
- 'YER018C'
- 'YER133W'
- 'YER147C'
- 'YER177W'
- 'YFL008W'
- 'YFL009W'
- 'YFL033C'
- 'YFL034C-B'
- 'YFL037W'
- 'YFR003C'
- 'YFR027W'
- 'YFR028C'
- 'YFR031C'
- 'YFR036W'
- 'YFR046C'
- 'YGL003C'
- 'YGL021W'
- 'YGL056C'
- 'YGL061C'
- 'YGL071W'
- 'YGL086W'
- 'YGL093W'
- 'YGL097W'
- 'YGL116W'
- 'YGL190C'
- 'YGL216W'
- 'YGL238W'
- 'YGL240W'
- 'YGR044C'
- 'YGR092W'
- 'YGR098C'
- 'YGR108W'
- 'YGR113W'
- 'YGR140W'
- 'YGR179C'
- 'YGR188C'
- 'YGR238C'
- 'YHL007C'
- 'YHR061C'
- 'YHR115C'
- 'YHR127W'
- 'YHR129C'
- 'YHR152W'
- 'YHR158C'
- 'YHR166C'
- 'YHR172W'
- 'YHR191C'
- 'YIL026C'
- 'YIL031W'
- 'YIL106W'
- 'YIL122W'
- 'YIL144W'
- 'YIR010W'
- 'YIR025W'
- 'YJL013C'
- 'YJL019W'
- 'YJL030W'
- 'YJL047C'
- 'YJL074C'
- 'YJL076W'
- 'YJL187C'
- 'YJR046W'
- 'YJR053W'
- 'YJR072C'
- 'YJR089W'
- 'YJR112W'
- 'YJR135C'
- 'YKL022C'
- 'YKL049C'
- 'YKL052C'
- 'YKL089W'
- 'YKL092C'
- 'YKL138C-A'
- 'YKL193C'
- 'YKR010C'
- 'YKR037C'
- 'YKR054C'
- 'YKR083C'
- 'YKR088C'
- 'YKR095W'
- 'YLL003W'
- 'YLR086W'
- 'YLR102C'
- 'YLR127C'
- 'YLR175W'
- 'YLR210W'
- 'YLR226W'
- 'YLR229C'
- 'YLR234W'
- 'YLR243W'
- 'YLR254C'
- 'YLR272C'
- 'YLR310C'
- 'YLR315W'
- 'YLR381W'
- 'YML034W'
- 'YML064C'
- 'YML085C'
- 'YML124C'
- 'YMR001C'
- 'YMR032W'
- 'YMR036C'
- 'YMR048W'
- 'YMR055C'
- 'YMR076C'
- 'YMR078C'
- 'YMR117C'
- 'YMR153W'
- 'YMR168C'
- 'YMR190C'
- 'YMR198W'
- 'YMR308C'
- 'YNL001W'
- 'YNL031C'
- 'YNL078W'
- 'YNL116W'
- 'YNL126W'
- 'YNL152W'
- 'YNL164C'
- 'YNL172W'
- 'YNL188W'
- 'YNL262W'
- 'YNL273W'
- 'YNL298W'
- 'YNL299W'
- 'YNL307C'
- 'YNL327W'
- 'YOL006C'
- 'YOL069W'
- 'YOL115W'
- 'YOR014W'
- 'YOR026W'
- 'YOR058C'
- 'YOR073W'
- 'YOR119C'
- 'YOR144C'
- 'YOR178C'
- 'YOR195W'
- 'YOR198C'
- 'YOR233W'
- 'YOR249C'
- 'YOR257W'
- 'YOR262W'
- 'YOR269W'
- 'YOR279C'
- 'YOR339C'
- 'YOR373W'
- 'YPL008W'
- 'YPL017C'
- 'YPL018W'
- 'YPL024W'
- 'YPL155C'
- 'YPL209C'
- 'YPL233W'
- 'YPL253C'
- 'YPL267W'
- 'YPL269W'
- 'YPR046W'
- 'YPR082C'
- 'YPR111W'
- 'YPR119W'
- 'YPR120C'
- 'YPR135W'
- 'YPR141C'
- $`GO:0010791`
- 'YGL175C'
- 'YMR224C'
- $`GO:0000723`
- 'TLC1'
- 'YAR003W'
- 'YAR007C'
- 'YBL032W'
- 'YBL035C'
- 'YBL088C'
- 'YBR136W'
- 'YBR160W'
- 'YBR175W'
- 'YBR233W'
- 'YBR275C'
- 'YCL011C'
- 'YCL061C'
- 'YDL002C'
- 'YDL013W'
- 'YDL059C'
- 'YDL074C'
- 'YDL220C'
- 'YDR004W'
- 'YDR082W'
- 'YDR369C'
- 'YDR499W'
- 'YDR545W'
- 'YER092W'
- 'YER095W'
- 'YER116C'
- 'YER190W'
- 'YGL058W'
- 'YGL150C'
- 'YGL163C'
- 'YGL169W'
- 'YGL175C'
- 'YGR042W'
- 'YGR099W'
- 'YGR262C'
- 'YGR280C'
- 'YGR296W'
- 'YHR031C'
- 'YHR119W'
- 'YHR164C'
- 'YHR167W'
- 'YIL009C-A'
- 'YJL173C'
- 'YJL184W'
- 'YKL018W'
- 'YKL054C'
- 'YKL117W'
- 'YKR038C'
- 'YKR095W-A'
- 'YLR010C'
- 'YLR015W'
- 'YLR052W'
- 'YLR150W'
- 'YLR233C'
- 'YLR234W'
- 'YLR288C'
- 'YLR318W'
- 'YLR453C'
- 'YLR466W'
- 'YLR467W'
- 'YML032C'
- 'YML036W'
- 'YML060W'
- 'YML061C'
- 'YMR106C'
- 'YMR137C'
- 'YMR186W'
- 'YMR190C'
- 'YMR224C'
- 'YMR284W'
- 'YNL216W'
- 'YNL250W'
- 'YNL312W'
- 'YNL339C'
- 'YOR033C'
- 'YOR144C'
- 'YOR189W'
- 'YPL128C'
- 'YPL157W'
- 'YPL240C'
- 'YPL283C'
- $`GO:0000730`
- 'YDR004W'
- 'YDR076W'
- 'YDR078C'
- 'YER095W'
- 'YER179W'
- 'YHL006C'
- 'YHR079C-A'
- 'YIL132C'
- 'YLR376C'
- 'YML032C'
- 'YPL121C'
- $`GO:0051028`
- 'YAL029C'
- 'YAR002W'
- 'YBL032W'
- 'YBL079W'
- 'YBR017C'
- 'YBR034C'
- 'YBR111W-A'
- 'YBR130C'
- 'YBR170C'
- 'YCL011C'
- 'YDL084W'
- 'YDL088C'
- 'YDL116W'
- 'YDL160C'
- 'YDL207W'
- 'YDR138W'
- 'YDR159W'
- 'YDR192C'
- 'YDR363W-A'
- 'YDR381W'
- 'YDR395W'
- 'YDR432W'
- 'YDR457W'
- 'YER063W'
- 'YER105C'
- 'YER107C'
- 'YER110C'
- 'YER125W'
- 'YER165W'
- 'YFR001W'
- 'YFR002W'
- 'YGL066W'
- 'YGL092W'
- 'YGL100W'
- 'YGL122C'
- 'YGL172W'
- 'YGL174W'
- 'YGL241W'
- 'YGL247W'
- 'YGR116W'
- 'YGR119C'
- 'YGR218W'
- 'YHR015W'
- 'YHR036W'
- 'YHR167W'
- 'YIL040W'
- 'YIL115C'
- 'YIL143C'
- 'YIL149C'
- 'YIR005W'
- 'YJL039C'
- 'YJL041W'
- 'YJL061W'
- 'YJL140W'
- 'YJR042W'
- 'YJR074W'
- 'YKL057C'
- 'YKL068W'
- 'YKL130C'
- 'YKL186C'
- 'YKL214C'
- 'YKR082W'
- 'YKR095W'
- 'YLL023C'
- 'YLR016C'
- 'YLR018C'
- 'YLR064W'
- 'YLR208W'
- 'YLR335W'
- 'YLR347C'
- 'YLR368W'
- 'YML031W'
- 'YML062C'
- 'YML103C'
- 'YML107C'
- 'YMR047C'
- 'YMR125W'
- 'YMR129W'
- 'YMR153W'
- 'YMR255W'
- 'YMR273C'
- 'YMR308C'
- 'YNL004W'
- 'YNL139C'
- 'YNL253W'
- 'YOL072W'
- 'YOR046C'
- 'YOR098C'
- 'YOR257W'
- 'YPL169C'
- 'YPL178W'
- 'YPL226W'
- 'YPR133C'
- $`GO:0051017`
- 'YAL003W'
- 'YAR014C'
- 'YDR129C'
- 'YDR309C'
- 'YFR024C-A'
- 'YGR238C'
- 'YHR016C'
- 'YHR158C'
- 'YIL105C'
- 'YIL118W'
- 'YIL138C'
- 'YIL159W'
- 'YKR055W'
- 'YLR319C'
- 'YMR032W'
- 'YNL047C'
- 'YNL079C'
- 'YNL271C'
- 'YNL293W'
- 'YOL112W'
- 'YOR094W'
- 'YOR122C'
- 'YOR181W'
- 'YOR239W'
- 'YOR304C-A'
- $`GO:0006607`
- 'YAR002W'
- 'YBR017C'
- 'YBR109C'
- 'YDR192C'
- 'YER110C'
- 'YGL092W'
- 'YGR119C'
- 'YIL115C'
- 'YJL041W'
- 'YKL068W'
- 'YLR335W'
- 'YLR347C'
- 'YMR153W'
- 'YMR308C'
- 'YNL189W'
- 'YOR098C'
- $`GO:0006468`
- 'YAL017W'
- 'YAL040C'
- 'YAR018C'
- 'YAR019C'
- 'YBL009W'
- 'YBL016W'
- 'YBL056W'
- 'YBL088C'
- 'YBL105C'
- 'YBR028C'
- 'YBR059C'
- 'YBR097W'
- 'YBR103W'
- 'YBR136W'
- 'YBR156C'
- 'YBR160W'
- 'YBR182C'
- 'YBR274W'
- 'YBR276C'
- 'YBR279W'
- 'YCL024W'
- 'YCL032W'
- 'YCL051W'
- 'YCR008W'
- 'YCR033W'
- 'YCR059C'
- 'YCR073C'
- 'YCR091W'
- 'YDL006W'
- 'YDL017W'
- 'YDL025C'
- 'YDL028C'
- 'YDL073W'
- 'YDL079C'
- 'YDL081C'
- 'YDL101C'
- 'YDL108W'
- 'YDL127W'
- 'YDL130W'
- 'YDL155W'
- 'YDL159W'
- 'YDL179W'
- 'YDL214C'
- 'YDR052C'
- 'YDR079C-A'
- 'YDR103W'
- 'YDR122W'
- 'YDR162C'
- 'YDR168W'
- 'YDR247W'
- 'YDR283C'
- 'YDR311W'
- 'YDR382W'
- 'YDR422C'
- 'YDR460W'
- 'YDR466W'
- 'YDR477W'
- 'YDR490C'
- 'YDR507C'
- 'YDR523C'
- 'YER027C'
- 'YER059W'
- 'YER075C'
- 'YER089C'
- 'YER123W'
- 'YER129W'
- 'YER171W'
- 'YFL029C'
- 'YFL033C'
- 'YFL034C-B'
- 'YFR014C'
- 'YGL019W'
- 'YGL021W'
- 'YGL059W'
- 'YGL083W'
- 'YGL115W'
- 'YGL134W'
- 'YGL158W'
- 'YGL179C'
- 'YGL180W'
- 'YGL194C'
- 'YGL208W'
- 'YGL244W'
- 'YGR040W'
- 'YGR052W'
- 'YGR086C'
- 'YGR092W'
- 'YGR108W'
- 'YGR109C'
- 'YGR188C'
- 'YGR262C'
- 'YHL007C'
- 'YHR030C'
- 'YHR079C'
- 'YHR082C'
- 'YHR102W'
- 'YHR107C'
- 'YHR135C'
- 'YHR205W'
- 'YIL033C'
- 'YIL035C'
- 'YIL042C'
- 'YIL050W'
- 'YIL095W'
- 'YIL106W'
- 'YIL113W'
- 'YIL143C'
- 'YIL147C'
- 'YJL006C'
- 'YJL057C'
- 'YJL090C'
- 'YJL095W'
- 'YJL106W'
- 'YJL115W'
- 'YJL128C'
- 'YJL141C'
- 'YJL164C'
- 'YJL165C'
- 'YJL187C'
- 'YJR017C'
- 'YJR059W'
- 'YJR066W'
- 'YJR076C'
- 'YKL025C'
- 'YKL048C'
- 'YKL101W'
- 'YKL116C'
- 'YKL126W'
- 'YKL139W'
- 'YKL161C'
- 'YKL166C'
- 'YKL168C'
- 'YKL171W'
- 'YKL198C'
- 'YKL203C'
- 'YKR007W'
- 'YLL019C'
- 'YLR005W'
- 'YLR006C'
- 'YLR079W'
- 'YLR096W'
- 'YLR113W'
- 'YLR210W'
- 'YLR226W'
- 'YLR240W'
- 'YLR248W'
- 'YLR362W'
- 'YLR418C'
- 'YLR423C'
- 'YML112W'
- 'YMR001C'
- 'YMR036C'
- 'YMR104C'
- 'YMR139W'
- 'YMR199W'
- 'YMR216C'
- 'YMR291W'
- 'YNL020C'
- 'YNL025C'
- 'YNL053W'
- 'YNL154C'
- 'YNL161W'
- 'YNL183C'
- 'YNL207W'
- 'YNL289W'
- 'YNL298W'
- 'YNL307C'
- 'YNR031C'
- 'YNR047W'
- 'YOL001W'
- 'YOL016C'
- 'YOL045W'
- 'YOL100W'
- 'YOL113W'
- 'YOL128C'
- 'YOL145C'
- 'YOR039W'
- 'YOR048C'
- 'YOR061W'
- 'YOR119C'
- 'YOR208W'
- 'YOR231W'
- 'YOR233W'
- 'YOR242C'
- 'YOR267C'
- 'YOR351C'
- 'YPL004C'
- 'YPL014W'
- 'YPL026C'
- 'YPL031C'
- 'YPL042C'
- 'YPL089C'
- 'YPL122C'
- 'YPL140C'
- 'YPL141C'
- 'YPL150W'
- 'YPL153C'
- 'YPL203W'
- 'YPL204W'
- 'YPL209C'
- 'YPL219W'
- 'YPL236C'
- 'YPL256C'
- 'YPR025C'
- 'YPR054W'
- 'YPR056W'
- 'YPR106W'
- 'YPR111W'
- 'YPR119W'
- 'YPR120C'
- 'YPR149W'
- 'YPR161C'
- 'YPR185W'
- $`GO:0031087`
- 'YDL165W'
- 'YEL015W'
- 'YER068W'
- 'YGL246C'
- 'YNL118C'
- 'YOL149W'
- 'YPR072W'
- $`GO:0080009`
- 'YBR057C'
- 'YGL192W'
- 'YNL196C'
- $`GO:0043007`
- 'YDL089W'
- 'YDR078C'
- 'YDR110W'
- 'YGR178C'
- 'YHL006C'
- 'YIL132C'
- 'YLL002W'
- 'YLR272C'
- 'YML034W'
In [1]:
## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("ReactomePA")
Bioconductor version 3.4 (BiocInstaller 1.24.0), ?biocLite for help
BioC_mirror: https://bioconductor.org
Using Bioconductor 3.4 (BiocInstaller 1.24.0), R 3.3.2 (2016-10-31).
Installing package(s) ‘ReactomePA’
also installing the dependencies ‘rappdirs’, ‘graphite’
Updating HTML index of packages in '.Library'
Making 'packages.html' ... done
Old packages: 'AnnotationHub', 'assertthat', 'backports', 'BiocParallel',
'broom', 'cluster', 'colorspace', 'corpcor', 'curl', 'data.table', 'DBI',
'digest', 'forcats', 'ggplot2', 'IRanges', 'jsonlite', 'KEGGREST', 'lattice',
'Matrix', 'matrixStats', 'mgcv', 'nlme', 'openssl', 'pbdZMQ', 'pbkrtest',
'psych', 'Rcpp', 'RcppEigen', 'readr', 'repr', 'rmarkdown', 'rprojroot',
'S4Vectors', 'selectr', 'shiny', 'sourcetools', 'SparseM', 'stringi',
'stringr', 'survival', 'tibble', 'tidyr', 'tidyverse', 'XML', 'xml2',
'XVector', 'zoo'
In [23]:
library(ReactomePA)
data(geneList)
de <- names(geneList)[abs(geneList) > 1.5]
head(de)
Loading required package: DOSE
DOSE v3.0.10 For help: https://guangchuangyu.github.io/DOSE
If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609
ReactomePA v1.18.1 For help: https://guangchuangyu.github.io/ReactomePA
If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479
- '4312'
- '8318'
- '10874'
- '55143'
- '55388'
- '991'
In [27]:
length(de)
513
In [30]:
x <- enrichPathway(gene=de, pvalueCutoff=0.05, readable=T)
nrow(as.data.frame(x))
49
In [31]:
entrezCommunities <- sapply(1:max(assignments), function(i){
orfs <- getGenes(i)
orfs <- orfs[orfs%in%allGenesInDB]
return(as.character(org.Sc.sgdENTREZID[orfs]))
})
In [33]:
length(entrezCommunities[[1]])
250
In [26]:
pathwayEnrichments <- sapply(entrezCommunities[2:length(entrezCommunities)],
function(i) enrichPathway(gene=i, organism = "yeast", universe = entrezCommunities[[1]],
pvalueCutoff = 0.05))
Error in .testForValidCols(x, cols): Invalid columns: SYMBOL. Please use the columns method to see a listing of valid arguments.
Traceback:
1. sapply(entrezCommunities[2:length(entrezCommunities)], function(i) enrichPathway(gene = i,
. organism = "yeast", universe = entrezCommunities[[1]], pvalueCutoff = 0.05,
. readable = T))
2. sapply(entrezCommunities[2:length(entrezCommunities)], function(i) enrichPathway(gene = i,
. organism = "yeast", universe = entrezCommunities[[1]], pvalueCutoff = 0.05,
. readable = T))
3. lapply(X = X, FUN = FUN, ...)
4. FUN(X[[i]], ...)
5. enrichPathway(gene = i, organism = "yeast", universe = entrezCommunities[[1]],
. pvalueCutoff = 0.05, readable = T) # at line 2-3 of file <text>
6. setReadable(res, OrgDb)
7. EXTID2NAME(OrgDb, genes, keytype)
8. suppressMessages(select(OrgDb, keys = geneID, keytype = keytype,
. columns = "SYMBOL"))
9. withCallingHandlers(expr, message = function(c) invokeRestart("muffleMessage"))
10. select(OrgDb, keys = geneID, keytype = keytype, columns = "SYMBOL")
11. select(OrgDb, keys = geneID, keytype = keytype, columns = "SYMBOL")
12. .select(x, keys, columns, keytype, jointype = jointype, ...)
13. testSelectArgs(x, keys = keys, cols = cols, keytype = keytype,
. fks = fks, skipValidKeysTest = skipValidKeysTest)
14. .testForValidCols(x, cols)
15. stop(msg)
In [34]:
orfs <- getGenes(2)
orfs <- orfs[orfs%in%allGenesInDB]
entrez <- as.character(org.Sc.sgdENTREZID[orfs])
In [35]:
orfs
- 'YKR037C'
- 'YPL151C'
In [36]:
entrez
- YKR037C
- '853909'
- YPL151C
- '855952'
In [37]:
allGenes <- allGenes[allGenes%in%allGenesInDB]
entrezAll <- as.character(org.Sc.sgdENTREZID[allGenes])
entrezAllDB <- as.character(org.Sc.sgdENTREZID[allGenesInDB])
In [38]:
x <- enrichPathway(gene = entrezAll, organism = "yeast", universe = entrezAllDB)
In [39]:
head(as.data.frame(x))
ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count 5719840 5719840 Regulation of AMPK activity via LKB1 4/49 10/1145 0.000515426 0.03685296 0.03309577 852088/856749/852763/852664 4 5719841 5719841 Energy dependent regulation of mTOR by LKB1-AMPK 4/49 10/1145 0.000515426 0.03685296 0.03309577 852088/856749/852763/852664 4
In [40]:
unionAllGenes <- scan(character(), file="../yeast_union_all_genes.txt")
In [41]:
unionAllGenes <- unionAllGenes[unionAllGenes%in%allGenesInDB]
In [42]:
x <- enrichPathway(gene = as.character(org.Sc.sgdENTREZID[unionAllGenes]), organism = "yeast", universe = entrezAllDB)
In [44]:
head(as.data.frame(x))
ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count
In [2]:
library(WGCNA)
Loading required package: dynamicTreeCut
Loading required package: fastcluster
Attaching package: ‘fastcluster’
The following object is masked from ‘package:stats’:
hclust
==========================================================================
*
* Package WGCNA 1.51 loaded.
*
* Important note: It appears that your system supports multi-threading,
* but it is not enabled within WGCNA in R.
* To allow multi-threading within WGCNA with all available cores, use
*
* allowWGCNAThreads()
*
* within R. Use disableWGCNAThreads() to disable threading if necessary.
* Alternatively, set the following environment variable on your system:
*
* ALLOW_WGCNA_THREADS=<number_of_processors>
*
* for example
*
* ALLOW_WGCNA_THREADS=8
*
* To set the environment variable in linux bash shell, type
*
* export ALLOW_WGCNA_THREADS=8
*
* before running R. Other operating systems or shells will
* have a similar command to achieve the same aim.
*
==========================================================================
Attaching package: ‘WGCNA’
The following object is masked from ‘package:stats’:
cor
Content source: DavidMcDonald1993/ghsom
Similar notebooks: