In [1]:
#libraries
library(GO.db)
library(topGO)
library(org.Sc.sgd.db)
library(GOSemSim)
library(gridExtra)
library(GOSim)
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
GOSemSim v2.0.4 For help: https://guangchuangyu.github.io/GOSemSim
If you use GOSemSim in published research, please cite:
Guangchuang Yu, Fei Li, Yide Qin, Xiaochen Bo, Yibo Wu, Shengqi Wang. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products Bioinformatics 2010, 26(7):976-978
Attaching package: ‘gridExtra’
The following object is masked from ‘package:Biobase’:
combine
The following object is masked from ‘package:BiocGenerics’:
combine
Loading required package: annotate
Loading required package: XML
Attaching package: ‘XML’
The following object is masked from ‘package:graph’:
addNode
In [2]:
file <- "yeast_uetz"
ont <- "BP"
p <- 0.1
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", file, p))
scGO <- godata(OrgDb = mapping, keytype = ID, ont = ont)
setOntology(ont, loadIC=TRUE)
setEvidenceLevel(evidences="all", organism=org.Sc.sgdORGANISM, gomap=org.Sc.sgdGO)
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
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
allGenesInDB <- keys(db)
In [23]:
shortestPaths
1_shortest_path.csv 0 1 1 2 1 2 3 3 2 2 2 3 4 2 3 5 6 1 3 3 3 3 2 7 7 6 3 1 4 4 ⋮ 9 6 2 2 3 7 6 8 8 8 10 3 4 4 8 8 10 7 6 5 4 5 2 3 2 5 8 9 9 0
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]]))
}
getNeighbours <- function(com){
superCommunity <- getSuperCommunity(com)
map <- getShortestPath(superCommunity)
v <- map[com,] == 1
return (names(v[v]))
}
In [6]:
communitySimilarity <- function(community) {
geneSims <- mgeneSim(genes = getGenes(as.character(community)),
semData = scGO, measure = "Wang", combine = "BMA", verbose=F)
if (length(geneSims) > 1) {
return(mean(geneSims[upper.tri(geneSims)]))
} else {
return (NaN)
}
}
In [7]:
layerSimilarity <- function(layer) {
communitiesSimilarity <- sapply(unique(assignments[,layer][assignments[,layer] != -1]), communitySimilarity)
communitiesSimilarity <- communitiesSimilarity[!is.na(communitiesSimilarity)]
return(mean(communitiesSimilarity))
}
In [8]:
layerMeanSimilarities <- sapply(colnames, layerSimilarity)
In [9]:
layerMeanSimilarities
- 0
- 0.234247261044177
- 1
- 0.324240455790639
In [10]:
plot(colnames, layerMeanSimilarities, xlab="Layer", ylab="Mean Similarity", type = "l")
In [11]:
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 [12]:
rownames(enrichmentResults) <- c("terms","p-values","genes")
colnames(enrichmentResults) <- 1:max(assignments)
In [13]:
enrichmentResults["p-values",]
- $`1`
- GO:0032265
- 0.039258451472192
- GO:0030950
- 0.0101082256488166
- GO:0001100
- 0.0206693619456892
- GO:0032258
- 0.00535663564789109
- GO:0044206
- 0.00874053399737461
- GO:0030242
- 0.00168606124730799
- GO:0032986
- 0.0392248788873263
- GO:0010621
- 0.039258451472192
- GO:0051312
- 0.039258451472192
- GO:0006995
- 0.047167037909388
- GO:0006999
- 1.86115383174225e-05
- GO:0071940
- 0.0394096404459098
- GO:0046081
- 0.039258451472192
- GO:0006279
- 0.0367582896271415
- GO:0051301
- 0.000247378002819304
- GO:0044774
- 0.00184954302497703
- GO:2000221
- 0.0367582896271415
- GO:0000422
- 0.0427109248731116
- GO:0006913
- 0.0432319504663786
- GO:0033194
- 0.039258451472192
- GO:0030041
- 0.0392494136043784
- GO:0016973
- 0.0199838052691842
- GO:0034503
- 0.0141916404039552
- GO:0042148
- 0.0282915639098445
- GO:1901300
- 0.039258451472192
- GO:0001081
- 0.039258451472192
- GO:0090368
- 0.039258451472192
- GO:0051441
- 0.039258451472192
- GO:0000184
- 0.00166700786727578
- GO:2000042
- 0.039258451472192
- GO:0042262
- 0.039258451472192
- GO:0000132
- 0.0157824823516756
- GO:1990022
- 0.00448641628469648
- GO:0044805
- 0.0270403827114027
- GO:0033262
- 0.0282915639098445
- GO:0034497
- 0.046057217299538
- GO:0000290
- 0.000134094048551179
- GO:0051123
- 0.00245289974545606
- GO:0032837
- 0.039258451472192
- GO:0070987
- 0.00780484511090381
- GO:0017148
- 0.00483779050551011
- GO:0045429
- 0.039258451472192
- GO:0000288
- 0.0433563768712343
- GO:0033234
- 0.039258451472192
- GO:0070941
- 0.0367582896271415
- GO:0051292
- 0.046057217299538
- GO:0038203
- 0.00023229857201835
- GO:0010212
- 0.0367582896271415
- GO:0007094
- 0.00419574127412856
- GO:0000050
- 0.00109504705730622
- GO:0000053
- 0.039258451472192
- GO:0001558
- 0.0099743869179752
- GO:0044376
- 0.000563874387503784
- GO:0007067
- 0.00391938573977879
- GO:0045143
- 0.0364830783431343
- GO:0010791
- 0.00153534922398258
- GO:0019918
- 0.039258451472192
- GO:0000722
- 0.0388405312925765
- GO:0000723
- 0.00125144136972518
- GO:1903833
- 0.0367582896271415
- GO:0000730
- 3.22898345221797e-05
- GO:0007049
- 0.0270226584328084
- GO:1900475
- 0.039258451472192
- GO:1900476
- 0.039258451472192
- GO:0000706
- 0.0282915639098445
- GO:0030643
- 0.039258451472192
- GO:0051028
- 0.000113257699617022
- GO:0051017
- 0.00419432094777694
- GO:2001158
- 0.039258451472192
- GO:0035863
- 0.039258451472192
- GO:0051037
- 0.0391514584308218
- GO:0048213
- 0.039258451472192
- GO:0034213
- 0.039258451472192
- GO:1900152
- 0.039258451472192
- GO:1900153
- 0.0141916404039552
- GO:0006607
- 0.00290553028161677
- GO:0070550
- 0.046057217299538
- GO:0009847
- 0.039258451472192
- GO:0006468
- 0.00937238530764923
- GO:0034063
- 0.0367582896271415
- GO:0045722
- 0.0207399370772796
- GO:0033512
- 0.039258451472192
- GO:0031087
- 7.39498058912123e-05
- GO:0080009
- 0.00448641628469648
- GO:0043007
- 0.0042120427737538
- GO:1903343
- 0.039258451472192
- GO:0032147
- 0.0270403827114027
- GO:0019551
- 0.039258451472192
- GO:0019547
- 0.039258451472192
- $`2`
- GO:0031116
- 0.00373569603444879
- GO:0051987
- 0.00311356549927666
- GO:0000398
- 0.0367365775033613
- $`3`
- GO:0070941
- 2.03389691197433e-05
- GO:0038203
- 4.36560438133572e-06
- GO:0000709
- 0.00373453272056001
- GO:0042148
- 0.00652779880229259
- GO:0010212
- 0.00745743592131709
- GO:0032366
- 0.00838634800749294
- GO:0045454
- 0.0313748341573868
- GO:0001558
- 2.22864050148807e-06
- GO:0016226
- 0.0213152417452613
- GO:0090002
- 0.000123645963434209
- GO:0030950
- 9.95481732097397e-08
- GO:0000730
- 0.0102419988908394
- GO:0051017
- 0.000216381751611881
- GO:0031929
- 0.0198090289462545
- GO:0007130
- 0.0148684696691151
- GO:0016197
- 0.0043235306411222
- GO:0006915
- 0.0404450018182335
- $`4`
- GO:0031087
- 0.00326847995245849
- GO:0006972
- 0.00931960440165069
- GO:0017148
- 0.0148834751129375
- GO:1902626
- 0.004667074046106
- GO:0007232
- 0.00280199094151556
- GO:0000290
- 0.00652983016020781
- GO:0032968
- 0.0208874505877665
- GO:0042255
- 0.0351060695339087
- GO:0000463
- 0.0208874505877665
- GO:0001402
- 0.00559874288618423
- GO:0000749
- 0.0378420620791873
- GO:0000289
- 0.00652983016020781
- GO:0030010
- 0.0373864229609931
- $`5`
- GO:0030242
- 0.0213152417452613
- GO:0009968
- 0.0386366535660857
- GO:0006094
- 0.0313748341573868
- GO:0006096
- 0.0304639013995409
- GO:0006098
- 0.0167140048873777
- GO:0051292
- 0.00838634800749294
- GO:0034497
- 0.00838634800749294
- GO:0000973
- 0.0148684696691151
- $`6`
- GO:0031055
- 0.00233535640288263
- GO:0016559
- 0.00621791976261754
- GO:0000266
- 0.00621791976261754
- GO:0006915
- 0.0338169924116405
- GO:0043044
- 0.0300208983350099
- GO:0000413
- 0.0108610301302677
- GO:0006337
- 0.0154867695694072
- GO:0000288
- 0.0428791423869481
- GO:0034629
- 0.0216274850207683
- GO:0001300
- 0.0231578657046981
- GO:0033262
- 0.00544237576368101
- GO:0051654
- 0.00388983520961372
- $`7`
- GO:0046854
- 0.00621840341947566
- GO:0007049
- 0.0270912478753322
- GO:0000422
- 0.0210242339994802
- GO:0072462
- 0.00124600876439429
- GO:0006470
- 0.0320195633384796
- GO:0090372
- 0.00124600876439429
- GO:0048015
- 0.00186857631386328
- GO:2000002
- 0.00186857631386328
- GO:2001034
- 0.00124600876439429
- GO:1902660
- 0.00124600876439429
- GO:0006081
- 0.0228632612866471
- $`8`
- GO:0016584
- 0.0337701811404872
- GO:0043007
- 0.027711853948342
- GO:0051983
- 0.0381892158452053
- GO:2000042
- 0.00311575011684062
- GO:0032079
- 0.0154867695694072
- GO:0000023
- 0.048759494163056
- GO:0042766
- 0.000597496972401713
- GO:0006348
- 0.0288315302829081
- GO:0005995
- 0.0307455143104369
- GO:0034213
- 0.00311575011684062
- GO:0006556
- 0.00931960440165114
- GO:0070987
- 0.0337701811404872
- GO:0000722
- 0.000132989656048417
- GO:0034354
- 0.0185566580334879
- GO:0000730
- 0.0337701811404872
- $`9`
- GO:0017148
- 0.0487818340086967
- GO:0045429
- 0.00155787505842031
- GO:1901300
- 0.00155787505842031
- GO:0060196
- 0.00311356549927666
- GO:0009749
- 0.0200827524002063
- GO:0072363
- 0.00621840341947599
- GO:0000290
- 0.0216124390183768
- GO:0016226
- 0.0352832152661825
- GO:0060963
- 0.0108593436599707
- GO:0018105
- 0.0428035779376964
- GO:0000453
- 0.00466707404610645
- GO:0016055
- 0.00621840341947599
- GO:0008360
- 0.0352832152661825
- GO:0000122
- 0.0213142595585028
- $`10`
- GO:0033194
- 0.00342732512852451
- GO:0006526
- 0.0469746636368398
- GO:0035863
- 0.00342732512852451
- GO:0006097
- 0.0370895191227606
- GO:0046838
- 0.0170248309444226
- GO:0006226
- 0.00684343588688774
- GO:0000050
- 0.000166822120581806
- GO:0000053
- 0.00342732512852451
- GO:0018890
- 0.00684343588688774
- GO:0009229
- 0.0271065487502027
- GO:0019547
- 0.00342732512852451
- GO:0046100
- 0.00684343588688774
- GO:0061077
- 0.0237569938319722
- GO:0030643
- 0.00342732512852451
- GO:0001015
- 0.0136421539978834
- GO:0019858
- 0.00684343588688774
- GO:0019318
- 0.0344552222228075
- GO:0046475
- 0.0102483672271536
- GO:0019344
- 0.0403953915602294
- GO:0042262
- 0.00342732512852451
- GO:0009116
- 0.0362540417752698
- GO:0032265
- 0.00342732512852451
- GO:0055114
- 0.0128122709612934
- GO:0090368
- 0.00342732512852451
- GO:0032211
- 0.0170248309444226
- GO:0046081
- 0.00342732512852451
- GO:0044206
- 0.0136421539978834
- GO:0046087
- 0.0102483672271536
- $`11`
- GO:0048478
- 0.00373453272056024
- GO:0005978
- 0.0136421539978837
- GO:1900027
- 0.00124600876439429
- GO:0000077
- 0.0197967744889329
- GO:0051666
- 0.00931670699229759
- GO:0051017
- 0.0154915745071952
- GO:0006406
- 0.0386931737766998
- $`12`
- GO:0031087
- 0.008695599107444
- GO:0006995
- 0.0258885287799374
- GO:0017148
- 0.000661719720935067
- GO:0006279
- 0.0099324105870352
- GO:0006468
- 0.00178314757171125
- GO:0006986
- 0.0440194483125926
- GO:0080009
- 4.0754108177545e-06
- GO:0007155
- 0.000161700006391224
- GO:0000290
- 0.0173249644011398
- GO:0030435
- 0.0177757000345667
- GO:1900436
- 0.00745743592131709
- GO:0007126
- 0.000975173342322362
- GO:0016239
- 0.014866164726264
- GO:2000221
- 3.79187503034839e-05
- GO:0090606
- 0.0037348234584067
- GO:0001403
- 0.00232044154395616
- GO:0007329
- 0.008695599107444
- GO:0000184
- 0.000620749611639538
- GO:0045722
- 2.03389691197433e-05
- GO:0000742
- 0.0185523493011844
- GO:0001302
- 0.00142873053021727
- GO:0046020
- 0.0136347474321712
- $`13`
- GO:0031087
- 3.05687585936435e-06
- GO:0006397
- 0.00333700649512975
- GO:0051028
- 0.0428446063887961
- GO:0033962
- 8.00277467306543e-06
- GO:1900153
- 0.00233535640288263
- GO:0000184
- 0.0144206169180024
- GO:0000290
- 1.32368237604417e-05
- GO:0060261
- 0.00699515576668996
- GO:0010501
- 0.0125677343084346
- GO:0034063
- 4.07541081775449e-06
- GO:0045900
- 0.00140165065176157
- $`14`
- GO:1903343
- 0.00155787505842031
- GO:0031938
- 0.0413037402346842
- GO:0008360
- 0.0352832152661825
- GO:0001100
- 0.030745514310437
- GO:0006909
- 0.00311356549927666
- GO:0006279
- 0.0124019834858284
- GO:0042450
- 0.00311356549927666
- GO:0007004
- 0.036791527937749
- GO:0000727
- 0.03980178776756
- GO:0018105
- 0.0428035779376964
- GO:0045332
- 0.0261886129551226
- GO:0000050
- 0.00931453551325823
- $`15`
- GO:0038203: 0.000623150023367991
- $`16`
- GO:0006446
- 0.0167230758255287
- GO:0016973
- 0.0167230758255287
- GO:0098789
- 0.00931670699229759
- GO:0043488
- 0.00807825623539449
- GO:1900152
- 0.000623150023368213
- GO:0072423
- 0.00186857631386328
- GO:0006378
- 0.00839286881001589
- GO:0019509
- 0.00745859561145978
- GO:0045945
- 0.00186857631386328
- GO:1900364
- 0.00124600876439429
- GO:1903833
- 0.00497704936431898
- GO:0000184
- 0.0191826120669333
- $`17`
- GO:0006606
- 0.0278233057659767
- GO:0006607
- 0.0124048709686941
- GO:0051028
- 3.78446165841866e-07
- GO:0015031
- 0.00298113798086943
- GO:0060188
- 0.00311283820119712
- GO:0006913
- 0.00287815650504064
- GO:0051301
- 0.0191947525943116
- GO:0031990
- 0.00854164936527357
- GO:0006999
- 2.73708555411426e-10
- GO:0034501
- 0.00854164936527357
- GO:0072417
- 0.00388983520961372
- GO:0030474
- 5.80026576292668e-05
- $`18`
- GO:0034727
- 0.000236920291798616
- GO:0000422
- 0.00016232485249279
- GO:0000045
- 0.00933265080905232
- GO:0061709
- 0.00559787167012815
- GO:0006995
- 0.0130251018577507
- GO:0046777
- 0.0136421539978837
- GO:0034497
- 0.00559787167012815
- GO:0032147
- 0.0105539976986055
- GO:0010506
- 0.0185681610799747
- GO:0010499
- 0.00869762666546914
- GO:0044206
- 0.00249085276254546
- GO:0032258
- 0.000427805287978722
- GO:0044805
- 3.9491032486955e-05
- $`19`
- GO:0000350
- 0.0160962366292118
- GO:0048278
- 0.0380089007801886
- GO:0042148
- 0.008695599107444
- GO:0006000
- 0.008695599107444
- GO:0006003
- 0.00497704936431886
- GO:0000707
- 0.00621791976261765
- GO:0043007
- 0.0111678716251422
- GO:0010212
- 0.0099324105870352
- GO:0006110
- 0.008695599107444
- GO:0071051
- 0.000141575800503533
- GO:0006096
- 0.0280542866258642
- GO:0000011
- 0.0222264593466784
- GO:0071028
- 0.000469737168217714
- GO:0006896
- 0.0476099818957457
- GO:0070481
- 0.000339431754741892
- GO:0071042
- 0.000141575800503533
- GO:0006086
- 0.00497704936431886
- GO:0070478
- 0.000183145711227674
- GO:0071035
- 0.000229991630059329
- GO:0070987
- 7.43440223966901e-05
- GO:0071038
- 0.000161700006391224
- GO:0071039
- 0.0124019834858284
- GO:0034473
- 0.00745743592131709
- GO:0034475
- 0.000141575800503533
- GO:0034476
- 0.0136347474321712
- GO:0000467
- 0.000205910417357369
- GO:0000730
- 0.00653287560613469
- GO:0070651
- 0.000282093610954114
- $`20`
- GO:0036228
- 0.00280199094151556
- GO:0097064
- 0.000934579393881529
- GO:0006606
- 0.00702795564579162
- GO:0006607
- 1.74515208281603e-05
- GO:0016973
- 5.0987278541742e-05
- GO:0006999
- 0.008855005185332
- GO:0006409
- 1.74515208281603e-05
- GO:0051028
- 0.00471994965387035
- GO:0000055
- 5.49036574622341e-05
- GO:0000056
- 0.00652983016020781
- $`21`
- GO:0051090
- 0.00218000559359199
- GO:0016573
- 6.68978445155249e-05
- GO:0051123
- 5.47610802710538e-05
- $`22`
- GO:0000723
- 0.025080283481801
- GO:0051568
- 0.00233790523690769
- GO:0080182
- 0.000934579393881196
- GO:0006348
- 0.000177439493254168
- $`23`
- GO:0009060
- 0.0256955203348816
- GO:0019551
- 0.000311575011684107
- GO:0055114
- 0.0447070094204055
- $`24`
- GO:0045732
- 0.0136347474321712
- GO:0006282
- 0.0185523493011844
- GO:0045842
- 0.00497704936431886
- GO:0033567
- 0.0136347474321712
- GO:0042144
- 0.0499971103350841
- GO:0000083
- 0.0331766810815137
- GO:0051441
- 0.0012463000467362
- GO:0008361
- 0.0440194483125926
- GO:0051301
- 0.000264230010122046
- GO:0031145
- 0.0173249644011398
- GO:0000079
- 0.0343867227822877
- GO:0007094
- 2.00383763278628e-08
- GO:0000733
- 0.0111678716251422
- GO:0044774
- 1.22010939070791e-06
- GO:0032837
- 0.0012463000467362
- GO:0051312
- 0.0012463000467362
- $`25`
- GO:0007032
- 2.22460590420552e-05
- GO:0032880
- 0.00021508627203908
- $`26`
- GO:0015693
- 0.00233535640288263
- GO:0032511
- 0.0199653543694108
- GO:0006446
- 0.0125677343084346
- GO:0006895
- 0.0083902607363725
- GO:0036010
- 0.00233535640288263
- GO:0098655
- 0.0392081167078571
- GO:0019509
- 0.00559874288618423
- $`27`
- GO:0031098
- 0.0116331916992525
- GO:0000902
- 0.0345747830058277
- GO:0023014
- 0.0292602497295642
- GO:0000920
- 0.0154867695694072
- GO:0042981
- 0.0162560407292999
- GO:0001113
- 0.00388983520961372
- GO:0007118
- 0.000101346371554625
- GO:0006289
- 0.044382866999285
- GO:0070816
- 0.0177931404403234
- GO:0032147
- 0.0131760681645641
- GO:0006367
- 0.0356914372448136
- $`28`
- GO:1900153
- 0.00155738958785068
- GO:0042149
- 0.00342465504039091
- GO:0001403
- 0.018608572410193
- GO:0001302
- 0.0145915461805582
- GO:0006407
- 0.00560092251059552
- GO:0043254
- 0.0266180115115753
- $`29`
- GO:0051315
- 0.00544237576368101
- GO:0007052
- 0.0343946574518742
- GO:0000132
- 2.87664477914366e-07
- GO:0007020
- 0.0194738907305135
- GO:0031578
- 0.0151746908002668
- GO:0031116
- 0.0130190387851259
- GO:0051301
- 0.0379243263976651
- GO:0000722
- 0.0354529379275024
- GO:0034501
- 0.0119396970059497
- GO:0051987
- 0.0108593436599701
- GO:0007097
- 0.0258925247816356
- GO:0000032
- 0.0258925247816356
- GO:0007067
- 0.00131209940938967
- $`30`
- GO:1904263
- 0.004667074046106
- GO:0070682
- 0.00513298117325012
- GO:0043547
- 0.00978405840802643
- GO:0000972
- 0.008855005185332
- GO:2001158
- 0.000467362517526215
- $`31`
- GO:0034247
- 0.00311283820119712
- GO:0071586
- 0.00155738958785057
- GO:0000349
- 0.00699297987822989
- GO:0032958
- 0.00388983520961372
- GO:0000472
- 0.0269754402952977
- GO:0070481
- 0.0177931404403234
- GO:0071432
- 0.00388983520961372
- GO:0000447
- 0.0353320980489671
- $`32`
- GO:0007064
- 0.00027947618208749
- GO:0044376
- 1.2694988441645e-08
- GO:0015840
- 0.00249124077587737
- GO:1990022
- 4.0754108177545e-06
- GO:0043558
- 0.00249124077587737
- GO:0016579
- 0.0343867227822877
- GO:1902499
- 0.00249124077587737
- GO:0034087
- 0.0185523493011844
- GO:0043066
- 0.008695599107444
- GO:1903833
- 0.0099324105870352
- GO:0030041
- 0.0307526166566258
- GO:0032007
- 0.00497704936431886
- GO:2000785
- 0.00497704936431886
- GO:0006995
- 0.0258885287799374
- GO:0000147
- 0.019778392587504
- GO:0010508
- 0.0210030955172718
- GO:0031929
- 0.0216709887584008
- GO:0015824
- 0.00497704936431886
- $`33`
- GO:0035376
- 0.00807825623539449
- GO:0006356
- 0.0222505403343257
- GO:0090579
- 0.00249085276254546
- GO:0006855
- 0.0111722082592444
- GO:0060962
- 0.00683864470303397
- GO:0001174
- 0.00559787167012815
- GO:0035690
- 0.00931670699229759
- GO:0051123
- 0.000325434321008248
- GO:0030261
- 0.0124077602482791
- $`34`
- GO:0000086
- 0.0392136485562939
- GO:0000209
- 0.0355954391361822
- GO:0051301
- 0.0490562586615835
- GO:0033617
- 0.0258885287799374
- GO:0032049
- 0.00497704936431886
- GO:0031146
- 0.0258885287799374
- GO:0006122
- 0.0160962366292118
- GO:0016024
- 0.00621791976261765
- GO:0070202
- 0.0037348234584067
- GO:0051865
- 0.0111678716251422
- GO:0042787
- 0.0428199745965485
- GO:0006611
- 0.0210030955172718
- $`35`
- $`36`
- GO:0071590
- 0.000311575011684107
- GO:0071592
- 0.000311575011684107
- GO:0006006
- 0.0109051254089423
- GO:0009651
- 0.00529677519862903
- $`37`
- GO:0042138
- 0.0258885287799374
- GO:0000706
- 0.008695599107444
- GO:0070550
- 4.87222821794973e-05
- GO:0007089
- 0.008695599107444
- GO:0031860
- 0.0037348234584067
- GO:0070814
- 0.00745743592131709
- GO:0000183
- 0.0392136485562939
- GO:0003333
- 0.0428199745965485
- GO:0034503
- 1.35677711246622e-05
- GO:0010791
- 0.00249124077587737
- GO:0090282
- 0.00497704936431886
- GO:0019343
- 0.00249124077587737
- GO:0019346
- 0.00497704936431886
- GO:0031292
- 0.0037348234584067
- GO:0000737
- 0.00497704936431886
- GO:0045143
- 0.000703971717466696
- GO:0015827
- 0.00249124077587737
- GO:0006535
- 0.00249124077587737
- $`38`
- GO:0016241
- 0.0290793478246449
- GO:0046685
- 0.020848670430569
- GO:0001731
- 0.000207597742893594
- GO:0033262
- 0.00977797795727209
- GO:0000754
- 0.0331715775259899
- GO:0006446
- 0.037248468573449
- GO:1990145
- 0.00420102148205748
- GO:0061393
- 0.0125560309069039
- GO:0007120
- 0.0153271538864297
- GO:0007121
- 0.0222247419430807
- GO:0009967
- 0.0426605501446895
- GO:0003400
- 0.00699297987822967
- GO:0034316
- 0.00559787167012804
- GO:0007231
- 0.0331715775259899
- GO:0006972
- 0.0277118539483413
- GO:0031505
- 0.0205197790476248
- $`39`
- GO:0006183
- 0.000934579393881196
- GO:0051123
- 0.0149008394805638
- GO:0006177
- 0.00155738958785068
- GO:0016573
- 0.0164465777747399
- $`40`
- GO:0051026
- 0.00124600876439429
- GO:0019918
- 0.000623150023368213
- GO:0000086
- 0.0197967744889329
- GO:0000710
- 0.00745859561145978
- GO:0051123
- 0.0295842020969787
- GO:0034969
- 0.00124600876439429
- $`41`
- GO:0051438
- 0.0153271538864297
- GO:0031047
- 0.00699297987822967
- GO:0033512
- 0.0014020875525782
- GO:0043007
- 0.0125560309069039
- GO:0031938
- 0.037248468573449
- GO:0016575
- 0.0358912060761369
- GO:0001308
- 0.00699297987822967
- GO:0006281
- 0.0180318271191694
- GO:0010621
- 0.0014020875525782
- GO:0090295
- 0.00420102148205748
- GO:0016925
- 0.0148799899972756
- GO:0000122
- 0.0173123897688793
- GO:0010978
- 0.00420102148205748
- GO:0051457
- 0.00559787167012804
- GO:0006099
- 0.0426605501446895
- GO:0000723
- 0.00534596526202138
- GO:0006103
- 0.00699297987822967
- GO:0042787
- 0.0480455713660258
- GO:0000730
- 0.0153271538864297
- GO:0033234
- 0.0014020875525782
- $`42`
- GO:0006797
- 0.00745859561145978
- GO:0070072
- 0.00373453272056024
- GO:0015991
- 0.00993549730648136
- GO:0007030
- 0.00621840341947566
- GO:0007034
- 0.000600087236367658
- GO:0007035
- 9.41953158782391e-05
- GO:0042144
- 0.0253112657684993
- GO:0006890
- 0.0197967744889329
- GO:0048213
- 0.000623150023368213
- GO:0016237
- 0.0308024564188107
- GO:0000301
- 0.00683864470303397
- GO:0032258
- 0.0338430739153368
- GO:0030242
- 0.0142589167591791
- $`43`
- GO:0032147
- 0.00529017279888011
- GO:0000045
- 0.0090159652337044
- GO:0034727
- 0.0127347668923244
- GO:0000422
- 0.0105663154982932
- GO:0006888
- 0.0287687917768665
- GO:0016236
- 0.0115805946791863
- GO:0044805
- 0.00529017279888011
- GO:0000301
- 0.00342465504039091
- GO:0032258
- 0.0170645332630111
- GO:0030242
- 1.22824054154048e-05
- $`44`
- GO:0090337
- 0.00280242741110559
- GO:0016226
- 0.00715394286331783
- GO:0060627
- 0.0155192804394058
- GO:0008360
- 0.00715394286331783
- GO:0032465
- 0.00435763238139208
- GO:0001100
- 0.00622227629285499
- GO:0000755
- 0.00373569603444879
- $`45`
- GO:0032258
- 0.0254892567584971
- GO:0006914
- 0.00205562004457106
- GO:0030433
- 0.0250297263860164
- GO:0046777
- 0.0102483672271536
- GO:0016485
- 0.0218089682359599
- GO:0016050
- 0.0396631807510962
- GO:0031503
- 0.0148834751129375
- GO:0006887
- 0.0282434070605891
- GO:0006888
- 0.0428446063887961
- $`46`
- GO:0007039: 0.00140208755257865
- $`47`
- GO:0010673
- 0.000311575011684107
- GO:0010674
- 0.000623150023367991
- GO:0007070
- 0.00093472503505232
- GO:1900471
- 0.000311575011684107
- GO:1900472
- 0.000311575011684107
- GO:0009847
- 0.000155787505841998
- GO:1900475
- 0.000155787505841998
- GO:1900476
- 0.000155787505841998
- GO:0001081
- 0.000155787505841998
- GO:0034389
- 0.0012463000467362
- GO:0016569
- 0.0281975385574077
- GO:0007124
- 0.0116840629381525
- GO:1900525
- 0.000311575011684107
- GO:0006338
- 0.0141766630316248
- $`48`
- GO:0045048
- 0.0046663476548825
- GO:0006620
- 0.0157915977625069
- GO:0030242
- 0.0213152417452613
- GO:0051321
- 0.0308328861214759
- GO:0051301
- 0.0279257809290435
- GO:0007266
- 0.0213152417452613
- GO:0007067
- 0.0180137636302785
- GO:0006890
- 0.0295522548564279
- GO:0016560
- 0.00559743619776842
- $`49`
- GO:0030437
- 0.0169808381367815
- GO:0000723
- 0.0126187879732049
- GO:0000727
- 0.00405047515189327
- GO:0097552
- 0.000623150023367991
- GO:0007131
- 0.00997040037388996
- GO:0010791
- 0.000311575011684107
- GO:0031573
- 0.00155787505841987
- GO:0042138
- 0.00327153762268262
- GO:0035753
- 0.00171366256426231
- GO:0006284
- 0.00280417510515696
- GO:0051037
- 0.00140208755257865
- GO:0000706
- 0.00109051254089465
- GO:0006303
- 0.0046736251752606
- $`50`
- GO:0000122: 0.0232123383704627
- $`51`
- GO:0008298
- 0.0240673610394746
- GO:0006607
- 0.0148684696691151
- GO:0007533
- 0.0222333325128552
- GO:0035786
- 0.00559743619776842
- GO:0042407
- 0.00931453551325756
- GO:0015986
- 0.0176356914942637
- GO:0031144
- 0.00186872186425013
- GO:0010499
- 0.0130200487735759
- GO:0046839
- 0.0167140048873777
- $`52`
- GO:0006606
- 0.0095030378563643
- GO:0016973
- 0.00420626265773483
- GO:0051292
- 0.00140208755257865
- GO:0000055
- 0.00436205016357749
In [14]:
mapSimilarity <- function(mapID) {
map <- getShortestPath(as.character(mapID))
if (is.null(map)) return (NaN)
communities <- sapply(rownames(map), function(rowname) getGenes(rowname))
communitySimilarities <- mclusterSim(clusters = communities, semData = scGO, measure = "Wang", combine = "BMA")
return(mean(communitySimilarities[upper.tri(communitySimilarities)]))
}
In [15]:
similarityOfAllMapsOnLayer <- function(layer) {
mapSimilarities <- sapply(unique(assignments[,layer][assignments[,layer] != -1]), mapSimilarity)
mapSimilarities <- mapSimilarities[!is.na(mapSimilarities)]
return(mean(mapSimilarities))
}
In [16]:
layerMeanMapSimilarities <- sapply(colnames[1:length(colnames)-1], similarityOfAllMapsOnLayer)
Error in clusters[[i]]: subscript out of bounds
Traceback:
1. sapply(colnames[1:length(colnames) - 1], similarityOfAllMapsOnLayer)
2. sapply(colnames[1:length(colnames) - 1], similarityOfAllMapsOnLayer)
3. lapply(X = X, FUN = FUN, ...)
4. FUN(X[[i]], ...)
5. sapply(unique(assignments[, layer][assignments[, layer] != -1]),
. mapSimilarity) # at line 2 of file <text>
6. sapply(unique(assignments[, layer][assignments[, layer] != -1]),
. mapSimilarity)
7. lapply(X = X, FUN = FUN, ...)
8. FUN(X[[i]], ...)
9. mclusterSim(clusters = communities, semData = scGO, measure = "Wang",
. combine = "BMA") # at line 5 of file <text>
10. sapply(clusters[[i]], gene2GO, semData, dropCodes = drop)
11. lapply(X = X, FUN = FUN, ...)
In [ ]:
In [17]:
getNeighbours("4")
Error in map[com, ]: incorrect number of dimensions
Traceback:
1. getNeighbours("4")
In [62]:
numSharedTerms <- function(c1, c2) {
return(length(intersect(names(enrichmentResults[["p-values", c1]]),
names(enrichmentResults[["p-values", c2]]))))
}
In [18]:
getShortestPath("1")
In [19]:
assignments
0 1 YGR046W 1 34 YCL046W 1 28 YJL030W 1 24 YBR057C 1 12 YKR026C 1 16 YFL056C 1 7 YFL061W 1 10 YDR174W 1 33 YLR303W 1 10 YPL211W 1 4 YOL020W 1 37 YLR432W 1 39 YPL229W 1 8 YMR236W 1 21 YLR015W 1 22 YCR050C 1 14 YOR372C 1 37 YLR046C 1 41 YGR136W 1 38 YJR072C 1 32 YDR469W 1 22 YBR196C 1 5 YHR140W 1 8 YDL088C 1 17 YEL062W 1 32 YJL178C 1 5 YKR037C 1 2 YNL047C 1 3 YLR113W 1 38 YGR268C 1 32 ⋮ ⋮ ⋮ YOR264W 1 38 YER116C 1 41 YPL151C 1 2 YNL164C 1 8 YER027C 1 12 YOR006C 1 14 YDL013W 1 41 YDR167W 1 39 YNL023C 1 50 YMR180C 1 9 YJL110C 1 41 YNL199C 1 9 YHR057C 1 6 YFR002W 1 52 YOR164C 1 48 YLR291C 1 26 YNR025C 1 43 YGL025C 1 40 YML114C 1 33 YHR129C 1 29 YLR376C 1 19 YDR311W 1 27 YDR206W 1 12 YOL149W 1 12 YER065C 1 10 YNL218W 1 24 YOR078W 1 31 YPL019C 1 42 YGL153W 1 48 YDL002C 1 8
In [65]:
sapply(as.character(2:7), function(i) sapply(as.character(2:7), function(j) numSharedTerms(i, j)))
2 3 4 5 6 7 2 51 0 0 0 2 2 3 0 51 1 1 1 0 4 0 1 36 1 0 1 5 0 1 1 32 0 0 6 2 1 0 0 41 0 7 2 0 1 0 0 40
In [66]:
getShortestPath("7")
15 16 15 0 1 16 1 0
In [58]:
intersect(names(enrichmentResults[["p-values", "4"]]), names(enrichmentResults[["p-values", "2"]]))
In [59]:
intersect(names(enrichmentResults[["p-values", "4"]]), names(enrichmentResults[["p-values", "14"]]))
- 'GO:0015031'
- 'GO:0006999'
- 'GO:0001113'
- 'GO:0006913'
- 'GO:0046777'
- 'GO:0072417'
- 'GO:0032147'
- 'GO:0007118'
- 'GO:0051028'
- 'GO:0060188'
- 'GO:0030474'
In [60]:
intersect(names(enrichmentResults[["p-values", "4"]]), names(enrichmentResults[["p-values", "13"]]))
- 'GO:0032880'
- 'GO:0007032'
- 'GO:0036010'
- 'GO:0006810'
- 'GO:0015693'
In [61]:
intersect(names(enrichmentResults[["p-values", "13"]]), names(enrichmentResults[["p-values", "14"]]))
In [22]:
select(GO.db, keys=names(enrichmentResults[["p-values", 4]]), columns=c("GOID", "TERM", "DEFINITION"))
'select()' returned 1:1 mapping between keys and columns
GOID TERM DEFINITION 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. GO:0000045 autophagosome assembly The formation of a double membrane-bounded structure, the autophagosome, that occurs when a specialized membrane sac, called the isolation membrane, starts to enclose a portion of the cytoplasm. GO:0000422 mitophagy The autophagic process in which mitochondria are delivered to the vacuole and degraded in response to changing cellular conditions. GO:0015031 protein transport The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. GO:0032880 regulation of protein localization Any process that modulates the frequency, rate or extent of any process in which a protein is transported to, or maintained in, a specific location. GO:0006606 protein import into nucleus The directed movement of a protein from the cytoplasm to the nucleus. GO:0030242 pexophagy The process in which peroxisomes are delivered to the vacuole and degraded in response to changing nutrient conditions. GO:0007032 endosome organization A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of endosomes. GO:0007034 vacuolar transport The directed movement of substances into, out of or within a vacuole. GO:0007035 vacuolar acidification Any process that reduces the pH of the vacuole, measured by the concentration of the hydrogen ion. GO:0044206 UMP salvage Any process which produces UMP, uridine monophosphate, from derivatives of it (e.g. cytidine, uridine, cytosine) without de novo synthesis. GO:0044805 late nucleophagy A type of nucleophagy, distinct from piecemeal microautophagy of the nucleus (PNM) where the nuclear material is delivered to the vacuole/lysosome for breakdown and recycling later than observed for PNM. 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. GO:0034727 piecemeal microautophagy of nucleus Degradation of a cell nucleus by lysosomal microautophagy. GO:0001113 transcriptional open complex formation at RNA polymerase II promoter Any process involved in the melting of the DNA hybrid of the core promoter region within the transcriptional closed complex of an RNA polymerase II preinitiation complex (PIC) to produce an open complex where the DNA duplex around the transcription initiation site is unwound to form the transcription bubble. GO:0070072 vacuolar proton-transporting V-type ATPase complex assembly The aggregation, arrangement and bonding together of a vacuolar proton-transporting V-type ATPase complex, proton-transporting two-sector ATPase complex that couples ATP hydrolysis to the transport of protons across the vacuolar membrane. 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. GO:0006913 nucleocytoplasmic transport The directed movement of molecules between the nucleus and the cytoplasm. GO:0006914 autophagy The process in which cells digest parts of their own cytoplasm; allows for both recycling of macromolecular constituents under conditions of cellular stress and remodeling the intracellular structure for cell differentiation. GO:0046777 protein autophosphorylation The phosphorylation by a protein of one or more of its own amino acid residues (cis-autophosphorylation), or residues on an identical protein (trans-autophosphorylation). GO:0072417 response to spindle checkpoint signaling A process that occurs in response to signals generated as a result of spindle checkpoint signaling. GO:0006890 retrograde vesicle-mediated transport, Golgi to ER The directed movement of substances from the Golgi back to the endoplasmic reticulum, mediated by vesicles bearing specific protein coats such as COPI or COG. GO:0045048 protein insertion into ER membrane The process that results in incorporation of a protein into an endoplasmic reticulum (ER) membrane. It depends on specific topogenic sequences of amino acids that ensure that a protein acquires the proper orientation during its insertion into the ER membrane. GO:0032147 activation of protein kinase activity Any process that initiates the activity of an inactive protein kinase. GO:0007118 budding cell apical bud growth Growth at the tip of a bud, in a cell that reproduces by budding. GO:0048213 Golgi vesicle prefusion complex stabilization The binding of specific proteins to the t-SNARE/v-SNARE/SNAP25 complex, by which the Golgi vesicle prefusion complex is stabilized. 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. GO:0000301 retrograde transport, vesicle recycling within Golgi The retrograde movement of substances within the Golgi, mediated by COP I vesicles. Cis-Golgi vesicles are constantly moving forward through the Golgi stack by cisternal progression, eventually becoming trans-Golgi vesicles. They then selectively transport membrane and luminal proteins from the trans- to the medial-Golgi while leaving others behind in the trans-Golgi cisternae; similarly, they selectively move proteins from the medial- to the cis-Golgi. GO:0036010 protein localization to endosome A process in which a protein is transported to, or maintained in, a location within an endosome. GO:0060188 regulation of protein desumoylation Any process that modulates the frequency, rate or extent of protein desumoylation. Protein desumoylation is the process in which a SUMO protein (small ubiquitin-related modifier) is cleaved from its target protein. GO:0016560 protein import into peroxisome matrix, docking The process in which a complex formed of a peroxisome targeting sequence (PTS) receptor bound to a PTS-bearing protein docks with translocation machinery in the peroxisomal membrane. GO:0000055 ribosomal large subunit export from nucleus The directed movement of a ribosomal large subunit from the nucleus into the cytoplasm. GO:0006810 transport The directed movement of substances (such as macromolecules, small molecules, ions) or cellular components (such as complexes and organelles) into, out of or within a cell, or between cells, or within a multicellular organism by means of some agent such as a transporter, pore or motor protein. GO:0015693 magnesium ion transport The directed movement of magnesium (Mg) ions into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. GO:0030474 spindle pole body duplication Construction of a new spindle pole body. GO:0016973 poly(A)+ mRNA export from nucleus The directed movement of poly(A)+ mRNA out of the nucleus into the cytoplasm.
In [25]:
select(GO.db, keys=names(enrichmentResults[["p-values", 14]]), columns=c("GOID", "TERM", "DEFINITION"))
'select()' returned 1:1 mapping between keys and columns
GOID TERM DEFINITION GO:0046777 protein autophosphorylation The phosphorylation by a protein of one or more of its own amino acid residues (cis-autophosphorylation), or residues on an identical protein (trans-autophosphorylation). GO:0006913 nucleocytoplasmic transport The directed movement of molecules between the nucleus and the cytoplasm. GO:0000920 cell separation after cytokinesis The process of physically separating progeny cells after cytokinesis; this may involve enzymatic digestion of septum or cell wall components. GO:0001113 transcriptional open complex formation at RNA polymerase II promoter Any process involved in the melting of the DNA hybrid of the core promoter region within the transcriptional closed complex of an RNA polymerase II preinitiation complex (PIC) to produce an open complex where the DNA duplex around the transcription initiation site is unwound to form the transcription bubble. 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. GO:0042981 regulation of apoptotic process Any process that modulates the occurrence or rate of cell death by apoptotic process. GO:0070816 phosphorylation of RNA polymerase II C-terminal domain The process of introducing a phosphate group on to an amino acid residue in the C-terminal domain of RNA polymerase II. Typically, this occurs during the transcription cycle and results in production of an RNA polymerase II enzyme where the carboxy-terminal domain (CTD) of the largest subunit is extensively phosphorylated, often referred to as hyperphosphorylated or the II(0) form. Specific types of phosphorylation within the CTD are usually associated with specific regions of genes, though there are exceptions. The phosphorylation state regulates the association of specific complexes such as the capping enzyme or 3'-RNA processing machinery to the elongating RNA polymerase complex. GO:0031990 mRNA export from nucleus in response to heat stress The directed movement of mRNA from the nucleus to the cytoplasm during a heat stimulus, a temperature stimulus above the optimal temperature for the organism; in particular, a process that enables an organism withstand exposure to temperatures that would otherwise lethally impair poly(A)+ mRNA-nucleus export. GO:0031098 stress-activated protein kinase signaling cascade A series of molecular signals in which a stress-activated protein kinase (SAPK) cascade relays one or more of the signals. GO:0007039 protein catabolic process in the vacuole The chemical reactions and pathways resulting in the breakdown of a protein in the vacuole, usually by the action of vacuolar proteases. GO:0015031 protein transport The directed movement of proteins into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. 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. GO:0034501 protein localization to kinetochore Any process in which a protein is transported to, or maintained at, the kinetochore. GO:0030474 spindle pole body duplication Construction of a new spindle pole body. GO:0060188 regulation of protein desumoylation Any process that modulates the frequency, rate or extent of protein desumoylation. Protein desumoylation is the process in which a SUMO protein (small ubiquitin-related modifier) is cleaved from its target protein. 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. GO:0032147 activation of protein kinase activity Any process that initiates the activity of an inactive protein kinase. GO:0007118 budding cell apical bud growth Growth at the tip of a bud, in a cell that reproduces by budding. GO:0072417 response to spindle checkpoint signaling A process that occurs in response to signals generated as a result of spindle checkpoint signaling.
In [23]:
getSubCommunities(4)
- '14'
- '10'
- '12'
- '11'
- '13'
In [20]:
layerMeanMapSimilarities
- 0
- 0.641066666666667
- 1
- 0.461577777777778
- 2
- 0.365219367588933
In [ ]:
getDistance <- function(c1, c2) {
m1 <- getSuperCommunity(c1)
m2 <- getSuperCommunity(c2)
if (m1 == m2) return(getShortestPath(m1)[c1, c2])
d1 <- getDepth(c1)
d2 <- getDepth(c2)
if (d1 == d2) return (2 + getDistance(m1, m2))
if (d1 > d2) return (1 + getDistance(m1, c2))
if (d2 > d1) return (1 + getDistance(c1, m2))
}
In [78]:
distances <- sapply(as.character(2:max(assignments)), function(i)
sapply(as.character(2:max(assignments)), function(j) {
return(getDistance(i, j))
}))
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In [80]:
head(distances)
2 3 4 5 6 7 8 9 10 11 ⋯ 49 50 51 52 53 54 55 56 57 58 2 0 1 1 2 2 1 3 2 2 3 ⋯ 4 3 3 3 3 3 3 5 5 5 3 1 0 1 1 1 1 2 1 1 2 ⋯ 3 3 3 3 3 3 3 4 4 4 4 1 1 0 1 2 2 2 2 2 3 ⋯ 4 4 4 4 4 4 4 5 5 5 5 2 1 1 0 1 2 1 2 2 3 ⋯ 3 4 4 4 4 4 4 4 4 4 6 2 1 2 1 0 2 1 1 2 2 ⋯ 2 4 4 4 4 4 4 3 3 3 7 1 1 2 2 2 0 3 2 2 3 ⋯ 4 2 2 2 2 2 2 5 5 5
In [84]:
similarities <- sapply(2:max(assignments), function(i)
sapply(2:max(assignments), function(j) {
return(clusterSim(cluster1 = geneCommunities[[i]],
cluster2 = geneCommunities[[j]], semData = scGO, measure = "Wang", combine = "BMA"))
}))
In [86]:
head(similarities)
1.000 0.336 0.661 0.620 0.617 0.662 0.578 0.551 0.626 0.418 ⋯ 0.443 0.177 0.528 0.286 0.418 0.319 0.212 0.436 0.270 0.420 0.336 1.000 0.318 0.355 0.314 0.324 0.303 0.323 0.433 0.389 ⋯ 0.339 0.234 0.276 0.375 0.394 0.296 0.178 0.285 0.252 0.221 0.661 0.318 1.000 0.627 0.675 0.639 0.639 0.485 0.594 0.398 ⋯ 0.477 0.170 0.508 0.350 0.444 0.303 0.238 0.467 0.242 0.421 0.620 0.355 0.627 1.000 0.626 0.630 0.604 0.452 0.557 0.412 ⋯ 0.408 0.198 0.501 0.323 0.432 0.350 0.222 0.432 0.238 0.361 0.617 0.314 0.675 0.626 1.000 0.640 0.653 0.499 0.593 0.469 ⋯ 0.528 0.184 0.493 0.325 0.433 0.318 0.212 0.561 0.358 1.000 0.662 0.324 0.639 0.630 0.640 1.000 0.586 0.424 0.588 0.451 ⋯ 0.439 0.189 0.597 0.305 0.455 0.359 0.269 0.423 0.265 0.388
In [14]:
uniprots <- sapply(1:max(assignments), function(i) {
orfs <- getGenes(i)
orfs <- orfs[orfs %in% allGenesInDB]
return(as.character(org.Sc.sgdUNIPROT[orfs]))
})
Warning message in .local(x, ...):
“returned vector has duplicated names”Warning message in .local(x, ...):
“returned vector has duplicated names”Warning message in .local(x, ...):
“returned vector has duplicated names”Warning message in .local(x, ...):
“returned vector has duplicated names”
In [15]:
uniprots[1]
- YAL032C
- 'P28004'
- YAL040C
- 'P13365'
- YBL007C
- 'P32790'
- YBR057C
- 'P38236'
- YBR094W
- 'P38254'
- YBR133C
- 'P38274'
- YBR196C
- 'P12709'
- YBR221C
- 'P32473'
- YBR252W
- 'P33317'
- YBR270C
- 'P38346'
- YBR274W
- 'P38147'
- YCL054W
- 'P25582'
- YCL055W
- 'P25583'
- YCR011C
- 'P25371'
- YCR050C
- 'P25630'
- YCR086W
- 'P25651'
- YDL002C
- 'Q03435'
- YDL012C
- 'Q12489'
- YDL013W
- 'P32828'
- YDL017W
- 'P06243'
- YDL088C
- 'Q05166'
- YDL110C
- 'Q12513'
- YDL111C
- 'Q12277'
- YDL154W
- 'Q12175'
- YDL160C
- 'P39517'
- YDL165W
- 'P06100'
- YDL203C
- 'Q07622'
- YDL215C
- 'P33327'
- YDL236W
- 'P19881'
- YDL246C
- 'Q07786'
- YDR054C
- 'P14682'
- YDR061W
- 'Q12298'
- YDR078C
- 'P38957'
- YDR106W
- 'Q04549'
- YDR122W
- 'P13185'
- YDR148C
- 'P19262'
- YDR151C
- 'P47976'
- YDR167W
- 'Q12030'
- YDR174W
- 'Q03973'
- YDR201W
- 'Q03954'
- YDR206W
- 'Q03466'
- YDR207C
- 'P39001'
- YDR214W
- 'Q12449'
- YDR273W
- 'Q05610'
- YDR311W
- 'P32776'
- YDR315C
- 'Q06667'
- YDR326C
- 'Q06681'
- YDR383C
- 'Q12493'
- YDR439W
- 'Q04087'
- YDR469W
- 'Q03323'
- YDR477W
- 'P06782'
- YDR503C
- 'Q04396'
- YDR510W
- 'Q12306'
- YEL015W
- 'P39998'
- YEL023C
- 'P39992'
- YEL062W
- 'P39923'
- YER027C
- 'Q04739'
- YER065C
- 'P28240'
- YER079W
- 'P40052'
- YER092W
- 'P40060'
- YER105C
- 'P40064'
- YER116C
- 'P40072'
- YER179W
- 'P25453'
- YFL061W
- 'P0CH63'
- YFL061W
- 'P0CH64'
- YFR002W
- 'P34077'
- YFR033C
- 'P00127'
- YFR047C
- 'P43619'
- YFR052W
- 'P32496'
- YFR057W
- 'P43625'
- YGL025C
- 'P40356'
- YGL112C
- 'P53040'
- YGL115W
- 'P12904'
- YGL116W
- 'P26309'
- YGL122C
- 'P32505'
- YGL145W
- 'P33891'
- YGL150C
- 'P53115'
- YGL153W
- 'P53112'
- YGL158W
- 'P38622'
- YGL170C
- 'P45819'
- YGL172W
- 'Q02199'
- YGL175C
- 'P46946'
- YGL180W
- 'P53104'
- YGL192W
- 'P41833'
- YGL208W
- 'P34164'
- YGL221C
- 'P53081'
- YGL229C
- 'P53036'
- YGL238W
- 'P33307'
- YGR014W
- 'P32334'
- YGR024C
- 'P53215'
- YGR046W
- 'P53230'
- YGR057C
- 'P53237'
- YGR058W
- 'P53238'
- YGR099W
- 'P53038'
- YGR117C
- 'P53270'
- YGR119C
- 'P48837'
- YGR120C
- 'P53271'
- YGR136W
- 'P53281'
- YGR155W
- 'P32582'
- YGR158C
- 'P48240'
- YGR253C
- 'P32379'
- YGR268C
- 'P40325'
- YHL006C
- 'P38751'
- YHL009C
- 'P38749'
- YHL046C
- 'P38725'
- YHR016C
- 'P32793'
- YHR032W
- 'P38767'
- YHR057C
- 'P23285'
- YHR060W
- 'P38784'
- YHR102W
- 'P38692'
- YHR128W
- 'P18562'
- YHR129C
- 'P38696'
- YHR140W
- 'P38842'
- YHR158C
- 'P38853'
- YIL013C
- 'P40550'
- YIL065C
- 'P40515'
- YIL105C
- 'P40485'
- YIL132C
- 'P40465'
- YIL144W
- 'P40460'
- YIL172C
- 'P0CW40'
- YIL172C
- 'P0CW41'
- YJL013C
- 'P47074'
- YJL030W
- 'P40958'
- YJL041W
- 'P14907'
- YJL048C
- 'P47049'
- YJL057C
- 'P47042'
- YJL065C
- 'P40366'
- YJL088W
- 'P05150'
- YJL092W
- 'P12954'
- YJL110C
- 'P42944'
- YJL178C
- 'P46989'
- YJL184W
- 'P46984'
- YJR034W
- 'Q02772'
- YJR072C
- 'P47122'
- YJR091C
- 'P47135'
- YJR117W
- 'P47154'
- YJR122W
- 'P47158'
- YJR133W
- 'P47165'
- YJR159W
- 'P35497'
- YKL012W
- 'P33203'
- YKL015W
- 'P25502'
- YKL028W
- 'P36100'
- YKL039W
- 'P32857'
- YKL061W
- 'P35727'
- YKL075C
- 'P36083'
- YKL090W
- 'P36075'
- YKL103C
- 'P14904'
- YKL130C
- 'P36068'
- YKL204W
- 'P36041'
- YKR026C
- 'P14741'
- YKR037C
- 'P36131'
- YKR062W
- 'P36145'
- YLL046C
- 'P32385'
- YLR015W
- 'P43132'
- YLR046C
- 'Q12253'
- YLR113W
- 'P32485'
- YLR150W
- 'P39015'
- YLR243W
- 'Q06543'
- YLR258W
- 'P27472'
- YLR264W
- 'P0C0X0'
- YLR291C
- 'P32502'
- YLR303W
- 'P06106'
- YLR305C
- 'P37297'
- YLR315W
- 'Q06162'
- YLR321C
- 'Q06168'
- YLR323C
- 'P53769'
- YLR345W
- 'Q06137'
- YLR376C
- 'Q12318'
- YLR392C
- 'P18634'
- YLR423C
- 'Q06410'
- YLR432W
- 'P50095'
- YLR433C
- 'P23287'
- YLR447C
- 'P32366'
- YML015C
- 'Q04226'
- YML028W
- 'P34760'
- YML031W
- 'P32500'
- YML114C
- 'Q03750'
- YMR068W
- 'Q04749'
- YMR102C
- 'Q03177'
- YMR129W
- 'P39685'
- YMR153W
- 'Q03790'
- YMR180C
- 'Q03220'
- YMR181C
- 'Q03231'
- YMR224C
- 'P32829'
- YMR226C
- 'Q05016'
- YMR236W
- 'Q05027'
- YMR255W
- 'Q04839'
- YMR267W
- 'P28239'
- YMR269W
- 'Q03525'
- YMR309C
- 'P32497'
- YMR314W
- 'P40302'
- YNL021W
- 'P53973'
- YNL023C
- 'P53971'
- YNL047C
- 'P53955'
- YNL086W
- 'P48232'
- YNL091W
- 'P53935'
- YNL118C
- 'P53550'
- YNL122C
- 'P53921'
- YNL154C
- 'P23292'
- YNL164C
- 'P53892'
- YNL189W
- 'Q02821'
- YNL199C
- 'Q01722'
- YNL201C
- 'P40164'
- YNL218W
- 'P40151'
- YNL236W
- 'P32259'
- YNL244C
- 'P32911'
- YNL288W
- 'P53829'
- YNR029C
- 'P53729'
- YNR048W
- 'P53740'
- YOL020W
- 'P38967'
- YOL058W
- 'P22768'
- YOL082W
- 'P35193'
- YOL105C
- 'Q12215'
- YOL123W
- 'Q99383'
- YOL130W
- 'Q08269'
- YOL149W
- 'Q12517'
- YOR006C
- 'Q12094'
- YOR026W
- 'P26449'
- YOR036W
- 'P32854'
- YOR047C
- 'Q02794'
- YOR062C
- 'P36025'
- YOR078W
- 'Q08492'
- YOR115C
- 'Q99394'
- YOR138C
- 'Q12242'
- YOR164C
- 'Q12125'
- YOR262W
- 'Q08726'
- YOR264W
- 'Q08729'
- YOR284W
- 'Q12134'
- YOR353C
- 'Q08817'
- YOR355W
- 'P41913'
- YOR372C
- 'Q08887'
- YPL019C
- 'Q02725'
- YPL059W
- 'Q02784'
- YPL070W
- 'Q02866'
- YPL110C
- 'Q02979'
- YPL111W
- 'P00812'
- YPL128C
- 'Q02457'
- YPL151C
- 'Q12417'
- YPL174C
- 'P33420'
- YPL211W
- 'Q08962'
- YPL214C
- 'P41835'
- YPL229W
- 'Q99395'
- YPL260W
- 'Q08977'
- YPR020W
- 'Q12233'
- YPR040W
- 'Q12199'
- YPR048W
- 'Q12181'
- YPR062W
- 'Q12178'
- YPR070W
- 'Q12321'
- YPR105C
- 'Q06096'
- YPR115W
- 'Q06108'
- YPR185W
- 'Q06628'
In [12]:
g <- getGenes(2)[getGenes(2) %in% allGenesInDB]
In [13]:
org.Sc.sgdUNIPROT[g]
UNIPROT submap for Yeast (object of class "AnnDbBimap")
In [ ]:
Content source: DavidMcDonald1993/ghsom
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