In [3]:

    

#libraries
library(GO.db)
library(topGO)
library(org.Hs.eg.db)
library(org.Sc.sgd.db)
library(GOSemSim)


    

    




Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

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    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, xtabs

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    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")'.

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Attaching package: ‘S4Vectors’

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Loading required package: graph
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Attaching package: ‘SparseM’

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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

In [30]:

    

file <- "yeast_union"

ont="BP"
db <- org.Sc.sgd.db
mapping <- "org.Sc.sgd.db"
ID <- "ENSEMBL"


    

In [31]:

    

##load all community gene lists
setwd(sprintf("/home/david/Documents/ghsom/"))

#background gene list
backgroundFilename <- sprintf("%s_all_genes.txt", file)
allGenes <- scan(backgroundFilename, character())


    

In [7]:

    

scGO <- godata(mapping, ont=ont, keytype=ID)


    

    




[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

In [12]:

    

semanticDistances <- mgeneSim(allGenes, semData=scGO, measure="Resnik", combine="BMA", verbose=FALSE)


    

In [14]:

    

ncol(semanticDistances)


    

    





224

In [10]:

    

semanticDistances <- 1 - semanticDistances


    

In [11]:

    

head(semanticDistances)


    

    






YGR046W
YBR057C
YJR122W
YDR503C
YGR136W
YPR048W
YGL175C
YOR262W
YDL246C
YNL122C
⋯
YGR024C
YGL116W
YJL057C
YOL082W
YJL110C
YFR002W
YGL025C
YHR129C
YOR078W
YGL153W

	
YGR046W
0.000
0.704
0.814
0.367
0.949
0.769
0.751
0.882
0.786
0.672
⋯    
0.850
0.865
0.748
0.807
0.826
0.919
0.832
0.916
0.878
0.906
	
YBR057C
0.704
0.000
0.784
0.719
0.782
0.788
0.487
0.771
0.788
0.554
⋯    
0.381
0.685
0.652
0.787
0.769
0.845
0.743
0.805
0.720
0.736
	
YJR122W
0.814
0.784
0.000
0.809
0.845
0.000
0.784
0.809
0.845
0.754
⋯    
0.864
0.862
0.836
0.608
0.834
0.694
0.689
0.856
0.779
0.862
	
YDR503C
0.367
0.719
0.809
0.000
0.812
0.738
0.667
0.851
0.643
0.681
⋯    
0.756
0.801
0.577
0.787
0.746
0.888
0.843
0.876
0.817
0.842
	
YGR136W
0.949
0.782
0.845
0.812
0.000
0.845
0.795
0.847
0.960
0.843
⋯    
0.969
0.760
0.966
0.829
0.890
0.676
0.860
0.845
0.942
0.877
	
YPR048W
0.769
0.788
0.000
0.738
0.845
0.000
0.778
0.889
0.560
0.678
⋯    
0.766
0.841
0.824
0.799
0.786
0.838
0.720
0.903
0.840
0.862

In [ ]:

    

semanticDistances2 <- sapply(allGenes, function(i) {
    sapply(allGenes, function(j) {
        geneSim(i, j, semData=scGO, measure="Wang", combine="BMA")["geneSim"]
    })
})


    

In [ ]:

    

length(allGenes)


    

In [ ]:

    

nrow(semanticDistances2)


    

In [ ]:

    

semanticDistances2[is.na(semanticDistances2)] <- 0


    

In [ ]:

    

write.table(semanticDistances, sep=",", file = sprintf("%s_wang_similarity.csv", file), row.names=FALSE, col.names=FALSE)


    

In [15]:

    

library(GOSim)

setOntology(ont, loadIC=FALSE)
setEvidenceLevel(evidences="all",organism=org.Sc.sgdORGANISM, gomap=org.Sc.sgdGO)

head(allGenes)


    

    




Loading required package: annotate
Loading required package: XML

Attaching package: ‘XML’

The following object is masked from ‘package:graph’:

    addNode

In [33]:

    

allGeneSims <- 1 - getGeneSim(allGenes, similarity="funSimMax", 
                          similarityTerm="relevance", normalization = TRUE)

allGeneSims[is.na(allGeneSims)] <- 1


    

    




filtering out genes not mapping to the currently set GO category ... ===> list of  1647 genes reduced to  1529 






    




Warning message in getGeneSim(allGenes, similarity = "funSimMax", similarityTerm = "relevance", :
“Similarity matrix contains values > 1! This may happen with simlarity='funSimMax', if one gene's GO annotation is a complete subset of another gene's GO annotation.”

In [36]:

    

head(allGeneSims)


    

    






YLR268W
YJL155C
YBR255W
YFR027W
YJR122W
YLR244C
YPL144W
YBR135W
YBR160W
YDL238C
⋯
YDR076W
YDR508C
YPR028W
YMR117C
YLR096W
YCR067C
YMR047C
YML055W
YGL015C
YPL013C

	
YLR268W
0.0000000 
0.9938983 
1         
0.8118516 
0.7318703 
1.0000000 
0.7339676 
0.97257857
0.27822262
0.9833591 
⋯         
0.8929767 
0.175877  
0.2725629 
0.8150920 
0.32610054
0.1846623 
0.2633827 
0.5411890 
1         
0.79901481
	
YJL155C
0.9938983 
0.0000000 
1         
0.4416787 
0.8477656 
0.1645404 
0.9849700 
0.40300985
0.40931441
0.5005958 
⋯         
0.4386883 
1.000000  
0.9874740 
0.9814483 
0.38235845
1.0000000 
0.5504462 
0.5971853 
1         
0.28307242
	
YBR255W
1.0000000 
1.0000000 
0         
1.0000000 
1.0000000 
1.0000000 
1.0000000 
1.00000000
1.00000000
1.0000000 
⋯         
1.0000000 
1.000000  
1.0000000 
1.0000000 
1.00000000
1.0000000 
1.0000000 
1.0000000 
1         
1.00000000
	
YFR027W
0.8118516 
0.4416787 
1         
0.0000000 
0.6558710 
0.1292241 
0.6593126 
0.15098605
0.11972270
0.3179332 
⋯         
0.2183519 
1.000000  
0.5952518 
0.3655197 
0.53420515
0.9757161 
0.5486758 
0.7136951 
1         
0.26000288
	
YJR122W
0.7318703 
0.8477656 
1         
0.6558710 
0.0000000 
0.8896328 
0.5773167 
0.71418507
0.59620407
0.9044549 
⋯         
0.5980483 
1.000000  
0.6167872 
0.5772192 
0.85076796
1.0000000 
0.7613731 
0.8959624 
1         
0.71567447
	
YLR244C
1.0000000 
0.1645404 
1         
0.1292241 
0.8896328 
0.0000000 
1.0000000 
0.05909364
0.06133358
0.4717284 
⋯         
0.1245602 
1.000000  
1.0000000 
1.0000000 
0.02449147
0.9400956 
0.3237669 
0.3793700 
1         
0.06517584

In [37]:

    

write.table(allGeneSims, sep=",", file = sprintf("%s_rel_similarity_GOSim.csv", file), row.names=TRUE, col.names=FALSE)