MeSH over-representation analysis (Selective sweep data)
1. Create a vector of background genes
We first create a vector of background genes.
library(biomaRt)
## access to biomaRt
#mart <- useMart(biomart = "ensembl", dataset = "ggallus_gene_ensembl")
mart <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = "ggallus_gene_ensembl",
host = "www.ensembl.org")
univ.geneID <- getBM(attributes=c("ensembl_gene_id", "entrezgene",
"hgnc_symbol"), mart = mart) # 17680
## remove genes with no corresponding Entrez Gene ID
univ.geneID2 <- univ.geneID[!is.na(univ.geneID[,2]),] # 14124
## remove duplicated Entrez Gene ID
univ.geneID3 <- univ.geneID2[ !duplicated(univ.geneID2[,2]),] # 13984 2. Create a vector of selected genes
Secondly, we create a vector of significant genes by reading the input file.
## read data
my.geneID <- read.table("allSelectedGenes.txt", colClasses = c("NULL", "character", "NULL"),
sep="\t", header=TRUE) # 397
colnames(my.geneID) <- "ensembl_gene_id"
## merge two files
my.geneID2 <- merge(my.geneID, univ.geneID3, by ="ensembl_gene_id") # 352
## remove duplicated Entrez Gene ID
my.geneID3 <- my.geneID2[ !duplicated(my.geneID2$entrezgene),] # 3523. GO enrichment analysis
We perform a GO analysis using the GOstats package.
library("org.Gg.eg.db")
library("GOstats")
library("GOSemSim")
paraGO <- new("GOHyperGParams", geneIds=my.geneID3[,2], universeGeneIds=univ.geneID3[,2],
annotation="org.Gg.eg.db", ontology="BP", pvalueCutoff=0.05,
conditional=TRUE, testDirection="over")GO enrichment analysis for BP
BP <- hyperGTest(paraGO)
summary(BP)[,c(1,2,7)] # 109## GOBPID Pvalue
## 1 GO:0051893 0.0005051324
## 2 GO:1901888 0.0005051324
## 3 GO:0002092 0.0018768234
## 4 GO:0072273 0.0018768234
## 5 GO:0007045 0.0022162536
## 6 GO:0072077 0.0036927123
## 7 GO:0034332 0.0037062867
## 8 GO:0045807 0.0060547770
## 9 GO:0090184 0.0060547770
## 10 GO:0010810 0.0068780676
## 11 GO:0061061 0.0080357348
## 12 GO:0007160 0.0082041922
## 13 GO:0048259 0.0089352211
## 14 GO:0051145 0.0089352211
## 15 GO:0051147 0.0096710216
## 16 GO:0001838 0.0123072565
## 17 GO:0048599 0.0123072565
## 18 GO:0048634 0.0130382162
## 19 GO:1901861 0.0130382162
## 20 GO:0071705 0.0143637030
## 21 GO:0023061 0.0158148709
## 22 GO:0010830 0.0161450728
## 23 GO:0030500 0.0161450728
## 24 GO:0051155 0.0161450728
## 25 GO:0015833 0.0169978114
## 26 GO:0034330 0.0169978114
## 27 GO:1903530 0.0187634040
## 28 GO:0035148 0.0204238057
## 29 GO:0060538 0.0240708694
## 30 GO:0060627 0.0240708694
## 31 GO:0048791 0.0249433107
## 32 GO:0001656 0.0251195079
## 33 GO:0048477 0.0251195079
## 34 GO:0048641 0.0251195079
## 35 GO:0002026 0.0254957507
## 36 GO:0003257 0.0254957507
## 37 GO:0003337 0.0254957507
## 38 GO:0006203 0.0254957507
## 39 GO:0006700 0.0254957507
## 40 GO:0006702 0.0254957507
## 41 GO:0006705 0.0254957507
## 42 GO:0006788 0.0254957507
## 43 GO:0007403 0.0254957507
## 44 GO:0009151 0.0254957507
## 45 GO:0009215 0.0254957507
## 46 GO:0009264 0.0254957507
## 47 GO:0010579 0.0254957507
## 48 GO:0010882 0.0254957507
## 49 GO:0030237 0.0254957507
## 50 GO:0031945 0.0254957507
## 51 GO:0031946 0.0254957507
## 52 GO:0032148 0.0254957507
## 53 GO:0032239 0.0254957507
## 54 GO:0032344 0.0254957507
## 55 GO:0032349 0.0254957507
## 56 GO:0032353 0.0254957507
## 57 GO:0032470 0.0254957507
## 58 GO:0033015 0.0254957507
## 59 GO:0033292 0.0254957507
## 60 GO:0034633 0.0254957507
## 61 GO:0034650 0.0254957507
## 62 GO:0038030 0.0254957507
## 63 GO:0045822 0.0254957507
## 64 GO:0045948 0.0254957507
## 65 GO:0046061 0.0254957507
## 66 GO:0046084 0.0254957507
## 67 GO:0046386 0.0254957507
## 68 GO:0046833 0.0254957507
## 69 GO:0046851 0.0254957507
## 70 GO:0046864 0.0254957507
## 71 GO:0046886 0.0254957507
## 72 GO:0050921 0.0254957507
## 73 GO:0050926 0.0254957507
## 74 GO:0050930 0.0254957507
## 75 GO:0051187 0.0254957507
## 76 GO:0051458 0.0254957507
## 77 GO:0051973 0.0254957507
## 78 GO:0055119 0.0254957507
## 79 GO:0060126 0.0254957507
## 80 GO:0060129 0.0254957507
## 81 GO:0061369 0.0254957507
## 82 GO:0070296 0.0254957507
## 83 GO:0071376 0.0254957507
## 84 GO:0090191 0.0254957507
## 85 GO:0090291 0.0254957507
## 86 GO:1900086 0.0254957507
## 87 GO:1900155 0.0254957507
## 88 GO:1900158 0.0254957507
## 89 GO:1902811 0.0254957507
## 90 GO:1903233 0.0254957507
## 91 GO:1903515 0.0254957507
## 92 GO:1903611 0.0254957507
## 93 GO:2000019 0.0254957507
## 94 GO:2000066 0.0254957507
## 95 GO:2000225 0.0254957507
## 96 GO:2000852 0.0254957507
## 97 GO:0010717 0.0302091200
## 98 GO:0034754 0.0302091200
## 99 GO:0060675 0.0302091200
## 100 GO:0072078 0.0302091200
## 101 GO:0051641 0.0302884399
## 102 GO:0051493 0.0338616187
## 103 GO:0019932 0.0356704421
## 104 GO:0042440 0.0356704421
## 105 GO:0043112 0.0356704421
## 106 GO:0060993 0.0356704421
## 107 GO:0061326 0.0356704421
## 108 GO:1903651 0.0356704421
## 109 GO:0034654 0.0372961140
## 110 GO:0014031 0.0388975273
## 111 GO:0043547 0.0388975273
## 112 GO:0045844 0.0414821067
## 113 GO:0051336 0.0416897666
## 114 GO:0031326 0.0420860147
## 115 GO:0032879 0.0465892706
## 116 GO:0006836 0.0476235523
## 117 GO:0007623 0.0476235523
## 118 GO:0008585 0.0476235523
## 119 GO:0072009 0.0476235523
## 120 GO:0090090 0.0476235523
## 121 GO:0090288 0.0476235523
## 122 GO:0072521 0.0488073627
## 123 GO:0060231 0.0492782479
## Term
## 1 regulation of focal adhesion assembly
## 2 regulation of cell junction assembly
## 3 positive regulation of receptor internalization
## 4 metanephric nephron morphogenesis
## 5 cell-substrate adherens junction assembly
## 6 renal vesicle morphogenesis
## 7 adherens junction organization
## 8 positive regulation of endocytosis
## 9 positive regulation of kidney development
## 10 regulation of cell-substrate adhesion
## 11 muscle structure development
## 12 cell-matrix adhesion
## 13 regulation of receptor-mediated endocytosis
## 14 smooth muscle cell differentiation
## 15 regulation of muscle cell differentiation
## 16 embryonic epithelial tube formation
## 17 oocyte development
## 18 regulation of muscle organ development
## 19 regulation of muscle tissue development
## 20 nitrogen compound transport
## 21 signal release
## 22 regulation of myotube differentiation
## 23 regulation of bone mineralization
## 24 positive regulation of striated muscle cell differentiation
## 25 peptide transport
## 26 cell junction organization
## 27 regulation of secretion by cell
## 28 tube formation
## 29 skeletal muscle organ development
## 30 regulation of vesicle-mediated transport
## 31 calcium ion-dependent exocytosis of neurotransmitter
## 32 metanephros development
## 33 oogenesis
## 34 regulation of skeletal muscle tissue development
## 35 regulation of the force of heart contraction
## 36 positive regulation of transcription from RNA polymerase II promoter involved in myocardial precursor cell differentiation
## 37 mesenchymal to epithelial transition involved in metanephros morphogenesis
## 38 dGTP catabolic process
## 39 C21-steroid hormone biosynthetic process
## 40 androgen biosynthetic process
## 41 mineralocorticoid biosynthetic process
## 42 heme oxidation
## 43 glial cell fate determination
## 44 purine deoxyribonucleotide metabolic process
## 45 purine deoxyribonucleoside triphosphate metabolic process
## 46 deoxyribonucleotide catabolic process
## 47 positive regulation of adenylate cyclase activity involved in G-protein coupled receptor signaling pathway
## 48 regulation of cardiac muscle contraction by calcium ion signaling
## 49 female sex determination
## 50 positive regulation of glucocorticoid metabolic process
## 51 regulation of glucocorticoid biosynthetic process
## 52 activation of protein kinase B activity
## 53 regulation of nucleobase-containing compound transport
## 54 regulation of aldosterone metabolic process
## 55 positive regulation of aldosterone biosynthetic process
## 56 negative regulation of hormone biosynthetic process
## 57 positive regulation of endoplasmic reticulum calcium ion concentration
## 58 tetrapyrrole catabolic process
## 59 T-tubule organization
## 60 retinol transport
## 61 cortisol metabolic process
## 62 non-canonical Wnt signaling pathway via MAPK cascade
## 63 negative regulation of heart contraction
## 64 positive regulation of translational initiation
## 65 dATP catabolic process
## 66 adenine biosynthetic process
## 67 deoxyribose phosphate catabolic process
## 68 positive regulation of RNA export from nucleus
## 69 negative regulation of bone remodeling
## 70 isoprenoid transport
## 71 positive regulation of hormone biosynthetic process
## 72 positive regulation of chemotaxis
## 73 regulation of positive chemotaxis
## 74 induction of positive chemotaxis
## 75 cofactor catabolic process
## 76 corticotropin secretion
## 77 positive regulation of telomerase activity
## 78 relaxation of cardiac muscle
## 79 somatotropin secreting cell differentiation
## 80 thyroid-stimulating hormone-secreting cell differentiation
## 81 negative regulation of testicular blood vessel morphogenesis
## 82 sarcoplasmic reticulum calcium ion transport
## 83 cellular response to corticotropin-releasing hormone stimulus
## 84 negative regulation of branching involved in ureteric bud morphogenesis
## 85 negative regulation of osteoclast proliferation
## 86 positive regulation of peptidyl-tyrosine autophosphorylation
## 87 negative regulation of bone trabecula formation
## 88 negative regulation of bone mineralization involved in bone maturation
## 89 positive regulation of skeletal muscle fiber differentiation
## 90 regulation of calcium ion-dependent exocytosis of neurotransmitter
## 91 calcium ion transport from cytosol to endoplasmic reticulum
## 92 negative regulation of calcium-dependent ATPase activity
## 93 negative regulation of male gonad development
## 94 positive regulation of cortisol biosynthetic process
## 95 negative regulation of testosterone biosynthetic process
## 96 regulation of corticosterone secretion
## 97 regulation of epithelial to mesenchymal transition
## 98 cellular hormone metabolic process
## 99 ureteric bud morphogenesis
## 100 nephron tubule morphogenesis
## 101 cellular localization
## 102 regulation of cytoskeleton organization
## 103 second-messenger-mediated signaling
## 104 pigment metabolic process
## 105 receptor metabolic process
## 106 kidney morphogenesis
## 107 renal tubule development
## 108 positive regulation of cytoplasmic transport
## 109 nucleobase-containing compound biosynthetic process
## 110 mesenchymal cell development
## 111 positive regulation of GTPase activity
## 112 positive regulation of striated muscle tissue development
## 113 regulation of hydrolase activity
## 114 regulation of cellular biosynthetic process
## 115 regulation of localization
## 116 neurotransmitter transport
## 117 circadian rhythm
## 118 female gonad development
## 119 nephron epithelium development
## 120 negative regulation of canonical Wnt signaling pathway
## 121 negative regulation of cellular response to growth factor stimulus
## 122 purine-containing compound metabolic process
## 123 mesenchymal to epithelial transition# GO similarity
library(corrplot)## Warning: package 'corrplot' was built under R version 3.2.5goListBP <- summary(BP)[,c(1)]
goSimMatBP <- mgoSim(goListBP, goListBP, ont="BP", measure="Jiang", organism="chicken", combine=NULL)
goSimMatBP <- goSimMatBP[-66, -66]
corrplot(goSimMatBP, is.corr = FALSE, type="lower", tl.col = "black", tl.cex = 0.8)
GO enrichment analysis for MF
ontology(paraGO) <- "MF"
MF <- hyperGTest(paraGO)
summary(MF)[,c(1,2,7)] # 18## GOMFID Pvalue
## 1 GO:0017124 5.625388e-05
## 2 GO:0030276 5.625388e-05
## 3 GO:0044548 5.763240e-03
## 4 GO:0046872 1.452494e-02
## 5 GO:0003714 1.538956e-02
## 6 GO:0005488 1.853059e-02
## 7 GO:0004112 2.489110e-02
## 8 GO:0004392 2.489110e-02
## 9 GO:0004638 2.489110e-02
## 10 GO:0004639 2.489110e-02
## 11 GO:0004687 2.489110e-02
## 12 GO:0008832 2.489110e-02
## 13 GO:0015056 2.489110e-02
## 14 GO:0032554 2.489110e-02
## 15 GO:0032567 2.489110e-02
## 16 GO:0034632 2.489110e-02
## 17 GO:0043023 2.489110e-02
## 18 GO:0043184 2.489110e-02
## 19 GO:0045159 2.489110e-02
## 20 GO:0047555 2.489110e-02
## 21 GO:0005165 4.917775e-02
## 22 GO:0015020 4.917775e-02
## 23 GO:0030297 4.917775e-02
## 24 GO:0030553 4.917775e-02
## 25 GO:0042056 4.917775e-02
## 26 GO:0048018 4.917775e-02
## 27 GO:0048039 4.917775e-02
## Term
## 1 SH3 domain binding
## 2 clathrin binding
## 3 S100 protein binding
## 4 metal ion binding
## 5 transcription corepressor activity
## 6 binding
## 7 cyclic-nucleotide phosphodiesterase activity
## 8 heme oxygenase (decyclizing) activity
## 9 phosphoribosylaminoimidazole carboxylase activity
## 10 phosphoribosylaminoimidazolesuccinocarboxamide synthase activity
## 11 myosin light chain kinase activity
## 12 dGTPase activity
## 13 corticotrophin-releasing factor receptor activity
## 14 purine deoxyribonucleotide binding
## 15 dGTP binding
## 16 retinol transporter activity
## 17 ribosomal large subunit binding
## 18 vascular endothelial growth factor receptor 2 binding
## 19 myosin II binding
## 20 3',5'-cyclic-GMP phosphodiesterase activity
## 21 neurotrophin receptor binding
## 22 glucuronosyltransferase activity
## 23 transmembrane receptor protein tyrosine kinase activator activity
## 24 cGMP binding
## 25 chemoattractant activity
## 26 receptor agonist activity
## 27 ubiquinone binding# GO similarity
goListMF <- summary(MF)[,c(1)]
goSimMatMF <- mgoSim(goListMF, goListMF, ont="MF", measure="Jiang", organism="chicken", combine=NULL)
corrplot(goSimMatMF, is.corr = FALSE, type="lower", tl.col = "black", tl.cex = 0.8)
GO enrichment analysis for CC
ontology(paraGO) <- "CC"
CC <- hyperGTest(paraGO)
summary(CC)[,c(1,2,7)] # 4## GOCCID Pvalue Term
## 1 GO:0014801 0.02493369 longitudinal sarcoplasmic reticulum
## 2 GO:0042584 0.02493369 chromaffin granule membrane
## 3 GO:0042734 0.02493369 presynaptic membrane
## 4 GO:0060418 0.02493369 yolk plasma
## 5 GO:0090534 0.02493369 calcium ion-transporting ATPase complex
## 6 GO:0097470 0.02493369 ribbon synapse
## 7 GO:0031011 0.04925859 Ino80 complex# GO similarity
goListCC <- summary(CC)[,c(1)]
goSimMatCC <- mgoSim(goListCC, goListCC, ont="CC", measure="Jiang", organism="chicken", combine=NULL)
corrplot(goSimMatCC[-4,-4], is.corr = FALSE, type="lower", tl.col = "black", tl.cex = 0.8)
4. MeSH enrichment analysis
Then, we perform a MeSH ORA for the category Chemicals and Drugs by setting ‘category=“D”’.
library(meshr)
library(MeSH.db)
library("MeSH.Gga.eg.db")
meshParams <- new("MeSHHyperGParams", geneIds = my.geneID3[,2], universeGeneIds = univ.geneID3[,2],
annotation = "MeSH.Gga.eg.db", category = "D", database = "gene2pubmed",
pvalueCutoff = 0.05, pAdjust = "none")
meshR <- meshHyperGTest(meshParams)
summary(meshR)[!duplicated(summary(meshR)[,7]),c(1,2,7)] # 63## MESHID Pvalue MESHTERM
## 2390 D020933 0.001663627 Neurotrophin 3
## 2802 D056950 0.003665786 Period Circadian Proteins
## 1078 D005982 0.004311957 Glutathione Transferase
## 2678 D051499 0.008862701 Receptor, Fibroblast Growth Factor, Type 4
## 2790 D056930 0.008862701 ARNTL Transcription Factors
## 2808 D064235 0.012202441 Matrilin Proteins
## 1990 D016203 0.016001235 CDC2 Protein Kinase
## 2004 D017526 0.016001235 Receptor, IGF Type 1
## 2021 D018808 0.016001235 Transcription Factor AP-1
## 2337 D020107 0.016001235 Troponin T
## 2712 D055435 0.016001235 Myostatin
## 2768 D056926 0.016001235 CLOCK Proteins
## 7 D002148 0.020233991 Calmodulin-Binding Proteins
## 2031 D019208 0.020233991 Brain-Derived Neurotrophic Factor
## 2753 D056504 0.020233991 Chromatin Assembly Factor-1
## 4 D000953 0.025171625 Antigens, Protozoan
## 5 D001664 0.025171625 Biliverdine
## 6 D002122 0.025171625 Calcium Chloride
## 41 D005395 0.025171625 Fish Oils
## 1948 D010908 0.025171625 Pituitary Hormones, Anterior
## 2020 D018739 0.025171625 Oncogene Proteins v-erbB
## 2357 D020410 0.025171625 Activated-Leukocyte Cell Adhesion Molecule
## 2365 D020747 0.025171625 Calcium Channels, T-Type
## 2423 D037241 0.025171625 Mannose-Binding Lectins
## 2460 D044925 0.025171625 Oxidoreductases Acting on CH-CH Group Donors
## 2664 D050051 0.025171625 Transient Receptor Potential Channels
## 2675 D050863 0.025171625 Synaptotagmin I
## 2695 D053779 0.025171625 Latent TGF-beta Binding Proteins
## 2710 D054507 0.025171625 Receptors, Phospholipase A2
## 2711 D054677 0.025171625 Cyclic Nucleotide Phosphodiesterases, Type 1
## 2066 D020033 0.026778865 Protein Isoforms
## 42 D005819 0.029738908 Genetic Markers
## 1949 D011972 0.029905815 Receptor, Insulin
## 1970 D015222 0.029905815 Sodium Channels
## 2461 D049452 0.031102079 Green Fluorescent Proteins
## 1316 D007328 0.032538124 Insulin
## 1134 D006023 0.033241254 Glycoproteins
## 2369 D020932 0.041035920 Nerve Growth Factor
## 1876 D009479 0.044032856 Neuropeptides
## 2424 D044767 0.045796226 Ubiquitin-Protein Ligases
## 1437 D008565 0.045977714 Membrane Proteins
## 1 D000582 0.049711394 Amidophosphoribosyltransferase
## 39 D002262 0.049711394 Carboxy-Lyases
## 1433 D007329 0.049711394 Insulin Antagonists
## 1435 D008043 0.049711394 Linseed Oil
## 1872 D008747 0.049711394 Methylcellulose
## 1874 D009251 0.049711394 NADPH-Ferrihemoprotein Reductase
## 1945 D010453 0.049711394 Peptide Synthases
## 1967 D015081 0.049711394 2-Naphthylamine
## 1988 D015240 0.049711394 Phorbol 12,13-Dibutyrate
## 2002 D017493 0.049711394 Antigens, CD45
## 2017 D018028 0.049711394 Receptors, Neurotensin
## 2367 D020848 0.049711394 Chimerin 1
## 2412 D024502 0.049711394 alpha-Tocopherol
## 2414 D024745 0.049711394 Smooth Muscle Myosins
## 2417 D027341 0.049711394 Excitatory Amino Acid Transporter 1
## 2419 D036121 0.049711394 Receptor, EphA3
## 2665 D050559 0.049711394 beta-Carotene 15,15'-Monooxygenase
## 2685 D051547 0.049711394 Heme Oxygenase-1
## 2688 D051959 0.049711394 Hu Paraneoplastic Encephalomyelitis Antigens
## 2693 D053613 0.049711394 Janus Kinase 1
## 2697 D054411 0.049711394 Receptors, Leptin
## 2707 D054419 0.049711394 Receptors, Adiponectin
## 2751 D055767 0.049711394 Immobilized Proteins
## 2806 D062367 0.049711394 ortho-Aminobenzoates# MeSH similarity
library("MeSHSim")
headingListD <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(7)]
meshSimMatD <- mheadingSim(headingListD, headingListD, method="JC")
rownames(meshSimMatD) <- colnames(meshSimMatD) <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(1)]
indexD <- which(meshSimMatD > 0.15 & meshSimMatD != 1, arr.ind = TRUE)
corrplot(meshSimMatD[unique(rownames(meshSimMatD)[indexD[,1]]),
unique(rownames(meshSimMatD)[indexD[,1]])], is.corr = FALSE, type="lower",
tl.col = "black", tl.cex = 0.8)
Switching to a different category is easily done by the ‘category<-’ function. Here, we use Diseases (category = “C”).
category(meshParams) <- "C"
meshR <- meshHyperGTest(meshParams)
summary(meshR)[!duplicated(summary(meshR)[,7]),c(1,2,7)] # 12## MESHID Pvalue MESHTERM
## 10 D006528 0.001863934 Carcinoma, Hepatocellular
## 1 D001715 0.025171625 Bird Diseases
## 6 D003327 0.025171625 Coronary Disease
## 7 D003645 0.025171625 Death, Sudden
## 15 D007333 0.025171625 Insulin Resistance
## 32 D010049 0.025171625 Ovarian Diseases
## 33 D013217 0.025171625 Starvation
## 18 D009136 0.029905815 Muscular Dystrophies
## 8 D004370 0.049711394 Duane Retraction Syndrome
## 13 D006956 0.049711394 Hyperopia
## 30 D009374 0.049711394 Neoplasms, Experimental
## 35 D063646 0.049711394 Carcinogenesis# MeSH similarity
headingListC <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(7)]
meshSimMatC <- mheadingSim(headingListC, headingListC, method="JC")
rownames(meshSimMatC) <- colnames(meshSimMatC) <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(1)]
indexC <- which(meshSimMatC > 0.025 & meshSimMatC != 1, arr.ind = TRUE)
corrplot(meshSimMatC, is.corr = FALSE, type="lower", tl.col = "black", tl.cex = 0.8)
MeSH ORA for Anatomy (category = “A”).
category(meshParams) <- "A"
meshR <- meshHyperGTest(meshParams)
summary(meshR)[!duplicated(summary(meshR)[,7]),c(1,2,7)] # 27## MESHID Pvalue MESHTERM
## 2 D002478 2.066345e-05 Cells, Cultured
## 1199 D007903 2.989189e-04 Lens Capsule, Crystalline
## 1518 D010521 6.318558e-04 Periosteum
## 1408 D009475 4.381019e-03 Neurons, Afferent
## 1552 D014276 6.008107e-03 Trigeminal Nerve
## 1638 D017949 1.502469e-02 Retinal Cone Photoreceptor Cells
## 1666 D017950 1.600124e-02 Ganglia, Sensory
## 1024 D003599 1.971117e-02 Cytoskeleton
## 1187 D007596 2.023399e-02 Joints
## 1 D000010 2.517162e-02 Abducens Nerve
## 1023 D002529 2.517162e-02 Cerebellar Nuclei
## 1111 D006413 2.517162e-02 Hematopoietic System
## 1447 D009847 2.517162e-02 Olivary Nucleus
## 1558 D014327 2.517162e-02 Trophoblasts
## 1522 D013687 2.678796e-02 Telencephalon
## 1575 D016501 2.809079e-02 Neurites
## 1561 D015672 2.990582e-02 Erythroid Precursor Cells
## 938 D002490 3.139094e-02 Central Nervous System
## 1207 D009432 4.454088e-02 Neural Crest
## 1678 D020897 4.709480e-02 Organizers, Embryonic
## 1449 D009865 4.935090e-02 Oocytes
## 1112 D006614 4.946814e-02 Hindlimb
## 1021 D002525 4.971139e-02 Cerebellar Cortex
## 1197 D007685 4.971139e-02 Kidney Tubules, Collecting
## 1445 D009802 4.971139e-02 Oculomotor Nerve
## 1520 D012540 4.971139e-02 Scapula
## 1559 D014928 4.971139e-02 Wolffian Ducts
## 1676 D019581 4.971139e-02 NeuropilheadingListA <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(7)]
meshSimMatA <- mheadingSim(headingListA, headingListA, method="JC")
rownames(meshSimMatA) <- colnames(meshSimMatA) <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(1)]
indexA <- which(meshSimMatA > 0.15 & meshSimMatA != 1, arr.ind = TRUE)
corrplot(meshSimMatA, is.corr = FALSE, type="lower", tl.col = "black", tl.cex = 0.8)
MeSH ORA for Phenomena and Processes (category = “G”).
category(meshParams) <- "G"
meshR <- meshHyperGTest(meshParams)
summary(meshR)[!duplicated(summary(meshR)[,7]),c(1,2,7)] # 22## MESHID Pvalue MESHTERM
## 3513 D016764 0.001130317 Cell Polarity
## 2961 D014162 0.001826469 Transfection
## 815 D002455 0.003074289 Cell Division
## 2841 D013091 0.004682665 Spermatogenesis
## 941 D004789 0.005589435 Enzyme Activation
## 3622 D024721 0.016001235 E-Box Elements
## 3489 D016385 0.017078715 TATA Box
## 2857 D013379 0.023522569 Substrate Specificity
## 2321 D007333 0.025171625 Insulin Resistance
## 2840 D009747 0.025171625 Nutritional Physiological Phenomena
## 1096 D005819 0.029738908 Genetic Markers
## 3609 D020449 0.029905815 Repetitive Sequences, Amino Acid
## 2132 D005822 0.036687202 Genetic Vectors
## 3549 D020125 0.038877808 Mutation, Missense
## 2324 D008027 0.042265490 Light
## 22 D002454 0.046348140 Cell Differentiation
## 1 D001683 0.047094796 Biological Clocks
## 2364 D009154 0.049287108 Mutation
## 2221 D006824 0.049711394 Hybridization, Genetic
## 2319 D007112 0.049711394 Immunity, Maternally-Acquired
## 3487 D016009 0.049711394 Chi-Square Distribution
## 3630 D058453 0.049711394 EmmetropiaheadingListG <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(7)]
meshSimMatG <- mheadingSim(headingListG, headingListG, method="JC")
rownames(meshSimMatG) <- colnames(meshSimMatG) <- summary(meshR)[!duplicated(summary(meshR)[,7]),c(1)]
indexG <- which(meshSimMatG > 0.1 & meshSimMatG != 1, arr.ind = TRUE)
corrplot(meshSimMatG, is.corr = FALSE, type="lower", tl.col = "black", tl.cex = 0.8)
5. Session Information
sessionInfo()## R version 3.2.4 (2016-03-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] MeSHSim_1.2.0 MeSH.Gga.eg.db_1.5.0
## [3] meshr_1.6.2 MeSH.Syn.eg.db_1.5.0
## [5] MeSH.Bsu.168.eg.db_1.5.0 MeSH.Aca.eg.db_1.5.0
## [7] MeSH.Hsa.eg.db_1.5.0 MeSH.PCR.db_1.5.0
## [9] MeSH.AOR.db_1.5.0 MeSH.db_1.5.0
## [11] MeSHDbi_1.6.0 org.Hs.eg.db_3.2.3
## [13] cummeRbund_2.12.1 Gviz_1.14.7
## [15] rtracklayer_1.30.4 GenomicRanges_1.22.4
## [17] GenomeInfoDb_1.6.3 fastcluster_1.1.20
## [19] reshape2_1.4.1 ggplot2_2.1.0
## [21] fdrtool_1.2.15 corrplot_0.77
## [23] GOSemSim_1.28.2 GOstats_2.36.0
## [25] graph_1.48.0 Category_2.36.0
## [27] GO.db_3.2.2 Matrix_1.2-6
## [29] org.Gg.eg.db_3.2.3 RSQLite_1.0.0
## [31] DBI_0.3.1 AnnotationDbi_1.32.3
## [33] IRanges_2.4.8 S4Vectors_0.8.11
## [35] Biobase_2.30.0 BiocGenerics_0.16.1
## [37] biomaRt_2.26.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 matrixStats_0.50.2
## [3] RColorBrewer_1.1-2 tools_3.2.4
## [5] rpart_4.1-10 Hmisc_3.17-3
## [7] colorspace_1.2-6 nnet_7.3-12
## [9] gridExtra_2.2.1 formatR_1.3
## [11] scales_0.4.0 genefilter_1.52.1
## [13] RBGL_1.46.0 stringr_1.0.0
## [15] digest_0.6.9 Rsamtools_1.22.0
## [17] foreign_0.8-66 rmarkdown_0.9.5
## [19] AnnotationForge_1.12.2 XVector_0.10.0
## [21] dichromat_2.0-0 htmltools_0.3.5
## [23] BSgenome_1.38.0 BiocParallel_1.4.3
## [25] acepack_1.3-3.3 VariantAnnotation_1.16.4
## [27] RCurl_1.95-4.8 magrittr_1.5
## [29] Formula_1.2-1 futile.logger_1.4.1
## [31] Rcpp_0.12.4 munsell_0.4.3
## [33] stringi_1.0-1 yaml_2.1.13
## [35] SummarizedExperiment_1.0.2 zlibbioc_1.16.0
## [37] plyr_1.8.3 lattice_0.20-33
## [39] Biostrings_2.38.4 splines_3.2.4
## [41] GenomicFeatures_1.22.13 annotate_1.48.0
## [43] knitr_1.12.3 futile.options_1.0.0
## [45] XML_3.98-1.4 evaluate_0.8.3
## [47] biovizBase_1.18.0 latticeExtra_0.6-28
## [49] lambda.r_1.1.7 gtable_0.2.0
## [51] xtable_1.8-2 survival_2.39-2
## [53] GenomicAlignments_1.6.3 cluster_2.0.4
## [55] GSEABase_1.32.0