MeSH over-representation analysis (RNA-seq 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",host = "www.ensembl.org")
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("rnaseqGenes.txt", header=FALSE) # 286
colnames(my.geneID) <- "ensembl_gene_id"
## merge two files
my.geneID2 <- merge(my.geneID, univ.geneID3, by ="ensembl_gene_id") # 263
## remove duplicated Entrez Gene ID
my.geneID3 <- my.geneID2[ !duplicated(my.geneID2$entrezgene),] # 2633. 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)] # 136## GOBPID Pvalue
## 1 GO:0006066 0.0001346985
## 2 GO:0034433 0.0023178686
## 3 GO:0034435 0.0023178686
## 4 GO:0072273 0.0023178686
## 5 GO:0090190 0.0023178686
## 6 GO:0008203 0.0039322704
## 7 GO:0031640 0.0045517534
## 8 GO:0046470 0.0045517534
## 9 GO:0061217 0.0045517534
## 10 GO:0072077 0.0045517534
## 11 GO:0045445 0.0050105534
## 12 GO:0090090 0.0062507020
## 13 GO:0006639 0.0074490804
## 14 GO:0043534 0.0074490804
## 15 GO:0050832 0.0074490804
## 16 GO:0090184 0.0074490804
## 17 GO:0042572 0.0109719667
## 18 GO:0051057 0.0109719667
## 19 GO:0051496 0.0109719667
## 20 GO:0030198 0.0109979825
## 21 GO:0048588 0.0109979825
## 22 GO:0042445 0.0129371294
## 23 GO:0008202 0.0135341138
## 24 GO:0032501 0.0135775266
## 25 GO:0030111 0.0141272414
## 26 GO:0007399 0.0144044785
## 27 GO:0001838 0.0150840702
## 28 GO:0016126 0.0150840702
## 29 GO:0032231 0.0150840702
## 30 GO:0032331 0.0150840702
## 31 GO:0048771 0.0150840702
## 32 GO:0070271 0.0164713600
## 33 GO:0018108 0.0165179070
## 34 GO:0016101 0.0197505358
## 35 GO:0070167 0.0197505358
## 36 GO:0007015 0.0225843317
## 37 GO:0006576 0.0249379427
## 38 GO:0030168 0.0249379427
## 39 GO:0035148 0.0249379427
## 40 GO:0051651 0.0249379427
## 41 GO:0007169 0.0251089934
## 42 GO:0010631 0.0254228285
## 43 GO:0071822 0.0271832002
## 44 GO:0030502 0.0277777778
## 45 GO:0032488 0.0277777778
## 46 GO:1903011 0.0277777778
## 47 GO:0001907 0.0283286119
## 48 GO:0003257 0.0283286119
## 49 GO:0003337 0.0283286119
## 50 GO:0006000 0.0283286119
## 51 GO:0006116 0.0283286119
## 52 GO:0006930 0.0283286119
## 53 GO:0007538 0.0283286119
## 54 GO:0010804 0.0283286119
## 55 GO:0010898 0.0283286119
## 56 GO:0010903 0.0283286119
## 57 GO:0014029 0.0283286119
## 58 GO:0014036 0.0283286119
## 59 GO:0018158 0.0283286119
## 60 GO:0018206 0.0283286119
## 61 GO:0019100 0.0283286119
## 62 GO:0019217 0.0283286119
## 63 GO:0019432 0.0283286119
## 64 GO:0019836 0.0283286119
## 65 GO:0021800 0.0283286119
## 66 GO:0030300 0.0283286119
## 67 GO:0032371 0.0283286119
## 68 GO:0032489 0.0283286119
## 69 GO:0032692 0.0283286119
## 70 GO:0033344 0.0283286119
## 71 GO:0033564 0.0283286119
## 72 GO:0033700 0.0283286119
## 73 GO:0034115 0.0283286119
## 74 GO:0034380 0.0283286119
## 75 GO:0035025 0.0283286119
## 76 GO:0035382 0.0283286119
## 77 GO:0035622 0.0283286119
## 78 GO:0036032 0.0283286119
## 79 GO:0042632 0.0283286119
## 80 GO:0044058 0.0283286119
## 81 GO:0044179 0.0283286119
## 82 GO:0044241 0.0283286119
## 83 GO:0045723 0.0283286119
## 84 GO:0046460 0.0283286119
## 85 GO:0046851 0.0283286119
## 86 GO:0050713 0.0283286119
## 87 GO:0050994 0.0283286119
## 88 GO:0051006 0.0283286119
## 89 GO:0051457 0.0283286119
## 90 GO:0051659 0.0283286119
## 91 GO:0051801 0.0283286119
## 92 GO:0051818 0.0283286119
## 93 GO:0051973 0.0283286119
## 94 GO:0055091 0.0283286119
## 95 GO:0060009 0.0283286119
## 96 GO:0060221 0.0283286119
## 97 GO:0060354 0.0283286119
## 98 GO:0060517 0.0283286119
## 99 GO:0060532 0.0283286119
## 100 GO:0060534 0.0283286119
## 101 GO:0060729 0.0283286119
## 102 GO:0060761 0.0283286119
## 103 GO:0060784 0.0283286119
## 104 GO:0061046 0.0283286119
## 105 GO:0061145 0.0283286119
## 106 GO:0065005 0.0283286119
## 107 GO:0070327 0.0283286119
## 108 GO:0070384 0.0283286119
## 109 GO:0070508 0.0283286119
## 110 GO:0071504 0.0283286119
## 111 GO:0072190 0.0283286119
## 112 GO:0072193 0.0283286119
## 113 GO:0072197 0.0283286119
## 114 GO:0072289 0.0283286119
## 115 GO:0090037 0.0283286119
## 116 GO:0090191 0.0283286119
## 117 GO:0090207 0.0283286119
## 118 GO:0090291 0.0283286119
## 119 GO:1900086 0.0283286119
## 120 GO:1900155 0.0283286119
## 121 GO:1900158 0.0283286119
## 122 GO:1902027 0.0283286119
## 123 GO:2000020 0.0283286119
## 124 GO:2000138 0.0283286119
## 125 GO:2000741 0.0283286119
## 126 GO:2001054 0.0283286119
## 127 GO:0001656 0.0306142539
## 128 GO:0006720 0.0306142539
## 129 GO:0035821 0.0306142539
## 130 GO:0043623 0.0322004563
## 131 GO:0009605 0.0332254655
## 132 GO:0051707 0.0341520596
## 133 GO:0090130 0.0351728486
## 134 GO:0007155 0.0354251530
## 135 GO:0001525 0.0361338416
## 136 GO:0009308 0.0367487668
## 137 GO:0050829 0.0367487668
## 138 GO:0060675 0.0367487668
## 139 GO:0061008 0.0367487668
## 140 GO:0072078 0.0367487668
## 141 GO:0009607 0.0382746520
## 142 GO:0006644 0.0387962052
## 143 GO:2000147 0.0426036065
## 144 GO:0016055 0.0431949255
## 145 GO:0046474 0.0433120661
## 146 GO:0060993 0.0433120661
## 147 GO:0061326 0.0433120661
## 148 GO:0072330 0.0433120661
## 149 GO:0022607 0.0450528552
## 150 GO:0007275 0.0496555223
## Term
## 1 alcohol metabolic process
## 2 steroid esterification
## 3 cholesterol esterification
## 4 metanephric nephron morphogenesis
## 5 positive regulation of branching involved in ureteric bud morphogenesis
## 6 cholesterol metabolic process
## 7 killing of cells of other organism
## 8 phosphatidylcholine metabolic process
## 9 regulation of mesonephros development
## 10 renal vesicle morphogenesis
## 11 myoblast differentiation
## 12 negative regulation of canonical Wnt signaling pathway
## 13 acylglycerol metabolic process
## 14 blood vessel endothelial cell migration
## 15 defense response to fungus
## 16 positive regulation of kidney development
## 17 retinol metabolic process
## 18 positive regulation of small GTPase mediated signal transduction
## 19 positive regulation of stress fiber assembly
## 20 extracellular matrix organization
## 21 developmental cell growth
## 22 hormone metabolic process
## 23 steroid metabolic process
## 24 multicellular organismal process
## 25 regulation of Wnt signaling pathway
## 26 nervous system development
## 27 embryonic epithelial tube formation
## 28 sterol biosynthetic process
## 29 regulation of actin filament bundle assembly
## 30 negative regulation of chondrocyte differentiation
## 31 tissue remodeling
## 32 protein complex biogenesis
## 33 peptidyl-tyrosine phosphorylation
## 34 diterpenoid metabolic process
## 35 regulation of biomineral tissue development
## 36 actin filament organization
## 37 cellular biogenic amine metabolic process
## 38 platelet activation
## 39 tube formation
## 40 maintenance of location in cell
## 41 transmembrane receptor protein tyrosine kinase signaling pathway
## 42 epithelial cell migration
## 43 protein complex subunit organization
## 44 negative regulation of bone mineralization
## 45 Cdc42 protein signal transduction
## 46 negative regulation of bone development
## 47 killing by symbiont of host cells
## 48 positive regulation of transcription from RNA polymerase II promoter involved in myocardial precursor cell differentiation
## 49 mesenchymal to epithelial transition involved in metanephros morphogenesis
## 50 fructose metabolic process
## 51 NADH oxidation
## 52 substrate-dependent cell migration, cell extension
## 53 primary sex determination
## 54 negative regulation of tumor necrosis factor-mediated signaling pathway
## 55 positive regulation of triglyceride catabolic process
## 56 negative regulation of very-low-density lipoprotein particle remodeling
## 57 neural crest formation
## 58 neural crest cell fate specification
## 59 protein oxidation
## 60 peptidyl-methionine modification
## 61 male germ-line sex determination
## 62 regulation of fatty acid metabolic process
## 63 triglyceride biosynthetic process
## 64 hemolysis by symbiont of host erythrocytes
## 65 cerebral cortex tangential migration
## 66 regulation of intestinal cholesterol absorption
## 67 regulation of sterol transport
## 68 regulation of Cdc42 protein signal transduction
## 69 negative regulation of interleukin-1 production
## 70 cholesterol efflux
## 71 anterior/posterior axon guidance
## 72 phospholipid efflux
## 73 negative regulation of heterotypic cell-cell adhesion
## 74 high-density lipoprotein particle assembly
## 75 positive regulation of Rho protein signal transduction
## 76 sterol transmembrane transport
## 77 intrahepatic bile duct development
## 78 neural crest cell delamination
## 79 cholesterol homeostasis
## 80 regulation of digestive system process
## 81 hemolysis in other organism
## 82 lipid digestion
## 83 positive regulation of fatty acid biosynthetic process
## 84 neutral lipid biosynthetic process
## 85 negative regulation of bone remodeling
## 86 negative regulation of interleukin-1 beta secretion
## 87 regulation of lipid catabolic process
## 88 positive regulation of lipoprotein lipase activity
## 89 maintenance of protein location in nucleus
## 90 maintenance of mitochondrion location
## 91 cytolysis in other organism involved in symbiotic interaction
## 92 disruption of cells of other organism involved in symbiotic interaction
## 93 positive regulation of telomerase activity
## 94 phospholipid homeostasis
## 95 Sertoli cell development
## 96 retinal rod cell differentiation
## 97 negative regulation of cell adhesion molecule production
## 98 epithelial cell proliferation involved in prostatic bud elongation
## 99 bronchus cartilage development
## 100 trachea cartilage development
## 101 intestinal epithelial structure maintenance
## 102 negative regulation of response to cytokine stimulus
## 103 regulation of cell proliferation involved in tissue homeostasis
## 104 regulation of branching involved in lung morphogenesis
## 105 lung smooth muscle development
## 106 protein-lipid complex assembly
## 107 thyroid hormone transport
## 108 Harderian gland development
## 109 cholesterol import
## 110 cellular response to heparin
## 111 ureter urothelium development
## 112 ureter smooth muscle cell differentiation
## 113 ureter morphogenesis
## 114 metanephric nephron tubule formation
## 115 positive regulation of protein kinase C signaling
## 116 negative regulation of branching involved in ureteric bud morphogenesis
## 117 regulation of triglyceride metabolic process
## 118 negative regulation of osteoclast proliferation
## 119 positive regulation of peptidyl-tyrosine autophosphorylation
## 120 negative regulation of bone trabecula formation
## 121 negative regulation of bone mineralization involved in bone maturation
## 122 positive regulation of cartilage condensation
## 123 positive regulation of male gonad development
## 124 positive regulation of cell proliferation involved in heart morphogenesis
## 125 positive regulation of mesenchymal stem cell differentiation
## 126 negative regulation of mesenchymal cell apoptotic process
## 127 metanephros development
## 128 isoprenoid metabolic process
## 129 modification of morphology or physiology of other organism
## 130 cellular protein complex assembly
## 131 response to external stimulus
## 132 response to other organism
## 133 tissue migration
## 134 cell adhesion
## 135 angiogenesis
## 136 amine metabolic process
## 137 defense response to Gram-negative bacterium
## 138 ureteric bud morphogenesis
## 139 hepaticobiliary system development
## 140 nephron tubule morphogenesis
## 141 response to biotic stimulus
## 142 phospholipid metabolic process
## 143 positive regulation of cell motility
## 144 Wnt signaling pathway
## 145 glycerophospholipid biosynthetic process
## 146 kidney morphogenesis
## 147 renal tubule development
## 148 monocarboxylic acid biosynthetic process
## 149 cellular component assembly
## 150 multicellular organismal development# GO similarity
library(corrplot)## Warning: package 'corrplot' was built under R version 3.2.5goListBP <- summary(BP)[,c(1)][1:50] # top 50
goSimMatBP <- mgoSim(goListBP, goListBP, ont="BP", measure="Jiang", organism="chicken", combine=NULL)
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)] # 26## GOMFID Pvalue
## 1 GO:0030295 0.003340377
## 2 GO:0005504 0.005481988
## 3 GO:0016747 0.007104929
## 4 GO:0030674 0.011162870
## 5 GO:0008237 0.018557479
## 6 GO:0004180 0.023676012
## 7 GO:0008047 0.024166328
## 8 GO:0000254 0.024268824
## 9 GO:0004056 0.024268824
## 10 GO:0004063 0.024268824
## 11 GO:0004366 0.024268824
## 12 GO:0004508 0.024268824
## 13 GO:0004607 0.024268824
## 14 GO:0008035 0.024268824
## 15 GO:0008241 0.024268824
## 16 GO:0019863 0.024268824
## 17 GO:0019864 0.024268824
## 18 GO:0031994 0.024268824
## 19 GO:0031995 0.024268824
## 20 GO:0043184 0.024268824
## 21 GO:0047442 0.024268824
## 22 GO:0060228 0.024268824
## 23 GO:0061609 0.024268824
## 24 GO:0070061 0.024268824
## 25 GO:0070653 0.024268824
## 26 GO:0071814 0.024268824
## 27 GO:0097157 0.024268824
## 28 GO:0016830 0.027496725
## 29 GO:0001758 0.047963417
## 30 GO:0003841 0.047963417
## 31 GO:0004181 0.047963417
## 32 GO:0004332 0.047963417
## 33 GO:0005030 0.047963417
## 34 GO:0015485 0.047963417
## 35 GO:0015643 0.047963417
## 36 GO:0030297 0.047963417
## 37 GO:0031014 0.047963417
## 38 GO:0031210 0.047963417
## 39 GO:0034191 0.047963417
## 40 GO:0048018 0.047963417
## 41 GO:0019207 0.049390009
## Term
## 1 protein kinase activator activity
## 2 fatty acid binding
## 3 transferase activity, transferring acyl groups other than amino-acyl groups
## 4 protein binding, bridging
## 5 metallopeptidase activity
## 6 carboxypeptidase activity
## 7 enzyme activator activity
## 8 C-4 methylsterol oxidase activity
## 9 argininosuccinate lyase activity
## 10 aryldialkylphosphatase activity
## 11 glycerol-3-phosphate O-acyltransferase activity
## 12 steroid 17-alpha-monooxygenase activity
## 13 phosphatidylcholine-sterol O-acyltransferase activity
## 14 high-density lipoprotein particle binding
## 15 peptidyl-dipeptidase activity
## 16 IgE binding
## 17 IgG binding
## 18 insulin-like growth factor I binding
## 19 insulin-like growth factor II binding
## 20 vascular endothelial growth factor receptor 2 binding
## 21 17-alpha-hydroxyprogesterone aldolase activity
## 22 phosphatidylcholine-sterol O-acyltransferase activator activity
## 23 fructose-1-phosphate aldolase activity
## 24 fructose binding
## 25 high-density lipoprotein particle receptor binding
## 26 protein-lipid complex binding
## 27 pre-mRNA intronic binding
## 28 carbon-carbon lyase activity
## 29 retinal dehydrogenase activity
## 30 1-acylglycerol-3-phosphate O-acyltransferase activity
## 31 metallocarboxypeptidase activity
## 32 fructose-bisphosphate aldolase activity
## 33 neurotrophin receptor activity
## 34 cholesterol binding
## 35 toxic substance binding
## 36 transmembrane receptor protein tyrosine kinase activator activity
## 37 troponin T binding
## 38 phosphatidylcholine binding
## 39 apolipoprotein A-I receptor binding
## 40 receptor agonist activity
## 41 kinase regulator activity# 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)] # 7## GOCCID Pvalue Term
## 1 GO:0005576 1.420690e-08 extracellular region
## 2 GO:0005577 8.363040e-04 fibrinogen complex
## 3 GO:0060417 2.461834e-03 yolk
## 4 GO:0005604 6.813459e-03 basement membrane
## 5 GO:0005578 1.656586e-02 proteinaceous extracellular matrix
## 6 GO:0034366 2.917772e-02 spherical high-density lipoprotein particle
## 7 GO:0030017 3.083484e-02 sarcomere# GO similarity
goListCC <- summary(CC)[,c(1)]
goSimMatCC <- mgoSim(goListCC, goListCC, ont="CC", measure="Jiang", organism="chicken", combine=NULL)
corrplot(goSimMatCC[-3,-3], 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)] # 179## MESHID Pvalue
## 3460 D006023 7.968146e-09
## 4557 D009414 1.000085e-05
## 4120 D008565 6.593672e-05
## 3875 D008074 1.055079e-04
## 6864 D012697 2.578723e-04
## 9410 D051153 3.350551e-04
## 5148 D009439 3.434309e-04
## 9880 D060528 3.434309e-04
## 7250 D014874 3.523914e-04
## 7777 D017931 4.006199e-04
## 9258 D040121 5.079934e-04
## 3929 D008562 5.545764e-04
## 9239 D039961 9.703630e-04
## 405 D000806 1.044018e-03
## 2196 D004549 1.044018e-03
## 5226 D011228 1.044018e-03
## 7772 D017038 1.044018e-03
## 9720 D054387 1.044018e-03
## 9748 D054839 1.044018e-03
## 7351 D015850 1.133017e-03
## 474 D002352 1.243176e-03
## 306 D000596 1.725201e-03
## 7634 D016326 1.860294e-03
## 993 D003851 2.062091e-03
## 6914 D012709 2.062091e-03
## 8393 D019718 2.062091e-03
## 9303 D045702 2.062091e-03
## 5275 D011506 2.132027e-03
## 898 D003459 3.258086e-03
## 1 D000117 3.394179e-03
## 408 D001053 3.394179e-03
## 2414 D005419 3.394179e-03
## 5142 D009438 3.394179e-03
## 6928 D013381 3.394179e-03
## 9214 D039942 3.394179e-03
## 9297 D045683 3.394179e-03
## 9340 D050556 3.678819e-03
## 8425 D020033 4.006211e-03
## 5702 D011518 4.566675e-03
## 1009 D004247 4.893274e-03
## 2395 D005340 5.028200e-03
## 3642 D007073 5.028200e-03
## 5220 D011108 5.028200e-03
## 6838 D012177 5.028200e-03
## 6942 D013917 5.028200e-03
## 8406 D019743 5.028200e-03
## 9231 D039943 5.028200e-03
## 9395 D050796 5.028200e-03
## 9702 D054377 5.028200e-03
## 6185 D011993 5.480342e-03
## 4639 D009419 5.618871e-03
## 8327 D019063 5.916373e-03
## 434 D001054 6.952412e-03
## 445 D001141 6.952412e-03
## 459 D001216 6.952412e-03
## 857 D002630 6.952412e-03
## 9327 D047493 6.952412e-03
## 9896 D064235 6.952412e-03
## 9035 D036341 7.745054e-03
## 6456 D011994 7.816209e-03
## 7048 D014357 7.962759e-03
## 3897 D008079 9.155405e-03
## 9287 D043484 9.155405e-03
## 9762 D055748 1.114071e-02
## 999 D004054 1.162609e-02
## 6933 D013739 1.162609e-02
## 7228 D014801 1.162609e-02
## 9726 D054834 1.162609e-02
## 5955 D011956 1.238517e-02
## 3649 D007136 1.416071e-02
## 8932 D029867 1.416071e-02
## 7144 D014443 1.430798e-02
## 8696 D020662 1.435371e-02
## 8708 D020746 1.435371e-02
## 8372 D019151 1.732779e-02
## 8783 D024043 1.732779e-02
## 9641 D051780 1.732779e-02
## 404 D000726 1.880721e-02
## 472 D002122 1.880721e-02
## 473 D002216 1.880721e-02
## 864 D002785 1.880721e-02
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## 2202 D004851 1.880721e-02
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## 2394 D005338 1.880721e-02
## 2412 D005344 1.880721e-02
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## 5158 D009961 1.880721e-02
## 5217 D010127 1.880721e-02
## 5218 D010416 1.880721e-02
## 5219 D010928 1.880721e-02
## 6863 D012450 1.880721e-02
## 6926 D013196 1.880721e-02
## 6927 D013254 1.880721e-02
## 6952 D014049 1.880721e-02
## 7140 D014359 1.880721e-02
## 7254 D015728 1.880721e-02
## 7389 D016173 1.880721e-02
## 7776 D017367 1.880721e-02
## 8391 D019307 1.880721e-02
## 8392 D019426 1.880721e-02
## 8720 D020781 1.880721e-02
## 8721 D020913 1.880721e-02
## 9034 D036082 1.880721e-02
## 9286 D043303 1.880721e-02
## 9339 D050494 1.880721e-02
## 9407 D050890 1.880721e-02
## 9639 D051199 1.880721e-02
## 9640 D051398 1.880721e-02
## 9672 D053606 1.880721e-02
## 9699 D053676 1.880721e-02
## 9700 D053779 1.880721e-02
## 9724 D054589 1.880721e-02
## 9879 D059002 1.880721e-02
## 261 D000242 2.005160e-02
## 6166 D011991 2.053816e-02
## 9193 D038681 2.053816e-02
## 9673 D053674 2.053816e-02
## 2204 D005136 2.228595e-02
## 5159 D010047 2.397497e-02
## 8 D000199 2.489505e-02
## 3747 D007527 2.509153e-02
## 8309 D018925 2.762862e-02
## 8754 D024002 2.762862e-02
## 7259 D015816 2.764424e-02
## 7394 D016212 2.967478e-02
## 5250 D011374 3.148981e-02
## 6953 D014212 3.225025e-02
## 9814 D055758 3.420595e-02
## 300 D000516 3.726203e-02
## 467 D001789 3.726203e-02
## 469 D002070 3.726203e-02
## 865 D002794 3.726203e-02
## 950 D003486 3.726203e-02
## 989 D003580 3.726203e-02
## 2420 D005634 3.726203e-02
## 3867 D007703 3.726203e-02
## 3873 D008043 3.726203e-02
## 4555 D009251 3.726203e-02
## 6859 D012337 3.726203e-02
## 7141 D014361 3.726203e-02
## 7226 D014760 3.726203e-02
## 7252 D015230 3.726203e-02
## 7255 D015786 3.726203e-02
## 7387 D015926 3.726203e-02
## 7391 D016186 3.726203e-02
## 8386 D019272 3.726203e-02
## 8389 D019301 3.726203e-02
## 8419 D019809 3.726203e-02
## 8780 D024003 3.726203e-02
## 8797 D024082 3.726203e-02
## 8832 D028884 3.726203e-02
## 9211 D038702 3.726203e-02
## 9295 D044139 3.726203e-02
## 9336 D050486 3.726203e-02
## 9390 D050784 3.726203e-02
## 9403 D050822 3.726203e-02
## 9405 D050882 3.726203e-02
## 9670 D052244 3.726203e-02
## 9811 D055751 3.726203e-02
## 9911 D064236 3.726203e-02
## 869 D002918 3.979893e-02
## 8722 D023181 3.979893e-02
## 9307 D047428 3.979893e-02
## 2424 D005819 4.084448e-02
## 8251 D018160 4.409130e-02
## 2081 D004262 4.461181e-02
## 8834 D029721 4.656610e-02
## 958 D003577 4.883317e-02
## 8799 D026901 4.883317e-02
## 9605 D051155 4.883317e-02
## MESHTERM
## 3460 Glycoproteins
## 4557 Nerve Growth Factors
## 4120 Membrane Proteins
## 3875 Lipoproteins
## 6864 Serine Endopeptidases
## 9410 Wnt Proteins
## 5148 Neuraminidase
## 9880 Wnt4 Protein
## 7250 Water Pollutants, Chemical
## 7777 DNA Primers
## 9258 Semaphorin-3A
## 3929 Membrane Glycoproteins
## 9239 Semaphorins
## 405 Angiotensin-Converting Enzyme Inhibitors
## 2196 Elastin
## 5226 Prealbumin
## 7772 Mannosyl-Glycoprotein Endo-beta-N-Acetylglucosaminidase
## 9720 Receptors, CXCR
## 9748 Retinol-Binding Proteins, Plasma
## 7351 Interleukin-6
## 474 Carrier Proteins
## 306 Amino Acids
## 7634 Extracellular Matrix Proteins
## 993 Deoxyribonucleases
## 6914 Serum Albumin
## 8393 Receptors, CXCR4
## 9303 Proprotein Convertase 5
## 5275 Proteins
## 898 Crystallins
## 1 Acetylglucosamine
## 408 Apolipoproteins
## 2414 Flavonoids
## 5142 Neuraminic Acids
## 6928 Subtilisins
## 9214 Neuropilin-1
## 9297 Furin
## 9340 Fatty Acid-Binding Proteins
## 8425 Protein Isoforms
## 5702 Proto-Oncogene Proteins
## 1009 DNA
## 2395 Fibrinogen
## 3642 Immunoglobulin E
## 5220 Polymers
## 6838 Retinol-Binding Proteins
## 6942 Thrombin
## 8406 Chemokines, CXC
## 9231 Neuropilin-2
## 9395 STAT3 Transcription Factor
## 9702 Chemokine CXCL12
## 6185 Recombinant Fusion Proteins
## 4639 Nerve Tissue Proteins
## 8327 Tenascin
## 434 Apolipoproteins A
## 445 Aromatase
## 459 Asparagine
## 857 Chemotactic Factors
## 9327 PPAR alpha
## 9896 Matrilin Proteins
## 9035 Intercellular Signaling Peptides and Proteins
## 6456 Recombinant Proteins
## 7048 Trypsin
## 3897 Lipoproteins, VLDL
## 9287 Proprotein Convertases
## 9762 SOXB1 Transcription Factors
## 999 Diethylstilbestrol
## 6933 Testosterone
## 7228 Vitamin A
## 9726 Lipocalins
## 5955 Receptors, Cell Surface
## 3649 Immunoglobulins
## 8932 Xenopus Proteins
## 7144 Tyrosine
## 8696 Guanine Nucleotide Exchange Factors
## 8708 Calcium Channels, L-Type
## 8372 Peptide Library
## 8783 Collagen Type II
## 9641 Sterol Regulatory Element Binding Protein 1
## 404 Androgen Antagonists
## 472 Calcium Chloride
## 473 Captopril
## 864 Sterol O-Acyltransferase
## 868 Chromans
## 893 Complement System Proteins
## 894 Complement C1 Inactivator Proteins
## 895 Complement C3
## 956 Cytochrome b Group
## 992 Dantrolene
## 2202 Epoxide Hydrolases
## 2203 Epoxy Compounds
## 2394 Fibrin Fibrinogen Degradation Products
## 2412 Fibrinopeptide A
## 2413 Fibrinopeptide B
## 2419 Flutamide
## 3872 Phosphatidylcholine-Sterol O-Acyltransferase
## 5157 Nifedipine
## 5158 Orosomucoid
## 5217 P Blood-Group System
## 5218 Pentachlorophenol
## 5219 Placental Lactogen
## 6863 Safflower Oil
## 6926 Dihydrotestosterone
## 6927 Steroid 17-alpha-Hydroxylase
## 6952 Tolperisone
## 7140 Trypsin Inhibitor, Kazal Pancreatic
## 7254 Receptors, HIV
## 7389 Macrophage Colony-Stimulating Factor
## 7776 Serotonin Uptake Inhibitors
## 8391 1-(5-Isoquinolinesulfonyl)-2-Methylpiperazine
## 8392 Integrases
## 8720 Matrix Metalloproteinase 1
## 8721 Perindopril
## 9034 Receptor, EphA1
## 9286 Aryldialkylphosphatase
## 9339 Vesicular Acetylcholine Transport Proteins
## 9407 HSP30 Heat-Shock Proteins
## 9639 Toll-Like Receptor 7
## 9640 Aquaporin 1
## 9672 Receptors, OSM-LIF
## 9699 Syntenins
## 9700 Latent TGF-beta Binding Proteins
## 9724 Protein Tyrosine Phosphatase, Non-Receptor Type 3
## 9879 Androgen Receptor Antagonists
## 261 Cyclic AMP
## 6166 Receptors, Virus
## 9193 Follistatin
## 9673 Aggrecans
## 2204 Eye Proteins
## 5159 Ovalbumin
## 8 Actins
## 3747 Isoenzymes
## 8309 Chemokines
## 8754 HMGB Proteins
## 7259 Cell Adhesion Molecules, Neuronal
## 7394 Transforming Growth Factor beta
## 5250 Progesterone
## 6953 Tretinoin
## 9814 SOX9 Transcription Factor
## 300 alpha-Amylases
## 467 Blood Group Antigens
## 469 Butadienes
## 865 Choline
## 950 Cyanides
## 989 Cytochromes
## 2420 Fructose-Bisphosphate Aldolase
## 3867 Peptidyl-Dipeptidase A
## 3873 Linseed Oil
## 4555 NADPH-Ferrihemoprotein Reductase
## 6859 RNA, Ribosomal, 18S
## 7141 Trypsin Inhibitors
## 7226 Viral Fusion Proteins
## 7252 Prostaglandin D2
## 7255 Cytochromes b5
## 7387 Complement C3a
## 7391 Receptor, Macrophage Colony-Stimulating Factor
## 8386 Leukocyte Elastase
## 8389 Oleic Acid
## 8419 Streptavidin
## 8780 HMGA Proteins
## 8797 Collagen Type XII
## 8832 Karyopherins
## 9211 Follistatin-Related Proteins
## 9295 Receptor, Angiotensin, Type 2
## 9336 Serotonin Plasma Membrane Transport Proteins
## 9390 Proto-Oncogene Protein c-ets-1
## 9403 Cytokine Receptor gp130
## 9405 CREB-Binding Protein
## 9670 Endocrine Disruptors
## 9811 SOXC Transcription Factors
## 9911 Cartilage Oligomeric Matrix Protein
## 869 Chymotrypsin
## 8722 Antimicrobial Cationic Peptides
## 9307 Protein Kinase Inhibitors
## 2424 Genetic Markers
## 8251 Receptors, Cytoplasmic and Nuclear
## 2081 DNA Restriction Enzymes
## 8834 Drosophila Proteins
## 958 Cytochrome P-450 Enzyme System
## 8799 Membrane Transport Proteins
## 9605 Wnt1 Protein# 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 > 0.35 & 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)] # 29## MESHID Pvalue MESHTERM
## 113 D009521 0.0005079934 Newcastle Disease
## 7 D003333 0.0010440180 Coronaviridae Infections
## 123 D012088 0.0020620909 Reoviridae Infections
## 108 D009336 0.0033941788 Necrosis
## 130 D014802 0.0033941788 Vitamin A Deficiency
## 12 D004195 0.0042746874 Disease Models, Animal
## 3 D000419 0.0188072082 Albuminuria
## 4 D001249 0.0188072082 Asthma
## 5 D002114 0.0188072082 Calcinosis
## 6 D003233 0.0188072082 Conjunctivitis, Allergic
## 83 D005923 0.0188072082 Glomerulosclerosis, Focal Segmental
## 86 D006349 0.0188072082 Heart Valve Diseases
## 89 D006967 0.0188072082 Hypersensitivity
## 92 D007057 0.0188072082 Ichthyosis
## 127 D012221 0.0188072082 Rhinitis, Allergic, Perennial
## 136 D014855 0.0188072082 Wallerian Degeneration
## 144 D058186 0.0188072082 Acute Kidney Injury
## 93 D007249 0.0205381626 Inflammation
## 1 D000130 0.0372620251 Achondroplasia
## 10 D003924 0.0372620251 Diabetes Mellitus, Type 2
## 81 D004535 0.0372620251 Ehlers-Danlos Syndrome
## 84 D006345 0.0372620251 Heart Septal Defects, Ventricular
## 87 D006965 0.0372620251 Hyperplasia
## 90 D007039 0.0372620251 Hypotrichosis
## 106 D007842 0.0372620251 Lathyrism
## 128 D013733 0.0372620251 Testicular Diseases
## 137 D015427 0.0372620251 Reperfusion Injury
## 140 D020347 0.0372620251 Lithiasis
## 142 D021181 0.0372620251 Egg Hypersensitivity# 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)] # 66## MESHID Pvalue MESHTERM
## 7619 D008099 3.523638e-07 Liver
## 10532 D018699 2.594517e-05 Coated Vesicles
## 6825 D005121 1.330416e-04 Extremities
## 9173 D009900 1.417543e-04 Optic Nerve
## 417 D001853 1.858506e-04 Bone Marrow
## 8062 D008334 2.176694e-04 Mandible
## 8070 D008648 2.679905e-04 Mesoderm
## 10536 D018878 2.881693e-04 Limb Buds
## 1476 D002642 3.705599e-04 Chick Embryo
## 453 D002478 4.049824e-04 Cells, Cultured
## 9722 D013116 4.361241e-04 Spinal Cord
## 7448 D007668 6.293158e-04 Kidney
## 6599 D004530 1.275901e-03 Egg Yolk
## 7069 D005123 2.154362e-03 Eye
## 8487 D009432 3.029153e-03 Neural Crest
## 6377 D002889 3.394179e-03 Chromosomes, Human, Pair 2
## 6472 D004475 3.465261e-03 Ectoderm
## 9104 D009865 3.660892e-03 Oocytes
## 8386 D008862 5.028200e-03 Microsomes, Liver
## 11459 D059630 5.916373e-03 Mesenchymal Stromal Cells
## 7180 D005855 6.952412e-03 Germ Layers
## 11025 D020439 7.812322e-03 Growth Cones
## 8688 D009474 8.991802e-03 Neurons
## 6726 D005109 9.570570e-03 Extracellular Matrix
## 11368 D053595 1.114071e-02 Embryonic Stem Cells
## 6717 D004727 1.162609e-02 Endothelium
## 7187 D006107 1.242174e-02 Granulosa Cells
## 9207 D010053 1.369892e-02 Ovary
## 9338 D012160 1.398331e-02 Retina
## 9901 D013154 1.491848e-02 Spleen
## 11077 D032446 1.522718e-02 Myoblasts
## 11115 D050153 1.522718e-02 Abdominal Fat
## 10130 D013564 1.732779e-02 Sympathetic Nervous System
## 10804 D019556 1.774163e-02 COS Cells
## 25 D001133 1.880721e-02 Bones of Upper Extremity
## 26 D001339 1.880721e-02 Autonomic Fibers, Preganglionic
## 6383 D003228 1.880721e-02 Conjunctiva
## 6384 D003239 1.880721e-02 Connective Tissue Cells
## 8393 D008943 1.880721e-02 Mitral Valve
## 9335 D011708 1.880721e-02 Pylorus
## 10483 D014261 1.880721e-02 Tricuspid Valve
## 11367 D050527 1.880721e-02 Mesangial Cells
## 11400 D054258 2.053816e-02 Neural Plate
## 10045 D013477 2.347781e-02 Superior Colliculi
## 7576 D007962 2.368206e-02 Leukocytes
## 27 D001808 2.397497e-02 Blood Vessels
## 9191 D010006 2.397497e-02 Osteoblasts
## 10467 D013799 2.397497e-02 Theca Cells
## 10145 D013710 2.764424e-02 Tendons
## 6385 D003599 2.914083e-02 Cytoskeleton
## 7158 D005145 3.148981e-02 Face
## 10671 D019170 3.215174e-02 Somites
## 10188 D013737 3.235859e-02 Testis
## 11412 D054259 3.655662e-02 Neural Tube
## 1389 D002479 3.726203e-02 Inclusion Bodies
## 9333 D010949 3.726203e-02 Plasma
## 9718 D012540 3.726203e-02 Scapula
## 9720 D012708 3.726203e-02 Sertoli Cells
## 10481 D014066 3.726203e-02 Palatine Tonsil
## 10484 D016548 3.898816e-02 Prosencephalon
## 8395 D009046 4.084448e-02 Motor Neurons
## 1393 D002490 4.270459e-02 Central Nervous System
## 8004 D008264 4.409130e-02 Macrophages
## 1 D001011 4.422957e-02 Aorta
## 7221 D006321 4.617024e-02 Heart
## 43 D001842 4.950968e-02 Bone and BonesheadingListA <- 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[unique(rownames(meshSimMatA)[indexA[,1]]),
unique(rownames(meshSimMatA)[indexA[,1]])], 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)] # 61## MESHID Pvalue MESHTERM
## 277 D000595 3.389919e-09 Amino Acid Sequence
## 18384 D015870 4.028123e-07 Gene Expression
## 19403 D017386 5.978909e-06 Sequence Homology, Amino Acid
## 15835 D013997 3.031943e-04 Time Factors
## 13592 D011485 4.970937e-04 Protein Binding
## 4369 D001483 1.883463e-03 Base Sequence
## 15724 D013047 2.053056e-03 Specific Pathogen-Free Organisms
## 13351 D007814 2.062091e-03 Larva
## 9053 D001923 3.378975e-03 Brain Chemistry
## 9915 D002889 3.394179e-03 Chromosomes, Human, Pair 2
## 13365 D009336 3.394179e-03 Necrosis
## 13370 D009693 4.371130e-03 Nucleic Acid Hybridization
## 16324 D015398 5.256284e-03 Signal Transduction
## 14505 D011487 5.278250e-03 Protein Conformation
## 15091 D013045 6.709171e-03 Species Specificity
## 11504 D005838 7.745054e-03 Genotype
## 14816 D011489 7.774415e-03 Protein Denaturation
## 1 D000375 8.476071e-03 Aging
## 16287 D014961 8.818046e-03 X-Ray Diffraction
## 4359 D001343 9.155405e-03 Autophagy
## 9958 D005075 9.747289e-03 Biological Evolution
## 10115 D005091 1.119189e-02 Exons
## 8997 D001667 1.157491e-02 Binding, Competitive
## 13355 D008390 1.162609e-02 Markov Chains
## 23388 D054338 1.162609e-02 Cell Transdifferentiation
## 18217 D015854 1.191336e-02 Up-Regulation
## 20925 D018507 1.289404e-02 Gene Expression Regulation, Developmental
## 13295 D006031 1.335579e-02 Glycosylation
## 20762 D017433 1.430758e-02 Protein Structure, Secondary
## 23201 D019943 1.430798e-02 Amino Acid Substitution
## 23374 D040681 1.435371e-02 Structural Homology, Protein
## 17605 D015723 1.445543e-02 Gene Library
## 23307 D024510 1.633875e-02 Muscle Development
## 8362 D001665 1.720409e-02 Binding Sites
## 13280 D005865 1.732779e-02 Gestational Age
## 22928 D019151 1.732779e-02 Peptide Library
## 276 D000478 1.880721e-02 Alkylation
## 9957 D004306 1.880721e-02 Dose-Response Relationship, Immunologic
## 14855 D012079 1.880721e-02 Renal Circulation
## 14856 D012119 1.880721e-02 Respiration
## 19175 D016163 1.880721e-02 Genes, DCC
## 19176 D017102 1.880721e-02 Fracture Healing
## 23306 D024282 1.880721e-02 Genes, sry
## 15742 D013312 2.053816e-02 Stress, Physiological
## 22942 D019521 2.187740e-02 Body Patterning
## 9921 D004032 2.368206e-02 Diet
## 23399 D055503 2.368206e-02 Protein Multimerization
## 19179 D017209 2.729612e-02 Apoptosis
## 14857 D012727 2.762862e-02 Sex Characteristics
## 15006 D012741 2.762862e-02 Sexual Maturation
## 9122 D002454 2.953272e-02 Cell Differentiation
## 15779 D013696 2.974933e-02 Temperature
## 22850 D018628 3.148981e-02 Gene Dosage
## 14982 D012733 3.554951e-02 Sex Differentiation
## 23261 D020013 3.655662e-02 Calcium Signaling
## 22847 D018598 3.726203e-02 Minisatellite Repeats
## 23386 D054337 3.726203e-02 Cell Dedifferentiation
## 10468 D005819 4.084448e-02 Genetic Markers
## 13572 D010012 4.883317e-02 Osteogenesis
## 22872 D018919 4.950968e-02 Neovascularization, PhysiologicheadingListG <- 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[unique(rownames(meshSimMatG)[indexG[,1]]),
unique(rownames(meshSimMatG)[indexG[,1]])], 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_1.2-5
## [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