MeSH Enrichment and Semantic Analyses
2017-10-30
Contents
1 Introduction
MeSH (Medical Subject Headings) is the NLM (U.S. National Library of Medicine) controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH is comprehensive life science vocabulary. MeSH has 19 categories and MeSH.db contains 16 of them. That is:
| Abbreviation | Category |
|---|---|
| A | Anatomy |
| B | Organisms |
| C | Diseases |
| D | Chemicals and Drugs |
| E | Analytical, Diagnostic and Therapeutic Techniques and Equipment |
| F | Psychiatry and Psychology |
| G | Phenomena and Processes |
| H | Disciplines and Occupations |
| I | Anthropology, Education, Sociology and Social Phenomena |
| J | Technology and Food and Beverages |
| K | Humanities |
| L | Information Science |
| M | Persons |
| N | Health Care |
| V | Publication Type |
| Z | Geographical Locations |
MeSH terms were associated with Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH (Reciprocal Blast Best Hit).
| Method | Way of corresponding Entrez Gene IDs and MeSH IDs |
|---|---|
| Gendoo | Text-mining |
| gene2pubmed | Manual curation by NCBI teams |
| RBBH | sequence homology with BLASTP search (E-value<10-50) |
2 Enrichment Analysis
meshes supports enrichment analysis (over-representation analysis and gene set enrichment analysis) of gene list or whole expression profile using MeSH annotation. Data source from gendoo, gene2pubmed and RBBH are all supported. User can selecte interesting category to test. All 16 categories are supported. The analysis supports >70 species listed in MeSHDb BiocView.
For algorithm details, please refer to the vignettes of DOSE1 package.
library(meshes)
data(geneList, package="DOSE")
de <- names(geneList)[1:100]
x <- enrichMeSH(de, MeSHDb = "MeSH.Hsa.eg.db", database='gendoo', category = 'C')
head(x)## ID Description GeneRatio BgRatio pvalue
## D043171 D043171 Chromosomal Instability 16/96 198/16528 2.794765e-14
## D000782 D000782 Aneuploidy 17/96 320/16528 3.866830e-12
## D042822 D042822 Genomic Instability 16/96 312/16528 3.007419e-11
## D012595 D012595 Scleroderma, Systemic 11/96 279/16528 6.449334e-07
## D009303 D009303 Nasopharyngeal Neoplasms 11/96 314/16528 2.049315e-06
## D019698 D019698 Hepatitis C, Chronic 11/96 317/16528 2.246856e-06
## p.adjust qvalue
## D043171 2.459394e-11 1.815127e-11
## D000782 1.701405e-09 1.255702e-09
## D042822 8.821761e-09 6.510798e-09
## D012595 1.418854e-04 1.047168e-04
## D009303 3.295389e-04 2.432123e-04
## D019698 3.295389e-04 2.432123e-04
## geneID
## D043171 4312/991/2305/1062/4605/10403/7153/55355/4751/4085/81620/332/7272/9212/1111/6790
## D000782 4312/55143/991/1062/7153/4751/79019/55839/890/983/4085/332/7272/9212/8208/1111/6790
## D042822 55143/991/1062/4605/7153/1381/9787/4751/10635/890/4085/81620/332/9212/1111/6790
## D012595 4312/6280/1062/4605/7153/3627/4283/6362/7850/3002/4321
## D009303 4312/7153/3627/6241/983/4085/5918/332/3002/4321/6790
## D019698 4312/3627/10563/6373/4283/983/6362/7850/332/3002/3620
## Count
## D043171 16
## D000782 17
## D042822 16
## D012595 11
## D009303 11
## D019698 11In the over-representation analysis, we use data source from gendoo and C (Diseases) category.
In the following example, we use data source from gene2pubmed and test category G (Phenomena and Processes) using GSEA.
y <- gseMeSH(geneList, MeSHDb = "MeSH.Hsa.eg.db", database = 'gene2pubmed', category = "G")
head(y)## ID Description setSize enrichmentScore NES
## D009929 D009929 Organ Size 489 -0.3508778 -1.568321
## D009043 D009043 Motor Activity 424 -0.3321683 -1.474033
## D009119 D009119 Muscle Contraction 404 -0.3261438 -1.434361
## D050156 D050156 Adipogenesis 404 -0.3414986 -1.501890
## D001846 D001846 Bone Development 314 -0.3751600 -1.621060
## D006339 D006339 Heart Rate 321 -0.3697848 -1.601069
## pvalue p.adjust qvalues rank
## D009929 0.001215067 0.03467782 0.02522292 2309
## D009043 0.001246883 0.03467782 0.02522292 2126
## D009119 0.001290323 0.03467782 0.02522292 2781
## D050156 0.001290323 0.03467782 0.02522292 2207
## D001846 0.001298701 0.03467782 0.02522292 2100
## D006339 0.001300390 0.03467782 0.02522292 2405
## leading_edge
## D009929 tags=27%, list=18%, signal=23%
## D009043 tags=23%, list=17%, signal=20%
## D009119 tags=29%, list=22%, signal=23%
## D050156 tags=25%, list=18%, signal=21%
## D001846 tags=27%, list=17%, signal=23%
## D006339 tags=29%, list=19%, signal=24%
## core_enrichment
## D009929 154/9846/3315/6716/9732/5139/7337/5530/23001/4086/80114/6532/6416/1499/8945/7157/627/2252/22891/2908/8654/4088/27445/22846/4057/860/23286/268/2735/2104/23522/5480/51131/3082/10253/831/604/1028/182/7173/5624/8743/23047/596/9905/1548/2272/22829/948/27303/4314/196/6019/595/5021/7248/4212/2488/54820/5334/6403/2246/4803/866/5919/2308/79789/1907/7048/1831/4060/23387/2247/5468/8076/5793/3485/1733/3952/126/3778/79068/79633/6653/5244/4313/3625/10468/9201/1501/6720/2273/2099/3480/5764/6387/1471/1462/4016/2690/8817/6678/8821/5125/1191/5350/2162/5744/23541/185/367/4982/25802/4128/150/64084/3479/10451/9370/10699/125/4857/1308/2167/652/57502/4137/8614/5241
## D009043 1499/6453/8945/7157/627/408/2908/22881/27445/11132/2752/9445/2571/23621/3082/1291/2915/1543/7466/3240/3350/55304/181/3632/2169/27306/80169/9627/196/8678/8863/23284/81627/4692/5799/2259/3087/1278/1277/3953/4747/2247/6414/210/4744/5468/8835/89795/4023/8522/3485/3952/79068/8864/4313/2944/2273/2099/3480/8528/4908/56892/3339/5138/57161/4741/4306/6571/79750/4915/5744/2487/58503/347/6863/2952/5327/367/4982/4128/4059/3572/150/7060/9358/7166/3479/9254/5348/4129/9370/3708/1311/5105/4137/1408/5241
## D009119 23411/10724/4026/8502/1215/3672/7169/9455/10014/5742/10174/2150/5562/3611/4604/7070/4985/7139/3784/1760/3315/9732/72/5595/3092/6416/9759/270/6558/627/953/408/2908/7138/5563/6794/5564/3567/2104/845/3371/6548/831/3554/126393/7402/1129/7201/3350/5590/5592/7168/2149/4628/8082/5021/2318/844/79026/3790/2308/1907/253959/54795/4311/10580/1848/2281/10398/5166/50507/1012/6876/10203/83700/11167/2317/3952/3778/1009/5733/10468/3693/6253/9499/5159/3991/857/1909/6678/7041/32/8639/5350/3551/1264/2697/185/7043/3357/2205/253190/5327/25802/1634/3572/8490/3679/3479/5348/9370/9122/4629/652/7021/5241
## D050156 5595/8609/9563/27332/1499/79738/4837/7157/79960/5729/408/2908/4088/23741/6500/8038/4057/6649/5564/860/8648/10365/10253/54884/4602/8452/7474/6776/79875/596/25956/8644/80781/79923/1490/50486/7840/84162/6041/4692/2246/4208/11075/63924/5919/284119/2308/9411/54795/5950/79365/2247/5468/373/50507/6469/8553/4023/594/7350/81029/3952/79068/5733/4313/10468/10628/6720/2099/3480/11213/55893/290/6678/63895/4035/633/23414/8639/2162/165/3551/10788/185/3357/367/4982/3667/1634/4128/23024/3479/6424/9370/2167/652/8839/54829/2625/79689/10974
## D001846 8945/7157/57798/79048/627/6500/8038/860/2752/4882/3371/2915/63971/54455/3791/819/57045/596/2034/54808/80781/1280/64388/2261/4054/11059/3483/9900/26234/4734/9452/4208/4322/253461/1278/7048/51280/10903/7869/1277/3953/10516/10411/8835/79776/11167/2317/3485/3952/5274/54681/4488/10486/1009/2202/91851/2099/5764/23327/3339/8817/83716/6678/4915/633/658/54361/5744/165/5654/10631/3487/367/4982/3667/79971/1634/3479/114899/9370/652/8614/4969
## D006339 4985/7139/8929/3784/3375/154/1760/9781/5139/118/2702/6532/6416/2869/270/7157/627/2908/7138/5563/3643/1129/7779/947/1901/2034/4179/4804/64388/1621/4881/8863/5021/844/4212/11030/5797/6403/4803/84059/79789/5176/3953/5243/5468/1012/2868/5793/4023/7056/3952/5577/126/2946/3778/477/5733/4313/2944/9201/3075/9499/2273/2099/1471/857/775/5138/4306/4487/213/5350/5744/23245/2152/2697/2791/185/6863/2952/5327/80206/9607/3572/150/3479/2006/55259/9370/125/652/55351User can use visualization methods implemented in DOSE (i.e.barplot, dotplot, cnetplot, enrichMap, upsetplot and gseaplot) to visualize these enrichment results. With these visualization methods, it’s much easier to interpret enriched results.
dotplot(x)
gseaplot(y, y[1,1], title=y[1,2])
3 Semantic Similarity
meshes implemented four IC-based methods (i.e. Resnik2, Jiang3, Lin4 and Schlicker5) and one graph-structure based method (i.e. Wang6). For algorithm details, please refer to the vignette of GOSemSim package7
meshSim function is designed to measure semantic similarity between two MeSH term vectors.
library(meshes)
## hsamd <- meshdata("MeSH.Hsa.eg.db", category='A', computeIC=T, database="gendoo")
data(hsamd)
meshSim("D000009", "D009130", semData=hsamd, measure="Resnik")## [1] 0.2910261meshSim("D000009", "D009130", semData=hsamd, measure="Rel")## [1] 0.521396meshSim("D000009", "D009130", semData=hsamd, measure="Jiang")## [1] 0.4914785meshSim("D000009", "D009130", semData=hsamd, measure="Wang")## [1] 0.5557103meshSim(c("D001369", "D002462"), c("D017629", "D002890", "D008928"), semData=hsamd, measure="Wang")## D017629 D002890 D008928
## D001369 0.2886598 0.1923711 0.2193326
## D002462 0.6521739 0.2381925 0.2809552geneSim function is designed to measure semantic similarity among two gene vectors.
geneSim("241", "251", semData=hsamd, measure="Wang", combine="BMA")## [1] 0.487geneSim(c("241", "251"), c("835", "5261","241", "994"), semData=hsamd, measure="Wang", combine="BMA")## 835 5261 241 994
## 241 0.732 0.337 1.000 0.438
## 251 0.526 0.588 0.487 0.5975 Session Information
Here is the output of sessionInfo() on the system on which this document was compiled:
## R version 3.4.2 (2017-09-28)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.6-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] meshes_1.4.0 DOSE_3.4.0 MeSH.db_1.9.0
## [4] MeSH.Hsa.eg.db_1.9.0 MeSHDbi_1.14.0 BiocGenerics_0.24.0
## [7] BiocStyle_2.6.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.13 plyr_1.8.4 compiler_3.4.2
## [4] tools_3.4.2 digest_0.6.12 bit_1.1-12
## [7] gtable_0.2.0 RSQLite_2.0 evaluate_0.10.1
## [10] memoise_1.1.0 tibble_1.3.4 pkgconfig_2.0.1
## [13] rlang_0.1.2 igraph_1.1.2 fastmatch_1.1-0
## [16] DBI_0.7 rvcheck_0.0.9 yaml_2.1.14
## [19] gridExtra_2.3 fgsea_1.4.0 stringr_1.2.0
## [22] knitr_1.17 S4Vectors_0.16.0 IRanges_2.12.0
## [25] grid_3.4.2 stats4_3.4.2 rprojroot_1.2
## [28] bit64_0.9-7 qvalue_2.10.0 data.table_1.10.4-3
## [31] Biobase_2.38.0 AnnotationDbi_1.40.0 BiocParallel_1.12.0
## [34] GOSemSim_2.4.0 rmarkdown_1.6 bookdown_0.5
## [37] reshape2_1.4.2 ggplot2_2.2.1 GO.db_3.4.2
## [40] DO.db_2.9 blob_1.1.0 magrittr_1.5
## [43] splines_3.4.2 scales_0.5.0 backports_1.1.1
## [46] htmltools_0.3.6 colorspace_1.3-2 labeling_0.3
## [49] stringi_1.1.5 lazyeval_0.2.1 munsell_0.4.3References
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