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)

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 DOSE(Yu et al. 2015) package.

##              ID              Description GeneRatio   BgRatio       pvalue
## 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
## D001471 D001471        Barrett Esophagus      9/96 213/16528 4.070611e-06
##             p.adjust       qvalue
## D000782 6.457606e-10 3.744719e-10
## D042822 2.511195e-09 1.456224e-09
## D012595 3.590129e-05 2.081890e-05
## D009303 7.504499e-05 4.351805e-05
## D019698 7.504499e-05 4.351805e-05
## D001471 1.132987e-04 6.570108e-05
##                                                                                      geneID
## 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
## D001471                                         6280/7153/10563/890/4085/332/2146/4321/6790
##         Count
## D000782    17
## D042822    16
## D012595    11
## D009303    11
## D019698    11
## D001471     9

In 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.

##              ID            Description setSize enrichmentScore       NES
## D006339 D006339             Heart Rate     331      -0.3686389 -1.587342
## D001846 D001846       Bone Development     321      -0.3706355 -1.590429
## D020257 D020257 Ventricular Remodeling     274      -0.3923590 -1.657664
## D055105 D055105    Waist Circumference     267      -0.3963353 -1.672229
## D066253 D066253    Vascular Remodeling     249      -0.5372751 -2.250013
## D050156 D050156           Adipogenesis     447      -0.3380419 -1.495481
##              pvalue   p.adjust     qvalues rank
## D006339 0.001265823 0.01853172 0.009659752 2405
## D001846 0.001267427 0.01853172 0.009659752 2100
## D020257 0.001298701 0.01853172 0.009659752 2385
## D055105 0.001302083 0.01853172 0.009659752 1625
## D066253 0.001310616 0.01853172 0.009659752 2016
## D050156 0.002463054 0.01853172 0.009659752 2508
##                           leading_edge
## D006339 tags=29%, list=19%, signal=24%
## D001846 tags=27%, list=17%, signal=23%
## D020257 tags=32%, list=19%, signal=27%
## D055105 tags=24%, list=13%, signal=21%
## D066253 tags=44%, list=16%, signal=37%
## D050156 tags=28%, list=20%, signal=23%
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   core_enrichment
## D006339                                                                                                                                                                      4985/7139/8929/3784/10681/3375/154/1760/9781/5139/118/2702/6532/6416/2869/270/7157/627/2908/7138/5563/3643/1129/7779/947/1901/2034/4179/4804/64388/1621/4881/8863/5021/844/4212/11030/5797/6403/4803/84059/79789/5176/3953/5243/5468/1012/2868/5793/4023/7056/3952/5577/126/2946/3778/477/5733/4313/2944/9201/3075/9499/2273/2099/1471/857/775/5138/4306/4487/213/5350/5744/23245/2152/2697/2791/185/6863/2952/5327/80206/2200/9607/3572/150/8490/3479/2006/55259/9370/125/652/55351
## D001846                                                                                                                                                                                              8945/7157/57798/79048/627/6500/8038/860/2752/4882/3371/2915/5745/63971/54455/3791/819/57045/596/2034/54808/80781/1280/64388/2261/4054/11059/3483/9900/26234/4734/9452/4208/4322/253461/1278/7048/51280/10903/30008/7869/1277/3953/10516/10411/8835/79776/11167/2317/3485/3952/5274/54681/4488/10486/1009/2202/91851/2099/5764/23327/3339/8817/83716/6678/4915/633/658/54361/5744/165/5654/10631/3487/367/4982/3667/79971/1634/3479/114899/9370/652/8614/4969
## D020257                                                                                                                                                                                                3624/7139/6385/154/5530/1238/5595/5228/1499/7157/4776/25970/408/8654/4811/3910/3371/6548/3082/5914/1291/947/3685/80757/80781/5592/1490/1306/4314/80070/10272/4881/3875/7042/3912/1511/2934/4060/283/1277/7078/5549/22795/1293/2247/50507/1281/5136/11167/4319/1513/6310/4313/2199/1294/2273/2099/6387/3339/7079/1462/1292/775/3908/4306/4035/633/5167/5350/11188/10418/10631/9429/1805/2487/2697/185/7043/6863/3913/4982/1634/7060/3479/7373/9370/2167/652
## D055105                                                                                                                                                                                                                                                                                                                                7466/181/2078/2169/948/4887/3931/4018/4212/9208/6403/3087/84059/5919/253461/2308/79789/5176/3953/5950/2166/6414/5243/5468/1012/4023/582/4214/3485/7350/3952/5577/126/585/79068/2202/4313/9369/2621/2099/1471/2690/1363/4306/174/5167/23245/10631/4886/185/9863/80206/3667/9607/1489/3479/9370/23704/2167/5346/7021/79689/9
## D066253                                                                                                627/953/4088/6649/7010/6778/4811/3248/57124/94/3910/3371/2662/1291/1958/3490/7450/80781/948/1280/1306/8425/10085/4054/4256/3912/2817/9353/2308/1278/8829/1511/2934/4060/1277/7078/5549/22795/10516/1293/1295/6591/5468/50507/358/1012/6876/8076/5118/2192/1281/4319/1290/3952/1513/7075/7058/2202/4313/2199/5159/11117/857/3339/1462/1289/1292/3908/4016/1909/3909/4053/6678/633/1191/165/5654/10631/1805/2331/347/2152/1264/247/55107/7043/1359/3913/4982/8404/2200/1634/4059/7177/8490/563/2053/3479/7373/1307/9370/79148/1311/2167/8483/54829/4239/4969
## D050156 5562/8626/8434/7070/5071/10499/2067/2874/9611/6716/5925/65989/5595/8609/9563/27332/1499/79738/4837/7157/79960/5729/408/2908/4088/23741/6500/8038/4057/6649/5564/860/8648/10365/10253/54884/4602/8452/7474/6776/8743/79875/596/25956/8644/80781/79923/1490/50486/7840/84162/6041/4692/2246/4208/11075/63924/5919/284119/2308/9411/10216/54795/5950/79365/1293/2247/5468/373/50507/6876/6469/8553/4023/2530/594/7350/81029/3952/1675/79068/5733/4313/10468/10628/6720/9052/2099/3480/11213/857/55893/290/6678/63895/4035/633/23414/8639/2162/165/3551/10788/185/3357/367/4982/3667/1634/4128/23024/3479/6424/9370/2167/652/8839/5346/54829/2625/79689/10974

User can use visualization methods implemented in enrichplot (i.e.barplot, dotplot, cnetplot, emapplot and gseaplot) to visualize these enrichment results. With these visualization methods, it’s much easier to interpret enriched results.

Semantic Similarity

meshes implemented four IC-based methods (i.e. Resnik(Philip 1999), Jiang(Jiang and Conrath 1997), Lin(Lin 1998) and Schlicker(Schlicker et al. 2006)) and one graph-structure based method (i.e. Wang(Wang et al. 2007)). For algorithm details, please refer to the vignette of GOSemSim package(Yu et al. 2010)

meshSim function is designed to measure semantic similarity between two MeSH term vectors.

## [1] 0.2910261
## [1] 0.521396
## [1] 0.4914785
## [1] 0.5557103
##           D017629   D002890   D008928
## D001369 0.2886598 0.1923711 0.2193326
## D002462 0.6521739 0.2381925 0.2809552

geneSim function is designed to measure semantic similarity among two gene vectors.

## [1] 0.487
##       835  5261   241   994
## 241 0.732 0.337 1.000 0.438
## 251 0.526 0.588 0.487 0.597

Need helps?

If you have questions/issues, please visit meshes homepage first. Your problems are mostly documented. If you think you found a bug, please follow the guide and provide a reproducible example to be posted on github issue tracker. For questions, please post to Bioconductor support site and tag your post with meshes.

For Chinese user, you can follow me on WeChat (微信).

Session Information

Here is the output of sessionInfo() on the system on which this document was compiled:

## R version 3.5.0 (2018-04-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.7-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_1.6.0          DOSE_3.6.0            MeSH.db_1.10.0       
## [4] MeSH.Hsa.eg.db_1.10.0 MeSHDbi_1.16.0        BiocGenerics_0.26.0  
## 
## loaded via a namespace (and not attached):
##  [1] Biobase_2.40.0       viridis_0.5.1        bit64_0.9-7         
##  [4] viridisLite_0.3.0    splines_3.5.0        ggraph_1.0.1        
##  [7] prettydoc_0.2.1      assertthat_0.2.0     DO.db_2.9           
## [10] rvcheck_0.0.9        stats4_3.5.0         blob_1.1.1          
## [13] yaml_2.1.18          ggrepel_0.7.0        pillar_1.2.2        
## [16] RSQLite_2.1.0        backports_1.1.2      lattice_0.20-35     
## [19] glue_1.2.0           digest_0.6.15        qvalue_2.12.0       
## [22] colorspace_1.3-2     cowplot_0.9.2        htmltools_0.3.6     
## [25] Matrix_1.2-14        plyr_1.8.4           pkgconfig_2.0.1     
## [28] GO.db_3.6.0          scales_0.5.0         tweenr_0.1.5        
## [31] enrichplot_1.0.0     BiocParallel_1.14.0  ggforce_0.1.1       
## [34] tibble_1.4.2         IRanges_2.14.0       ggplot2_2.2.1       
## [37] UpSetR_1.3.3         lazyeval_0.2.1       magrittr_1.5        
## [40] memoise_1.1.0        evaluate_0.10.1      MASS_7.3-50         
## [43] tools_3.5.0          data.table_1.10.4-3  stringr_1.3.0       
## [46] S4Vectors_0.18.0     munsell_0.4.3        AnnotationDbi_1.42.0
## [49] bindrcpp_0.2.2       compiler_3.5.0       rlang_0.2.0         
## [52] ggridges_0.5.0       units_0.5-1          grid_3.5.0          
## [55] igraph_1.2.1         labeling_0.3         rmarkdown_1.9       
## [58] gtable_0.2.0         DBI_0.8              reshape2_1.4.3      
## [61] R6_2.2.2             gridExtra_2.3        knitr_1.20          
## [64] dplyr_0.7.4          bit_1.1-12           udunits2_0.13       
## [67] bindr_0.1.1          fastmatch_1.1-0      fgsea_1.6.0         
## [70] rprojroot_1.3-2      GOSemSim_2.6.0       stringi_1.1.7       
## [73] Rcpp_0.12.16

References

Jiang, Jay J., and David W. Conrath. 1997. “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy.” Proceedings of 10th International Conference on Research in Computational Linguistics. http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9709008.

Lin, Dekang. 1998. “An Information-Theoretic Definition of Similarity.” In Proceedings of the 15th International Conference on Machine Learning, 296—304. https://doi.org/10.1.1.55.1832.

Philip, Resnik. 1999. “Semantic Similarity in a Taxonomy: An Information-Based Measure and Its Application to Problems of Ambiguity in Natural Language.” Journal of Artificial Intelligence Research 11:95–130.

Schlicker, Andreas, Francisco S Domingues, Jorg Rahnenfuhrer, and Thomas Lengauer. 2006. “A New Measure for Functional Similarity of Gene Products Based on Gene Ontology.” BMC Bioinformatics 7:302. https://doi.org/1471-2105-7-302.

Wang, James Z, Zhidian Du, Rapeeporn Payattakool, Philip S Yu, and Chin-Fu Chen. 2007. “A New Method to Measure the Semantic Similarity of Go Terms.” Bioinformatics (Oxford, England) 23 (May):1274–81. https://doi.org/btm087.

Yu, Guangchuang, and Qing-Yu He. 2016. “ReactomePA: An R/Bioconductor Package for Reactome Pathway Analysis and Visualization.” Molecular BioSystems 12 (2):477–79. https://doi.org/10.1039/C5MB00663E.

Yu, Guangchuang, Fei Li, Yide Qin, Xiaochen Bo, Yibo Wu, and Shengqi Wang. 2010. “GOSemSim: An R Package for Measuring Semantic Similarity Among Go Terms and Gene Products.” Bioinformatics 26 (april):976–78. https://doi.org/10.1093/bioinformatics/btq064.

Yu, Guangchuang, Li-Gen Wang, Yanyan Han, and Qing-Yu He. 2012. “clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters.” OMICS: A Journal of Integrative Biology 16 (5):284–87. https://doi.org/10.1089/omi.2011.0118.

Yu, Guangchuang, Li-Gen Wang, Guang-Rong Yan, and Qing-Yu He. 2015. “DOSE: An R/Bioconductor Package for Disease Ontology Semantic and Enrichment Analysis.” Bioinformatics 31 (4):608–9. https://doi.org/10.1093/bioinformatics/btu684.