using meshes for MeSH Enrichment Analysis
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:
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) |
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.
library(meshes)
data(geneList)
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 2.434241e-11 1.794534e-11
## D000782 1.684004e-09 1.241456e-09
## D042822 8.731539e-09 6.436931e-09
## D012595 1.404343e-04 1.035288e-04
## D009303 3.261686e-04 2.404530e-04
## D019698 3.261686e-04 2.404530e-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 11
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.
y <- gseMeSH(geneList, MeSHDb = "MeSH.Hsa.eg.db", database = 'gene2pubmed', category = "G")
## [1] "preparing geneSet collections..."
## [1] "GSEA analysis..."
## [1] "leading edge analysis..."
## [1] "done..."
head(y)
## ID Description setSize enrichmentScore
## D009929 D009929 Organ Size 449 -0.3458797
## D059647 D059647 Gene-Environment Interaction 455 -0.3551242
## D009043 D009043 Motor Activity 398 -0.3391521
## D050156 D050156 Adipogenesis 368 -0.3618413
## D004041 D004041 Dietary Fats 314 -0.3427588
## D006339 D006339 Heart Rate 312 -0.3695689
## NES pvalue p.adjust qvalues rank
## D009929 -1.524164 0.001248439 0.03715088 0.02756207 2309
## D059647 -1.564984 0.001251564 0.03715088 0.02756207 2237
## D009043 -1.483672 0.001256281 0.03715088 0.02756207 1757
## D050156 -1.577000 0.001256281 0.03715088 0.02756207 2207
## D004041 -1.473730 0.001269036 0.03715088 0.02756207 1684
## D006339 -1.588315 0.001270648 0.03715088 0.02756207 2405
## leading_edge
## D009929 tags=27%, list=18%, signal=22%
## D059647 tags=26%, list=18%, signal=22%
## D009043 tags=21%, list=14%, signal=18%
## D050156 tags=26%, list=18%, signal=22%
## D004041 tags=21%, list=13%, signal=19%
## D006339 tags=29%, list=19%, signal=24%
## core_enrichment
## D009929 154/9846/3315/6716/9732/5139/7337/5530/4086/6532/1499/7157/627/2252/22891/2908/8654/4088/22846/4057/860/268/2735/2104/23522/5480/51131/3082/10253/831/604/1028/182/7173/5624/8743/23047/596/9905/1548/2272/22829/948/27303/4314/196/6019/595/5021/7248/4212/2488/54820/5334/6403/2246/4803/866/5919/79789/1907/7048/1831/4060/2247/5468/8076/5793/3485/1733/3952/126/3778/79068/79633/6653/5244/4313/3625/10468/9201/1501/6720/2273/2099/3480/5764/6387/1471/1462/4016/2690/8817/8821/5125/1191/5350/2162/5744/23541/185/367/4982/25802/4128/150/3479/10451/9370/125/4857/1308/2167/652/57502/4137/8614/5241
## D059647 9497/118/8859/6532/23405/7424/2295/7157/8631/627/2774/22891/2908/4088/51151/11132/1387/860/268/7366/2104/4153/29119/3791/1543/3643/22841/1129/5624/3240/3174/3350/5590/55304/55213/1548/2169/196/8204/8863/5021/23284/9162/11005/4256/3426/84159/5334/629/1793/4208/4322/7048/6817/553/56172/3953/22795/2638/210/5243/5468/1393/1012/27136/51314/4023/5172/4319/4214/3952/5577/126/7832/79068/4313/2944/9369/3075/6720/7494/2099/857/57161/9223/4306/79750/4035/4915/10443/5744/5654/100126791/3551/2487/1746/185/2952/6935/4128/4059/4582/27324/9358/64084/7166/6505/9370/3708/3117/80129/125/5105/2018/2167/652/4137/1524/5241
## D009043 23621/3082/1291/2915/1543/7466/3240/3350/55304/181/2169/27306/80169/9627/196/8678/8863/23284/81627/4692/5799/2259/3087/1278/1277/3953/4747/2247/6414/210/4744/5468/89795/4023/8522/3485/3952/79068/8864/4313/2944/2273/2099/3480/8528/4908/56892/3339/57161/4741/4306/6571/79750/4915/5744/2487/58503/347/6863/2952/5327/367/4982/4128/4059/3572/150/7060/9358/7166/3479/9254/5348/4129/9370/3708/1311/5105/4137/1408/5241
## D050156 5595/8609/9563/27332/1499/79738/4837/7157/79960/5729/408/2908/4088/6500/8038/4057/6649/5564/860/8648/10365/10253/54884/4602/7474/6776/79875/596/25956/8644/80781/79923/1490/50486/7840/84162/6041/4692/2246/4208/11075/63924/5919/284119/2308/9411/54795/5950/79365/2247/5468/50507/6469/8553/4023/594/7350/81029/3952/79068/5733/4313/10468/10628/6720/11213/55893/290/6678/63895/4035/633/23414/8639/2162/165/3551/10788/185/3357/367/4982/3667/1634/4128/23024/3479/6424/9370/2167/652/8839/54829/2625/79689/10974
## D004041 3554/4925/22841/7466/2181/3350/201134/181/2169/948/55911/324/4018/3426/3087/6785/2308/1581/56172/3953/1384/5950/2166/60481/5468/5166/50507/1012/27136/4023/7056/4214/9365/7350/3952/3778/79068/8864/2944/6720/5159/3991/2203/2819/9223/4035/32/213/165/347/2152/185/3487/5327/3667/54898/150/64084/3479/9370/5105/5174/2018/5346/7021/79689
## D006339 4985/7139/8929/3784/3375/154/1760/9781/5139/118/2702/6532/6416/2869/270/7157/627/2908/7138/5563/3643/1129/7779/947/2034/4179/64388/1621/4881/8863/5021/844/4212/11030/5797/6403/4803/84059/79789/5176/3953/5243/5468/1012/2868/5793/4023/7056/3952/5577/126/2946/3778/477/5733/4313/2944/9201/3075/9499/2273/2099/1471/857/775/4306/4487/213/5350/5744/23245/2152/2697/2791/185/6863/2952/5327/80206/9607/3572/150/3479/2006/55259/9370/125/652/55351
Users can use visualization methods implemented in DOSE (i.e. barplot
, dotplot
, cnetplot
, enrichMap
, upsetplot
, gseaplot
) to help interpreting enriched results.
gseaplot(y, y[1,1], title=y[1,2])