Last updated: 2021-01-27
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 687a6ca | davetang | 2021-01-27 | Gene Ontology Enrichment Analysis |
The Gene Ontology Enrichment Analysis (GOEA) is a typical analysis carried out on transcriptome data. Online tools for performing a GOEA include DAVID, Enrichr, and PANTHER just to name a few. While web-based tools are easy to use, it becomes tedious when you have to analyse (or re-analyse) lots of datasets. Therefore, it is preferable to use a programmatic approach and in this post we will check out some Bioconductor packages that allow to perform a GOEA.
First install the following packages, if necessary, and then load them.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
my_packages <- c("clusterProfiler",
"GOstats",
"GO.db",
"org.Hs.eg.db")
to_install <- my_packages[!my_packages %in% installed.packages()]
BiocManager::install(pkgs = to_install)
# load all packages and suppress output of sapply
invisible(sapply(my_packages, library, character.only = TRUE))Create a positive control where the gene set are composed of genes that are all associated with GO:0007411 (axon guidance); we will use the org.Hs.eg.db package to achieve this based on the vignette.
Methods that can be applied to AnnotationDbi objects such as org.Hs.eg.db include: columns, keytypes, keys, and select.
Use columns to find out what data can be retrived using select.
columns(org.Hs.eg.db) [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
[6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
[11] "GO" "GOALL" "IPI" "MAP" "OMIM"
[16] "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM" "PMID"
[21] "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG" "UNIGENE"
[26] "UNIPROT" Use keytypes to find out what fields we can use as keys to query the database.
keytypes(org.Hs.eg.db) [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
[6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
[11] "GO" "GOALL" "IPI" "MAP" "OMIM"
[16] "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM" "PMID"
[21] "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG" "UNIGENE"
[26] "UNIPROT" Select all genes with GO:0007411.
go_to_entrez <- select(org.Hs.eg.db,
keys = "GO:0007411",
columns = "ENTREZID",
keytype = "GO")'select()' returned 1:many mapping between keys and columnsaxon_gene <- unique(go_to_entrez$ENTREZID)
length(axon_gene)[1] 205To perform the GOEA we need to create a gene background called the universe and we will use all genes with a GO term. Normally the universe should be the list of genes that were actually assayed in your transcriptome analysis.
all_go_terms <- keys(org.Hs.eg.db, keytype = "GO")
all_go <- select(org.Hs.eg.db, keys = all_go_terms, columns = c("ENTREZID", "GO"), keytype = "GO")'select()' returned 1:many mapping between keys and columnsuniverse <- unique(all_go$ENTREZID)
length(universe)[1] 20488The function hyperGTest will perform the GOEA based on a set of parameters; in this example, we are testing for the over-representation of biological process (BP) terms and using a p-value cutoff of 0.001 or less.
params <- new('GOHyperGParams',
geneIds = axon_gene,
universeGeneIds = universe,
ontology = 'BP',
pvalueCutoff = 0.001,
conditional = FALSE,
testDirection = 'over',
annotation = "org.Hs.eg.db"
)
my_test <- hyperGTest(params)
my_testGene to GO BP test for over-representation
4723 GO BP ids tested (1069 have p < 0.001)
Selected gene set size: 205
Gene universe size: 18670
Annotation package: org.Hs.eg Use summary to get a summary of the results. The summary contains the GOID, Pvalue, OddsRatio, ExpCount, Count, and Size.
ExpCount is the expected countCount is how many instances of that term were actually observed in your gene listSize is the number that could have been found in your gene list if every instance had turned uphead(summary(my_test)) GOBPID Pvalue OddsRatio ExpCount Count Size
1 GO:0007409 0.00000e+00 Inf 5.138725 205 468
2 GO:0007411 0.00000e+00 Inf 3.030530 205 276
3 GO:0048667 0.00000e+00 Inf 6.401446 205 583
4 GO:0061564 0.00000e+00 Inf 5.643814 205 514
5 GO:0097485 0.00000e+00 Inf 3.041510 205 277
6 GO:0006935 6.87054e-316 Inf 7.071237 205 644
Term
1 axonogenesis
2 axon guidance
3 cell morphogenesis involved in neuron differentiation
4 axon development
5 neuron projection guidance
6 chemotaxisGO terms associated to axons are enriched as expected. Note that the Count and Size for GO:0007411 is not identical even though we had selected all genes associated with GO:0007411.
If we manually select Entrez gene IDs using org.Hs.egGO, we still get the same list of genes, so I’m not sure how the size is calculated by hyperGTest.
my_df <- as.data.frame(org.Hs.egGO)
my_idx <- my_df$go_id == "GO:0007411"
length(unique(my_df[my_idx, "gene_id"])) == length(axon_gene)[1] TRUEWe can use the biomaRt package for converting between different gene identifiers and in this example, we will convert Ensembl gene IDs to Entrez gene IDs.
if (!"biomaRt" %in% installed.packages()){
BiocManager::install("biomaRt")
}
library("biomaRt")We will fetch every Ensembl gene ID and randomly select 10 IDs to convert into Entrez gene IDs.
ensembl <- useMart("ensembl", dataset="hsapiens_gene_ensembl")
my_chr <- c(1:22, 'M', 'X', 'Y')
my_ensembl_gene <- getBM(attributes = 'ensembl_gene_id',
filters = 'chromosome_name',
values = my_chr,
mart = ensembl)
head(my_ensembl_gene) ensembl_gene_id
1 ENSG00000223972
2 ENSG00000227232
3 ENSG00000278267
4 ENSG00000243485
5 ENSG00000284332
6 ENSG00000237613Select 10 Ensembl gene IDs.
set.seed(1984)
to_convert <- sample(x = my_ensembl_gene$ensembl_gene_id, size = 10, replace = FALSE)Now to convert the IDs.
to_entrez <- getBM(attributes = c('ensembl_gene_id', 'entrezgene_id'),
filters = 'ensembl_gene_id',
values = to_convert,
mart = ensembl)
to_entrez ensembl_gene_id entrezgene_id
1 ENSG00000124568 6568
2 ENSG00000131400 9476
3 ENSG00000212191 NA
4 ENSG00000225315 NA
5 ENSG00000228658 NA
6 ENSG00000256659 101927694
7 ENSG00000257890 NA
8 ENSG00000267552 NA
9 ENSG00000280344 NA
10 ENSG00000281133 NANote that not all Ensembl IDs have Entrez IDs. We can find out how many Ensembl IDs do not have Entrez IDs.
my_entrez_gene <- getBM(attributes = c('ensembl_gene_id', 'entrezgene_id'),
filters = 'ensembl_gene_id',
values = my_ensembl_gene,
mart = ensembl)
table(is.na(my_entrez_gene$entrezgene_id))
FALSE TRUE
25628 35099 35099 out of 60727 Ensembl gene IDs do not have corresponding Entrez gene IDs. To learn more about the missing Entrez ID values from the Ensembl conversion see this useful post on BioStars.
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] biomaRt_2.44.4 org.Hs.eg.db_3.11.4 GO.db_3.11.4
[4] GOstats_2.54.0 graph_1.66.0 Category_2.54.0
[7] Matrix_1.3-2 AnnotationDbi_1.50.3 IRanges_2.22.2
[10] S4Vectors_0.26.1 Biobase_2.48.0 BiocGenerics_0.34.0
[13] clusterProfiler_3.16.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fgsea_1.14.0 colorspace_2.0-0 ellipsis_0.3.1
[4] ggridges_0.5.3 rprojroot_2.0.2 qvalue_2.20.0
[7] fs_1.5.0 rstudioapi_0.13 farver_2.0.3
[10] urltools_1.7.3 graphlayouts_0.7.1 ggrepel_0.9.1
[13] bit64_4.0.5 scatterpie_0.1.5 xml2_1.3.2
[16] splines_4.0.3 cachem_1.0.1 GOSemSim_2.14.2
[19] knitr_1.30 polyclip_1.10-0 jsonlite_1.7.2
[22] annotate_1.66.0 dbplyr_2.0.0 ggforce_0.3.2
[25] BiocManager_1.30.10 compiler_4.0.3 httr_1.4.2
[28] rvcheck_0.1.8 assertthat_0.2.1 fastmap_1.1.0
[31] later_1.1.0.1 tweenr_1.0.1 htmltools_0.5.1.1
[34] prettyunits_1.1.1 tools_4.0.3 igraph_1.2.6
[37] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4
[40] DO.db_2.9 dplyr_1.0.3 rappdirs_0.3.1
[43] fastmatch_1.1-0 Rcpp_1.0.6 enrichplot_1.8.1
[46] vctrs_0.3.6 ggraph_2.0.4 xfun_0.20
[49] stringr_1.4.0 lifecycle_0.2.0 XML_3.99-0.5
[52] DOSE_3.14.0 europepmc_0.4 MASS_7.3-53
[55] scales_1.1.1 tidygraph_1.2.0 hms_1.0.0
[58] promises_1.1.1 RBGL_1.64.0 RColorBrewer_1.1-2
[61] curl_4.3 yaml_2.2.1 memoise_2.0.0
[64] gridExtra_2.3 ggplot2_3.3.3 downloader_0.4
[67] triebeard_0.3.0 stringi_1.5.3 RSQLite_2.2.3
[70] genefilter_1.70.0 BiocParallel_1.22.0 rlang_0.4.10
[73] pkgconfig_2.0.3 bitops_1.0-6 evaluate_0.14
[76] lattice_0.20-41 purrr_0.3.4 cowplot_1.1.1
[79] bit_4.0.4 tidyselect_1.1.0 AnnotationForge_1.30.1
[82] GSEABase_1.50.1 plyr_1.8.6 magrittr_2.0.1
[85] R6_2.5.0 generics_0.1.0 DBI_1.1.1
[88] withr_2.4.1 pillar_1.4.7 whisker_0.4
[91] survival_3.2-7 RCurl_1.98-1.2 tibble_3.0.5
[94] crayon_1.3.4 BiocFileCache_1.12.1 rmarkdown_2.6
[97] viridis_0.5.1 progress_1.2.2 grid_4.0.3
[100] data.table_1.13.6 Rgraphviz_2.32.0 blob_1.2.1
[103] git2r_0.28.0 digest_0.6.27 xtable_1.8-4
[106] tidyr_1.1.2 httpuv_1.5.5 gridGraphics_0.5-1
[109] openssl_1.4.3 munsell_0.5.0 viridisLite_0.3.0
[112] ggplotify_0.0.5 askpass_1.1