This vignette is meant as an extension of what already exists in the GOstatsHyperG vignette. It is intended to explain how a user can run hypergeometric testing on GO terms or KEGG terms when the organism in question is not supported by an annotation package. The 1st thing for a user to determine then is whether or not their organism is supported by an organism package through AnnotationForge. In order to do that, they need only to call the available.dbschemas() method from AnnotationForge.
library("AnnotationForge")
available.dbschemas()## [1] "ARABIDOPSISCHIP_DB" "BOVINECHIP_DB" "BOVINE_DB"
## [4] "CANINECHIP_DB" "CANINE_DB" "CHICKENCHIP_DB"
## [7] "CHICKEN_DB" "ECOLICHIP_DB" "ECOLI_DB"
## [10] "FLYCHIP_DB" "FLY_DB" "GO_DB"
## [13] "HUMANCHIP_DB" "HUMANCROSSCHIP_DB" "HUMAN_DB"
## [16] "INPARANOIDDROME_DB" "INPARANOIDHOMSA_DB" "INPARANOIDMUSMU_DB"
## [19] "INPARANOIDRATNO_DB" "INPARANOIDSACCE_DB" "KEGG_DB"
## [22] "MALARIA_DB" "MOUSECHIP_DB" "MOUSE_DB"
## [25] "PFAM_DB" "PIGCHIP_DB" "PIG_DB"
## [28] "RATCHIP_DB" "RAT_DB" "WORMCHIP_DB"
## [31] "WORM_DB" "YEASTCHIP_DB" "YEAST_DB"
## [34] "ZEBRAFISHCHIP_DB" "ZEBRAFISH_DB"If the organism that you are using is listed here, then your organism is supported. If not, then you will need to find a source or GO (org KEGG) to gene mappings. One source for GO to gene mappings is the blast2GO project. But you might also find such mappings at Ensembl or NCBI. If your organism is not a typical model organism, then the GO terms you will find are probably going to be predictions based on sequence similarity measures instead of direct measurements. This is something that you might want to bear in mind when you draw conclusions later.
In preparation for our subsequent demonstrations, lets get some data to work
with by borrowing from an organism package. We will assume that you will use
something like read.table to load your own annotation packages into a
data.frame object. The starting object needs to be a data.frame with the GO Id’s
in the 1st col, the evidence codes in the 2nd column and the gene Id’s in the
3rd.
library("org.Hs.eg.db")
frame = toTable(org.Hs.egGO)
goframeData = data.frame(frame$go_id, frame$Evidence, frame$gene_id)
head(goframeData)## frame.go_id frame.Evidence frame.gene_id
## 1 GO:0002764 IBA 1
## 2 GO:0008150 ND 1
## 3 GO:0001553 IEA 2
## 4 GO:0001869 IDA 2
## 5 GO:0002438 IEA 2
## 6 GO:0006953 IEA 2When using GO mappings, it is important to consider the data structure of the GO ontologies. The terms are organized into a directed acyclic graph. The structure of the graph creates implications about the mappings of less specific terms to genes that are mapped to more specific terms. The Category and GOstats packages normally deal with this kind of complexity by using a special GO2All mapping in the annotation packages. You won’t have one of those, so instead you will have to make one. AnnotationDbi provides some simple tools to represent your GO term to gene mappings so that this process will be easy. First you need to put your data into a GOFrame object. Then the simple act of casting this object to a GOAllFrame object will tap into the GO.db package and populate this object with the implicated GO2All mappings for you.
goFrame=GOFrame(goframeData,organism="Homo sapiens")
goAllFrame=GOAllFrame(goFrame)In an effort to standardize the way that we pass this kind of custom information around, we have chosen to support geneSetCollection objects from GSEABase package. You can generate one of these objects in the following way:
library("GSEABase")
gsc <- GeneSetCollection(goAllFrame, setType = GOCollection())Now we can make a parameter object for GOstats by using a special constructor function. For the sake of demonstration, I will just use all the EGs as the universe and grab some arbitrarily to be the geneIds tested. For your case, you need to make sure that the gene IDs you use are unique and that they are the same type for both the universe, the geneIds and the IDs that are part of your geneSetCollection.
library("GOstats")
universe = Lkeys(org.Hs.egGO)
genes = universe[1:500]
params <- GSEAGOHyperGParams(name="My Custom GSEA based annot Params",
geneSetCollection=gsc,
geneIds = genes,
universeGeneIds = universe,
ontology = "MF",
pvalueCutoff = 0.05,
conditional = FALSE,
testDirection = "over")And finally we can call hyperGTest in the same way we always have before.
Over <- hyperGTest(params)
head(summary(Over))## GOMFID Pvalue OddsRatio ExpCount Count Size
## 1 GO:0042626 7.375129e-33 26.132589 2.2124139 35 98
## 2 GO:0019829 1.720940e-30 46.179293 1.2190852 27 54
## 3 GO:0003824 1.915221e-27 2.941782 126.0624433 232 5584
## 4 GO:0015399 4.104615e-24 12.520756 3.7475583 35 166
## 5 GO:0015662 6.850323e-23 95.813424 0.5643913 17 25
## 6 GO:0140358 6.850323e-23 95.813424 0.5643913 17 25
## Term
## 1 ATPase-coupled transmembrane transporter activity
## 2 ATPase-coupled monoatomic cation transmembrane transporter activity
## 3 catalytic activity
## 4 primary active transmembrane transporter activity
## 5 P-type ion transporter activity
## 6 P-type transmembrane transporter activityThis is much the same as what you did with the GO mappings except for two important simplifications. First of all you will no longer need to track evidence codes, so the object you start with only needs to hold KEGG Ids and gene IDs. Seconly, since KEGG is not a directed graph, there is no need for a KEGG to All mapping. Once again I will borrow some data to use as an example. Notice that we have to put the KEGG Ids in the left hand column of our initial two column data.frame.
frame = toTable(org.Hs.egPATH)
keggframeData = data.frame(frame$path_id, frame$gene_id)
head(keggframeData)## frame.path_id frame.gene_id
## 1 04610 2
## 2 00232 9
## 3 00983 9
## 4 01100 9
## 5 00232 10
## 6 00983 10keggFrame=KEGGFrame(keggframeData,organism="Homo sapiens")The rest of the process should be very similar.
gsc <- GeneSetCollection(keggFrame, setType = KEGGCollection())
universe = Lkeys(org.Hs.egGO)
genes = universe[1:500]
kparams <- GSEAKEGGHyperGParams(name="My Custom GSEA based annot Params",
geneSetCollection=gsc,
geneIds = genes,
universeGeneIds = universe,
pvalueCutoff = 0.05,
testDirection = "over")
kOver <- hyperGTest(params)
head(summary(kOver))## GOMFID Pvalue OddsRatio ExpCount Count Size
## 1 GO:0042626 7.375129e-33 26.132589 2.2124139 35 98
## 2 GO:0019829 1.720940e-30 46.179293 1.2190852 27 54
## 3 GO:0003824 1.915221e-27 2.941782 126.0624433 232 5584
## 4 GO:0015399 4.104615e-24 12.520756 3.7475583 35 166
## 5 GO:0015662 6.850323e-23 95.813424 0.5643913 17 25
## 6 GO:0140358 6.850323e-23 95.813424 0.5643913 17 25
## Term
## 1 ATPase-coupled transmembrane transporter activity
## 2 ATPase-coupled monoatomic cation transmembrane transporter activity
## 3 catalytic activity
## 4 primary active transmembrane transporter activity
## 5 P-type ion transporter activity
## 6 P-type transmembrane transporter activitysessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
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## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
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## time zone: America/New_York
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] GOstats_2.72.0 Category_2.72.0 Matrix_1.7-1
## [4] GO.db_3.20.0 GSEABase_1.68.0 graph_1.84.0
## [7] annotate_1.84.0 XML_3.99-0.17 org.Hs.eg.db_3.20.0
## [10] AnnotationForge_1.48.0 AnnotationDbi_1.68.0 IRanges_2.40.0
## [13] S4Vectors_0.44.0 Biobase_2.66.0 BiocGenerics_0.52.0
## [16] BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.46.0 xfun_0.48 bslib_0.8.0
## [4] lattice_0.22-6 vctrs_0.6.5 tools_4.4.1
## [7] bitops_1.0-9 curl_5.2.3 RSQLite_2.3.7
## [10] blob_1.2.4 pkgconfig_2.0.3 lifecycle_1.0.4
## [13] GenomeInfoDbData_1.2.13 compiler_4.4.1 Biostrings_2.74.0
## [16] GenomeInfoDb_1.42.0 htmltools_0.5.8.1 sass_0.4.9
## [19] RCurl_1.98-1.16 yaml_2.3.10 crayon_1.5.3
## [22] jquerylib_0.1.4 cachem_1.1.0 genefilter_1.88.0
## [25] digest_0.6.37 bookdown_0.41 splines_4.4.1
## [28] fastmap_1.2.0 grid_4.4.1 cli_3.6.3
## [31] RBGL_1.82.0 survival_3.7-0 UCSC.utils_1.2.0
## [34] bit64_4.5.2 rmarkdown_2.28 XVector_0.46.0
## [37] httr_1.4.7 matrixStats_1.4.1 bit_4.5.0
## [40] png_0.1-8 memoise_2.0.1 evaluate_1.0.1
## [43] knitr_1.48 rlang_1.1.4 xtable_1.8-4
## [46] DBI_1.2.3 Rgraphviz_2.50.0 BiocManager_1.30.25
## [49] jsonlite_1.8.9 R6_2.5.1 MatrixGenerics_1.18.0
## [52] zlibbioc_1.52.0