GOstats 2.68.0
The GOstats package has extensive facilities for testing the association of Gene Ontology (GO) The Gene Ontology Consortium (2000) terms to genes in a gene list. You can test for both over and under representation of GO terms using either the standard Hypergeometric test or a conditional Hypergeometric test that uses the relationships among the GO terms for conditioning (similar to that presented in Alexa et al. (2006)).
In this vignette we describe the preprocessing required to construct
inputs for the main testing function, hyperGTest, the algorithms used,
and the structure of the return value. We use a microarray data set
(Chiaretti et al., 2004) from a clinical trial in acute lymphoblastic leukemia
(ALL) to work an example analysis. In the ALL data, we focus on the
patients with B-cell derived ALL, and in particular on comparing the
group with ALL1/AF4 to those with no observed cytogenetic abnormalities.
To get started, load the packages needed for this analysis:
library("ALL")
library("hgu95av2.db")
library("GO.db")
library("annotate")
library("genefilter")
library("GOstats")
library("RColorBrewer")
library("xtable")
library("Rgraphviz")To perform an analysis using the Hypergeometric-based tests, one needs to define a gene universe (usually conceptualized as the number of balls in an urn) and a list of selected genes from the universe. While it is clear that the selected gene list determines to a large degree the results of the analysis, the fact that the universe has a large effect on the conclusions is, perhaps, less obvious.
For microarray data, one can use the unique gene identifiers assayed in the experiment as the gene universe. However, the presence of a gene on the array does not necessarily mean much. Some arrays, such as those from Affymetrix, attempt to include probes for as much of the genome as possible. Since not all genes will be expressed under all conditions (a widely held belief is that about 40% of the genome is expressed in any tissue), it may be sensible to reduce the universe to those that are expressed.
To identify the set of expressed genes from a microarray experiment, we propose that a non-specific filter be applied and that the genes that pass the filter be used to form the universe for any subsequent functional analyses. Below, we outline the non-specific filtering procedure used for the example analysis.
Once a gene universe has been established, one can apply any number of methods to select genes. For the example analysis we use a simple \(t\)-test to identify differentially expressed genes among the two subgroups in the sample population.
It is worth noting that the effect of increasing the universe size with genes that are irrelevant to the questions at hand, in general, has the effect of making the resultant \(p\)-values look more significant. For example, in a universe of 1000 genes where 400 have been selected, suppose that a GO term has 40 gene annotations from the universe of 1000. If 10 of the genes in the selected gene list are among the 40 genes annotated at this category, then the Hypergeometric \(p\)-value is 0.99. However, if the gene universe contained 5000 genes, the \(p\)-value would drop to 0.001.
First we load the ALL data object and extract the subset of the data
we wish to analyze: subjects with either no cytogenetic abnormality
(“NEG”) or those with “ALL1/AF4”.
data(ALL, package="ALL")
## For this data we can have ALL1/AF4 or BCR/ABL
subsetType <- "ALL1/AF4"
## Subset of interest: 37BRC/ABL + 42NEG = 79 samples
Bcell <- grep("^B", as.character(ALL$BT))
bcrAblOrNegIdx <- which(as.character(ALL$mol) %in% c("NEG", subsetType))
bcrAblOrNeg <- ALL[, intersect(Bcell, bcrAblOrNegIdx)]
bcrAblOrNeg$mol.biol = factor(bcrAblOrNeg$mol.biol)We begin our non-specific filtering by removing probe sets that have no Entrez Gene identifier in our annotation data.
##Remove genes that have no entrezGene id
entrezIds <- mget(featureNames(bcrAblOrNeg), envir=hgu95av2ENTREZID)
haveEntrezId <- names(entrezIds)[sapply(entrezIds, function(x) !is.na(x))]
numNoEntrezId <- length(featureNames(bcrAblOrNeg)) - length(haveEntrezId)
bcrAblOrNeg <- bcrAblOrNeg[haveEntrezId, ]Similarly, we remove probe sets for which we have no GO annotation.
## Remove genes with no GO mapping
haveGo <- sapply(mget(featureNames(bcrAblOrNeg), hgu95av2GO),
function(x){
if (length(x) == 1 && is.na(x))
FALSE
else TRUE
})
numNoGO <- sum(!haveGo)
bcrAblOrNeg <- bcrAblOrNeg[haveGo, ]Now use the IQR of each probe set across samples to remove those probe sets that have little variation across samples. Also, since there is an imbalance of men and women by group, we remove probe sets that measure genes on the Y chromosome.
## Non-specific filtering based on IQR
iqrCutoff <- 0.5
bcrAblOrNegIqr <- apply(exprs(bcrAblOrNeg), 1, IQR)
selected <- bcrAblOrNegIqr > iqrCutoff
## Drop those that are on the Y chromosome
## because there is an imbalance of men and women by group
chrN <- mget(featureNames(bcrAblOrNeg), envir=hgu95av2CHR)
onY <- sapply(chrN, function(x) any(x=="Y"))
onY[is.na(onY)] <- FALSE
selected <- selected & !onY
nsFiltered <- bcrAblOrNeg[selected, ]Here we ensure that each probe set maps to exactly one Entrez Gene ID. If multiple probes are found to map to the same Entrez Gene ID, we select the probe with largest IQR (from the computation above).
numNsWithDups <- length(featureNames(nsFiltered))
nsFilteredIqr <- bcrAblOrNegIqr[selected]
uniqGenes <- findLargest(featureNames(nsFiltered), nsFilteredIqr,
"hgu95av2")
nsFiltered <- nsFiltered[uniqGenes, ]
numSelected <- length(featureNames(nsFiltered))
##set up some colors
BCRcols = ifelse(nsFiltered$mol == subsetType, "goldenrod", "skyblue")
cols = brewer.pal(10, "RdBu")Finally, we can define the gene universe we will use for the Hypergeometric tests.
## Define gene universe based on results of non-specific filtering
affyUniverse <- featureNames(nsFiltered)
entrezUniverse <- unlist(mget(affyUniverse, hgu95av2ENTREZID))
if (any(duplicated(entrezUniverse)))
stop("error in gene universe: can't have duplicate Entrez Gene Ids")
## Also define an alternate universe based on the entire chip
chipAffyUniverse <- featureNames(bcrAblOrNeg)
chipEntrezUniverse <- mget(chipAffyUniverse, hgu95av2ENTREZID)
chipEntrezUniverse <- unique(unlist(chipEntrezUniverse)) Summary of non-specific filtering: Our non-specific filtering procedure removed probes missing either Entrez Gene identifies or mappings to GO terms. Because of an imbalance of men and women by group, probes measuring genes on the Y chromosome were dropped. The inter-quartile range was used with a cutoff of 0.5 to select probes with sufficient variability across samples to be informative; probes with little variability across all samples are inherently uninteresting. Finally, the set of remaining probes was refined by ensuring that each probe maps to exactly one Entrez Gene identifier. For those probes mapping to the same Entrez Gene ID, the probe with largest IQR was selected.
Producing a set of Entrez Gene identifiers that map to a unique set of probes at the non-specific filtering stage is important because genes are mapped to GO categories using Entrez Gene IDs and we want to avoid double counting any GO categories. In all, the filtering left 3398 genes.
We apply a standard \(t\)-test to identify a set of genes with differential expression between the ALL1/AF4 and NEG groups.
ttestCutoff <- 0.05
ttests = rowttests(nsFiltered, "mol.biol")
smPV = ttests$p.value < ttestCutoff
pvalFiltered <- nsFiltered[smPV, ]
selectedEntrezIds <- unlist(mget(featureNames(pvalFiltered),
hgu95av2ENTREZID))There are 666 genes with \(p\)-values less than 0.05. We do not make use of any \(p\)-value correction methods since we are interested in a relatively long gene list.
A detail often omitted from GO association analyses is the fact that the \(t\)-test, and most similar statistics, are directional. For a given gene, average expression might be higher in the ALL1/AF4 group than in the NEG group, whereas for a different gene it might be the NEG group that shows the increased expression. By only looking at the \(p\)-values for the test statistics, the directionality is lost. The danger is that an association with a GO category may be found where the genes are not differentially expressed in the same direction. One way to tackle this problem is by separating the selected gene list into two lists according to direction and running two analyses. A more elegant approach is the subject of further research.
Often one wishes to perform many similar analyses using slightly
different sets of parameters and to facilitate this pattern of usage the
main interface to the Hypergeometric tests, hyperGTest, takes a single
parameter object as its argument. This argument is an instance of class
GOHyperGParams. There are also parameter classes KEGGHyperGParams
and PFAMHyperGParams defined in the Category package
that allow for testing for association with KEGG pathways and PFAM
protein domains, respectively.
Using a parameter class instead of individual arguments makes it easier
to organize and execute a series of related analyses. For example, one
can create a list of GOHyperGParams instances and perform the
Hypergeometric test on each using R’s lapply function:
resultList <- lapply(lisOfParamObjs, hyperGTest)In the absence of a parameter class, this could be achieved using
mapply, but the result would be less readable. Because parameter
objects can be copied and modified, they tend to reduce duplication of
code. We’ll demonstrate this in the following example.
Below, we create a parameter instance by specifying the gene list, the
universe, the name of the annotation data package, and the GO ontology
we wish to interrogate. For the example analysis, we have stored the
vector of Entrez Gene identifiers making up the gene universe in
entrezUniverse. The selected genes are stored in selectedEntrezIds.
If you are following along with your own data and have an
ExpressionSet instance resulting from a non-specific filtering
procedure, you can create the entrezUniverse and selectedEntrezIds
vectors using code similar to that shown here:
entrezUniverse <- unlist(mget(featureNames(yourData),
hgu95av2ENTREZID))
if (any(duplicated(entrezUniverse)))
stop(\"error in gene universe: can't have duplicate Entrez Gene Ids")
pvalFiltered <- yourData[hasSmallPvalue, ]
selectedEntrezIds <-unlist(mget(featureNames(pvalFiltered),
hgu95av2ENTREZID))Here is a description of all the arguments needed to construct a GOHyperGParams instance.
geneIds A vector of gene identifiers that defines the selected list of genes. This is often the output of a test for differential expression among two sample groups. For experiments using Affymetrix expression arrays, this should be a vector of Entrez Gene IDs. If you are using the YEAST annotation package, the vector will consist of yeast systematic names.
universeGeneIds A vector of gene identifiers that defines the universe of
possible genes. We recommend using the set of gene IDs that result from
non-specific filtering.The identifiers should be of the same type as the
geneIds; for Affymetrix arrays, these will be Entrez Gene IDs.
annotation A string giving the name of the annotation data package that corresponds to the chip used in the experiment.
ontology A two-letter string specifying one of the three GO ontologies: BP,
CC, or MF. The hyperGTest function only tests a single GO ontology at one time.
pvalueCutoff A numeric values between zero and one used as a \(p\)-value cutoff
for \(p\)-values generated by the Hypergeometric test. When the test being
performed is non-conditional, this is only used as a default value for printing
and summarizing the results. For a conditional analysis, the cutoff is used
during the computation to determine perform the conditioning: child terms with
a \(p\)-value less than pvalueCutoff are conditioned out of the test for their
parent term.
conditional A logical value. If TRUE, the test performed uses the
conditional algorithm. Otherwise, a standard Hypergeometric test is performed.
When conditional(p) == TRUE, the hyperGTest function uses the structure of
the GO graph to estimate for each term whether or not there is evidence beyond
that which is provided by the term’s children to call the term in question
statistically overrepresented.The algorithm conditions on all child terms that
are themselves significant at the specified p-value cutoff. Given a subgraph of
one of the three GO ontologies, the terms with no child categories are tested
first. Next the nodes whose children have already been tested are tested. If any
of a given node’s children tested significant, the appropriate conditioning is
performed.
testDirection A string which can be either “over” or “under”. This determines whether the test performed detects over or under represented GO terms.
hgCutoff<- 0.001
params <- new("GOHyperGParams",
geneIds=selectedEntrezIds,
universeGeneIds=entrezUniverse,
annotation="hgu95av2.db",
ontology="BP",
pvalueCutoff=hgCutoff,
conditional=FALSE,
testDirection="over")We would also like to perform a conditional test. Instead of having to define a new GOHyperGParams instance by hand, we can create a copy and update just the parameter of interest.
paramsCond <- params
conditional(paramsCond) <- TRUE A similar approach would work to create a parameter object for testing a different GO ontology or to create an object for testing under representation.
The hyperGTest function returns an instance of class GOHyperGResult.
When the input parameter object is a KEGGHyperGParams or
PFAMHyperGParams instance, the result will instead be a HyperGResult
object. Most of the reporting and summarization methods demonstrated
here will work the same, except for those that deal specifically with GO
or the GO graph.
As shown below, printing the result at the R prompt provides a brief summary of the test performed and the number of significant terms found. Depending on how you pre-processed your gene list and gene universe, The hyperGTest function may have to do even more filtering on both of these for you. Genes that are not marked with a GO term from the ontology that you specified will have to be discarded, and so you might notice that your gene list and gene universe had shrank somewhat when you print the results.
hgOver <- hyperGTest(params)
hgCondOver <- hyperGTest(paramsCond)hgOver ## Gene to GO BP test for over-representation
## 6854 GO BP ids tested (132 have p < 0.001)
## Selected gene set size: 653
## Gene universe size: 3317
## Annotation package: hgu95av2hgCondOver## Gene to GO BP Conditional test for over-representation
## 6854 GO BP ids tested (64 have p < 0.001)
## Selected gene set size: 653
## Gene universe size: 3317
## Annotation package: hgu95av2The summary function returns a data.frame summarizing the result. By
default, only the results for terms with a \(p\)-value less than the
cutoff specified in the parameter instance will be shown. However, you
can set a new cutoff using the pvalue argument. You can also set a
minimum number of genes for each term using the categorySize argument.
For GOHyperGResult objects, the summary method also has a
htmlLinks argument. When TRUE, the GO term names are printed as HTML
links to the GO website.
df <- summary(hgOver)
names(df) # the columns ## [1] "GOBPID" "Pvalue" "OddsRatio" "ExpCount" "Count" "Size"
## [7] "Term"dim(summary(hgOver, pvalue=0.1))## [1] 1143 7dim(summary(hgOver, categorySize=10)) ## [1] 127 7Now we demonstrate some of the accessor functions that can be used to extract detail from a result object. These functions are all detailed in their respective manual pages.
pvalues(hgOver)[1:3]## GO:0032501 GO:0098609 GO:0048731
## 2.456842e-10 3.145824e-10 1.725728e-08oddsRatios(hgOver)[1:3]## GO:0032501 GO:0098609 GO:0048731
## 1.732476 2.485244 1.697559expectedCounts(hgOver)[1:3]## GO:0032501 GO:0098609 GO:0048731
## 301.39976 49.41302 171.66596geneCounts(hgOver)[1:3]## GO:0032501 GO:0098609 GO:0048731
## 373 90 229universeCounts(hgOver)[1:3]## GO:0032501 GO:0098609 GO:0048731
## 1531 251 872length(geneIds(hgOver)) ## [1] 653length(geneIdUniverse(hgOver)[[3]])## [1] 872## GOHyperGResult _only_
## (NB: edges go from parent to child)
goDag(hgOver)## A graphNEL graph with directed edges
## Number of Nodes = 6854
## Number of Edges = 15159geneMappedCount(hgOver) ## [1] 653universeMappedCount(hgOver)## [1] 3317conditional(hgOver) ## [1] FALSEtestDirection(hgOver)## [1] "over"testName(hgOver)## [1] "GO" "BP"To make it easy for non-technical users to review the results, the
htmlReport function generates an HTML file that can be viewed in any
web browser. The output generated by htmlReport as called below is
output to your current working directory.
htmlReport(hgCondOver, file="ALL_hgco.html")In the Hypergeometric model, each term is treated as an independent classification. Each gene is cross-classified according to whether or not it has been selected and whether or not it is annotated, not necessarily specifically annotated, at a particular term. A Hypergeometric probability is computed to assess whether the number of selected genes associated with the term is larger than expected.
The hyperGTest function provides an implementation of the commonly
applied Hypergeometric calculation for over or under-representation of
GO terms in a specified gene list. This computation ignores the
structure of the GO terms, treating each term as independent from all
other terms.
Often an analysis for GO term associations results in the identification of directly related GO terms with considerable overlap of genes. This is because each GO term inherits all annotations from its more specific descendants. To alleviate this problem, we have implemented a method which conditions on all child terms that are themselves significant at a specified \(p\)-value cutoff. Given a subgraph of one of the three GO ontologies, we test the leaves of the graph, that is, those terms with no child terms. Before testing the terms whose children have already been tested, we remove all genes annotated at significant children from the parent’s gene list. This continues until all terms have been tested.
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## [46] MatrixGenerics_1.14.0 zlibbioc_1.48.0Alexa,A. et al. (2006) Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics, 22, 1600–7.
Chiaretti,S. et al. (2004) Gene expression profile of adult t-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood, 103, 2771–2778.
The Gene Ontology Consortium (2000) Gene Ontology: Tool for the unification of biology. Nature Genetics, 25, 25–29.