---
title: "How To Use GOstats Testing Gene Lists for GO Term Association"
author:
- name: S. Falcon
- name: R. Gentleman
- name: Dennis Ndubi
affiliation: "Vignette translation from Sweave to R Markdown / HTML"
date: "`r format(Sys.time(), '%B %d, %Y')`"
package: GOstats
bibliography: GOstats.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Hypergeometric Tests Using GOstats}
%\VignetteDepends{ALL, GO.db, annotate, hgu95av2.db, genefilter, RColorBrewer, xtable, Rgraphviz}
%\VignetteKeywords{Hypergeometric, GO, ontology}
%\VignettePackage{GOstats}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
output:
BiocStyle::html_document:
number_sections: yes
toc: no
toc_depth: 4
csl: bioinformatics.csl
---
# Introduction
The `r Biocpkg("GOstats")` package has extensive facilities for testing
the association of Gene Ontology (GO)Â @GO 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. [-@Alexa06]).
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
[@Chiaretti2004] 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:
```{r Setup, echo=TRUE, message=FALSE}
library("ALL")
library("hgu95av2.db")
library("GO.db")
library("annotate")
library("genefilter")
library("GOstats")
library("RColorBrewer")
library("xtable")
library("Rgraphviz")
```
# Preprocessing and Inputs
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.
```{r universeSizeVsPvalue, eval=TRUE, echo=FALSE, results='hide'}
hg_tester <-function(size){
numFound <- 10
numDrawn <- 400
numAtCat <- 40
numNotAtCat <- size - numAtCat
phyper(numFound-1, numAtCat,numNotAtCat, numDrawn, lower.tail=FALSE)
}
pv1000 <- hg_tester(1000)
pv5000 <- hg_tester(5000)
```
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 `r round(pv1000,2)`.
However, if the gene universe contained 5000 genes, the $p$-value would
drop to `r round(pv5000,3)`.
## Non-specific filtering
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".
```{r bcrAblOrNegSubset, results='hide', echo=TRUE, cache=TRUE}
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.
```{r nsFiltering-noEntrez, results='hide', echo=TRUE, cache=TRUE}
##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.
```{r nsFiltering-noGO, results='hide', echo=TRUE, cache=FALSE}
## 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.
```{r nsFiltering-IQR, results='hide', echo=TRUE, cache=TRUE}
## 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).
```{r nsFiltering-unique, results='hide', echo=TRUE, cache=TRUE}
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.
```{r defineGeneUniverse, echo=TRUE, results='hide', cache=TRUE}
## 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 `r iqrCutoff` 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
`r length(featureNames(nsFiltered))` genes.
## Gene selection via t-test
We apply a standard $t$-test to identify a set of genes with
differential expression between the `r subsetType` and NEG groups.
```{r parametric1, echo=TRUE, results='hide', cache=TRUE}
ttestCutoff <- 0.05
ttests = rowttests(nsFiltered, "mol.biol")
smPV = ttests$p.value < ttestCutoff
pvalFiltered <- nsFiltered[smPV, ]
selectedEntrezIds <- unlist(mget(featureNames(pvalFiltered),
hgu95av2ENTREZID))
```
There are `r sum(smPV)` genes with $p$-values less than `r ttestCutoff`.
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 `r subsetType` 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.
## Inputs
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 `r Biocpkg("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:
````{r eval=FALSE}
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:
```{r withYourData1, eval=FALSE}
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.
```{r standardHyperGeo, echo=TRUE, results='hide', cache=TRUE}
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.
```{r condHyperGeo, echo=TRUE, results='hide', cache=TRUE}
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.
# Outputs and Result Summarization
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.
```{r standardHGTEST, cache=TRUE, echo=TRUE, results='hide'}
hgOver <- hyperGTest(params)
hgCondOver <- hyperGTest(paramsCond)
```
```{r HGTestAns}
hgOver
hgCondOver
```
The `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.
```{r summaryEx}
df <- summary(hgOver)
names(df) # the columns
dim(summary(hgOver, pvalue=0.1))
dim(summary(hgOver, categorySize=10))
```
Now 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.
```{r resultAccessors}
pvalues(hgOver)[1:3]
oddsRatios(hgOver)[1:3]
expectedCounts(hgOver)[1:3]
geneCounts(hgOver)[1:3]
universeCounts(hgOver)[1:3]
length(geneIds(hgOver))
length(geneIdUniverse(hgOver)[[3]])
## GOHyperGResult _only_
## (NB: edges go from parent to child)
goDag(hgOver)
geneMappedCount(hgOver)
universeMappedCount(hgOver)
conditional(hgOver)
testDirection(hgOver)
testName(hgOver)
```
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.
```{r htmlReportExample, results='hide', echo=TRUE}
htmlReport(hgCondOver, file="ALL_hgco.html")
```
```{r helperFunc, echo=FALSE, results='hide', cache=TRUE}
sigCategories <- function(res, p){
if (missing(p))
p <- pvalueCutoff(res)
pv <- pvalues(res)
goIds <- names(pv[pv < p])
goIds
}
```
```{r plotFuns, echo=FALSE, results='hide', cache=TRUE}
coloredGoPlot <- function(ccMaxGraph, hgOver, hgCondOver) {
nodeColors <- sapply(nodes(ccMaxGraph),
function(n) {
if (n %in% sigCategories(hgCondOver))
"dark red"
else if (n %in% sigCategories(hgOver))
"pink"
else
"gray"
})
nattr <- makeNodeAttrs(ccMaxGraph,
label=nodes(ccMaxGraph),
shape="ellipse",
fillcolor=nodeColors,
fixedsize=FALSE)
plot(ccMaxGraph, nodeAttrs=nattr)
}
getMaxConnCompGraph <- function(hgOver, hgCondOver){
##uGoDagRev \<-ugraph(goDag(hgOver))
sigNodes <- sigCategories(hgOver)
##adjNodes \<- unlist(adj(uGoDagRev, sigNodes))
adjNodes <- unlist(adj(goDag(hgOver),sigNodes))
displayNodes <- unique(c(sigNodes, adjNodes))
displayGraph <- subGraph(displayNodes, goDag(hgOver))
cc <- connComp(displayGraph)
ccSizes <- listLen(cc)
ccMaxIdx <- which(ccSizes == max(ccSizes))
ccMaxGraph <- subGraph(cc[[ccMaxIdx]], displayGraph)
ccMaxGraph
}
```
# GOstats Capabilities
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.
```{r info}
sessionInfo()
```
# References