- Open Access
FUNC: a package for detecting significant associations between gene sets and ontological annotations
© Prüfer et al; licensee BioMed Central Ltd. 2007
Received: 04 September 2006
Accepted: 06 February 2007
Published: 06 February 2007
Genome-wide expression, sequence and association studies typically yield large sets of gene candidates, which must then be further analysed and interpreted. Information about these genes is increasingly being captured and organized in ontologies, such as the Gene Ontology. Relationships between the gene sets identified by experimental methods and biological knowledge can be made explicit and used in the interpretation of results. However, it is often difficult to assess the statistical significance of such analyses since many inter-dependent categories are tested simultaneously.
We developed the program package FUNC that includes and expands on currently available methods to identify significant associations between gene sets and ontological annotations. Implemented are several tests in particular well suited for genome wide sequence comparisons, estimates of the family-wise error rate, the false discovery rate, a sensitive estimator of the global significance of the results and an algorithm to reduce the complexity of the results.
FUNC is a versatile and useful tool for the analysis of genome-wide data. It is freely available under the GPL license and also accessible via a web service.
High-throughput genomic technologies are revolutionizing biology and medicine and provide new challenges in the way we analyse and interpret these large amounts of data. To this end it is necessary to integrate the acquired knowledge into a flexible data structure and the Gene Ontology (GO) Consortium has provided a widely used solution to this challenge by describing properties of genes using a controlled vocabulary and representing them in a directed acyclic graph, which groups genes in categories . Consequently, there has been an explosion in the number of methods to investigate large-scale gene expression data in the context of these functional annotations. The principle underlying most of these methods is the identification of an enrichment in particular gene annotations among a selected set of genes compared to a reference set – for example, an enrichment of differentially expressed genes in certain annotation categories compared to all other genes on a microarray. The significance of the enrichment is tested for example using tests based on the hypergeometric or the binomial distribution, Fisher's exact test, or a chi-square test. Various programs have implemented this approach (e.g. [2–7]). Ben-Shaul and others have argued that in many cases such a discrete distinction between "differentially expressed" and "not differentially expressed" genes is arbitrary and can reduce the power of the study to identify enriched categories (e.g. ). Therefore, methods have been proposed that instead use a measure of choice to rank the genes, and then use rank-based tests such as the Wilcoxon-rank test or the Kolmogorov-Smirnov test to test if genes that belong to one category differ in their ranks from genes that do not belong to the category (e.g. ). In both cases – rank-based classification or discrete categorization – and independently of the statistic that is used to define the significance of a single category, one needs to correct for the large number of tests performed in such an analysis. This is a challenging task, since the tested categories are highly inter-dependent: a single gene is usually annotated in many categories, and categories subsume other categories. One approach for the correction is to compute the family-wise error rate (FWER), i.e. to estimate the probability that at least one false positive category exists among those that are labelled significant. Applications exist that use a simple Bonferroni correction or other more powerful FWER correction procedures [9–11]. However, it has been noted that controlling the FWER might be too conservative in many genomic applications and that it might instead be more useful to determine the false discovery rate (FDR) , i.e. the proportion of false positives among all significant features, since a known proportion of false positives can easily be tolerated for an increase of power in these contexts (see e.g.  for an overview). Several approaches to estimate the FDR have been proposed (see e.g. [14, 15]) and some have been integrated in various functional profiling applications (e.g. [5, 9, 16–21]). Many methods rely on permutations to estimate the FWER or FDR, since the dependency and structure of the annotations makes it difficult to find analytical methods to do the same . Usually the gene lists are permuted, though some approaches also allow the permutation of sample labels [16, 20, 21, 23], This allows not only a correction of dependence among the categories, but also for dependence among the genes .
Properties of the four category tests
Gene associated variable
Alternative hypothesis (one side)
Alternative hypothesis (other side)
Category contains lower proportion of variable "1" as the root category
Category contains higher proportion of variable "1" as the root category
Gene list with detected genes on an array; "0" is not differently expressed, "1" is differently expressed
Sum of ranks of genes in the category is higher than all other genes
Sum of ranks of genes in the category is lower than all other genes
Gene list with detected genes on an array; continuous variable is the probability for being not differently expressed
Frequency of countA in category is lower than in root category
Frequency of countB in category is lower than in root category
CountA is amount of SAGE tags in experiment A, countB is amount of SAGE tags in experiment B
2 × 2 contingency
Counts are dependent and countA/countB < countC/countD
Counts are dependent and countA/countB > countC/countD
CountA and countC are differences at nonsynonymous sites between and within species, countB and countD are differences at synonymous sites between and within species, respectively
The output of FUNC is a summary of the above-mentioned statistics as well as a table listing the analysed categories and the associated raw and corrected p-values. After picking a desirable p-value cut-off, the user can run the refinement algorithm to identify those significant categories that provide the most concise information, i.e. to identify those categories whose significance does not depend solely on significant descendant categories.
Each of the four FUNC tools is designed for one of the possible category tests: hypergeometric test, Wilcoxon rank test, binomial test and 2 × 2 test (Table 1). For each test, two p-values for both sides of the test statistic are calculated, which allows the detection of an enrichment or a depletion of gene-associated variables among the categories. A detailed description of the algorithms used can be found in the Supplement. Briefly, the hypergeometric test takes a binary variable (e.g. "0" and "1") that is associated with each gene (e.g. "1" for differentially expressed and "0" for equally expressed) and uses the hypergeometric distribution to calculate for each category the probability to "draw" this many or more (respectively this many or less) differently expressed genes from the top category of the subtree selected by the user.
The Wilcoxon rank test differs from this scheme only in that it takes a floating point variable instead of a binary variable and compares the ranks of the genes in the tested category with the ranks of the remaining genes in the top category. This test is useful when it is not possible to clearly classify genes to two distinct classes – as is often the case in microarray experiments. This kind of test has also been used previously in the comparison between the human and the chimpanzee genome to identify GO categories, which contain an excess of fast or slowly evolving genes .
Whereas the hypergeometric and Wilcoxon rank tests compare the distribution of one gene-associated variable among categories, the "binomial test" compares the ratio of two gene-associated counts among categories. Each gene is associated with two counts, and the test determines whether the ratio of these counts in a category is significantly different from the ratio in the top category. The binomial test has been used to identify categories containing more amino acid changes on the human lineage compared to the number of changes on the chimpanzee lineage, and to identify categories that have a higher than expected number of amino acid changes between human and chimpanzee compared to changes between mouse and rat (see also the example below). This test might also be useful when comparing counts of expressed sequence tags e.g. from two SAGE (Serial Analysis of Gene Expression) libraries .
The fourth test takes a 2 × 2 table as the associated gene variables, sums them over each category, and uses a Fisher's exact test or a chi-square test (if all values in the tested category are larger then ten) to test whether the two properties (in rows and columns, each in two states) are independent of each other. Note that in contrast to the other three tests, the calculated p-value is not dependent on an expectation taken from the top category. This test can be useful to conduct a McDonald-Kreitman type test  on GO categories. A McDonald-Kreitman type of test compares the number of fixed substitutions and the number of polymorphisms at two classes of sites, such as synonymous and non-synonymous sites. An excess of fixed non-synonymous substitutions can indicate the action of positive selection, whereas an excess of non-synonymous polymorphisms can indicate the presence of slightly deleterious amino acid variants (for a review see ). The 2 × 2 contingency table test implemented in FUNC calculates two separate p-values to test for an excess of non-synonymous substitutions and an excess of non-synonymous polymorphisms, respectively [see Additional file 1]. The availability of a large (and largely unbiased) genome-wide measurement of polymorphisms in humans and other species together with the already available data on substitutions should make this test very useful in the near future.
It is important to keep in mind that for all the four tests the power to reject the null hypothesis differs among different categories since categories differ in their amount of genes and/or their amount of gene associated counts. Hence, the category with the biggest size effect is not necessarily the most significant category and vice versa (see also  and reply). Also note, for the binomial test and the 2 × 2 contingency table test, that the null hypothesis that FUNC tests is that the genes and not the gene associated counts are a random sample in a category. As a consequence the raw p-values of these two tests should be considered more like an arbitrary test statistic which is compared to the distribution of p-values obtained by permuting genes and not single gene counts (see also example below).
Correction for multiple testing
When many hypotheses are tested at the same time it is expected that a number will appear significant even if all the null hypotheses hold. Therefore, in order to confidently reject some of the null hypotheses, it is necessary to correct for multiple testing. The types of large-scale genomic experiments that have become possible during recent years, in particular microarrays, have revived interest in different statistical methods that deal with the issue of multiple testing (e.g. [13, 15]). The issue is somewhat complex, since (1) the tests are interdependent in a complex manner, (2) the power of each single test is often low, (3) more than one of the tested hypotheses are usually truly not null and (4) rejected hypotheses can be regarded as candidates for additional tests, so that less conservative significance levels can be tolerated for an increase in power. All of these issues are also relevant in the context described here, in particular the complex interdependency of the tests. To overcome the interdependency, we chose to use permutations, i.e. the randomization of gene-associated variables, in order to model the distribution under the null hypothesis that gene associated variables are independent of the gene annotation. This permuted data, the random sets, can be used to estimate the family wise error rate, i.e. the probability that among the tests that are declared significant one or more are false positives . This approach is more powerful than the conservative Bonferroni correction and has, in the context of GO analyses programs, been implemented for example in FuncAssociate  and is also implemented in FUNC. For any of the four tests described above one can calculate a raw p-value cut-off for which the FWER is e.g. 5%. This approach is, however, very strict, and a certain (known) fraction of false positives among the significantly labelled tests can often be tolerated for an increase of power. This is the reason why the false discovery rate (FDR) has gained popularity within the genomic community. The FDR is (loosely) defined as the expected proportion of falsely rejected hypothesis among all rejected hypotheses. Different methods exist to estimate the FDR, differing in how they treat the case that no hypothesis is rejected and in how they estimate the number of falsely rejected hypotheses (see e.g. [14, 15, 32] for a discussion). Several programs that analyse functional annotations have implemented FDR methods (e.g. [5, 9, 16–21]), most often using the procedure of Benjamini and Hochberg . In FUNC we implemented a similar method by Yekueteli and Benjamini that is well-suited for resampling methods . Although it has been shown that the method also works well under positive dependency among the tests  and an easy (conservative) correction method can be used under different kinds of dependencies , it is not entirely clear whether it is well-suited for the kind of strong dependencies that exist among the functional annotations. Further, it is noteworthy that the current methods for computing the FDR are strictly valid only under the assumption of subset pivotality, i.e. under the assumption that the significance of one test does not depend on the significance of another test. However, this assumption is violated for the hypergeometric test, the Wilcoxon rank sum test and the binomial test, since the expectation for each category is derived from the top category, which includes the other categories tested. Hence, if one or more categories truly deviate from the null hypothesis, this influences the null expectation of other categories. However, there is often no reasonable alternative for estimating the expectations independently. In practice, all these issues will not be relevant for data sets which deviate considerably from a random functional annotation. But for data sets with a weak signal, the FDR might not qualify as a well-suited measurement to determine whether there is any (or no) indication for a non-random distribution of the gene-associated variables among functional categories. Therefore, in addition to estimating the FDR and FWER rate of the data set, we developed a method to test directly the "global null hypothesis" that gene-associated variables are randomly distributed among categories.
Testing the global null hypothesis
This measure should be especially helpful, when the signal is weak and/or is distributed among many categories and should be more sensitive than an FDR estimate (see example below). The FDR can be interpreted using an analogy of how much money one would be willing to waste. Testing the global null hypothesis can determine whether it is worth spending any money in the first place, and the FDR can subsequently be used to estimate how much money one is willing to waste.
Results and discussion
To demonstrate the usefulness of FUNC we analysed a dataset of 7034 orthologous genes compared between human, chimpanzee, mouse and rat . We asked whether there are GO categories that evolve faster than expected in either rodents or primates. For this purpose we added the number of amino acid changes for a given gene between mouse and rat (rodent) and human and chimpanzee (primate) and performed the binomial test described above [see Additional file 2]. This procedure treats all genes in a category essentially as a single gene. Note that the p-value of this test is only nominal since it assumes independency among amino acid substitutions, but that the global p-value, the FWER and the FDR calculated by FUNC is based on the permutations across genes and hence controls for the dependency of amino acid substitutions within genes. For brevity, we limit the analysis here to the results from the ontology molecular function.
Categories evolving fast in humans and chimpanzees
nicotinic acetylcholine-activated cation-selective channel activity
olfactory receptor activity
neuropeptide hormone activity
intracellular ligand-gated ion channel activity
sodium channel activity
amino acid-polyamine transporter activity
neuropeptide receptor activity
sulphate porter activity
L-serine transporter activity
oxidoreductase activity, acting on NADH or NADPH, NAD or NADP as acceptor
sodium ion binding
chloride ion binding
We present the software package FUNC, which enhances the ability of researchers to correlate their data with gene annotations that are often provided in the form of ontologies. FUNC currently has two main drawbacks. First, it does not provide any graphical representation of the results such as those provided e.g. by GOMiner . Second, it allows no easy way to permute sample-associated variables instead of gene-associated variables. This has been shown to be useful in some cases  and has been implemented by some programs [16, 20, 23]. However, despite these two drawbacks, FUNC provides considerable advantages over existing tools: it integrates four kinds of tests suitable for the analysis of gene expression data and DNA sequence data of which two (binomial test on two gene-associated counts and 2 × 2 contingency table test on four gene-associated counts) are not implemented in other GO analysis programs. FUNC also provides two established multiple correction methods (FDR and FWER) as well as a new overall significance estimate, which should be useful especially for data with a weak signal. Further, the implemented refinement algorithm is a useful and transparent method for extracting the most specific biological information from lists of significant GO categories. Finally, FUNC is available as a well-documented stand-alone tool for UNIX/GNU Linux platforms as well as accessible via a web service, which makes its use more flexible than many other available GO analysis tools. Thus, FUNC provides flexible, statistically rigorous and novel tools to analyse the functional annotation of a variety of genome-wide data.
Availability and requirements
Project name: FUNC
Project home page: http://func.eva.mpg.de
Operating System: Unix/GNU Linux
Programming languages: C++, Perl, bash
Other requirements: R mathematical library (libRmath)
License: GNU GPL V2.0
We are grateful to Janet Kelso for comments on the manuscript. This work was supported by the Bundesministerium für Bildung und Forschung, the Max Planck Society, the European Commission's Sixth Framework Programme for New and Emerging Science and Technology (PKB140404) and the Deutsche Forschungsgemeinschaft.
- The_Gene_Ontology_Consortium: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25(1):25–29. 10.1038/75556PubMed CentralView ArticleGoogle Scholar
- Draghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA: Global functional profiling of gene expression. Genomics 2003, 81(2):98–104. 10.1016/S0888-7543(02)00021-6View ArticlePubMedGoogle Scholar
- Young A, Whitehouse N, Cho J, Shaw C: OntologyTraverser: an R package for GO analysis. Bioinformatics 2005, 21(2):275–276. 10.1093/bioinformatics/bth495View ArticlePubMedGoogle Scholar
- Pandey R, Guru RK, Mount DW: Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data. Bioinformatics 2004, 20(13):2156–2158. 10.1093/bioinformatics/bth215View ArticlePubMedGoogle Scholar
- Beissbarth T, Speed TP: GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 2004, 20(9):1464–1465. 10.1093/bioinformatics/bth088View ArticlePubMedGoogle Scholar
- Masseroli M, Martucci D, Pinciroli F: GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining. Nucleic Acids Res 2004, 32(Web Server issue):W293–300.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang B, Schmoyer D, Kirov S, Snoddy J: GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics 2004, 5: 16. 10.1186/1471-2105-5-16PubMed CentralView ArticlePubMedGoogle Scholar
- Ben-Shaul Y, Bergman H, Soreq H: Identifying subtle interrelated changes in functional gene categories using continuous measures of gene expression. Bioinformatics 2005, 21(7):1129–1137. 10.1093/bioinformatics/bti149View ArticlePubMedGoogle Scholar
- Al-Shahrour F, Diaz-Uriarte R, Dopazo J: FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 2004, 20(4):578–580. 10.1093/bioinformatics/btg455View ArticlePubMedGoogle Scholar
- Berriz GF, King OD, Bryant B, Sander C, Roth FP: Characterizing gene sets with FuncAssociate. Bioinformatics 2003, 19(18):2502–2504. 10.1093/bioinformatics/btg363View ArticlePubMedGoogle Scholar
- Castillo-Davis CI, Hartl DL: GeneMerge--post-genomic analysis, data mining, and hypothesis testing. Bioinformatics 2003, 19(7):891–892. 10.1093/bioinformatics/btg114View ArticlePubMedGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B 1995, 57(1):289–300.Google Scholar
- Manly KF, Nettleton D, Hwang JT: Genomics, prior probability, and statistical tests of multiple hypotheses. Genome Res 2004, 14(6):997–1001. 10.1101/gr.2156804View ArticlePubMedGoogle Scholar
- Reiner A, Yekutieli D, Benjamini Y: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003, 19(3):368–375. 10.1093/bioinformatics/btf877View ArticlePubMedGoogle Scholar
- Ge YC, Dudoit S, Speed TP: Resampling-based multiple testing for microarray data analysis. Test 2003, 12(1):1–77.View ArticleGoogle Scholar
- Barry WT, Nobel AB, Wright FA: Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 2005, 21(9):1943–1949. 10.1093/bioinformatics/bti260View ArticlePubMedGoogle Scholar
- Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003, 34(3):267–273. 10.1038/ng1180View ArticlePubMedGoogle Scholar
- Volinia S, Evangelisti R, Francioso F, Arcelli D, Carella M, Gasparini P: GOAL: automated Gene Ontology analysis of expression profiles. Nucleic Acids Res 2004, 32(Web Server issue):W492–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Zeeberg BR, Qin H, Narasimhan S, Sunshine M, Cao H, Kane DW, Reimers M, Stephens R, Bryant D, Burt SK, Elnekave E, Hari DM, Wynn TA, Cunningham-Rundles C, Stewart DM, Nelson D, Weinstein JN: High-Throughput GoMiner, an 'industrial-strength' integrative Gene Ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID). BMC Bioinformatics 2005, 6(1):168. 10.1186/1471-2105-6-168PubMed CentralView ArticlePubMedGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich AP, Pomeroy SL, Golub TR, Lander ES, Mesirov J: Gene set enrichment analysis: A knowledge based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102(43):15545–15550. 10.1073/pnas.0506580102PubMed CentralView ArticlePubMedGoogle Scholar
- Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ: Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci U S A 2005, 102(38):13544–13549. 10.1073/pnas.0506577102PubMed CentralView ArticlePubMedGoogle Scholar
- Osier MV, Zhao H, Cheung KH: Handling multiple testing while interpreting microarrays with the Gene Ontology Database. BMC Bioinformatics 2004, 5: 124. 10.1186/1471-2105-5-124PubMed CentralView ArticlePubMedGoogle Scholar
- Breslin T, Eden P, Krogh M: Comparing functional annotation analyses with Catmap. BMC Bioinformatics 2004, 5(1):193. 10.1186/1471-2105-5-193PubMed CentralView ArticlePubMedGoogle Scholar
- McDonald JH, Kreitman M: Adaptive protein evolution at the Adh locus in Drosophila. Nature 1991, 351(6328):652–654. 10.1038/351652a0View ArticlePubMedGoogle Scholar
- The_Chimpanzee_Sequencing_and_Analysis_Consortium: Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 2005, 437(7055):69–87. 10.1038/nature04072View ArticleGoogle Scholar
- Kelso J, Visagie J, Theiler G, Christoffels A, Bardien S, Smedley D, Otgaar D, Greyling G, Jongeneel CV, McCarthy MI, Hide T, Hide W: eVOC: a controlled vocabulary for unifying gene expression data. Genome Res 2003, 13(6A):1222–1230. 10.1101/gr.985203PubMed CentralView ArticlePubMedGoogle Scholar
- Yekutieli D, Benjamini Y: Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. J STAT PLAN INFER 1999, 82(1–2):171–196. 10.1016/S0378-3758(99)00041-5View ArticleGoogle Scholar
- Westfall PH, Young SS: Resampling-Based Multiple Testing: Examples and Methods for p-value Adjustment. New York , John Wiley & Sons, Inc.; 1993.Google Scholar
- Velculescu VE, Zhang L, Vogelstein B, Kinzler KW: Serial analysis of gene expression. Science 1995, 270(5235):484–487. 10.1126/science.270.5235.484View ArticlePubMedGoogle Scholar
- Fay JC, Wu CI: Sequence divergence, functional constraint, and selection in protein evolution. Annu Rev Genomics Hum Genet 2003, 4: 213–235. 10.1146/annurev.genom.4.020303.162528View ArticlePubMedGoogle Scholar
- Damian D, Gorfine M: Statistical concerns about the GSEA procedure. Nat Genet 2004, 36(7):663; author reply 663. 10.1038/ng0704-663aView ArticlePubMedGoogle Scholar
- Hsueh H, Chen JJ, Kodell RL: Comparison of methods for estimating the number of true null hypotheses in multplicity testing . J Biopharm Stat 2003, 13(4):675–689. 10.1081/BIP-120024202View ArticlePubMedGoogle Scholar
- Benjamini Y, Yekutieli D: The control of the false discovery rate in multiple testing under dependency. Ann Stat 2001, 29: 1165–1188. 10.1214/aos/1013699998View ArticleGoogle Scholar
- Alexa A, Rahnenfuhrer J, Lengauer T: Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 2006, 22(13):1600–1607. 10.1093/bioinformatics/btl140View ArticlePubMedGoogle Scholar
- Gilad Y, Man O, Paabo S, Lancet D: Human specific loss of olfactory receptor genes. Proc Natl Acad Sci U S A 2003, 100(6):3324–3327. 10.1073/pnas.0535697100PubMed CentralView ArticlePubMedGoogle Scholar
- Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S, Bussey KJ, Riss J, Barrett JC, Weinstein JN: GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 2003, 4(4):R28. 10.1186/gb-2003-4-4-r28PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.