Fast Gene Ontology based clustering for microarray experiments
© Ovaska et al; licensee BioMed Central Ltd. 2008
Received: 08 April 2008
Accepted: 21 November 2008
Published: 21 November 2008
Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses.
We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster.
Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.
A microarray experiment may result in hundreds of differentially expressed genes that are subject to interpretation and further analysis. As analysing these lists gene-by-gene is tedious and error prone, the genes in the lists are routinely annotated using Gene Ontology (GO) with an aim to identify statistically significant biological processes or pathways . However, statistical analysis of GO annotations can produce a very large number of significantly enriched or down-regulated biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses.
The GO project provides a species-independent controlled vocabulary for describing gene products (an RNA or protein product encoded by a gene) in terms of their biological processes, cellular components and molecular functions . The GO annotations are carried out by curators of several bioinformatics databases, so the GO database is constantly updated. The ontology defines terms that are linked together to form a directed acyclic graph. Gene products are annotated with a number of ontology terms. Annotation with a given term also implies annotation with all ancestors of the term.
In this study we present methodology and software to cluster genes based on their biological functionality using GO annotations. Integral part of the methodology is the ability to rapidly compute pair-wise distances between the gene annotation similarities.
Two approaches to gene similarity computation are graph structure -based (GS) and information content -based (IC) measures. GS-based methods use the hierarchical structure of GO in computing gene similarity. IC-based methods additionally consider the a priori probabilities, or information contents, of GO terms in a reference gene set. IC-based measures have been found to perform better than pure graph-based measures [2, 3].
where Δ is the symmetric set difference, # is the number of elements in a set and GO(G i ) is the set of GO annotations for gene G i . Similarity can be defined as 1 - d(G 1, G 2).
In Kappa statistics , each gene is represented as a binary vector (g 1,...,g N ), where g i is 1 if the gene is annotated with the GO term g i and 0 otherwise. N is the total number of GO terms under consideration.
where represents observed co-occurrence of GO terms and represents random co-occurrence. is the relative frequency of agreeing locations in the two binary vectors, i.e., locations that are either both 0 or both 1. is the expected relative frequency of such locations if the binary vectors were random, taking into account the observed probabilities of 0's and 1's.
The following discussion considers IC-based similarity measures. The information content of a GO term is computed by the frequency of the term occurring in annotations; a rarely used term contains a greater amount of information. Probability for observing a term t is defined as , where MaxFreq is the maximum frequency of all terms . The information content for a term t is given as IC(t) = -log2 p(t). Probabilities can be estimated from a corpus of annotations, such as the Gene Ontology database.
The MICA-based measures can be modified to take into account so called disjunctive ancestor terms . Two ancestors a 1 and a 2 of a term t are disjunctive if there are independent paths from a 1 to t and from a 2 to t. Such ancestors represent distinct interpretations of the term t. In the GraSM enhancement, all common disjunctive ancestors of terms t 1 and t 2 are considered when computing Sim(t 1, t 2) . GraSM modifies the computation of IC(A) and can be applied to the Resnik, Lin and Jiang-Conrath measures.
where GO(G i ) gives the GO annotations of gene G i . SimGIC is a hybrid of GS- and IC-based methods.
Given similarities between the genes we use hierarchical clustering with heat map presentation to visualise both semantic similarities and expression levels of the genes. First, similarity measures are converted to distances using d(x, y) = 1 - Sim(x, y) when the similarity range is [0, 1] (Czekanowski-Dice, Kappa, Lin, Jiang-Conrath, Relevance, Cosine, SimGIC) or using d(x, y) = 1/(Sim(x, y) + 1) when the range is [0, ∞) (Resnik). Second, a hierarchical clustering algorithm is run using the converted distances. The results are visualised as a dendrogram and heat map. The dendrogram is generated using the GO semantic distances and allows identification of clusters containing genes contributing to the same biological process. For each cluster we compute statistical significance with a permutation test. The heat map illustrates gene expression data obtained from microarray analysis. Thus, the visualisation framework integrates both functional gene expression levels to biological processes, which facilitates interpretation of the gene expression analysis results.
The semantic similarity package, csbl.go, is available for R . The package computes similarities for arbitrary number of genes and supports the following measures: Czekanowski-Dice, Kappa, Resnik (with GraSM as an option), Jiang-Conrath (GraSM), Lin (GraSM), Relevance, Cosine and SimGIC. The MICA-based measures (Resnik, Lin, Jiang-Conrath, Relevance and GraSM enhancements) are implemented as a combination of R and C++ code; the four other measures are implemented in R. In addition to the regular R package, csbl.go is available as a component for Anduril , a framework for high-throughput data analysis we recently developed. The package is extensively tested and includes a user guide.
Similarity computation needs GO term probabilities for the reference gene set. We provide precomputed probability tables for Homo sapiens, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Rattus norvegicus. The tables are computed based on all gene and protein annotations for the given organism found in the geneontology.org database. As GO is constantly updated and revised, we update the tables every six months. The package also has an option to use custom tables. The taxonomy ID of the organism is stored along with probability tables as metadata, which enables selection of a table by organism ID. The package also includes an option to compute GO term enrichment using Fisher's Exact Test .
Results and discussion
We evaluated the package by using a performance benchmark and by applying the methods to microarray data from a testicular germ cell tumor study .
We compared the performance of our package to two earlier introduced semantic similarity packages, SemSim 1.6.0  and GOSim 126.96.36.199 . The benchmark computes semantic similarities for GO term set sizes 50, 100 and 200. For csbl.go and SemSim, the measures Resnik, Jiang-Conrath, Lin and Relevance are used in the benchmark. GOSim does not support the Relevance measure so only the three other measures are used for it. The GraSM enhancement was not used in the benchmark as SemSim does not support GraSM.
The benchmark computes a symmetric n × n similarity matrix for the GO term sets. The three packages handle matrix computation in different ways. GOSim and csbl.go take a single term list and compute the symmetric matrix by computing half of of the pair-wise similarities (n 2/2) and mirroring the matrix by the diagonal. SemSim takes two potentially different term lists and computes all n 2 pair-wise similarities. To compare the packages, we halved the execution times of SemSim in order to consider a situation where all packages perform n 2/2 operations. The benchmark computes GO term similarities instead of gene similarities because the former is the most time-consuming part of similarity computation.
Number of GO terms
GOSim (vs. csbl.go)
SemSim (vs. csbl.go)
4.9 s (2399 ×)
4.5 s (2158 ×)
19.5 s (3219 ×)
17.4 s (2880 ×)
77.8 s (3245 ×)
71.0 s (2963 ×)
7.4 s (3612 ×)
4.5 s (2167 ×)
29.5 s (4284 ×)
17.4 s (2528 ×)
117.8 s (4894 ×)
71.0 s (2950 ×)
7.4 s (3590 ×)
4.5 s (2157 ×)
29.5 s (4274 ×)
17.4 s (2525 ×)
117.5 s (5043 ×)
70.9 s (3043 ×)
4.5 s (2062 ×)
17.4 s (2400 ×)
71.1 s (2866 ×)
As a case study, we applied similarity measures to identify common GO classes for differentially expressed genes involved in testicular germ cell tumors (TGCTs). The TGCT microarray study here consists of five undifferentiated embryonal carcinoma samples and 12 differentiated testicular cell samples, which include both tumors and healthy samples .
We re-analysed the data set with the goal of finding differentially expressed genes (DEGs) between four undifferentiated samples (EC_0502, EC_0564, EC_1017 and EC_1740) and 10 differentiated samples (Cc_0915, N_9013, N_9014, N_0140, Ter_0691, Ter_0696, YST_0216, YST_0307, YST_0738, YST_2110). Three samples (EC_1838, Ter_1282 and Ter_2201) were excluded due to data quality problems. Data from the two-channel Agilent Human 1A were background corrected and processed with LOWESS . DEGs were selected using t-test followed by false discovery rate correction . We obtained 65 genes that have q-value below 0.1 and have also fold change of at least 1.5. We found GO annotations for 58 of the 65 genes using Ensembl version 50 . Among the 58 genes, the median number of GO annotations per gene is eight.
Genes corresponding to the most statistically significant clusters found in the case study.
PDCL3 MAGED1 (two probe sets) PRKCE PRDX1 CLIC4 MRPS23
CBR3 RANBP17 NBEA FVT1
NANOGP8 ZNF215 POU5F1 MYBL2 L1TD1 CITED2 TCEA2 SMARCAD1
MKI67IP CPSF4 PPFIBP2 WDSUB1 PPP3CA ISG20L1 TIPARP CEP290
DPPA4 TJP2 NLRP7
TLR5 OR5R1 TMEM106C IFITM1 PCDHB5 PCDHB11 AC069513.28 PLK3
PDGFA IGSF21 GDF3 CCDC80 GAL TF
Most informative GO terms for the clusters obtained from microarray data.
aminoacyl-tRNA ligase activity
carbon-sulfur lyase activity
primary metabolic process
negative regulation of biological process
cellular protein metabolic process
integral to membrane
The cluster G 5 consists of two genes: cystathionase (CTH) and glyoxalase I (GLO1). These two genes correlate strongly in their GO terms as their extremely high IC-value of 12.0 indicates. Also their gene expression patterns are almost identical across the samples as shown in the heat map in Figure 1. GLO1 is a glutathione-binding protein that contributes to several pathways that are associated with various diseases, such as cancers . As glutathione plays a key role in the process where tumor cells acquire resistance to anti-cancer drugs, GLO1 inhibitors are considered as potential anti-cancer agents [22, 23]. CTH is a critical factor in glutathione synthesis and has recently been associated with increased risk of bladder cancer . While detailed discussion of the exact roles of CTH and GLO1 in embryonal carcinomas is out of scope of this study, our results suggest that GLO1 and CTH may function in concert, and contribute to tumor progression and drug resistance in embryonic cancers.
KEGG pathways for differentially expressed genes.
Regulation of autophagy
Tight junction, Fc epsilon RI signaling pathway, Type II diabetes mellitus
Arachidonic acid metabolism
Tryptophan metabolism, Aminoacyl-tRNA biosynthesis
MAPK signaling pathway, Calcium signaling pathway, Apoptosis, Wnt signaling pathway, Axon guidance, VEGF signaling pathway, Natural killer cell mediated cytotoxicity, T cell receptor signaling pathway, B cell receptor signaling pathway, Long-term potentiation, Amyotrophic lateral sclerosis (ALS)
Tight junction, Vibrio cholerae infection
Glycine, serine and threonine metabolism, Methionine metabolism, Cysteine metabolism, Selenoamino acid metabolism, Nitrogen metabolism
Complement and coagulation cascades
Glycerolipid metabolism, Glycerophospholipid metabolism, Ether lipid metabolism, Sphingolipid metabolism
Toll-like receptor signaling pathway, Pathogenic Escherichia coli infection -EHEC and EPEC
B cell receptor signaling pathway
MAPK signaling pathway, Focal adhesion, Gap junction, Regulation of actin cytoskeleton, Glioma, Prostate cancer, Melanoma
We have developed tools to cluster genes from microarray experiments using semantic similarity measures. Using benchmark tests we demonstrated clear speed gain as compared to existing implementations. Our efficient implementation of similarity measures enables analysis of gene sets with hundreds of genes that are typically seen in microarray experiments. We then combined expression data and GO annotations using hierarchical clustering and a heat map visualisation that together enable rapid identification of genes sharing similar biological functions. In our case study we further analysed genes that are differentially expressed in testicular germ cell tumors between undifferentiated embryonal carcinomas and differentiated testicular cells. Our results suggest that GO-based annotation analysis approaches may be able to take advantage of the accumulated knowledge available in literature over approaches using pathway databases, which are typically updated in a much slower pace than the GO database. In summary, the csbl.go package allows rapid visualisation of gene GO and expression profiles, and thereby facilitates hypothetisising gene functions in cells.
Availability and requirements
Project name: csbl.go
Project home page: http://www.ltdk.helsinki.fi/sysbio/csb/downloads/GeneOntologyHeatmap/
Operating system(s): Platform independent; tested on Windows and Linux
Programming language: R (version 2.6 or greater)
License: GNU General Public License
We thank Dr Rolf I Skotheim for providing the testicular germ cell tumor microarray data. Financial support from Helsinki University Funds, Sigrid Jusélius Foundation, Biocentrum Helsinki, Academy of Finland (project 125826) and the Graduate School in Computational Biology, Bioinformatics, and Biometry (ComBi) is gratefully acknowledged.
- Ashburner M, Ball C, Blake J, Botstein D, Butler H, Cherry J, Davis A, Dolinski K, Dwight S, Eppig J, Harris M, Hill D, Issel-Tarver L, Kasarskis A, Lewis S, Matese J, Richardson J, Ringwald M, Rubin G, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25-9. 10.1038/75556.View ArticlePubMedPubMed CentralGoogle Scholar
- Guo X, Liu R, Shriver C, Hu H, Liebman M: Assessing semantic similarity measures for the characterization of human regulatory pathways. Bioinformatics. 2006, 22 (8): 967-10.1093/bioinformatics/btl042.View ArticlePubMedGoogle Scholar
- Pesquita C, Faria D, Bastos H, Ferreira A, Falcão A, Couto F: Metrics for GO based protein semantic similarity: a systematic evaluation. BMC Bioinformatics. 2008, 9 (5): S4-10.1186/1471-2105-9-S5-S4.View ArticlePubMedPubMed CentralGoogle Scholar
- Brun C, Chevenet F, Martin D, Wojcik J, Guenoche A, Jacq B: Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. GENOME BIOLOGY. 2004, 5: 6-6. 10.1186/gb-2003-5-1-r6.View ArticleGoogle Scholar
- Huang D, Sherman B, Tan Q, Collins J, Alvord W, Roayaei J, Stephens R, Baseler M, Lane H, Lempicki R: The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007, 8 (9): R183-10.1186/gb-2007-8-9-r183.View ArticlePubMedPubMed CentralGoogle Scholar
- Couto FM, Silva MJ, Coutinho PM: Measuring semantic similarity between Gene Ontology terms. Data Knowl Eng. 2007, 61: 137-152. 10.1016/j.datak.2006.05.003.View ArticleGoogle Scholar
- Lord P, Stevens R, Brass A, Goble C: Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics. 2003, 19 (10): 1275-1283. 10.1093/bioinformatics/btg153.View ArticlePubMedGoogle Scholar
- Resnik P: Using information content to evaluate semantic similarity in a taxonomy. Proceedings of the 14th International Joint Conference on Artificial Intelligence. 1995, 1: 448-453.Google Scholar
- Lin D: An information-theoretic defiition of similarity. Proceedings of the 15th International Conference on Machine Learning. 1998, 296-304.Google Scholar
- Jiang J, Conrath D: Semantic similarity based on corpus statistics and lexical taxonomy. Proceedings of International Conference on Research in Computational Linguistics. 1997, 19-33.Google Scholar
- Schlicker A, Domingues F, Rahnenführer J, Lengauer T: A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics. 2006, 7: 302-10.1186/1471-2105-7-302.View ArticlePubMedPubMed CentralGoogle Scholar
- Frohlich H, Speer N, Poustka A, Beißbarth T: GOSim-An R-package for computation of information theoretic GO similarities between terms and gene products. BMC Bioinformatics. 2007, 8: 166-10.1186/1471-2105-8-166.View ArticlePubMedPubMed CentralGoogle Scholar
- Bodenreider O, Aubry M, Burgun A: Non-lexical approaches to identifying associative relations in the Gene Ontology. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2005, 91-Google Scholar
- R Development Core Team: R: A Language and Environment for Statistical Computing. 2007, R Foundation for Statistical Computing, Vienna, Austria, [http://www.R-project.org]Google Scholar
- Anduril framework. [http://csbi.ltdk.helsinki.fi/anduril/]
- Good P: Permutation tests: a practical guide to resampling methods for testing hypotheses. 2000, Springer series in statisticsView ArticleGoogle Scholar
- Skotheim R, Lind G, Monni O, Nesland J, Abeler V, Fossa S, Duale N, Brunborg G, Kallioniemi O, Andrews P, Lothe R: Differentiation of human embryonal carcinomas in vitro and in vivo reveals expression profiles relevant to normal development. Cancer Research. 2005, 65 (13): 5588-5598. 10.1158/0008-5472.CAN-05-0153.View ArticlePubMedGoogle Scholar
- SemSim package. [http://bioconductor.org/packages/2.1/bioc/html/SemSim.html]
- Draghici S: Data Analysis Tools for DNA Microarrays. 2003, Chapman & Hall/CRCView ArticleGoogle Scholar
- Pounds S, Cheng C: Robust estimation of the false discovery rate. Bioinformatics. 2006, 22 (16): 1979-10.1093/bioinformatics/btl328.View ArticlePubMedGoogle Scholar
- Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J, Curwen V, Down T, Durbin R, Eyras E, Gilbert J, Hammond M, Huminiecki L, Kasprzyk A, Lehvaslaiho H, Lijnzaad P, Melsopp C, Mongin E, Pettett R, Pocock M, Potter S, Rust A, Schmidt E, Searle S, Slater G, Smith J, Spooner W, Stabenau A: The Ensembl genome database project. Nucleic Acids Research. 2002, 30: 38-10.1093/nar/30.1.38.View ArticlePubMedPubMed CentralGoogle Scholar
- Laga M, Cottyn A, Van Herreweghe F, Berghe W, Haegeman G, Van Oostveldt P, Vandekerckhove J, Vancompernolle K: Methylglyoxal suppresses TNF-α-induced NF-κB activation by inhibiting NF-κB DNA-binding. Biochemical Pharmacology. 2007, 74 (4): 579-589. 10.1016/j.bcp.2007.05.026.View ArticlePubMedGoogle Scholar
- Balendiran G, Dabur R, Fraser D: The role of glutathione in cancer. Cell Biochemistry And Function. 2004, 22: 343-352. 10.1002/cbf.1149.View ArticlePubMedGoogle Scholar
- Moore L, Malats N, Rothman N, Real F, Kogevinas M, Karami S, Garcia-Closas R, Silverman D, Chanock S, Welch R, Tardffon A, Serra C, Carrato A, Dosemeci M, García-Closas M: Polymorphisms in one-carbon metabolism and trans-sulfuration pathway genes and susceptibility to bladder cancer. Int J Cancer. 2007, 120 (11): 2452-8. 10.1002/ijc.22565.View ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research. 2000, 28: 27-10.1093/nar/28.1.27.View ArticlePubMedPubMed CentralGoogle 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.