Pattern-Based Gene-Set Recognition for Interpreting Genome-Wide Gene Expression Profiles

Background: Accurate recognition of important gene sets from genome-wide gene expression profiles provides great insights into the underlying biological mechanisms that govern the gene expression dynamics. However, most gene set recognition algorithms rely solely on supervised sample phenotypic information, overlooking the unsupervised gene-gene expression correlations that are inherently informative in the gene expression profiles. Results: We developed a computational framework named PAGER (Pattern Acquisition and GEne-set Recognition) for identifying gene sets showing significant supervised and unsupervised patterns. We showcased the use of PAGER in several recent expression profiling studies including cadmium treated rat primary hepatocyte toxicogenomics study and adrenal gland periodical gene expression profiling. Our results indicate that PAGER achieved better performance in discovering truly important pathways from expression profiles which were undetected using current other existing tools. These results were further corroborated by literature and cytotoxicity experiments. Conclusions: PAGER integrated both supervised and unsupervised pattern metrics for gene set summarization. For each given gene set, PAGER provides a two-dimensional view showing its external activity and internal coherence pattern. PAGER employed statistical methods such as Relaxed Intersection-Union Tests, Stouffer’s method and Fisher’s method for integration of pattern significance. In addition, PAGER can be used for recognizing user-defined arbitrary gene set as demonstrated in one of our previous publications. PAGER is freely available for academic user at http://dengx.bol. ucla.edu/PAGER/PAGER.htm. BACKGROUND Gene set recognition, a major apparatus for interpreting gene expression profiles, has been employed in a wide range of microarray-based studies ranging from Drosophila heart formation to human cancer mechanisms [1-9]. Gene set recognition refers to the computational task of identifying previously curated gene sets such as regulatory pathways and gene ontology. The appealing feature of gene set recognition comes from its capability to identify signal genes as functional modules that give rise to more biologically interpretable results rather than sporadic significant genes. The basic and standard approach to gene set recognition is to use Fisher’s Exact Test (FET) to compare the microarray-derived gene list against each of the pathways, gene ontology groups in standard database such as GenMAPP [10;11] and Gene Ontology [12]. This simple FET approach has been implemented in many data analysis tools [13-17]. In fact, virtually all gene set recognition algorithms involves in three basic steps: (1) each gene is assigned a score according to certain hypothesis; (2) for a specific gene set, a summarization statistic, called the enrichment score is computed from individual gene scores; and (3) a significance value is computed for each gene set enrichment score. For instance, the FET approach requires the users to retrieve the candidate gene *Address correspondence to these authors at the 8700 Beverly Blvd., Davis Bldg. , G150, Los Angeles, CA90048, USA; Tel: 1-310-423-7361; Fax: 1310-423-2303; E-mail: charles.wang@cshs.org 8700 Beverly Blvd., Davis Bldg. , G151, Los Angeles, CA90048, USA; Tel: 1-310-423-7363; Fax: 1-310-423-2303; E-mail: dengx@ucla.edu lists from the gene expression profiles, which is essentially equivalent to assigning a binary score for each gene indicating whether the gene is of interest or not. Then for each predefined gene set the enrichment score is defined as the proportion interesting genes. Finally, this enrichment score is tested against background of all genes using FET. An obvious drawback of FET is that the dependence on gene lists requires an arbitrary cutoff value for binary scoring, which often leads to inconsistent biological interpretations [18-20]. Subramanian, Tamayo, and Mootha [18] proposed an algorithm named Gene Set Enrichment Analysis (GSEA) which used continuous scoring used for individual gene and gene sets. GSEA uses Kolmogorov–Smirnov statistic as an enrichment score for each pathway, and then significance acquisition and multiplicity adjustment procedures are performed based on the enrichment scores. Subsequently, an array of modifications, primarily targeting on different summarizing statistic and significance acquisition techniques, have been proposed [21-27]. One common feature of the above mentioned methods is their heavy dependence on sample phenotypic information such as disease status, drug treatment, age, gender, which makes them a group of supervised gene set recognition methods. This feature, however, limited their usage in gene expression profiling studies for the following three reasons: (1) the manually curated gene sets in knowledgebase represent established functional modules, and their inherent orchestrated expression patterns are a major signature of the gene sets but were overlooked by the supervised methods; (2) as the gene expression profiling studies become increas2 The Open Bioinformatics Journal, 2008, Volume 2 Deng and Wang ingly complex, often involved with multiple phenotypes and unknown subgroups, the supervised scoring metric based on gene-phenotype correlations are becoming more difficult to define; and (3) some gene expression profiling studies employ unsupervised design by focusing on discovering coregulated gene expression pattern [28-30]. Because of these drawbacks, the supervised gene set recognition methods are only effective in simple studies such as a comparison between a treatment group and control group. The challenge hence is to define and capture the true pattern of the gene sets given the expression profiles. We distinguish the two aspects of the gene set pattern: the supervised gene set activity defined by the gene-phenotype correlation structure and the unsupervised gene set coherence defined by the gene-gene correlation structure. The coherence of each gene set not only served as a complement to the gene set activity, in some cases, it must be incorporated into gene set activity to avoid reduced recognition sensitivity and specificity [31]. For instance, a highly coherent pathway with low activity may not be important at all if the pathway genes that are constantly expressed across all samples. On the other hand, a highly active pathway with low coherence profiles may indicate its genes were activated through other pathways. In this paper, we developed a computational framework named PAGER (Pattern Acquisition and GEne-set Recognition) which provides a two-dimensional view on gene sets defined by supervised activity and unsupervised coherence. PAGER allows one to choose activity or coherence or both for gene set recognition. For activity and coherence metrics, PAGER offers a set of expandable options based on specific study design. The major feature of PAGER hinges on its emphasis on its integration of both gene set coherence and activity for gene set recognition. Currently, the options on gene set coherence are generally not available in other software, but nevertheless important for studies employing complex design. Additionally, PAGER emphasizes a statistically justified integration method we developed before [32], in which the error is rigorously controlled and statistical power is improved over classical methods. As demonstrated in one of our published studies, PAGER was proved to be a powerful tool in identifying a set of known stem cell regulation genes in responding to the liver injury, providing evidence supporting our hypothesis that some factors released from the injured rat liver can promote bone marrow hepatic stem cells priming [33]. METHODS The computational framework of PAGER consists of five steps: scoring, summarization, significance acquisition, integration, and adjustment. As described, although the first three steps were similar to many existing algorithms, PAGER computes both gene set activity and coherence with new summarization options. Then for each gene set, its significance on coherence and activity were combined based on statistically principled methods to identify the gene sets that are active and/or coherent. In the scoring step, the pair-wise gene-gene coherence (GGC) and gene-phenotype activity (GPA) for each gene were computed according to user selected metrics. The gene level scoring was subsequently summarized into gene set level enrichment score which consists of coherence C(P) and activity A(P) for each gene set P. The summarization is followed by the significance acquisition in which the p-values of C(P) and A(P) were obtained based on the comparison with randomized gene sets which are used as the background null distribution. For each gene set P, its p-values of coherence and activity were then comFig. (1). Illustration of PAGER framework for gene set recognition. 1. Each gene set is superimposed onto the gene expression data 2. The GPA is computed for each gene in the gene set 3. The GGC is computed for each pair of genes 4. GPA vector is summarized into the gene set activity 5. GGC matrix is summarized into gene set coherence 6. The permuted version of the gene set is generated for significance acquisition 7. The significance of gene set activity and coherence are integrated 8. FDR is computed after all gene sets were processed for multiplicity adjustment. Coherence 0.0 0.2 0.4 0.6 0.8 1.0 A c ti v it y