SUMMARY: With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case-control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines. AVAILABILITY: http://www.biostat.pitt.edu/bioinfo/software.htm CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Systematic information integration of multiple-related microarray studies has become an important issue as the technology becomes mature and prevalent in the past decade. The aggregated information provides more robust and accurate biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multi-class studies and considering expression pattern concordance are rarely explored. RESULTS: In this article, we develop three integration methods for biomarker detection in multiple multi-class microarray studies: ANOVA-maxP, min-MCC and OW-min-MCC. We first consider a natural extension of combining P-values from the traditional ANOVA model. Since P-values from ANOVA do not guarantee to reflect the concordant expression pattern information across studies, we propose a multi-class correlation (MCC) measure to specifically seek for biomarkers of concordant inter-class patterns across a pair of studies. For both ANOVA and MCC approaches, we use extreme order statistics to identify biomarkers differentially expressed (DE) in all studies (i.e. ANOVA-maxP and min-MCC). The min-MCC method is further extended to identify biomarkers DE in partial studies by incorporating a recently developed optimally weighted (OW) technique (OW-min-MCC). All methods are evaluated by simulation studies and by three meta-analysis applications to multi-tissue mouse metabolism datasets, multi-condition mouse trauma datasets and multi-malignant-condition human prostate cancer datasets. The results show complementary strength of the three methods for different biological purposes. AVAILABILITY: http://www.biostat.pitt.edu/bioinfo/. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

MOTIVATION: Traditional genomic prediction models based on individual genes suffer from low reproducibility across microarray studies due to the lack of robustness to expression measurement noise and gene missingness when they are matched across platforms. It is common that some of the genes in the prediction model established in a training study cannot be matched to another test study because a different platform is applied. The failure of inter-study predictions has severely hindered the clinical applications of microarray. To overcome the drawbacks of traditional gene-based prediction (GBP) models, we propose a module-based prediction (MBP) strategy via unsupervised gene clustering. RESULTS: K-means clustering is used to group genes sharing similar expression profiles into gene modules, and small modules are merged into their nearest neighbors. Conventional univariate or multivariate feature selection procedure is applied and a representative gene from each selected module is identified to construct the final prediction model. As a result, the prediction model is portable to any test study as long as partial genes in each module exist in the test study. We demonstrate that K-means cluster sizes generally follow a multinomial distribution and the failure probability of inter-study prediction due to missing genes is diminished by merging small clusters into their nearest neighbors. By simulation and applications of real datasets in inter-study predictions, we show that the proposed MBP provides slightly improved accuracy while is considerably more robust than traditional GBP. AVAILABILITY: http://www.biostat.pitt.edu/bioinfo/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Many pathway analysis (or gene set enrichment analysis) methods have been developed to identify enriched pathways under different biological states within a genomic study. As more and more microarray datasets accumulate, meta-analysis methods have also been developed to integrate information among multiple studies. Currently, most meta-analysis methods for combining genomic studies focus on biomarker detection and meta-analysis for pathway analysis has not been systematically pursued. RESULTS: We investigated two approaches of meta-analysis for pathway enrichment (MAPE) by combining statistical significance across studies at the gene level (MAPE_G) or at the pathway level (MAPE_P). Simulation results showed increased statistical power of meta-analysis approaches compared to a single study analysis and showed complementary advantages of MAPE_G and MAPE_P under different scenarios. We also developed an integrated method (MAPE_I) that incorporates advantages of both approaches. Comprehensive simulations and applications to real data on drug response of breast cancer cell lines and lung cancer tissues were evaluated to compare the performance of three MAPE variations. MAPE_P has the advantage of not requiring gene matching across studies. When MAPE_G and MAPE_P show complementary advantages, the hybrid version of MAPE_I is generally recommended. AVAILABILITY: http://www.biostat.pitt.edu/bioinfo/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Reproducibility analyses of biologically relevant microarray studies have mostly focused on overlap of detected biomarkers or correlation of differential expression evidences across studies. For clinical utility, direct inter-study prediction (i.e. to establish a prediction model in one study and apply to another) for disease diagnosis or prognosis prediction is more important. Normalization plays a key role for such a task. Traditionally, sample-wise normalization has been a standard for inter-array and inter-study normalization. For gene-wise normalization, it has been implemented for intra-study or inter-study predictions in a few papers while its rationale, strategy and effect remain unexplored. RESULTS: In this article, we investigate the effect of gene-wise normalization in microarray inter-study prediction. Gene-specific intensity discrepancies across studies are commonly found even after proper sample-wise normalization. We explore the rationale and necessity of gene-wise normalization. We also show that the ratio of sample sizes in normal versus diseased groups can greatly affect the performance of gene-wise normalization and an analytical method is developed to adjust for the imbalanced ratio effect. Both simulation results and applications to three lung cancer and two prostate cancer data sets, considering both binary classification and survival risk predictions, showed significant and robust improvement of the new adjustment. A calibration scheme is developed to apply the ratio-adjusted gene-wise normalization for prospective clinical trials. The number of calibration samples needed is estimated from existing studies and suggested for future applications. The result has important implication to the translational research of microarray as a practical disease diagnosis and prognosis prediction tool.