SUMMARY: In this article, we introduce a hierarchical clustering and Gaussian mixture model with expectation-maximization (EM) algorithm for detecting copy number variants (CNVs) using whole exome sequencing (WES) data. The R shiny package 'HCMMCNVs' is also developed for processing user-provided bam files, running CNVs detection algorithm and conducting visualization. Through applying our approach to 325 cancer cell lines in 22 tumor types from Cancer Cell Line Encyclopedia (CCLE), we show that our algorithm is competitive with other existing methods and feasible in using multiple cancer cell lines for CNVs estimation. In addition, by applying our approach to WES data of 120 oral squamous cell carcinoma (OSCC) samples, our algorithm, using the tumor sample only, exhibits more power in detecting CNVs as compared with the methods using both tumors and matched normal counterparts. AVAILABILITY AND IMPLEMENTATION: HCMMCNVs R shiny software is freely available at github repository https://github.com/lunching/HCMM_CNVs.and Zenodo https://doi.org/10.5281/zenodo.4593371. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The rapid advances of omics technologies have generated abundant genomic data in public repositories and effective analytical approaches are critical to fully decipher biological knowledge inside these data. Meta-analysis combines multiple studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond individual study analysis. To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful guidelines exist. Here, we introduce a comprehensive analytical pipeline and browser-based software suite, called MetaOmics, to meta-analyze multiple transcriptomic studies for various biological purposes, including quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic meta-analytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions. AVAILABILITY AND IMPLEMENTATION: MetaOmics is freely available at https://github.com/metaOmics/metaOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

BACKGROUND: Detecting candidate markers in transcriptomic studies often encounters difficulties in complex diseases, particularly when overall signals are weak and sample size is small. Covariates including demographic, clinical and technical variables are often confounded with the underlying disease effects, which further hampers accurate biomarker detection. Our motivating example came from an analysis of five microarray studies in major depressive disorder (MDD), a heterogeneous psychiatric illness with mostly uncharacterized genetic mechanisms. RESULTS: We applied a random intercept model to account for confounding variables and case-control paired design. A variable selection scheme was developed to determine the effective confounders in each gene. Meta-analysis methods were used to integrate information from five studies and post hoc analyses enhanced biological interpretations. Simulations and application results showed that the adjustment for confounding variables and meta-analysis improved detection of biomarkers and associated pathways. CONCLUSIONS: The proposed framework simultaneously considers correction for confounding variables, selection of effective confounders, random effects from paired design and integration by meta-analysis. The approach improved disease-related biomarker and pathway detection, which greatly enhanced understanding of MDD neurobiology. The statistical framework can be applied to similar experimental design encountered in other complex and heterogeneous diseases.

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: 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.

BACKGROUND: The study of microRNAs (miRNAs) is attracting great considerations. Recent studies revealed that miRNAs play as important regulators of gene expression and some even as cancer players or inhibitors. Many studies try to discover new miRNAs and reveal the miRNA expression profile in cancer using a SAGE-based total RNA clone method. However, the data processing of this method is labor-intensive with several different biological databases and more than ten data processing steps involved. RESULTS: With miRAS, miRNAs and possible miRNA candidates contained in the submitted sequencing data were obtained together with their expression profile. The functions of known and predicted miRNAs were then analyzed by miRNA target prediction followed by target gene annotations. Finally, to extract the biological significance of the miRNAs in the samples, further annotations of the known miRNA and target genes were performed by collecting the public expression datasets of miRNA and target genes in normal and cancer tissues. CONCLUSION: We introduce a web-based analysis platform called miRNA Analysis System (miRAS), for processing and analyzing of the sequence data obtained from the total RNA clone method. The system was built on generalizing the study of a liver cancer cell line total RNA sequencing project. miRAS is freely available on the web.