MOTIVATION: Modern molecular technologies allow the collection of large amounts of high-throughput data on the functional attributes of genes. Often multiple technologies and study designs are used to address the same biological question such as which genes are overexpressed in a specific disease state. Consequently, there is considerable interest in methods that can integrate across datasets to present a unified set of predictions. RESULTS: An important aspect of data integration is being able to account for the fact that datasets may differ in how accurately they capture the biological signal of interest. While many methods to address this problem exist, they always rely either on dataset internal statistics, which reflect data structure and not necessarily biological relevance, or external gold standards, which may not always be available. We present a new rank aggregation method for data integration that requires neither external standards nor internal statistics but relies on Bayesian reasoning to assess dataset relevance. We demonstrate that our method outperforms established techniques and significantly improves the predictive power of rank-based aggregations. We show that our method, which does not require an external gold standard, provides reliable estimates of dataset relevance and allows the same set of data to be integrated differently depending on the specific signal of interest. AVAILABILITY: The method is implemented in R and is freely available at http://www.pitt.edu/~mchikina/BIRRA/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Identifying alterations in gene expression associated with different clinical states is important for the study of human biology. However, clinical samples used in gene expression studies are often derived from heterogeneous mixtures with variable cell-type composition, complicating statistical analysis. Considerable effort has been devoted to modeling sample heterogeneity, and presently, there are many methods that can estimate cell proportions or pure cell-type expression from mixture data. However, there is no method that comprehensively addresses mixture analysis in the context of differential expression without relying on additional proportion information, which can be inaccurate and is frequently unavailable. RESULTS: In this study, we consider a clinically relevant situation where neither accurate proportion estimates nor pure cell expression is of direct interest, but where we are rather interested in detecting and interpreting relevant differential expression in mixture samples. We develop a method, Cell-type COmputational Differential Estimation (CellCODE), that addresses the specific statistical question directly, without requiring a physical model for mixture components. Our approach is based on latent variable analysis and is computationally transparent; it requires no additional experimental data, yet outperforms existing methods that use independent proportion measurements. CellCODE has few parameters that are robust and easy to interpret. The method can be used to track changes in proportion, improve power to detect differential expression and assign the differentially expressed genes to the correct cell type.

MOTIVATION: ChIPseq is rapidly becoming a common technique for investigating protein-DNA interactions. However, results from individual experiments provide a limited understanding of chromatin structure, as various chromatin factors cooperate in complex ways to orchestrate transcription. In order to quantify chromtain interactions, it is thus necessary to devise a robust similarity metric applicable to ChIPseq data. Unfortunately, moving past simple overlap calculations to give statistically rigorous comparisons of ChIPseq datasets often involves arbitrary choices of distance metrics, with significance being estimated by computationally intensive permutation tests whose statistical power may be sensitive to non-biological experimental and post-processing variation. RESULTS: We show that it is in fact possible to compare ChIPseq datasets through the efficient computation of exact P-values for proximity. Our method is insensitive to non-biological variation in datasets such as peak width, and can rigorously model peak location biases by evaluating similarity conditioned on a restricted set of genomic regions (such as mappable genome or promoter regions). Applying our method to the well-studied dataset of Chen et al. (2008), we elucidate novel interactions which conform well with our biological understanding. By comparing ChIPseq data in an asymmetric way, we are able to observe clear interaction differences between cofactors such as p300 and factors that bind DNA directly. AVAILABILITY: Source code is available for download at http://sonorus.princeton.edu/IntervalStats/IntervalStats.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correctly evaluating functional similarities among homologous proteins is necessary for accurate transfer of experimental knowledge from one organism to another, and is of particular importance for the development of animal models of human disease. While the fact that sequence similarity implies functional similarity is a fundamental paradigm of molecular biology, sequence comparison does not directly assess the extent to which two proteins participate in the same biological processes, and has limited utility for analyzing families with several parologous members. Nevertheless, we show that it is possible to provide a cross-organism functional similarity measure in an unbiased way through the exclusive use of high-throughput gene-expression data. Our methodology is based on probabilistic cross-species mapping of functionally analogous proteins based on Bayesian integrative analysis of gene expression compendia. We demonstrate that even among closely related genes, our method is able to predict functionally analogous homolog pairs better than relying on sequence comparison alone. We also demonstrate that the landscape of functional similarity is often complex and that definitive "functional orthologs" do not always exist. Even in these cases, our method and the online interface we provide are designed to allow detailed exploration of sources of inferred functional similarity that can be evaluated by the user.

Tissue-specific gene expression plays a fundamental role in metazoan biology and is an important aspect of many complex diseases. Nevertheless, an organism-wide map of tissue-specific expression remains elusive due to difficulty in obtaining these data experimentally. Here, we leveraged existing whole-animal Caenorhabditis elegans microarray data representing diverse conditions and developmental stages to generate accurate predictions of tissue-specific gene expression and experimentally validated these predictions. These patterns of tissue-specific expression are more accurate than existing high-throughput experimental studies for nearly all tissues; they also complement existing experiments by addressing tissue-specific expression present at particular developmental stages and in small tissues. We used these predictions to address several experimentally challenging questions, including the identification of tissue-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues. We also investigate the role of tissue context in gene function through tissue-specific functional interaction networks. To our knowledge, this is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data.

MOTIVATION: Biological data generation has accelerated to the point where hundreds or thousands of whole-genome datasets of various types are available for many model organisms. This wealth of data can lead to valuable biological insights when analyzed in an integrated manner, but the computational challenge of managing such large data collections is substantial. In order to mine these data efficiently, it is necessary to develop methods that use storage, memory and processing resources carefully. RESULTS: The Sleipnir C++ library implements a variety of machine learning and data manipulation algorithms with a focus on heterogeneous data integration and efficiency for very large biological data collections. Sleipnir allows microarray processing, functional ontology mining, clustering, Bayesian learning and inference and support vector machine tasks to be performed for heterogeneous data on scales not previously practical. In addition to the library, which can easily be integrated into new computational systems, prebuilt tools are provided to perform a variety of common tasks. Many tools are multithreaded for parallelization in desktop or high-throughput computing environments, and most tasks can be performed in minutes for hundreds of datasets using a standard personal computer. AVAILABILITY: Source code (C++) and documentation are available at http://function.princeton.edu/sleipnir and compiled binaries are available from the authors on request.