Statistical Methods in Integrative Genomics

Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions

A Review of Integrative Imputation for Multi-Omics Datasets

Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and functional mechanisms for complex diseases, and facilitate new discoveries in precision medicine. However, omics datasets often contain missing values, and in multi-omics study designs it is common for individuals to be represented for some omics layers but not all. Since most statistical analyses cannot be applied directly to the incomplete datasets, imputation is typically performed to infer the missing values. Integrative imputation techniques which make use of the correlations and shared information among multi-omics datasets are expected to outperform approaches that rely on single-omics information alone, resulting in more accurate results for the subsequent downstream analyses. In this review, we provide an overview of the currently available imputation methods for handling missing values in bioinformatics data with an emphasis on multi-omics imputation. In addition, we also provide a perspective on how deep learning methods might be developed for the integrative imputation of multi-omics datasets

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Differential expression and feature selection in the analysis of multiple omics studies

With the rapid advances of high-throughput technologies in the past decades, various kinds of omics data have been generated from many labs and accumulated in the public domain. These studies have been designed for different biological purposes, including the identification of differentially expressed genes, the selection of predictive biomarkers, etc. Effective meta-analysis of omics data from multiple studies can improve statistical power, accuracy and reproducibility of single study. This dissertation covered a few methods for differential expression (Chapter 2 and 3) and feature selection (Chapter 4) in the analysis of multiple omics studies. In Chapter 2, we proposed a full Bayesian hierarchical model for RNA-seq meta-analysis by modeling count data, integrating information across genes and across studies, and modeling differential signals across studies via latent variables. A Dirichlet process mixture prior was further applied on the latent variables to provide categorization of detected biomarkers according to their differential expression patterns across studies. We used both simulations and a real application on multiple brain region HIV-1 transgenic rats to demonstrate improved sensitivity, accuracy and biological findings of our method. In Chapter 3, we extended the previous Bayesian model to jointly integrate transcriptomic data from the two platforms: microarray and RNA-seq. In Chapter 4, we considered a general framework for variable screening with multiple omics studies and further proposed a novel two-step screening procedure for high-dimensional regression analysis in this framework. Compared to the one-step procedure and rank-based sure independence screening procedure, our procedure greatly reduced false negative errors while keeping a low false positive rate. Theoretically, we showed that our procedure possesses the sure screening property with weaker assumptions on signal strengths and allows the number of features to grow at an exponential rate of the sample size. Public health significance: The proposed methods are useful in detecting important biomarkers that are either differentially expressed or predictive of clinical outcomes. This is essential for searching for potential drug targets and understanding the disease mechanism. Such findings in basic science can be translated into preventive medicine or potential treatment for disease to promote human health and improve the global healthcare system

Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology

Motivation: High-throughput data are now commonplace in biological research. Rapidly changing technologies and application mean that novel methods for detecting differential behaviour that account for a ‘large P, small n’ setting are required at an increasing rate. The development of such methods is, in general, being done on an ad hoc basis, requiring further development cycles and a lack of standardization between analyses. Results: We present here a generalized method for identifying differential behaviour within high-throughput biological data through empirical Bayesian methods. This approach is based on our baySeq algorithm for identification of differential expression in RNA-seq data based on a negative binomial distribution, and in paired data based on a beta-binomial distribution. Here we show how the same empirical Bayesian approach can be applied to any parametric distribution, removing the need for lengthy development of novel methods for differently distributed data. Comparisons with existing methods developed to address specific problems in high-throughput biological data show that these generic methods can achieve equivalent or better performance. A number of enhancements to the basic algorithm are also presented to increase flexibility and reduce computational costs. Availability and implementation: The methods are implemented in the R baySeq (v2) package, available on Bioconductor http://www.bioconductor.org/packages/release/bioc/html/baySeq.html. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.This work was supported by European Research Council Advanced Investigator Grant ERC-2013-AdG 340642 – TRIBE.This is the author accepted manuscript. The final version is available from Oxford University Press via http://dx.doi.org/10.1093/bioinformatics/btv56

An updated State-of-the-Art Overview of transcriptomic Deconvolution Methods

Although bulk transcriptomic analyses have significantly contributed to an enhanced comprehension of multifaceted diseases, their exploration capacity is impeded by the heterogeneous compositions of biological samples. Indeed, by averaging expression of multiple cell types, RNA-Seq analysis is oblivious to variations in cellular changes, hindering the identification of the internal constituents of tissues, involved in disease progression. On the other hand, single-cell techniques are still time, manpower and resource-consuming analyses.To address the intrinsic limitations of both bulk and single-cell methodologies, computational deconvolution techniques have been developed to estimate the frequencies of cell subtypes within complex tissues. These methods are especially valuable for dissecting intricate tissue niches, with a particular focus on tumour microenvironments (TME).In this paper, we offer a comprehensive overview of deconvolution techniques, classifying them based on their methodological characteristics, the type of prior knowledge required for the algorithm, and the statistical constraints they address. Within each category identified, we delve into the theoretical aspects for implementing the underlying method, while providing an in-depth discussion of their main advantages and disadvantages in supplementary materials.Notably, we emphasise the advantages of cutting-edge deconvolution tools based on probabilistic models, as they offer robust statistical frameworks that closely align with biological realities. We anticipate that this review will provide valuable guidelines for computational bioinformaticians in order to select the appropriate method in alignment with their statistical and biological objectives.We ultimately end this review by discussing open challenges that must be addressed to accurately quantify closely related cell types from RNA sequencing data, and the complementary role of single-cell RNA-Seq to that purpose

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871