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

Prediction with Dimension Reduction of Multiple Molecular Data Sources for Patient Survival

Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal components analysis (PCA). However, the application of PCA is not straightforward for multi-source data, wherein multiple sources of 'omics data measure different but related biological components. In this article we utilize recent advances in the dimension reduction of multi-source data for predictive modeling. In particular, we apply exploratory results from Joint and Individual Variation Explained (JIVE), an extension of PCA for multi-source data, for prediction of differing response types. We conduct illustrative simulations to illustrate the practical advantages and interpretability of our approach. As an application example we consider predicting survival for Glioblastoma Multiforme (GBM) patients from three data sources measuring mRNA expression, miRNA expression, and DNA methylation. We also introduce a method to estimate JIVE scores for new samples that were not used in the initial dimension reduction, and study its theoretical properties; this method is implemented in the R package R.JIVE on CRAN, in the function 'jive.predict'.Comment: 11 pages, 9 figure

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria—hypothesis, data types, strategies, study design and study focus— to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline

scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles.

Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms

Statistical learning methods for multi-omics data integration in dimension reduction, supervised and unsupervised machine learning

Over the decades, many statistical learning techniques such as supervised learning, unsupervised learning, dimension reduction technique have played ground breaking roles for important tasks in biomedical research. More recently, multi-omics data integration analysis has become increasingly popular to answer to many intractable biomedical questions, to improve statistical power by exploiting large size samples and different types omics data, and to replicate individual experiments for validation. This dissertation covers the several analytic methods and frameworks to tackle with practical problems in multi-omics data integration analysis. Supervised prediction rules have been widely applied to high-throughput omics data to predict disease diagnosis, prognosis or survival risk. The top scoring pair (TSP) algorithm is a supervised discriminant rule that applies a robust simple rank-based algorithm to identify rank-altered gene pairs in case/control classes. TSP usually generates greatly reduced accuracy in inter-study prediction (i.e., the prediction model is established in the training study and applied to an independent test study). In the first part, we introduce a MetaTSP algorithm that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies. One important objective of omics data analysis is clustering unlabeled patients in order to identify meaningful disease subtypes. In the second part, we propose a group structured integrative clustering method to incorporate a sparse overlapping group lasso technique and a tight clustering via regularization to integrate inter-omics regulation flow, and to encourage outlier samples scattering away from tight clusters. We show by two real examples and simulated data that our proposed methods improve the existing integrative clustering in clustering accuracy, biological interpretation, and are able to generate coherent tight clusters. Principal component analysis (PCA) is commonly used for projection to low-dimensional space for visualization. In the third part, we introduce two meta-analysis frameworks of PCA (Meta-PCA) for analyzing multiple high-dimensional studies in common principal component space. Theoretically, Meta-PCA specializes to identify meta principal component (Meta-PC) space; (1) by decomposing the sum of variances and (2) by minimizing the sum of squared cosines. Applications to various simulated data shows that Meta-PCAs outstandingly identify true principal component space, and retain robustness to noise features and outlier samples. We also propose sparse Meta-PCAs that penalize principal components in order to selectively accommodate significant principal component projections. With several simulated and real data applications, we found Meta-PCA efficient to detect significant transcriptomic features, and to recognize visual patterns for multi-omics data sets. In the future, the success of data integration analysis will play an important role in revealing the molecular and cellular process inside multiple data, and will facilitate disease subtype discovery and characterization that improve hypothesis generation towards precision medicine, and potentially advance public health research

A primer on correlation-based dimension reduction methods for multi-omics analysis

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will guide researchers navigate the emerging methods for multi-omics and help them integrate diverse omic datasets appropriately and embrace the opportunity of population multi-omics.Comment: 30 pages, 2 figures, 6 table

A survey on data integration for multi-omics sample clustering

Due to the current high availability of omics, data-driven biology has greatly expanded, and several papers have reviewed state-of-the-art technologies. Nowadays, two main types of investigation are available for a multi-omics dataset: extraction of relevant features for a meaningful biological interpretation and clustering of the samples. In the latter case, a few reviews refer to some outdated or no longer available methods, whereas others lack the description of relevant clustering metrics to compare the main approaches. This work provides a general overview of the major techniques in this area, divided into four groups: graph, dimensionality reduction, statistical and neural-based. Besides, eight tools have been tested both on a synthetic and a real biological dataset. An extensive performance comparison has been provided using four clustering evaluation scores: Peak Signal-to-Noise Ratio (PSNR), Davies-Bouldin(DB) index, Silhouette value and the harmonic mean of cluster purity and efficiency. The best results were obtained by using the dimensionality reduction, either explicitly or implicitly, as in the neural architecture

More effort — more results: recent advances in integrative ‘omics’ data analysis

The development of ‘omics’ technologies has progressed to address complex biological questions that underlie various plant functions thereby producing copious amounts of data. The need to assimilate large amounts of data into biologically meaningful interpretations has necessitated the development of statistical methods to integrate multidimensional information. Throughout this review, we provide examples of recent outcomes of ‘omics’ data integration together with an overview of available statistical methods and tools