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

Statistical Methods for Characterizing Genomic Heterogeneity in Mixed Samples

Recently, sequencing technologies have generated massive and heterogeneous data sets. However, interpretation of these data sets is a major barrier to understand genomic heterogeneity in complex diseases. In this dissertation, we develop a Bayesian statistical method for single nucleotide level analysis and a global optimization method for gene expression level analysis to characterize genomic heterogeneity in mixed samples. The detection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogeneity using next-generation sequencing (NGS) data. Various computational algorithms have been proposed to detect variants at the single nucleotide level in mixed samples. Yet, the noise inherent in the biological processes involved in NGS technology necessitates the development of statistically accurate methods to identify true rare variants. At the single nucleotide level, we propose a Bayesian probabilistic model and a variational expectation maximization (EM) algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations. We demonstrate that our variational EM algorithm has comparable sensitivity and specificity compared with a Markov Chain Monte Carlo (MCMC) sampling inference algorithm, and is more computationally efficient on tests of relatively low coverage (27x and 298x) data. Furthermore, we show that our model with a variational EM inference algorithm has higher specificity than many state-of-the-art algorithms. In an analysis of a directed evolution longitudinal yeast data set, we are able to identify a time-series trend in non-reference allele frequency and detect novel variants that have not yet been reported. Our model also detects the emergence of a beneficial variant earlier than was previously shown, and a pair of concomitant variants. Characterization of heterogeneity in gene expression data is a critical challenge for personalized treatment and drug resistance due to intra-tumor heterogeneity. Mixed membership factorization has become popular for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. At the gene expression level, we derive a global optimization (GOP) algorithm that provides a guaranteed epsilon-global optimum for a sparse mixed membership matrix factorization problem for molecular subtype classification. We test the algorithm on simulated data and find the algorithm always bounds the global optimum across random initializations and explores multiple modes efficiently. The GOP algorithm is well-suited for parallel computations in the key optimization steps

Development of methods for Omics Network inference and analysis and their application to disease modeling

With the advent of Next Generation Sequencing (NGS) technologies and the emergence of large publicly available genomics data comes an unprecedented opportunity to model biological networks through a holistic lens using a systems-based approach. Networks provide a mathematical framework for representing biological phenomena that go beyond standard one-gene-at-a-time analyses. Networks can model system-level patterns and the molecular rewiring (i.e. changes in connectivity) occurring in response to perturbations or between distinct phenotypic groups or cell types. This in turn supports the identification of putative mechanisms of actions of the biological processes under study, and thus have the potential to advance prevention and therapy. However, there are major challenges faced by researchers. Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput omics data. Furthermore, modeling biological networks involves complex analyses capable of integrating multiple sources of omics layers and summarizing large amounts of information. My dissertation aims to address these challenges by presenting new approaches for high-dimensional network inference with limit sample sizes as well as methods and tools for integrated network analysis applied to multiple research domains in cancer genomics. First, I introduce a novel method for reconstructing gene regulatory networks called SHINE (Structure Learning for Hierarchical Networks) and present an evaluation on simulated and real datasets including a Pan-Cancer analysis using The Cancer Genome Atlas (TCGA) data. Next, I summarize the challenges with executing and managing data processing workflows for large omics datasets on high performance computing environments and present multiple strategies for using Nextflow for reproducible scientific workflows including shine-nf - a collection of Nextflow modules for structure learning. Lastly, I introduce the methods, objects, and tools developed for the analysis of biological networks used throughout my dissertation work. Together - these contributions were used in focused analyses of understanding the molecular mechanisms of tumor maintenance and progression in subtype networks of Breast Cancer and Head and Neck Squamous Cell Carcinoma

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute\u27s Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues