Sparse integrative clustering of multiple omics data sets

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation and gene expression associated with a disease. An integrated genomic profiling approach measures multiple omics data types simultaneously in the same set of biological samples. Such approach renders an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996) 267-288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 301-320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 91-108] methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design [Monographs on Statistics and Applied Probability (1994) Chapman & Hall] is used to seek "experimental" points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic and transcriptomic data for subtype analysis in breast and lung cancer data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS578 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

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

Detecting Biomarkers among Subgroups with Structured Latent Features and Multitask Learning Methods

University of Minnesota Ph.D. dissertation. May 2017. Major: Computer Science. Advisor: Rui Kuang. 1 computer file (PDF); viii, 89 pages.Because of disease progression and heterogeneity in samples and single cells, biomarker detection among subgroups is important as it provides better understanding on population genetics and cancer causative. In this thesis, we proposed several structured latent features based and multitask learning based methods for biomarker detection on DNA Copy-Number Variations (CNVs) data and single cell RNA sequencing (scRNA-seq) data. By incorporating prior known group information or taking domain heterogeneity into consideration, our models are able to achieve meaningful biomarker detection and accurate sample classification. 1. By cooperating population relationship from human phylogenetic tree, we introduced a latent feature model to detect population-differentiation CNV markers. The algorithm, named tree-guided sparse group selection (treeSGS), detects sample sub- groups organized by a population phylogenetic tree such that the evolutionary relations among the populations are incorporated for more accurate detection of population- differentiation CNVs. 2. We applied transfer learning technic for cross-cancer-type CNV studies. We proposed Transfer Learning with Fused LASSO (TLFL) algorithm, which detects latent CNV components from multiple CNV datasets of different tumor types and distinguishes the CNVs that are common across the datasets and those that are specific in each dataset. Both the common and type-specific CNVs are detected as latent components in matrix factorization coupled with fused LASSO on adjacent CNV probe features. 3. We further applied multitask learning idea on scRNA-seq data. We introduced variance-driven multitask clustering on single-cell RNA-seq data (scV DMC) that utilizes multiple cell populations from biological replicates or related samples with significant biological variances. scVDMC clusters single cells of similar cell types and markers but varies expression patterns across different domains such that the scRNA-seq data are adjusted for better integration. We applied both simulations and several publicly available CNV and scRNA-seq datasets, including one in house scRNA-seq dataset, to evaluate the performance of our models. The promising results show that we achieve better biomarker prediction among subgroups

Model selection techniques for sparse weight-based principal component analysis

Many studies make use of multiple types of data that are collected for the same set of samples, resulting in so-called multiblock data (e.g., multiomics studies). A popular analysis framework is sparse principal component analysis (PCA) of the concatenated data. The sparseness in the component weights of these models is usually induced by penalties. A crucial factor in the use of such penalized methods is a proper tuning of the regularization parameters used to give more or less weight to the penalties. In this paper, we examine several model selection procedures to tune these regularization parameters for sparse PCA. The model selection procedures include cross-validation, Bayesian information criterion (BIC), index of sparseness, and the convex hull procedure. Furthermore, to account for the multiblock structure, we present a sparse PCA algorithm with a group least absolute shrinkage and selection operator (LASSO) penalty added to it, to either select or cancel out blocks of data in an automated way. Also, the tuning of the group LASSO parameter is studied for the proposed model selection procedures. We conclude that when the component weights are to be interpreted, cross-validation with the one standard error rule is preferred; alternatively, if the interest lies in obtaining component scores using a very limited set of variables, the convex hull, BIC, and index of sparseness are all suitable

Statistical Methods for High Dimensional Networked Data Analysis.

Networked data are frequently encountered in many scientific disciplines. One major challenges in the analysis of such data are its high dimensionality and complex dependence. My dissertation consists of three projects. The first project focuses on the development of sparse multivariate factor analysis regression model to construct the underlying sparse association map between gene expressions and biomarkers. This is motivated by the fact that some associations may be obscured by unknown confounding factors that are not collected in the data. I have shown that accounting for such unobserved confounding factors can increase both sensitivity and specificity for detecting important gene-biomarker associations and thus lead to more interpretable association maps. The second project concerns the reconstruction of the underlying gene regulatory network using directed acyclic graphical models. My project aims to reduce false discoveries by identifying and removing edges resulted from shared confounding factors. I propose sparse structural factor equation models, in which structural equation models are used to capture directed graphs while factor analysis models are used to account for potential latent factors. I have shown that the proposed method enables me to obtain a simpler and more interpretable topology of a gene regulatory network. The third project is devoted to the development of a new regression analysis methodology to analyze electroencephalogram (EEG) neuroimaging data that are correlated among electrodes within an EEG-net. To address analytic challenges pertaining to the integration of network topology into the analysis, I propose hybrid quadratic inference functions that utilize both prior and data-driven correlations among network nodes into statistical estimation and inference. The proposed method is conceptually simple and computationally fast and more importantly has appealing large-sample properties. In a real EEG data analysis I applied the proposed method to detect significant association of iron deficiency on event-related potential measured in two subregions, which was not found using the classical spatial ANOVA random-effects models.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111595/1/zhouyan_1.pd

Structured Sparse Methods for Imaging Genetics

abstract: Imaging genetics is an emerging and promising technique that investigates how genetic variations affect brain development, structure, and function. By exploiting disorder-related neuroimaging phenotypes, this class of studies provides a novel direction to reveal and understand the complex genetic mechanisms. Oftentimes, imaging genetics studies are challenging due to the relatively small number of subjects but extremely high-dimensionality of both imaging data and genomic data. In this dissertation, I carry on my research on imaging genetics with particular focuses on two tasks---building predictive models between neuroimaging data and genomic data, and identifying disorder-related genetic risk factors through image-based biomarkers. To this end, I consider a suite of structured sparse methods---that can produce interpretable models and are robust to overfitting---for imaging genetics. With carefully-designed sparse-inducing regularizers, different biological priors are incorporated into learning models. More specifically, in the Allen brain image--gene expression study, I adopt an advanced sparse coding approach for image feature extraction and employ a multi-task learning approach for multi-class annotation. Moreover, I propose a label structured-based two-stage learning framework, which utilizes the hierarchical structure among labels, for multi-label annotation. In the Alzheimer's disease neuroimaging initiative (ADNI) imaging genetics study, I employ Lasso together with EDPP (enhanced dual polytope projections) screening rules to fast identify Alzheimer's disease risk SNPs. I also adopt the tree-structured group Lasso with MLFre (multi-layer feature reduction) screening rules to incorporate linkage disequilibrium information into modeling. Moreover, I propose a novel absolute fused Lasso model for ADNI imaging genetics. This method utilizes SNP spatial structure and is robust to the choice of reference alleles of genotype coding. In addition, I propose a two-level structured sparse model that incorporates gene-level networks through a graph penalty into SNP-level model construction. Lastly, I explore a convolutional neural network approach for accurate predicting Alzheimer's disease related imaging phenotypes. Experimental results on real-world imaging genetics applications demonstrate the efficiency and effectiveness of the proposed structured sparse methods.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Tissue-specific identification of multi-omics features for pan-cancer drug response prediction

Publisher Copyright: © 2022 The Author(s)Current statistical models for drug response prediction and biomarker identification fall short in leveraging the shared and unique information from various cancer tissues and multi-omics profiles. We developed mix-lasso model that introduces an additional sample group penalty term to capture tissue-specific effects of features on pan-cancer response prediction. The mix-lasso model takes into account both the similarity between drug responses (i.e., multi-task learning), and the heterogeneity between multi-omics data (multi-modal learning). When applied to large-scale pharmacogenomics dataset from Cancer Therapeutics Response Portal, mix-lasso enabled accurate drug response predictions and identification of tissue-specific predictive features in the presence of various degrees of missing data, drug-drug correlations, and high-dimensional and correlated genomic and molecular features that often hinder the use of statistical approaches in drug response modeling. Compared to tree lasso model, mix-lasso identified a smaller number of tissue-specific features, hence making the model more interpretable and stable for drug discovery applications.Peer reviewe

Finding regions of aberrant DNA copy number associated with tumor phenotype

DNA copy number alterations are a hallmark of cancer. Understanding their role in tumor progression can help improve diagnosis, prognosis and therapy selection for cancer patients. High-resolution, genome-wide measurements of DNA copy number changes for large cohorts of tumors are currently available, owing to technologies like microarray-based array comparative hybridization (arrayCGH). In this thesis, we present a computational pipeline for statistical analysis of tumor cohorts, which can help extract relevant patterns of copy number aberrations and infer their association with various phenotypical indicators. The main challenges are the instability of classification models due to the high dimensionality of the arrays compared to the small number of tumor samples, as well as the large correlations between copy number estimates measured at neighboring loci. We show that the feature ranking given by several widely-used methods for feature selection is biased due to the large correlations between features. In order to correct for the bias and instability of the feature ranking, we introduce methods for consensus segmentation of the set of arrays. We present three algorithms for consensus segmentation, which are based on identifying recurrent DNA breakpoints or DNA regions of constant copy number profile. The segmentation constitutes the basis for computing a set of super-features, corresponding to the regions. We use the super-features for supervised classification and we compare the models to baseline models trained on probe data. We validated the methods by training models for prediction of the phenotype of breast cancers and neuroblastoma tumors. We show that the multivariate segmentation affords higher model stability, in general improves prediction accuracy and facilitates model interpretation. One of our most important biological results refers to the classification of neuroblastoma tumors. We show that patients belonging to different age subgroups are characterized by distinct copy number patterns, with largest discrepancy when the subgroups are defined as older or younger than 16-18 months. We thereby confirm the recommendation for a higher age cutoff than 12 months (current clinical practice) for differential diagnosis of neuroblastoma.Die abnormale Multiplizität bestimmter Segmente der DNS (copy number aberrations) ist eines der hervorstechenden Merkmale von Krebs. Das Verständnis der Rolle dieses Merkmals für das Tumorwachstum könnte massgeblich zur Verbesserung von Krebsdiagnose,-prognose und -therapie beitragen und somit bei der Auswahl individueller Therapien helfen. Micoroarray-basierte Technologien wie 'Array Comparative Hybridization' (array-CGH) erlauben es, hochauflösende, genomweite Kopiezahl-Karten von Tumorgeweben zu erstellen. Gegenstand dieser Arbeit ist die Entwicklung einer Software-Pipeline für die statistische Analyse von Tumorkohorten, die es ermöglicht, relevante Muster abnormaler Kopiezahlen abzuleiten und diese mit diversen phänotypischen Merkmalen zu assoziieren. Dies geschieht mithilfe maschineller Lernmethoden für Klassifikation und Merkmalselektion mit Fokus auf die Interpretierbarkeit der gelernten Modelle (regularisierte lineare Methoden sowie Entscheidungsbaum-basierte Modelle). Herausforderungen an die Methoden liegen vor allem in der hohen Dimensionalität der Daten, denen lediglich eine vergleichsweise geringe Anzahl von gemessenen Tumorproben gegenüber steht, sowie der hohen Korrelation zwischen den gemessenen Kopiezahlen in benachbarten genomischen Regionen. Folglich hängen die Resultate der Merkmalselektion stark von der Auswahl des Trainingsdatensatzes ab, was die Reproduzierbarkeit bei unterschiedlichen klinischen Datensätzen stark einschränkt. Diese Arbeit zeigt, dass die von diversen gängigen Methoden bestimmte Rangfolge von Features in Folge hoher Korrelationskoefizienten einzelner Prädiktoren stark verfälscht ist. Um diesen 'Bias' sowie die Instabilität der Merkmalsrangfolge zu korrigieren, führen wir in unserer Pipeline einen dimensions-reduzierenden Schritt ein, der darin besteht, die Arrays gemeinsam multivariat zu segmentieren. Wir präsentieren drei Algorithmen für diese multivariate Segmentierung,die auf der Identifikation rekurrenter DNA Breakpoints oder genomischer Regionen mit konstanten Kopiezahl-Profilen beruhen. Durch Zusammenfassen der DNA Kopiezahlwerte innerhalb einer Region bildet die multivariate Segmentierung die Grundlage für die Berechnung einer kleineren Menge von 'Super-Merkmalen'. Im Vergleich zu Klassifikationsverfahren,die auf Ebene einzelner Arrayproben beruhen, verbessern wir durch überwachte Klassifikation basierend auf den Super-Merkmalen die Interpretierbarkeit sowie die Stabilität der Modelle. Wir validieren die Methoden in dieser Arbeit durch das Trainieren von Vorhersagemodellen auf Brustkrebs und Neuroblastoma Datensätzen. Hier zeigen wir, dass der multivariate Segmentierungsschritt eine erhöhte Modellstabilität erzielt, wobei die Vorhersagequalität nicht abnimmt. Die Dimension des Problems wird erheblich reduziert (bis zu 200-fach weniger Merkmale), welches die multivariate Segmentierung nicht nur zu einem probaten Mittel für die Vorhersage von Phänotypen macht.Vielmehr eignet sich das Verfahren darüberhinaus auch als Vorverarbeitungschritt für spätere integrative Analysen mit anderen Datentypen. Auch die Interpretierbarkeit der Modelle wird verbessert. Dies ermöglicht die Identifikation von wichtigen Relationen zwischen Änderungen der Kopiezahl und Phänotyp. Beispielsweise zeigen wir, dass eine Koamplifikation in direkter Nachbarschaft des ERBB2 Genlokus einen höchst informativen Prädiktor für die Unterscheidung von entzündlichen und nicht-entzündlichen Brustkrebsarten darstellt. Damit bestätigen wir die in der Literatur gängige Hypothese, dass die Grösse eines Amplikons mit dem Krebssubtyp zusammenhängt. Im Fall von Neuroblastoma Tumoren zeigen wir, dass Untergruppen, die durch das Alter des Patienten deniert werden, durch Kopiezahl-Muster charakterisiert werden können. Insbesondere ist dies möglich, wenn ein Altersschwellenwert von 16 bis 18 Monaten zur Definition der Gruppen verwandt wird, bei dem ausserdem auch die höchste Vorhersagegenauigkeit vorliegt. Folglich geben wir weitere Evidenz für die Empfehlung, einen höheren Schwellenwert als zwölf Monate für die differentielle Diagnose von Neuroblastoma zu verwenden

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