TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain

BackgroundGene prioritization (gene ranking) aims to obtain the centrality of genes, which is critical for cancer diagnosis and therapy since keys genes correspond to the biomarkers or targets of drugs. Great efforts have been devoted to the gene ranking problem by exploring the similarity between candidate and known disease-causing genes. However, when the number of disease-causing genes is limited, they are not applicable largely due to the low accuracy. Actually, the number of disease-causing genes for cancers, particularly for these rare cancers, are really limited. Therefore, there is a critical needed to design effective and efficient algorithms for gene ranking with limited prior disease-causing genes.ResultsIn this study, we propose a transfer learning based algorithm for gene prioritization (called TLGP) in the cancer (target domain) without disease-causing genes by transferring knowledge from other cancers (source domain). The underlying assumption is that knowledge shared by similar cancers improves the accuracy of gene prioritization. Specifically, TLGP first quantifies the similarity between the target and source domain by calculating the affinity matrix for genes. Then, TLGP automatically learns a fusion network for the target cancer by fusing affinity matrix, pathogenic genes and genomic data of source cancers. Finally, genes in the target cancer are prioritized. The experimental results indicate that the learnt fusion network is more reliable than gene co-expression network, implying that transferring knowledge from other cancers improves the accuracy of network construction. Moreover, TLGP outperforms state-of-the-art approaches in terms of accuracy, improving at least 5%.ConclusionThe proposed model and method provide an effective and efficient strategy for gene ranking by integrating genomic data from various cancers

Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery

Driver event discovery is a crucial demand for breast cancer diagnosis and therapy. Especially, discovering subtype-specificity of drivers can prompt the personalized biomarker discovery and precision treatment of cancer patients. still, most of the existing computational driver discovery studies mainly exploit the information from DNA aberrations and gene interactions. Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers. On the one hand, the data formats of different aberration types also differ from each other, known as data format incompatibility. One the other hand, different types of aberrations demonstrate distinct patterns across samples, known as aberration type heterogeneity. To promote the integrated analysis of subtype-specific breast cancer drivers, we design a "splicing-and-fusing" framework to address the issues of data format incompatibility and aberration type heterogeneity respectively. To overcome the data format incompatibility, the "splicing-step" employs a knowledge graph structure to connect multi-type aberrations from the DNA and RNA data into a unified formation. To tackle the aberration type heterogeneity, the "fusing-step" adopts a dynamic mapping gene space integration approach to represent the multi-type information by vectorized profiles. The experiments also demonstrate the advantages of our approach in both the integration of multi-type aberrations from DNA and RNA and the discovery of subtype-specific breast cancer drivers. In summary, our "splicing-and-fusing" framework with knowledge graph connection and dynamic mapping gene space fusion of multi-type aberrations data from DNA and RNA can successfully discover potential breast cancer drivers with subtype-specificity indication.Comment: 14 pages, 5 figures, 1 tabl

Unsupervised multiple kernel learning approaches for integrating molecular cancer patient data

Cancer is the second leading cause of death worldwide. A characteristic of this disease is its complexity leading to a wide variety of genetic and molecular aberrations in the tumors. This heterogeneity necessitates personalized therapies for the patients. However, currently defined cancer subtypes used in clinical practice for treatment decision-making are based on relatively few selected markers and thus provide only a coarse classifcation of tumors. The increased availability in multi-omics data measured for cancer patients now offers the possibility of defining more informed cancer subtypes. Such a more fine-grained characterization of cancer subtypes harbors the potential of substantially expanding treatment options in personalized cancer therapy. In this thesis, we identify comprehensive cancer subtypes using multidimensional data. For this purpose, we apply and extend unsupervised multiple kernel learning methods. Three challenges of unsupervised multiple kernel learning are addressed: robustness, applicability, and interpretability. First, we show that regularization of the multiple kernel graph embedding framework, which enables the implementation of dimensionality reduction techniques, can increase the stability of the resulting patient subgroups. This improvement is especially beneficial for data sets with a small number of samples. Second, we adapt the objective function of kernel principal component analysis to enable the application of multiple kernel learning in combination with this widely used dimensionality reduction technique. Third, we improve the interpretability of kernel learning procedures by performing feature clustering prior to integrating the data via multiple kernel learning. On the basis of these clusters, we derive a score indicating the impact of a feature cluster on a patient cluster, thereby facilitating further analysis of the cluster-specific biological properties. All three procedures are successfully tested on real-world cancer data. Comparing our newly derived methodologies to established methods provides evidence that our work offers novel and beneficial ways of identifying patient subgroups and gaining insights into medically relevant characteristics of cancer subtypes.Krebs ist eine der häufigsten Todesursachen weltweit. Krebs ist gekennzeichnet durch seine Komplexität, die zu vielen verschiedenen genetischen und molekularen Aberrationen im Tumor führt. Die Unterschiede zwischen Tumoren erfordern personalisierte Therapien für die einzelnen Patienten. Die Krebssubtypen, die derzeit zur Behandlungsplanung in der klinischen Praxis verwendet werden, basieren auf relativ wenigen, genetischen oder molekularen Markern und können daher nur eine grobe Unterteilung der Tumoren liefern. Die zunehmende Verfügbarkeit von Multi-Omics-Daten für Krebspatienten ermöglicht die Neudefinition von fundierteren Krebssubtypen, die wiederum zu spezifischeren Behandlungen für Krebspatienten führen könnten. In dieser Dissertation identifizieren wir neue, potentielle Krebssubtypen basierend auf Multi-Omics-Daten. Hierfür verwenden wir unüberwachtes Multiple Kernel Learning, welches in der Lage ist mehrere Datentypen miteinander zu kombinieren. Drei Herausforderungen des unüberwachten Multiple Kernel Learnings werden adressiert: Robustheit, Anwendbarkeit und Interpretierbarkeit. Zunächst zeigen wir, dass die zusätzliche Regularisierung des Multiple Kernel Learning Frameworks zur Implementierung verschiedener Dimensionsreduktionstechniken die Stabilität der identifizierten Patientengruppen erhöht. Diese Robustheit ist besonders vorteilhaft für Datensätze mit einer geringen Anzahl von Proben. Zweitens passen wir die Zielfunktion der kernbasierten Hauptkomponentenanalyse an, um eine integrative Version dieser weit verbreiteten Dimensionsreduktionstechnik zu ermöglichen. Drittens verbessern wir die Interpretierbarkeit von kernbasierten Lernprozeduren, indem wir verwendete Merkmale in homogene Gruppen unterteilen bevor wir die Daten integrieren. Mit Hilfe dieser Gruppen definieren wir eine Bewertungsfunktion, die die weitere Auswertung der biologischen Eigenschaften von Patientengruppen erleichtert. Alle drei Verfahren werden an realen Krebsdaten getestet. Den Vergleich unserer Methodik mit etablierten Methoden weist nach, dass unsere Arbeit neue und nützliche Möglichkeiten bietet, um integrative Patientengruppen zu identifizieren und Einblicke in medizinisch relevante Eigenschaften von Krebssubtypen zu erhalten

Graphlet-adjacencies provide complementary views on the functional organisation of the cell and cancer mechanisms

Recent biotechnological advances have led to a wealth of biological network data. Topo- logical analysis of these networks (i.e., the analysis of their structure) has led to break- throughs in biology and medicine. The state-of-the-art topological node and network descriptors are based on graphlets, induced connected subgraphs of different shapes (e.g., paths, triangles). However, current graphlet-based methods ignore neighbourhood infor- mation (i.e., what nodes are connected). Therefore, to capture topology and connectivity information simultaneously, I introduce graphlet adjacency, which considers two nodes adjacent based on their frequency of co-occurrence on a given graphlet. I use graphlet adjacency to generalise spectral methods and apply these on molecular networks. I show that, depending on the chosen graphlet, graphlet spectral clustering uncovers clusters en- riched in different biological functions, and graphlet diffusion of gene mutation scores predicts different sets of cancer driver genes. This demonstrates that graphlet adjacency captures topology-function and topology-disease relationships in molecular networks. To further detail these relationships, I take a pathway-focused approach. To enable this investigation, I introduce graphlet eigencentrality to compute the importance of a gene in a pathway either from the local pathway perspective or from the global network perspective. I show that pathways are best described by the graphlet adjacencies that capture the importance of their functionally critical genes. I also show that cancer driver genes characteristically perform hub roles between pathways. Given the latter finding, I hypothesise that cancer pathways should be identified by changes in their pathway-pathway relationships. Within this context, I propose pathway- driven non-negative matrix tri-factorisation (PNMTF), which fuses molecular network data and pathway annotations to learn an embedding space that captures the organisation of a network as a composition of subnetworks. In this space, I measure the functional importance of a pathway or gene in the cell and its functional disruption in cancer. I apply this method to predict genes and the pathways involved in four major cancers. By using graphlet-adjacency, I can exploit the tendency of cancer-related genes to perform hub roles to improve the prediction accuracy

Integration of multi-scale protein interactions for biomedical data analysis

With the advancement of modern technologies, we observe an increasing accumulation of biomedical data about diseases. There is a need for computational methods to sift through and extract knowledge from the diverse data available in order to improve our mechanistic understanding of diseases and improve patient care. Biomedical data come in various forms as exemplified by the various omics data. Existing studies have shown that each form of omics data gives only partial information on cells state and motivated jointly mining multi-omics, multi-modal data to extract integrated system knowledge. The interactome is of particular importance as it enables the modelling of dependencies arising from molecular interactions. This Thesis takes a special interest in the multi-scale protein interactome and its integration with computational models to extract relevant information from biomedical data. We define multi-scale interactions at different omics scale that involve proteins: pairwise protein-protein interactions, multi-protein complexes, and biological pathways. Using hypergraph representations, we motivate considering higher-order protein interactions, highlighting the complementary biological information contained in the multi-scale interactome. Based on those results, we further investigate how those multi-scale protein interactions can be used as either prior knowledge, or auxiliary data to develop machine learning algorithms. First, we design a neural network using the multi-scale organization of proteins in a cell into biological pathways as prior knowledge and train it to predict a patient's diagnosis based on transcriptomics data. From the trained models, we develop a strategy to extract biomedical knowledge pertaining to the diseases investigated. Second, we propose a general framework based on Non-negative Matrix Factorization to integrate the multi-scale protein interactome with multi-omics data. We show that our approach outperforms the existing methods, provide biomedical insights and relevant hypotheses for specific cancer types

Computational solutions for addressing heterogeneity in DNA methylation data

DNA methylation, a reversible epigenetic modification, has been implicated with various bi- ological processes including gene regulation. Due to the multitude of datasets available, it is a premier candidate for computational tool development, especially for investigating hetero- geneity within and across samples. We differentiate between three levels of heterogeneity in DNA methylation data: between-group, between-sample, and within-sample heterogeneity. Here, we separately address these three levels and present new computational approaches to quantify and systematically investigate heterogeneity. Epigenome-wide association studies relate a DNA methylation aberration to a phenotype and therefore address between-group heterogeneity. To facilitate such studies, which necessar- ily include data processing, exploratory data analysis, and differential analysis of DNA methy- lation, we extended the R-package RnBeads. We implemented novel methods for calculating the epigenetic age of individuals, novel imputation methods, and differential variability analysis. A use-case of the new features is presented using samples from Ewing sarcoma patients. As an important driver of epigenetic differences between phenotypes, we systematically investigated associations between donor genotypes and DNA methylation states in methylation quantitative trait loci (methQTL). To that end, we developed a novel computational framework –MAGAR– for determining statistically significant associations between genetic and epigenetic variations. We applied the new pipeline to samples obtained from sorted blood cells and complex bowel tissues of healthy individuals and found that tissue-specific and common methQTLs have dis- tinct genomic locations and biological properties. To investigate cell-type-specific DNA methylation profiles, which are the main drivers of within-group heterogeneity, computational deconvolution methods can be used to dissect DNA methylation patterns into latent methylation components. Deconvolution methods require pro- files of high technical quality and the identified components need to be biologically interpreted. We developed a computational pipeline to perform deconvolution of complex DNA methyla- tion data, which implements crucial data processing steps and facilitates result interpretation. We applied the protocol to lung adenocarcinoma samples and found indications of tumor in- filtration by immune cells and associations of the detected components with patient survival. Within-sample heterogeneity (WSH), i.e., heterogeneous DNA methylation patterns at a ge- nomic locus within a biological sample, is often neglected in epigenomic studies. We present the first systematic benchmark of scores quantifying WSH genome-wide using simulated and experimental data. Additionally, we created two novel scores that quantify DNA methyla- tion heterogeneity at single CpG resolution with improved robustness toward technical biases. WSH scores describe different types of WSH in simulated data, quantify differential hetero- geneity, and serve as a reliable estimator of tumor purity. Due to the broad availability of DNA methylation data, the levels of heterogeneity in DNA methylation data can be comprehensively investigated. We contribute novel computational frameworks for analyzing DNA methylation data with respect to different levels of hetero- geneity. We envision that this toolbox will be indispensible for understanding the functional implications of DNA methylation patterns in health and disease.DNA Methylierung ist eine reversible, epigenetische Modifikation, die mit verschiedenen biologischen Prozessen wie beispielsweise der Genregulation in Verbindung steht. Eine Vielzahl von DNA Methylierungsdatensätzen bildet die perfekte Grundlage zur Entwicklung von Softwareanwendungen, insbesondere um Heterogenität innerhalb und zwischen Proben zu beschreiben. Wir unterscheiden drei Ebenen von Heterogenität in DNA Methylierungsdaten: zwischen Gruppen, zwischen Proben und innerhalb einer Probe. Hier betrachten wir die drei Ebenen von Heterogenität in DNA Methylierungsdaten unabhängig voneinander und präsentieren neue Ansätze um die Heterogenität zu beschreiben und zu quantifizieren. Epigenomweite Assoziationsstudien verknüpfen eine DNA Methylierungsveränderung mit einem Phänotypen und beschreiben Heterogenität zwischen Gruppen. Um solche Studien, welche Datenprozessierung, sowie exploratorische und differentielle Datenanalyse beinhalten, zu vereinfachen haben wir die R-basierte Softwareanwendung RnBeads erweitert. Die Erweiterungen beinhalten neue Methoden, um das epigenetische Alter vorherzusagen, neue Schätzungsmethoden für fehlende Datenpunkte und eine differentielle Variabilitätsanalyse. Die Analyse von Ewing-Sarkom Patientendaten wurde als Anwendungsbeispiel für die neu entwickelten Methoden gewählt. Wir untersuchten Assoziationen zwischen Genotypen und DNA Methylierung von einzelnen CpGs, um sogenannte methylation quantitative trait loci (methQTL) zu definieren. Diese stellen einen wichtiger Faktor dar, der epigenetische Unterschiede zwischen Gruppen induziert. Hierzu entwickelten wir ein neues Softwarepaket (MAGAR), um statistisch signifikante Assoziationen zwischen genetischer und epigenetischer Variation zu identifizieren. Wir wendeten diese Pipeline auf Blutzelltypen und komplexe Biopsien von gesunden Individuen an und konnten gemeinsame und gewebespezifische methQTLs in verschiedenen Bereichen des Genoms lokalisieren, die mit unterschiedlichen biologischen Eigenschaften verknüpft sind. Die Hauptursache für Heterogenität innerhalb einer Gruppe sind zelltypspezifische DNA Methylierungsmuster. Um diese genauer zu untersuchen kann Dekonvolutionssoftware die DNA Methylierungsmatrix in unabhängige Variationskomponenten zerlegen. Dekonvolutionsmethoden auf Basis von DNA Methylierung benötigen technisch hochwertige Profile und die identifizierten Komponenten müssen biologisch interpretiert werden. In dieser Arbeit entwickelten wir eine computerbasierte Pipeline zur Durchführung von Dekonvolutionsexperimenten, welche die Datenprozessierung und Interpretation der Resultate beinhaltet. Wir wendeten das entwickelte Protokoll auf Lungenadenokarzinome an und fanden Anzeichen für eine Tumorinfiltration durch Immunzellen, sowie Verbindungen zum Überleben der Patienten. Heterogenität innerhalb einer Probe (within-sample heterogeneity, WSH), d.h. heterogene Methylierungsmuster innerhalb einer Probe an einer genomischen Position, wird in epigenomischen Studien meist vernachlässigt. Wir präsentieren den ersten Vergleich verschiedener, genomweiter WSH Maße auf simulierten und experimentellen Daten. Zusätzlich entwickelten wir zwei neue Maße um WSH für einzelne CpGs zu berechnen, welche eine verbesserte Robustheit gegenüber technischen Faktoren aufweisen. WSH Maße beschreiben verschiedene Arten von WSH, quantifizieren differentielle Heterogenität und sagen Tumorreinheit vorher. Aufgrund der breiten Verfügbarkeit von DNA Methylierungsdaten können die Ebenen der Heterogenität ganzheitlich beschrieben werden. In dieser Arbeit präsentieren wir neue Softwarelösungen zur Analyse von DNA Methylierungsdaten in Bezug auf die verschiedenen Ebenen der Heterogenität. Wir sind davon überzeugt, dass die vorgestellten Softwarewerkzeuge unverzichtbar für das Verständnis von DNA Methylierung im kranken und gesunden Stadium sein werden

Integrative Transcriptomic Analysis of Long Intergenic Non-Coding RNAs in Cancer.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p