Data mining techniques for large-scale gene expression analysis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 238-256).Modern computational biology is awash in large-scale data mining problems. Several high-throughput technologies have been developed that enable us, with relative ease and little expense, to evaluate the coordinated expression levels of tens of thousands of genes, evaluate hundreds of thousands of single-nucleotide polymorphisms, and sequence individual genomes. The data produced by these assays has provided the research and commercial communities with the opportunity to derive improved clinical prognostic indicators, as well as develop an understanding, at the molecular level, of the systemic underpinnings of a variety of diseases. Aside from the statistical methods used to evaluate these assays, another, more subtle challenge is emerging. Despite the explosive growth in the amount of data being generated and submitted to the various publicly available data repositories, very little attention has been paid to managing the phenotypic characterization of their samples (i.e., managing class labels in a controlled fashion). If sense is to be made of the underlying assay data, the samples' descriptive metadata must first be standardized in a machine-readable format. In this thesis, we explore these issues, specifically within the context of curating and analyzing a large DNA microarray database. We address three main challenges. First, we acquire a large subset of a publicly available microarray repository and develop a principled method for extracting phenotype information from freetext sample labels, then use that information to generate an index of the sample's medically-relevant annotation. The indexing method we develop, Concordia, incorporates pre-existing expert knowledge relating to the hierarchical relationships between medical terms, allowing queries of arbitrary specificity to be efficiently answered. Second, we describe a highly flexible approach to answering the question: "Given a previously unseen gene expression sample, how can we compute its similarity to all of the labeled samples in our database, and how can we utilize those similarity scores to predict the phenotype of the new sample?" Third, we describe a method for identifying phenotype-specific transcriptional profiles within the context of this database, and explore a method for measuring the relative strength of those signatures across the rest of the database, allowing us to identify molecular signatures that are shared across various tissues ad diseases. These shared fingerprints may form a quantitative basis for optimal therapy selection and drug repositioning for a variety of diseases.by Nathan Patrick Palmer.Ph.D

An Archived Multi Objective Simulated Annealing Method to Discover Biclusters in Microarray Data

With the advent of microarray technology it has been possible to measure thousands of expression values of genes in a single experiment. Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering methods. Recently, biclustering techniques were proposed for revealing submatrices showing unique patterns. Biclustering or simultaneous clustering of both genes and conditions is challenging particularly for the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining. In biclustering of microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A multi objective model is very suitable for solving this problem. Our method proposes a algorithm which is based on multi objective Simulated Annealing for discovering biclusters in gene expression data. Experimental result in bench mark data base present a significant improvement in overlap among biclusters and coverage of elements in gene expression and quality of biclusters

Meta Analysis of Gene Expression Data within and Across Species

Since the second half of the 1990s, a large number of genome-wide analyses have been described that study gene expression at the transcript level. To this end, two major strategies have been adopted, a first one relying on hybridization techniques such as microarrays, and a second one based on sequencing techniques such as serial analysis of gene expression (SAGE), cDNA-AFLP, and analysis based on expressed sequence tags (ESTs). Despite both types of profiling experiments becoming routine techniques in many research groups, their application remains costly and laborious. As a result, the number of conditions profiled in individual studies is still relatively small and usually varies from only two to few hundreds of samples for the largest experiments. More and more, scientific journals require the deposit of these high throughput experiments in public databases upon publication. Mining the information present in these databases offers molecular biologists the possibility to view their own small-scale analysis in the light of what is already available. However, so far, the richness of the public information remains largely unexploited. Several obstacles such as the correct association between ESTs and microarray probes with the corresponding gene transcript, the incompleteness and inconsistency in the annotation of experimental conditions, and the lack of standardized experimental protocols to generate gene expression data, all impede the successful mining of these data. Here, we review the potential and difficulties of combining publicly available expression data from respectively EST analyses and microarray experiments. With examples from literature, we show how meta-analysis of expression profiling experiments can be used to study expression behavior in a single organism or between organisms, across a wide range of experimental conditions. We also provide an overview of the methods and tools that can aid molecular biologists in exploiting these public data

EgoNet: Identification of human disease ego-network modules

Background: Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks.Results: We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes.Conclusions: Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases

Association Analysis Techniques for Discovering Functional Modules from Microarray Data

An application of great interest in microarray data analysis is the identification of a group of genes that show very similar patterns of expression in a data set, and are expected to represent groups of genes that perform common/similar functions, also known as functional modules. Although clustering offers a natural solution to this problem, it suffers from the limitation that it uses all the conditions to compare two genes, whereas only a subset of them may be relevant. Association analysis offers an alternative route for finding such groups of genes that may be co-expressed only over a subset of the experimental conditions used to prepare the data set. The techniques in this field attempt to find groups of data objects that contain coherent values across a set of attributes, in an exhaustive and efficient manner. In this paper, we illustrate how a generalization of the techniques in association analysis for real-valued data can be utilized to extract coherent functional modules from large microarray data sets

Information visualization for DNA microarray data analysis: A critical review

Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use ldquoabstractrdquo graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work