Techniques for clustering gene expression data

Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered

Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

<p>Abstract</p> <p>Background</p> <p>The hierarchical clustering tree (HCT) with a dendrogram <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> and the singular value decomposition (SVD) with a dimension-reduced representative map <abbrgrp><abbr bid="B2">2</abbr></abbrgrp> are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures.</p> <p>Results</p> <p>This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose) seriation by Chen <abbrgrp><abbr bid="B3">3</abbr></abbrgrp> as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends.</p> <p>Conclusion</p> <p>We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at <url>http://gap.stat.sinica.edu.tw/Software/GAP</url>.</p

Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies

<p>Abstract</p> <p>Background</p> <p>The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters.</p> <p>Results</p> <p>In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods.</p> <p>Conclusion</p> <p>We demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.</p

Inferring interactions, expression programs and regulatory networks from high throughput biological data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (leaves 171-180).(cont.) For the networks level I present an algorithm that efficiently combines complementary large-scale expression and protein-DNA binding data to discover co-regulated modules of genes. This algorithm is extended so that it can infer sub-networks for specific systems in the cell. Finally, I present an algorithm which combines some of the above methods to automatically infer a dynamic sub-network for the cell cycle system.In this thesis I present algorithms for analyzing high throughput biological datasets. These algorithms work on a number of different analysis levels to infer interactions between genes, determine gene expression programs and model complex biological networks. Recent advances in high-throughput experimental methods in molecular biology hold great promise. DNA microarray technologies enable researchers to measure the expression levels of thousands of genes simultaneously. Time series expression data offers particularly rich opportunities for understanding the dynamics of biological processes. In addition to measuring expression data, microarrays have been recently exploited to measure genome-wide protein-DNA binding events. While these types of data are revolutionizing biology, they also present many computational challenges. Principled computational methods are required in order to make full use of each of these datasets, and to combine them to infer interactions and discover networks for modeling different systems in the cell. The algorithms presented in this thesis address three different analysis levels of high throughput biological data: Recovering individual gene values, pattern recognition and networks. For time series expression data, I present algorithms that permit the principled estimation of unobserved time-points, alignment and the identification of differentially expressed genes. For pattern recognition, I present algorithms for clustering continuous data, and for ordering the leaves of a clustering tree to infer expression programs.by Ziv Bar-Joseph.Ph.D

Towards a Holistic, Yet Gene-Centered Analysis of Gene Expression Profiles: A Case Study of Human Lung Cancers

Genome-wide gene expression profile studies encompass increasingly large number of samples, posing a challenge to their presentation and interpretation without losing the notion that each transcriptome constitutes a complex biological entity. Much like pathologists who visually analyze information-rich histological sections as a whole, we propose here an integrative approach. We use a self-organizing maps -based software, the gene expression dynamics inspector (GEDI) to analyze gene expression profiles of various lung tumors. GEDI allows the comparison of tumor profiles based on direct visual detection of transcriptome patterns. Such intuitive “gestalt” perception promotes the discovery of interesting relationships in the absence of an existing hypothesis. We uncovered qualitative relationships between squamous cell tumors, small-cell tumors, and carcinoid tumor that would have escaped existing algorithmic classifications. These results suggest that GEDI may be a valuable explorative tool that combines global and gene-centered analyses of molecular profiles from large-scale microarray experiments

Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases.

Inflammatory bowel diseases, which include Crohn's disease and ulcerative colitis, affect several million individuals worldwide. Crohn's disease and ulcerative colitis are complex diseases that are heterogeneous at the clinical, immunological, molecular, genetic, and microbial levels. Individual contributing factors have been the focus of extensive research. As part of the Integrative Human Microbiome Project (HMP2 or iHMP), we followed 132 subjects for one year each to generate integrated longitudinal molecular profiles of host and microbial activity during disease (up to 24 time points each; in total 2,965 stool, biopsy, and blood specimens). Here we present the results, which provide a comprehensive view of functional dysbiosis in the gut microbiome during inflammatory bowel disease activity. We demonstrate a characteristic increase in facultative anaerobes at the expense of obligate anaerobes, as well as molecular disruptions in microbial transcription (for example, among clostridia), metabolite pools (acylcarnitines, bile acids, and short-chain fatty acids), and levels of antibodies in host serum. Periods of disease activity were also marked by increases in temporal variability, with characteristic taxonomic, functional, and biochemical shifts. Finally, integrative analysis identified microbial, biochemical, and host factors central to this dysregulation. The study's infrastructure resources, results, and data, which are available through the Inflammatory Bowel Disease Multi'omics Database ( http://ibdmdb.org ), provide the most comprehensive description to date of host and microbial activities in inflammatory bowel diseases

Dendrogram seriation in data visualisation: algorithms and applications

Seriation is a data analytic tool for obtaining a permutation of a set of objects with the goal of revealing structural information within the set of objects. The purpose of this thesis is to investigate and develop tools for seriation with the goal of using these tools to enhance data visualisation. The particular focus of this thesis is on dendrogram seriation algorithms. A dendrogram is a tree-like structure used for visualising the results of a hierarchical clustering and the order of the leaves in a dendrogram provides a permutation of a set of objects. Dendrogram seriation algorithms rearrange the leaves of a dendrogram in order to find a permutation that optimises a given criterion. Dendrogram seriation algorithms are widely used, however, the research in this area is often confusing because of inconsistent or inadequate terminology. This thesis proposes new notation and terminology with the goal of better understanding and comparing dendrogram seriation algorithms. Seriation criteria measure the goodness of a permutation of a set of objects. Popular seriation criteria include the path length of a permutation and measuring anti-Robinson form in a symmetric matrix. This thesis proposes two new seriation criteria, lazy path length and banded anti-Robinson form, and demonstrates their effectiveness in improving a variety of visualisations. The main contribution of this thesis is a new dendrogram seriation algorithm. This algorithm improves on other dendrogram seriation algorithms and is also flexible because it allows the user to either choose from a variety of seriation criteria, including the new criteria mentioned above, or to input their own criteria. Finally, this thesis performs a comparison of several seriation algorithms, the results of which show that the proposed algorithm performs competitively against other algorithms. This leads to a set of general guidelines for choosing the most appropriate seriation algorithm for different seriation interests and visualisation settings