Hongya Zhao

A new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray data.

Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes.

Biclustering Analysis for Pattern Discovery: Current Techniques, Comparative Studies and Applications

Biclustering analysis is a useful methodology to discover the local coherent patterns hidden in a data matrix. Unlike the traditional clustering procedure, which searches for groups of coherent patterns using the entire feature set, biclustering performs simultaneous pattern classification in both row and column directions in a data matrix. The technique has found useful applications in many fields but notably in bioinformatics. In this paper, we give an overview of the biclustering problem and review some existing biclustering algorithms in terms of their underlying methodology, search strategy, detected bicluster patterns, and validation strategies. Moreover, we show that geometry of biclustering patterns can be used to solve biclustering problems effectively. Well-known methods in signal and image analysis, such as the Hough transform and relaxation labeling, can be employed to detect the geometrical biclustering patterns. We present performance evaluation results for several of the well known biclustering algorithms, on both artificial and real gene expression datasets. Finally, several interesting applications of biclustering are discussed.

A probabilistic relaxation labeling framework for reducing the noise effect in geometric biclustering of gene expression data

Biclustering is an important method in DNA microarray analysis which can be applied when only a subset of genes is co-expressed in a subset of conditions. Unlike standard clustering analyses, biclustering methodology can perform simultaneous classification on two dimensions of genes and conditions in a microarray data matrix. However, the performance of biclustering algorithms is affected by the inherent noise in data, types of biclusters and computational complexity. In this paper, we present a geometric biclustering method based on the Hough transform and the relaxation labeling technique. Unlike many existing biclustering algorithms, we first consider the biclustering patterns through geometric interpretation. Such a perspective makes it possible to unify the formulation of different types of biclusters as hyperplanes in spatial space and facilitates the use of a generic plane finding algorithm for bicluster detection. In our algorithm, the Hough transform is employed for hyperplane detection in sub-spaces to reduce the computational complexity. Then sub-biclusters are combined into larger ones under the probabilistic relaxation labeling framework. Our simulation studies demonstrate the robustness of the algorithm against noise and outliers. In addition, our method is able to extract biologically meaningful biclusters from real microarray gene expression data.

HoughFeature, a novel method for assessing drug effects in three-color cDNA microarray experiments

BackgroundThree-color microarray experiments can be performed to assess drug effects on the genomic scale. The methodology may be useful in shortening the cycle, reducing the cost, and improving the efficiency in drug discovery and development compared with the commonly used dual-color technology. A visualization tool, the hexaMplot, is able to show the interrelations of gene expressions in normal-disease-drug samples in three-color microarray data. However, it is not enough to assess the complicated drug therapeutic effects based on the plot alone. It is important to explore more effective tools so that a deeper insight into gene expression patterns can be gained with three-color microarrays.ResultsBased on the celebrated Hough transform, a novel algorithm, HoughFeature, is proposed to extract line features in the hexaMplot corresponding to different drug effects. Drug therapy results can then be divided into a number of levels in relation to different groups of genes. We apply the framework to experimental microarray data to assess the complex effects of Rg1 (an extract of Chinese medicine) on Hcy-related HUVECs in details. Differentially expressed genes are classified into 15 functional groups corresponding to different levels of drug effects.ConclusionOur study shows that the HoughFeature algorithm can reveal natural cluster patterns in gene expression data of normal-disease-drug samples. It provides both qualitative and quantitative information about up- or down-regulated genes. The methodology can be employed to predict disease susceptibility in gene therapy and assess drug effects on the disease based on three-color microarray data.

Efficient rotation- and scale-invariant texture classification method based on Gabor wavelets

An efficient texture classification method is proposed that considers the effects of both the rotation and scale of texture im- ages. In our method, the Gabor wavelets are adopted to extract local features of an image and the statistical properties of its gray- level intensities are used to represent the global features. Then, an adaptive, circular orientation normalization scheme is proposed to make the feature invariant to rotation, and an elastic cross- frequency searching mechanism is devised to reduce the effect of scaling. Our method is evaluated based on the Brodatz album and the Outex database, and the experimental results show that it out- performs the traditional algorithms.

A New Strategy of Geometrical Biclustering for Microarray Data Analysis

In this paper, we present a new biclustering algorithm to provide the geometrical interpretation of similar microarray gene expression profiles. Different from standard clustering analyses, biclustering methodology can perform simultaneous classification on the row and column dimensions of a data matrix. The main object of the strategy is to reveal the submatrix, in which a subset of genes exhibits a consistent pattern over a subset of conditions. However, the search for such subsets is a computationally complex task. We propose a new algorithm, based on the Hough transform in the column-pair space to perform pattern identification. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our simulation studies show that the method is robust to noise and computationally efficient. Furthermore, we have applied it to a large database of gene expression profiles of multiple human organs and the resulting biclusters show clear biological meanings.

Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments

BackgroundIdentification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular to solve this type of problems. These models show good performance in accommodating noise, variability and low replication of microarray data. However, the correlation between different fluorescent signals measured from a gene spot is ignored, which can diversely affect the data analysis step. In fact, the intensities of the two signals are significantly correlated across samples. The larger the log-transformed intensities are, the smaller the correlation is.ResultsMotivated by the complicated error relations in microarray data, we propose a multivariate hierarchical Bayesian framework for data analysis in the replicated microarray experiments. Gene expression data are modelled by a multivariate normal distribution, parameterized by the corresponding mean vectors and covariance matrixes with a conjugate prior distribution. Within the Bayesian framework, a generalized likelihood ratio test (GLRT) is also developed to infer the gene expression patterns. Simulation studies show that the proposed approach presents better operating characteristics and lower false discovery rate (FDR) than existing methods, especially when the correlation coefficient is large. The approach is illustrated with two examples of microarray analysis. The proposed method successfully detects significant genes closely related to the experimental states, which are verified by the biological information.ConclusionsThe multivariate Bayesian model, compatible with the dependence between mean and variance in the univariate Bayesian model, relaxes the constant coefficient of variation assumption between measurements by adding a covariance structure. This model improves the identification of differentially expressed genes significantly since the Bayesian model fit well with the microarray data.

Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform

The effects of a drug on the genomic scale can be assessed in a three-color cDNA microarray with the three color intensities represented through the so-called hexaMplot. In our recent study, we have shown that the Hough Transform (HT) applied to the hexaMplot can be used to detect groups of coexpressed genes in the normal-disease-drug samples. However, the standard HT is not well suited for the purpose because 1) the assayed genes need first to be hard-partitioned into equally and differentially expressed genes, with HT ignoring possible information in the former group; 2) the hexaMplot coordinates are negatively correlated and there is no direct way of expressing this in the standard HT and 3) it is not clear how to quantify the association of coexpressed genes with the line along which they cluster. We address these deficiencies by formulating a dedicated probabilistic model-based HT. The approach is demonstrated by assessing effects of the drug Rg1 on homocysteine-treated human umbilical vein endothetial cells. Compared with our previous study, we robustly detect stronger natural groupings of coexpressed genes. Moreover, the gene groups show coherent biological functions with high significance, as detected by the Gene Ontology analysis.

Identifying Multi-Dimensional Co-Clusters in Tensors Based on Hyperplane Detection in Singular Vector Spaces.

Co-clustering, often called biclustering for two-dimensional data, has found many applications, such as gene expression data analysis and text mining. Nowadays, a variety of multi-dimensional arrays (tensors) frequently occur in data analysis tasks, and co-clustering techniques play a key role in dealing with such datasets. Co-clusters represent coherent patterns and exhibit important properties along all the modes. Development of robust co-clustering techniques is important for the detection and analysis of these patterns. In this paper, a co-clustering method based on hyperplane detection in singular vector spaces (HDSVS) is proposed. Specifically in this method, higher-order singular value decomposition (HOSVD) transforms a tensor into a core part and a singular vector matrix along each mode, whose row vectors can be clustered by a linear grouping algorithm (LGA). Meanwhile, hyperplanar patterns are extracted and successfully supported the identification of multi-dimensional co-clusters. To validate HDSVS, a number of synthetic and biological tensors were adopted. The synthetic tensors attested a favorable performance of this algorithm on noisy or overlapped data. Experiments with gene expression data and lineage data of embryonic cells further verified the reliability of HDSVS to practical problems. Moreover, the detected co-clusters are well consistent with important genetic pathways and gene ontology annotations. Finally, a series of comparisons between HDSVS and state-of-the-art methods on synthetic tensors and a yeast gene expression tensor were implemented, verifying the robust and stable performance of our method.

Mining functional biclusters of DNA microarray gene expression data

A subset of genes sharing compatible expression patterns under a subset of conditions can be found from DNA microarray data using biclustering algorithms. In this paper, we present a novel geometrical biclustering algorithm in combination with gene ontology annotations to identify the gene functional biclusters. Unlike many existing biclustering algorithms, we first consider the biclustering patterns through geometrical interpretation. Such a perspective makes it possible to unify the formulation of different types of biclusters as hyperplanes in spatial space and facilitates the use of a generic plane finding algorithm for bicluster detection. In our bottom-up biclustering algorithm, the well-known Hough transform is first employed in pair-column spaces to reduce the computation complexity and then the resulting patterns are merged step by step into large-size biclusters incorporated with gene functional modules. The algorithm integrates the numerical characteristics in a gene expression matrix and the gene functions in the biological activities. Our experiments on real data show that the new algorithm outperforms most existing methods for mining gene functional biclusters.