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PubMed2004

How many samples are needed to build a classifier: a general sequential approach.

MOTIVATION: The standard paradigm for a classifier design is to obtain a sample of feature-label pairs and then to apply a classification rule to derive a classifier from the sample data. Typically in laboratory situations the sample size is limited by cost, time or availability of sample material. Thus, an investigator may wish to consider a sequential approach in which there is a sufficient number of patients to train a classifier in order to make a sound decision for diagnosis while at the same time keeping the number of patients as small as possible to make the studies affordable. RESULTS: A sequential classification procedure is studied via the martingale central limit theorem. It updates the classification rule at each step and provides stopping criteria to ensure with a certain confidence that at stopping a future subject will have misclassification probability smaller than a predetermined threshold. Simulation studies and applications to microarray data analysis are provided. The procedure possesses several attractive properties: (1) it updates the classification rule sequentially and thus does not rely on distributions of primary measurements from other studies; (2) it assesses the stopping criteria at each sequential step and thus can substantially reduce cost via early stopping; and (3) it is not restricted to any particular classification rule and therefore applies to any parametric or non-parametric method, including feature selection or extraction. AVAILABILITY: R-code for the sequential stopping rule is available at http://stat.tamu.edu/~wfu/microarray/sequential/R-code.html

Algorithms↗
PubMed2005

Classification of oligonucleotide fingerprints: application for microbial community and gene expression analyses.

MOTIVATION: Oligonucleotide fingerprinting of ribosomal RNA genes (OFRG) is a procedure that sorts rRNA gene (rDNA) clones into taxonomic groups through a series of hybridization experiments. The hybridization signals are classified into three discrete values 0, 1 and N, where 0 and 1, respectively, specify negative and positive hybridization events and N designates an uncertain assignment. This study examined various approaches for classifying the values including Bayesian classification with normally distributed signal data, Bayesian classification with the exponentially distributed data, and with gamma distributed data, along with tree-based classification. All classification data were clustered using the unweighted pair group method with arithmetic mean. RESULTS: The performance of each classification/clustering procedure was compared with results from known reference data. Comparisons indicated that the approach using the Bayesian classification with normal densities followed by tree clustering out-performed all others. The paper includes a discussion of how this Bayesian approach may be useful for the analysis of gene expression data.

Algorithms↗
PubMed2005

A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings.

MOTIVATION: Clustering is one of the most widely used methods in unsupervised gene expression data analysis. The use of different clustering algorithms or different parameters often produces rather different results on the same data. Biological interpretation of multiple clustering results requires understanding how different clusters relate to each other. It is particularly non-trivial to compare the results of a hierarchical and a flat, e.g. k-means, clustering. RESULTS: We present a new method for comparing and visualizing relationships between different clustering results, either flat versus flat, or flat versus hierarchical. When comparing a flat clustering to a hierarchical clustering, the algorithm cuts different branches in the hierarchical tree at different levels to optimize the correspondence between the clusters. The optimization function is based on graph layout aesthetics or on mutual information. The clusters are displayed using a bipartite graph where the edges are weighted proportionally to the number of common elements in the respective clusters and the weighted number of crossings is minimized. The performance of the algorithm is tested using simulated and real gene expression data. The algorithm is implemented in the online gene expression data analysis tool Expression Profiler. AVAILABILITY: http://www.ebi.ac.uk/expressionprofiler

Algorithms↗
PubMed2005

Effect of pooling samples on the efficiency of comparative studies using microarrays.

MOTIVATION: Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample pooling, and some approximate formulas were provided for the power and sample size calculations. It is desirable to have exact formulas for these calculations and have the approximate results checked against the exact ones. We show that the difference between the approximate and the exact results can be large. RESULTS: In this study, we have characterized quantitatively the effect of pooling samples on the efficiency of microarray experiments for the detection of differential gene expression between two classes. We present exact formulas for calculating the power of microarray experimental designs involving sample pooling and technical replications. The formulas can be used to determine the total number of arrays and biological subjects required in an experiment to achieve the desired power at a given significance level. The conditions under which pooled design becomes preferable to non-pooled design can then be derived given the unit cost associated with a microarray and that with a biological subject. This paper thus serves to provide guidance on sample pooling and cost-effectiveness. The formulation in this paper is outlined in the context of performing microarray comparative studies, but its applicability is not limited to microarray experiments. It is also applicable to a wide range of biomedical comparative studies where sample pooling may be involved.

Computational Biology↗
PubMed2005

PAGE: phase-shifted analysis of gene expression.

Grouping of gene expression patterns across biological experiments, treatments and time-series data is performed in q-intervals of measurements using phase-shifted analysis of gene expression (PAGE); a Java-based tool to find clusters of genes that share trends of expression profiles within the dataset. The patterns and genes within q-Clusters are visualized in trend plots and compared to determine biological relevance from the gene annotations.

Algorithms↗
PubMed2005

EDGE: extraction and analysis of differential gene expression.

EDGE (Extraction of Differential Gene Expression) is an open source, point-and-click software program for the significance analysis of DNA microarray experiments. EDGE can perform both standard and time course differential expression analysis. The functions are based on newly developed statistical theory and methods. This document introduces the EDGE software package.

Algorithms↗
PubMed2006

An application for assessing quality of RNA hybridized to Affymetrix GeneChips.

UNLABELLED: This paper describes a stand-alone application for estimating the 3' to 5' ratio by fitting a mixed effects model to the interior pixel intensities of perfect match probes for selected control probe sets from an Affymetrix *.DAT file. The effectiveness of this method was demonstrated previously by an application of the method to two microarray datasets for which external verification of RNA quality was known. This application provides a more objective assessment of sample quality in that both a point estimate and 95% confidence interval about the 3' to 5' ratio are provided. AVAILABILITY: The software and installation instructions are freely available for download at http://www.people.vcu.edu/~kjarcher/Research/Data.htm

Algorithms↗
PubMed2006

RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis.

UNLABELLED: While meta-analysis provides a powerful tool for analyzing microarray experiments by combining data from multiple studies, it presents unique computational challenges. The Bioconductor package RankProd provides a new and intuitive tool for this purpose in detecting differentially expressed genes under two experimental conditions. The package modifies and extends the rank product method proposed by Breitling et al., [(2004) FEBS Lett., 573, 83-92] to integrate multiple microarray studies from different laboratories and/or platforms. It offers several advantages over t-test based methods and accepts pre-processed expression datasets produced from a wide variety of platforms. The significance of the detection is assessed by a non-parametric permutation test, and the associated P-value and false discovery rate (FDR) are included in the output alongside the genes that are detected by user-defined criteria. A visualization plot is provided to view actual expression levels for each gene with estimated significance measurements. AVAILABILITY: RankProd is available at Bioconductor http://www.bioconductor.org. A web-based interface will soon be available at http://cactus.salk.edu/RankProd

Computational Biology↗
PubMed2006

Time ordering of gene coexpression.

Temporal microarray gene expression profiles allow characterization of gene function through time dynamics of gene coexpression within the same genetic pathway. In this paper, we define and estimate a global time shift characteristic for each gene via least squares, inferred from pairwise curve alignments. These time shift characteristics of individual genes reflect a time ordering that is derived from ob- served temporal gene expression profiles. Once these time shift characteristics are obtained for each gene, they can be entered into further analyses, such as clustering. We illustrate the proposed methodology using Drosophila embryonic development and yeast cell-cycle gene expression profiles, as well as simulations. Feasibility is demonstrated through the successful recovery of time ordering. Estimated time shifts for Drosophila maternal and zygotic genes provide excellent discrimination between these two categories and confirm known genetic pathways through the time order of gene expression. The application to yeast cell-cycle data establishes a natural time order of genes that is in line with cell-cycle phases. The method does not require periodicity of gene expression profiles. Asymptotic justifications are also provided.

Animals↗
PubMed2003

EXCAVATOR: a computer program for efficiently mining gene expression data.

Massive amounts of gene expression data are generated using microarrays for functional studies of genes and gene expression data clustering is a useful tool for studying the functional relationship among genes in a biological process. We have developed a computer package EXCAVATOR for clustering gene expression profiles based on our new framework for representing gene expression data as a minimum spanning tree. EXCAVATOR uses a number of rigorous and efficient clustering algorithms. This program has a number of unique features, including capabilities for: (i) data- constrained clustering; (ii) identification of genes with similar expression profiles to pre-specified seed genes; (iii) cluster identification from a noisy background; (iv) computational comparison between different clustering results of the same data set. EXCAVATOR can be run from a Unix/Linux/DOS shell, from a Java interface or from a Web server. The clustering results can be visualized as colored figures and 2-dimensional plots. Moreover, EXCAVATOR provides a wide range of options for data formats, distance measures, objective functions, clustering algorithms, methods to choose number of clusters, etc. The effectiveness of EXCAVATOR has been demonstrated on several experimental data sets. Its performance compares favorably against the popular K-means clustering method in terms of clustering quality and computing time.

Algorithms↗
PubMed2006

Expression profiling of endocrine-disrupting compounds using a customized Cyprinus carpio cDNA microarray.

Exposure to a variety of anthropogenic compounds has been shown to interfere with normal development, physiology, and reproduction in a wide range of organisms, both in laboratory studies and wildlife. We have developed a Cyprinus carpio cDNA microarray consisting of endocrine-related genes. In the current study, we investigated the applicability of this microarray (1) to study the molecular effects induced by exposure to a variety of endocrine-disrupting compounds (EDCs) in fish and (2) to discriminate the specific transcriptional profiles associated with these compounds. To that purpose, gene expression profiles were generated in livers of juvenile carp exposed to 14 Organization of Economical Cooperation Development (OECD)-recommended reference EDCs (17beta-estradiol, 17alpha-ethinylestradiol, 4-nonylphenol, bisphenol A, tamoxifen, 17alpha-methyltestosterone, 11-ketotestosterone, dibutyl phthalate, flutamide, vinclozolin, hydrocortisone, CuCl(2), propylthiouracil, and a mixture of L-triiodothyronine and L-thyroxine). Our results show that, in addition to some expression similarities between analogous acting substances, each individual compound produced its own unique expression pattern on the array, distinct from the profiles generated by the other compounds. In addition, we were able to identify a minimal subset of genes, which also allowed to discriminate between the different compounds. Overall, our findings suggest that the developed cDNA array has great promise to screen new and existing chemicals on their endocrine-disruptive potential and to identify distinct classes of EDCs.

Animals↗
PubMed2004

Tissue microarray analysis reveals prognostic significance of COX-2 expression for local relapse in T1-2N0 larynx cancer treated with primary radiation therapy.

OBJECTIVE: The purpose of this analysis is to determine whether a significant correlation exists between cyclooxygenase-2 (COX-2) expression and local relapse in a large cohort of patients with T1 to 2N0 laryngeal cancer treated with primary radiation therapy. METHODS AND MATERIALS: Clinical and molecular analyses were conducted on 123 patients with biopsy-proven T1 to 2N0 laryngeal cancer. Clinical prognostic factors included pretreatment hemoglobin, age, sex, race, T stage, tumor subsite, beam energy, biologically equivalent dose, therapy duration, and treatment date. Molecular prognostic factors included COX-2, p53, and Ki-67 expression. Expression levels were determined by immunohistochemistry on paraffin-embedded tissues arrayed on tissue microarrays. Multivariate analysis was done with the Cox proportional hazards model. RESULTS: Thirty-two patients have locally relapsed, for an actuarial 5-year local relapse-free rate of 70.4%. On multivariate analysis, positive COX-2 expression predicted local relapse after radiation therapy. The relative risk (RR) for local relapse with COX-2 positivity was 2.57 (95% CI, 1.21-5.47; P = .01). Other prognostic factors for local relapse included negative Ki-67 expression (RR = 5.72; 95% CI, 2.04-16.1; P < .001), T2 stage (RR = 2.98; 95% CI, 1.39-6.38; P = .005), and therapy duration greater than 43 days (RR = 6.04; 95% CI, 1.37-26.7; P = .02). CONCLUSIONS: Positive COX-2 expression predicts for local relapse in T1 to 2N0 larynx cancer in a multivariate model. This relationship may have potential therapeutic implications regarding the use of COX-2 inhibitors during radiation therapy for optimal outcome.

Adult↗
PubMed2006

Subdimension-based similarity measure for DNA microarray data clustering.

Microarray data analysis is useful for understanding biological processes. A number of clustering algorithms have been used to achieve this task. However, the performance of these methods can be significantly degraded due to the presence of nonsignificant conditions. In this paper, we propose a robust clustering algorithm based on a similarity measure. The key concept of the proposed similarity measure is to measure the similarity between two data points by their subdimensions. For example, assume that x1, x2, and x3 are ten-dimensional data vectors. The data point x3 is said to be closer to x1 than x2 if more than half of the dimensions of x1 and x3 are closer to x1 than x2. Thus, if two patterns are very similar except for a small amount of features, this measure will preserve the similarity. We have performed eight experiments to test the robustness of the proposed method, including three synthetic data sets, three real world data sets, and two microarray data sets. We also have compared the proposed method with four different clustering algorithms. Experimental results show that the proposed method yields better results than existing clustering algorithms.

Algorithms↗
PubMed2005

Multi-metric and multi-substructure biclustering analysis for gene expression data.

A good number of biclustering algorithms have been proposed for grouping gene expression data. Many of them have adopted matrix norms to define the similarity score of a bicluster. We shall show that almost all matrix metrics can be converted into vector norms while preserving the rank equivalence. Vector norms provide a much more efficient vehicle for biclustering analysis and computation. The advantages are two folds: ease of analysis and saving of computation. Most existing biclustering algorithms have also implicitly assumed the use of univariate (i.e., single metric) evaluation for identifying biclusters. Such an approach however overlooks the fundamental principle that genes (even though they may belong to the same gene group) (1) may be subdivided into different substructures; and (2) they may be co-expressed via a diversity of coherence models (a gene may participate in multiple pathways that may or may not be co-active under all conditions). The former leads to the adoption of a multi-substurcture analysis, while the latter to the multivariate analysis. This paper will show that the proposed multivariate and multi-subscluster analysis is very effective in identifying and classifying biologically relevant groups in genes and conditions. For example, it has successfully yielded highly discriminant and accurate classification based on known ribosomal gene groups.

Algorithms↗
PubMed2005

A robust meta-classification strategy for cancer diagnosis from gene expression data.

One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose a meta-classification scheme which uses a robust multivariate gene selection procedure and integrates the results of several machine learning tools trained on raw and pattern data. We validate our method by applying it to distinguish diffuse large B-cell lymphoma (DLBCL) from follicular lymphoma (FL) on two independent datasets: the HuGeneFL Affmetrixy dataset of Shipp et al. (www. genome.wi.mit.du/MPR /lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our meta-classification technique achieves higher predictive accuracies than each of the individual classifiers trained on the same dataset and is robust against various data perturbations. We also find that combinations of p53 responsive genes (e.g., p53, PLK1 and CDK2) are highly predictive of the phenotype.

Algorithms↗
PubMedDate not supplied

Semisupervised learning for molecular profiling.

Class prediction and feature selection are two learning tasks that are strictly paired in the search of molecular profiles from microarray data. Researchers have become aware how easy it is to incur a selection bias effect, and complex validation setups are required to avoid overly optimistic estimates of the predictive accuracy of the models and incorrect gene selections. This paper describes a semisupervised pattern discovery approach that uses the by-products of complete validation studies on experimental setups for gene profiling. In particular, we introduce the study of the patterns of single sample responses (sample-tracking profiles) to the gene selection process induced by typical supervised learning tasks in microarray studies. We originate sample-tracking profiles as the aggregated off-training evaluation of SVM models of increasing gene panel sizes. Genes are ranked by E-RFE, an entropy-based variant of the recursive feature elimination for support vector machines (RFE-SVM). A Dynamic Time Warping (DTW) algorithm is then applied to define a metric between sample-tracking profiles. An unsupervised clustering based on the DTW metric allows automating the discovery of outliers and of subtypes of different molecular profiles. Applications are described on synthetic data and in two gene expression studies.

Algorithms↗
PubMedDate not supplied

Parallel dual-color fluorescence cross-correlation spectroscopy using diffractive optical elements.

Dual-color cross-correlation spectroscopy allows the detection and quantification of labeled biomolecules at ultra-low concentrations, whereby the sensitivity of the assay correlates with the measurement time. We now describe a parallel multifocal dual-color spectroscopic configuration employing multiple avalanche photodiodes and hardware correlators. Cross-correlation curves are obtained from several dual-color excitation foci simultaneously. Multifocal dual-color excitation is achieved by splitting each of two laser beams (488 and 633 nm) into four sub-beams with the help of two 2x2 fan-out diffractive optical elements (DOEs), and subsequent superposition of the two sets of four foci. The fluorescence emission from double-labeled biomolecules is detected by two 2x2 fiber arrays.

Equipment Design↗