Ontologizing gene-expression microarray data: characterizing clusters with Gene Ontology

An XML-based Java application is described that provides a function-oriented overview of the results of cluster analysis of gene-expression microarray data based on Gene Ontology terms and associations. The application generates one HTML page with listings of the frequencies of explicit and implicit Gene Ontology annotations for each cluster, and separate, linked pages with listings of explicit annotations for each gene in a cluster.     

MAGIC Tool: integrated microarray data analysis

Summary: Several programs are now available for analyzing thelarge datasets arising from cDNA microarray experiments. Mostprograms are expensive commercial packages or require expensivethird party software. Some are freely available to academicresearchers, but are limited to one operating system. MicroArrayGenome Imaging and Clustering Tool (MAGIC Tool) is an open sourceprogram that works on all major platforms, and takes users ‘fromtiff to gif’. Several unique features of MAGIC Tool areparticularly useful for research and teaching. Availability: http://www.bio.davidson.edu/MAGIC Contact: laheyer{at}davidson.edu     

Statistical analysis of microarray data: a Bayesian approach

The potential of microarray data is enormous. It allows us to monitor the expression of thousands of genes simultaneously. A common task with microarray is to determine which genes are differentially expressed between two samples obtained under two different conditions. Recently, several statistical methods have been proposed to perform such a task when there are replicate samples under each condition. Two major problems arise with microarray data. The first one is that the number of replicates is very small (usually 2-10), leading to noisy point estimates. As a consequence, traditional statistics that are based on the means and standard deviations, e.g. t-statistic, are not suitable. The second problem is that the number of genes is usually very large (approximately 10,000), and one is faced with an extreme multiple testing problem. Most multiple testing adjustments are relatively conservative, especially when the number of replicates is small. In this paper we present an empirical Bayes analysis that handles both problems very well. Using different parametrizations, we develop four statistics that can be used to test hypotheses about the means and/or variances of the gene expression levels in both one- and two-sample problems. The methods are illustrated using experimental data with prior knowledge. In addition, we present the result of a simulation comparing our methods to well-known statistics and multiple testing adjustments.     

AVA: visual analysis of gene expression microarray data

SUMMARY: AVA (Array Visual Analyzer) is a Java program that provides a graphical environment for visualization and analysis of gene expression microarray data. Together with its interactive visualization tools and a variety of built-in data analysis and filtration methods, AVA effectively integrates microarray data normalization, quality assessment, and data mining into one application. AVAILABILITY: The software is freely available for academic users on request from the authors.     

MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data

MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP . The results are displayed in a searchable browser, allowing the user to rapidly identify GO terms with over-represented numbers of gene-expression changes. Clicking on GO terms generates GenMAPP graphical files where gene relationships can be explored, annotated, and files can be freely exchanged.     

MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data

MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP http://www.GenMAPP.org. The results are displayed in a searchable browser, allowing the user to rapidly identify GO terms with over-represented numbers of gene-expression changes. Clicking on GO terms generates GenMAPP graphical files where gene relationships can be explored, annotated, and files can be freely exchanged.     

Mining mouse microarray data

Microarrays of mouse genes are now available from several sources, and they have so far given new insights into gene expression in embryonic development, regions of the brain and during apoptosis. Microarray data posted on the internet can be reanalyzed to study a range of questions.     

Mining DNA microarray data using a novel approach based on graph theory.

The recent demonstration that biochemical pathways from diverse organisms are arranged in scale-free, rather than random, systems [Jeong et al., Nature 407 (2000) 651-654], emphasizes the importance of developing methods for the identification of biochemical nexuses--the nodes within biochemical pathways that serve as the major input/output hubs, and therefore represent potentially important targets for modulation. Here we describe a bioinformatics approach that identifies candidate nexuses for biochemical pathways without requiring functional gene annotation; we also provide proof-of-principle experiments to support this technique. This approach, called Nexxus, may lead to the identification of new signal transduction pathways and targets for drug design.     

Gene Class expression: analysis tool of Gene Ontology terms with gene expression data

Serial analysis of gene expression (SAGE) technology produces large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in these gene sets. We present an interactive web-based tool, called Gene Class, which allows functional annotation of SAGE data using the Gene Ontology (GO) database. This tool performs searches in the GO database for each SAGE tag, making associations in the selected GO category for a level selected in the hierarchy. This system provides user-friendly data navigation and visualization for mapping SAGE data onto the gene ontology structure. This tool also provides graphical visualization of the percentage of SAGE tags in each GO category, along with confidence intervals and hypothesis testing.     

Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data

Background  

Gene Ontology (GO) terms are often used to assess the results of microarray experiments. The most common way to do this is to perform Fisher's exact tests to find GO terms which are over-represented amongst the genes declared to be differentially expressed in the analysis of the microarray experiment. However, due to the high degree of dependence between GO terms, statistical testing is conservative, and interpretation is difficult.     

Nonparametric variance estimation in the analysis of microarray data: a measurement error approach

We investigate the effects of measurement error on the estimationof nonparametric variance functions. We show that either ignoringmeasurement error or direct application of the simulation extrapolation,SIMEX, method leads to inconsistent estimators. Nevertheless,the direct SIMEX method can reduce bias relative to a naiveestimator. We further propose a permutation SIMEX method thatleads to consistent estimators in theory. The performance ofboth the SIMEX methods depends on approximations to the exactextrapolants. Simulations show that both the SIMEX methods performbetter than ignoring measurement error. The methodology is illustratedusing microarray data from colon cancer patients.     

2HAPI: a microarray data analysis system

SUMMARY: 2HAPI (version 2 of High density Array Pattern Interpreter) is a web-based, publicly-available analytical tool designed to aid researchers in microarray data analysis. 2HAPI includes tools for searching, manipulating, visualizing, and clustering the large sets of data generated by microarray experiments. Other features include association of genes with NCBI information and linkage to external data resources. Unique to 2HAPI is the ability to retrieve upstream sequences of co-regulated genes for promoter analysis using MEME (Multiple Expectation-maximization for Motif Elicitation) AVAILABILITY: 2HAPI is freely available at http://array.sdsc.edu. Users can try 2HAPI anonymously with pre-loaded data or they can register as a 2HAPI user and upload their data.     

SpotWhatR: a user-friendly microarray data analysis system

SpotWhatR is a user-friendly microarray data analysis tool that runs under a widely and freely available R statistical language (http://www.r-project.org) for Windows and Linux operational systems. The aim of SpotWhatR is to help the researcher to analyze microarray data by providing basic tools for data visualization, normalization, determination of differentially expressed genes, summarization by Gene Ontology terms, and clustering analysis. SpotWhatR allows researchers who are not familiar with computational programming to choose the most suitable analysis for their microarray dataset. Along with well-known procedures used in microarray data analysis, we have introduced a stand-alone implementation of the HTself method, especially designed to find differentially expressed genes in low-replication contexts. This approach is more compatible with our local reality than the usual statistical methods. We provide several examples derived from the Blastocladiella emersonii and Xylella fastidiosa Microarray Projects. SpotWhatR is freely available at http://blasto.iq.usp.br/~tkoide/SpotWhatR, in English and Portuguese versions. In addition, the user can choose between "single experiment" and "batch processing" versions.     

AMADA: analysis of microarray data

SUMMARY: AMADA is a Windows program for identifying co-expressed genes from microarray data. It performs data transformation, principal component analysis, a variety of cluster analyses and extensive graphic functions for visualizing expression profiles.     

Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data

Background  

Many researchers are concerned with the comparability and reliability of microarray gene expression data. Recent completion of the MicroArray Quality Control (MAQC) project provides a unique opportunity to assess reproducibility across multiple sites and the comparability across multiple platforms. The MAQC analysis presented for the conclusion of inter- and intra-platform comparability/reproducibility of microarray gene expression measurements is inadequate. We evaluate the reproducibility/comparability of the MAQC data for 12901 common genes in four titration samples generated from five high-density one-color microarray platforms and the TaqMan technology. We discuss some of the problems with the use of correlation coefficient as metric to evaluate the inter- and intra-platform reproducibility and the percent of overlapping genes (POG) as a measure for evaluation of a gene selection procedure by MAQC.     

Comparison of different microarray data analysis programs and description of a database for microarray data management

Data analysis and management represent a major challenge for gene expression studies using microarrays. Here, we compare different methods of analysis and demonstrate the utility of a personal microarray database. Gene expression during HIV infection of cell lines was studied using Affymetrix U-133 A and B chips. The data were analyzed using Affymetrix Microarray Suite and Data Mining Tool, Silicon Genetics GeneSpring, and dChip from Harvard School of Public Health. A small-scale database was established with FileMaker Pro Developer to manage and analyze the data. There was great variability among the programs in the lists of significantly changed genes constructed from the same data. Similarly choices of different parameters for normalization, comparison, and standardization greatly affected the outcome. As many probe sets on the U133 chip target the same Unigene clusters, the Unigene information can be used as an internal control to confirm and interpret the probe set results. Algorithms used for the determination of changes in gene expression require further refinement and standardization. The use of a personal database powered with Unigene information can enhance the analysis of gene expression data.     

Mining microarray expression data by literature profiling

Background  

The rapidly expanding fields of genomics and proteomics have prompted the development of computational methods for managing, analyzing and visualizing expression data derived from microarray screening. Nevertheless, the lack of efficient techniques for assessing the biological implications of gene-expression data remains an important obstacle in exploiting this information.     

A spline function approach for detecting differentially expressed genes in microarray data analysis

MOTIVATION: A primary objective of microarray studies is to determine genes which are differentially expressed under various conditions. Parametric tests, such as two-sample t-tests, may be used to identify differentially expressed genes, but they require some assumptions that are not realistic for many practical problems. Non-parametric tests, such as empirical Bayes methods and mixture normal approaches, have been proposed, but the inferences are complicated and the tests may not have as much power as parametric models. RESULTS: We propose a weakly parametric method to model the distributions of summary statistics that are used to detect differentially expressed genes. Standard maximum likelihood methods can be employed to make inferences. For illustration purposes the proposed method is applied to the leukemia data (training part) discussed elsewhere. A simulation study is conducted to evaluate the performance of the proposed method.