Basic science for the practicing physician: gene expression microarrays

OBJECTIVE: To provide a general overview of gene expression microarray technology and its relevance to physicians practicing allergy/immunology. DATA SOURCES: The PubMed interface to MEDLINE was searched for primary and review articles on gene expression microarrays. Specific articles on clinical applications of microarrays were retrieved, along with articles on use of microarrays in models of allergy, asthma, and immunologic diseases. STUDY SELECTION: The author's knowledge of the field was used to include sources of information other than those obtained through the MEDLINE search. RESULTS: A synopsis of gene expression microarray technology, with emphasis on the relevance to allergy, asthma, and immunology, is presented. CONCLUSIONS: Gene expression microarray technology allows investigators to measure gene expression across the genome. This has allowed researchers to improve our understanding of immunologic mechanisms in disease models. Initially used solely as a research tool, microarray-based clinical tests are now available, and many more are in development. Use of microarrays in allergy, asthma, and immunology will support the development of novel diagnostic tests for the physician and facilitate exploration of the basic mechanisms underlying allergic and immunologic diseases.     

A classification-based machine learning approach for the analysis of genome-wide expression data

Three important areas of data analysis for global gene expression analysis are class discovery, class prediction, and finding dysregulated genes (biomarkers). The clinical application of microarray data will require marker genes whose expression patterns are sufficiently well understood to allow accurate predictions on disease subclass membership. Commonly used methods of analysis include hierarchical clustering algorithms, t-, F-, and Z-tests, and machine learning approaches. We describe an approach called the maximum difference subset (MDSS) algorithm that combines classification algorithms, classical statistics, and elements of machine learning and provides a coherent framework. By integrating prediction accuracy, the MDSS algorithm learns the critical threshold of statistical significance (the alpha or P-value), eliminating the arbitrariness of setting a threshold of statistical significance and minimizing the effect of the normality assumptions. To reduce the false positive rate and to increase external validity of the predictive gene set, a jackknife step is used. This step identifies and removes genes in the initial MDSS with low combined predictive utility. The overall MDSS provides a prediction that is less dependent on an arbitrary study design (sample inclusion or exclusion) and should thus have high external validity. We demonstrate that this approach, unlike other published methods, identifies biomarkers capable of predicting the outcome of anthracycline-cytarabine chemotherapy in cases of acute myeloid leukemia. By incorporating two criteria-statistical significance and predictive utility-the approach learns the significance level relevant for a given data set. The MDSS approach can be used with any test and classifier operator pair.     

DNA microarrays for gene expression profiling of breast cancer: principles and prognostic applications

Breast cancer is a major health problem in developed countries. Pathological and clinical heterogeneity, partly responsible of therapeutic failures, reflects its poorly documented complex and combinatory molecular basis. Thorough molecular typing could allow not only better tackling that diversity and improving the current prognostic classifications, but also could help in the identification of new molecular therapeutic targets. The recently developed DNA microarray technology allows the analysis of the RNA expression of several thousands of genes simultaneously in a sample. Recent studies have shown the promising prognostic impact of gene expression profiling in breast cancer by identifying new prognostic subclasses unidentifiable by conventional parameters.     

Analysing gene expression data from DNA microarrays to identify candidate genes

Microarray data analysis can be divided into two tasks: grouping of genes to discover broad patterns of biological behaviour, and filtering of genes to identify specific genes of interest. Whereas the gene-grouping task is largely addressed by cluster analysis, the gene-filtering task relies primarily on hypothesis testing. This review article surveys analytical methods for the gene-filtering task. Various types of data analysis are discussed for four basic types of experimental protocols: a comparison of two biological samples; a comparison of two biological conditions; each represented by a set of replicate samples; a comparison of multiple biological conditions; and analysis of covariate information.     

Algorithm of determination of circadian gene expression profiles analysed with DNA microarrays

DNA microarrays allow to simultaneously determine the expression level of thousands of genes. A nycthemeral study must enable to conclude which ones show a circadian rhythm. Two aspects prove this to be quite difficult: firstly, what does "circadian" exactly mean and how to quantify this qualification, and secondly which genes pertain to this definition. Our method, derived from linear optimisation procedures, consists in determining a cost function, depending from magnitudes characterising the notion of circadian rhythm. Given number of genes present on the microarray are known to be expressed rhythmically; their time series are considered as reference series. We have further constructed random series having the same temporal structure as the circadian gene series. We then carried out an optimisation procedure to determine the weighting coefficients in order to obtain a cost function value which orders the time series as follows: the reference series are in the first rows and the random series have low scores. We have tested this method on over 6000 genes expressed in mouse liver. We obtained a circadian gene detection probability of 100% with a false positive rate inferior to 1%.     

Acquiring genome-wide gene expression profiles in Guthrie card blood spots using microarrays

Standard Guthrie cards have been widely used to collect blood samples from essentially all USA and Japanese neonates for newborn screening programs. Thus, archival blood spot samples are a unique and comprehensive resource for molecular pathology studies. However, the challenge in using these samples is the presumed low quantity and degraded quality of nucleic acids that can be isolated from these samples, particularly the RNA. Here, we report a new assay using Agilent 4x44K microarrays for acquiring genome-wide gene expression profiles from blood spots on Guthrie cards. Due to the small amount of RNA obtained from each sample, major modifications, such as concentrating and amplifying the RNA and using a different labeling procedure, were performed. Approximately 9000 expressed genes can be detected after normalization of data, an increment of 260% in detection power compared with previously reported cDNA microarrays made in-house with standard procedures. The correlation coefficients in technical and biological replicates were 0.92 and 0.85, respectively, confirming the reproducibility of this study. This new and comprehensive assay will add value to the utility of archival Guthrie cards (e.g. neonatal blood spot cards) and open new opportunities to molecular epidemiology, pathology, genomic, and diagnostic studies of perinatal diseases.     

A novel ensemble machine learning for robust microarray data classification

Microarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents a novel ensemble machine learning approach for the development of robust microarray data classification. Different from the conventional ensemble learning techniques, the approach presented begins with generating a pool of candidate base classifiers based on the gene sub-sampling and then the selection of a sub-set of appropriate base classifiers to construct the classification committee based on classifier clustering. Experimental results have demonstrated that the classifiers constructed by the proposed method outperforms not only the classifiers generated by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods (bagging and boosting).     

A primer on microbial bioinformatics for nonbioinformaticians

BackgroundPresently, the bottleneck in the deployment of high-throughput sequencing technology is the ability to analyse the increasing amount of data produced in a fit-for-purpose manner. The field of microbial bioinformatics is thriving and quickly adapting to technological changes, which creates difficulties for nonbioinformaticians in following the complexity and increasingly obscure jargon of this field.AimsThis review is directed towards nonbioinformaticians who wish to gain understanding of the overall microbial bioinformatic processes, from raw data obtained from sequencers to final outputs.SourcesThe software and analytical strategies reviewed are based on the personal experience of the authors.ContentThe bioinformatic processes of transforming raw reads to actionable information in a clinical and epidemiologic context is explained. We review the advantages and limitations of two major strategies currently applied: read mapping, which is the comparison with a predefined reference genome, and de novo assembly, which is the unguided assembly of the raw data. Finally, we discuss the main analytical methodologies and the most frequently used freely available software and its application in the context of bacterial infectious disease management.ImplicationsHigh-throughput sequencing technologies are overhauling outbreak investigation and epidemiologic surveillance while creating new challenges due to the amount and complexity of data generated. The continuously evolving field of microbial bioinformatics is required for stakeholders to fully harness the power of these new technologies.     

A primer on using in ovo electroporation to analyze gene function.

The chicken embryo has served as a classic model system for developmental studies due to its easy access for surgical manipulations and a wealth of data about chicken embryogenesis. Notably, the mechanisms controlling limb development have been explored best in the chick. Recently, the method of in ovo electroporation has been used successfully to transfect particular cells/tissues during embryonic development, without the production or infectivity associated with retroviruses. With the sequencing of the chicken genome near completion, this approach will provide a powerful opportunity to examine the function of chicken genes and their counterparts in other species. In ovo electroporation has been most effectively used to date for ectopic or overexpression analyses. However, recent studies indicate that this approach can be used successfully for loss-of-function analyses, including protein knockdown experiments with morpholinos and RNAi. Here, I will discuss parameters for using in ovo electroporation successfully to study developmental processes.