Microarray Literature Research Articles

Analysis of the microarray gene expression for breast cancer progression after the application modified logistic regression

Methods based around statistics and linear algebra have been increasingly used in attempts to address emerging questions in microarray literature. Microarray technology is a long-used tool in the global analysis of gene expression, allowing for the simultaneous investigation of hundreds or thousands of genes in a sample. It is characterized by a low sample size and a large feature number created a non-square matrix, and by the incomplete rank, that can generate countless more solution in classifiers. To avoid the problem of the ‘curse of dimensionality’ many authors have performed feature selection or reduced the size of data matrix. In this work, we introduce a new logistic regression-based model to classify breast cancer tumor samples based on microarray expression data, including all features of gene expression and without reducing the microarray data matrix. If the user still deems it necessary to perform feature reduction, it can be done after the application of the methodology, still maintaining a good classification. This methodology allowed the correct classification of breast cancer sample data sets from Gene Expression Omnibus (GEO) data series GSE65194, GSE20711, and GSE25055, which contain the microarray data of said breast cancer samples. Classification had a minimum performance of 80% (sensitivity and specificity), and explored all possible data combinations, including breast cancer subtypes. This methodology highlighted genes not yet studied in breast cancer, some of which have been observed in Gene Regulatory Networks (GRNs). In this work we examine the patterns and features of a GRN composed of transcription factors (TFs) in MCF-7 breast cancer cell lines, providing valuable information regarding breast cancer. In particular, some genes whose αi ∗ associated parameter values revealed extreme positive and negative values, and, as such, can be identified as breast cancer prediction genes. We indicate that the PKN2, MKL1, MED23, CUL5 and GLI genes demonstrate a tumor suppressor profile, and that the MTR, ITGA2B, TELO2, MRPL9, MTTL1, WIPI1, KLHL20, PI4KB, FOLR1 and SHC1 genes demonstrate an oncogenic profile. We propose that these may serve as potential breast cancer prediction genes, and should be prioritized for further clinical studies on breast cancer. This new model allows for the assignment of values to the αi ∗ parameters associated with gene expression. It was noted that some αi ∗ parameters are associated with genes previously described as breast cancer biomarkers, as well as other genes not yet studied in relation to this disease.

Multidimensional BSS/WSS Criterion for Modified SBFS Feature Selection Method in Tumor Classification

To avoid the defect of BSS/WSS criterion, we propose a multidimensional BSS/WSS feature selection criterion and modify the sequential backward floating selection (SBFS) algorithm to deal with the case where the covariance matrix is singular in this study. Then, we use support vector machine (SVM) to classify the gene expression data based on the proposed feature selection algorithm. The performance of the proposed approach is compared with BSS/WSS criterion and some other popular methods in feature selection and classification via the wellknown colon cancer and prostate datasets in microarray literature, which demonstrates that the proposed criterion can take into account genes' joint discriminatory power, and the proposed feature selection method can obtain correct and informative gene subset for tumor classification.

Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia

The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n=315) and from ex-vivo blood tissues (n=578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia.

English

The countenance level measurement for thousands of genes is allowed in a parallel fashion by microarray technology. Clustering is one of the first stage accepted to reveal information from gene expression data. Choosing an appropriate proximity measure (similarity or distance) is having great significance in addition to selecting a clustering algorithm for attaining reasonable clustering results. Til today, there are no inclusive guidelines regarding how to elect proximity measures for clustering microarray data. The choice of proximity measures is studied for the clustering of microarray data by estimating the performance of twelve proximity measures in some data sets from time course and cancer experiments. Given that different measures hoisted out for time course and cancer data evaluations, their choice should be specific to each scenario. To estimate measures on time-course data, the pre-processed and collected data sets from the microarray literature in a benchmark is used along with a new methodology, called Intrinsic Biological Separation Ability (IBSA). Both can be employed in future research to assess the effectiveness of new measures for gene time-course data.

Guidelines for the choice of Microarray Adjustment methods for Class Comparison, Class Discovery and Class Prediction

Many data adjustment methods have been proposed in the microarray literature prior to detailed analyses such as class comparison, class discovery or class prediction. But guidelines have not yet been proposed for ensuring that the choice of an adjustment method is appropriate for the type of study envisaged. The aim of that article is to present guidelines for the choice of data pre-processing methods, for each of the three main areas of microarray applications: class comparison, class discovery and class prediction.

Systematic Omics Analysis Review (SOAR) tool to support risk assessment.

Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment.

Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis

Cluster analysis is usually the first step adopted to unveil information from gene expression microarray data. Besides selecting a clustering algorithm, choosing an appropriate proximity measure (similarity or distance) is of great importance to achieve satisfactory clustering results. Nevertheless, up to date, there are no comprehensive guidelines concerning how to choose proximity measures for clustering microarray data. Pearson is the most used proximity measure, whereas characteristics of other ones remain unexplored. In this paper, we investigate the choice of proximity measures for the clustering of microarray data by evaluating the performance of 16 proximity measures in 52 data sets from time course and cancer experiments. Our results support that measures rarely employed in the gene expression literature can provide better results than commonly employed ones, such as Pearson, Spearman, and euclidean distance. Given that different measures stood out for time course and cancer data evaluations, their choice should be specific to each scenario. To evaluate measures on time-course data, we preprocessed and compiled 17 data sets from the microarray literature in a benchmark along with a new methodology, called Intrinsic Biological Separation Ability (IBSA). Both can be employed in future research to assess the effectiveness of new measures for gene time-course data.

MetabR: an R script for linear model analysis of quantitative metabolomic data

BackgroundMetabolomics is an emerging high-throughput approach to systems biology, but data analysis tools are lacking compared to other systems level disciplines such as transcriptomics and proteomics. Metabolomic data analysis requires a normalization step to remove systematic effects of confounding variables on metabolite measurements. Current tools may not correctly normalize every metabolite when the relationships between each metabolite quantity and fixed-effect confounding variables are different, or for the effects of random-effect confounding variables. Linear mixed models, an established methodology in the microarray literature, offer a standardized and flexible approach for removing the effects of fixed- and random-effect confounding variables from metabolomic data.FindingsHere we present a simple menu-driven program, “MetabR”, designed to aid researchers with no programming background in statistical analysis of metabolomic data. Written in the open-source statistical programming language R, MetabR implements linear mixed models to normalize metabolomic data and analysis of variance (ANOVA) to test treatment differences. MetabR exports normalized data, checks statistical model assumptions, identifies differentially abundant metabolites, and produces output files to help with data interpretation. Example data are provided to illustrate normalization for common confounding variables and to demonstrate the utility of the MetabR program.ConclusionsWe developed MetabR as a simple and user-friendly tool for implementing linear mixed model-based normalization and statistical analysis of targeted metabolomic data, which helps to fill a lack of available data analysis tools in this field. The program, user guide, example data, and any future news or updates related to the program may be found at http://metabr.r-forge.r-project.org/.

A Bayesian Mixture Model for Comparative Spectral Count Data in Shotgun Proteomics

Recent developments in mass-spectrometry-based shotgun proteomics, especially methods using spectral counting, have enabled large-scale identification and differential profiling of complex proteomes. Most such proteomic studies are interested in identifying proteins, the abundance of which is different under various conditions. Several quantitative methods have recently been proposed and implemented for this purpose. Building on some techniques that are now widely accepted in the microarray literature, we developed and implemented a new method using a Bayesian model to calculate posterior probabilities of differential abundance for thousands of proteins in a given experiment simultaneously. Our Bayesian model is shown to deliver uniformly superior performance when compared with several existing methods.

Physically grounded approach for estimating gene expression from microarray data

High-throughput technologies, including gene-expression microarrays, hold great promise for the systems-level study of biological processes. Yet, challenges remain in comparing microarray data from different sources and extracting information about low-abundance transcripts. We demonstrate that these difficulties arise from limitations in the modeling of the data. We propose a physically motivated approach for estimating gene-expression levels from microarray data, an approach neglected in the microarray literature. We separately model the noises specific to sample amplification, hybridization, and fluorescence detection, combining these into a parsimonious description of the variability sources in a microarray experiment. We find that our model produces estimates of gene expression that are reproducible and unbiased. While the details of our model are specific to gene-expression microarrays, we argue that the physically grounded modeling approach we pursue is broadly applicable to other molecular biology technologies.

Bayesian variable selection for disease classification using gene expression data

An important application of gene expression microarray data is the classification of samples into categories. Accurate classification depends upon the method used to identify the most relevant genes. Owing to the large number of genes and relatively small sample size, the selection process can be unstable. Modification of existing methods for achieving better analysis of microarray data is needed. We propose a Bayesian stochastic variable selection approach for gene selection based on a probit regression model with a generalized singular g-prior distribution for regression coefficients. Using simulation-based Markov chain Monte Carlo methods for simulating parameters from the posterior distribution, an efficient and dependable algorithm is implemented. It is also shown that this algorithm is robust to the choices of initial values, and produces posterior probabilities of related genes for biological interpretation. The performance of the proposed approach is compared with other popular methods in gene selection and classification via the well-known colon cancer and leukemia datasets in microarray literature. A free Matlab code to perform gene selection is available at http://www.sta.cuhk.edu.hk/xysong/geneselection/. Supplementary data are available at Bioinformatics online.

Normalization and Statistical Analysis of Quantitative Proteomics Data Generated by Metabolic Labeling

Comparative proteomics is a powerful analytical method for learning about the responses of biological systems to changes in growth parameters. To make confident inferences about biological responses, proteomics approaches must incorporate appropriate statistical measures of quantitative data. In the present work we applied microarray-based normalization and statistical analysis (significance testing) methods to analyze quantitative proteomics data generated from the metabolic labeling of a marine bacterium (Sphingopyxis alaskensis). Quantitative data were generated for 1,172 proteins, representing 1,736 high confidence protein identifications (54% genome coverage). To test approaches for normalization, cells were grown at a single temperature, metabolically labeled with (14)N or (15)N, and combined in different ratios to give an artificially skewed data set. Inspection of ratio versus average (MA) plots determined that a fixed value median normalization was most suitable for the data. To determine an appropriate statistical method for assessing differential abundance, a -fold change approach, Student's t test, unmoderated t test, and empirical Bayes moderated t test were applied to proteomics data from cells grown at two temperatures. Inverse metabolic labeling was used with multiple technical and biological replicates, and proteomics was performed on cells that were combined based on equal optical density of cultures (providing skewed data) or on cell extracts that were combined to give equal amounts of protein (no skew). To account for arbitrarily complex experiment-specific parameters, a linear modeling approach was used to analyze the data using the limma package in R/Bioconductor. A high quality list of statistically significant differentially abundant proteins was obtained by using lowess normalization (after inspection of MA plots) and applying the empirical Bayes moderated t test. The approach also effectively controlled for the number of false discoveries and corrected for the multiple testing problem using the Storey-Tibshirani false discovery rate (Storey, J. D., and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440-9445). The approach we have developed is generally applicable to quantitative proteomics analyses of diverse biological systems.

Analyzing Gene Expression in Depression

Analyzing Gene Expression in Depression

Consolidated strategy for the analysis of microarray spike-in data

As the number of users of microarray technology continues to grow, so does the importance of platform assessments and comparisons. Spike-in experiments have been successfully used for internal technology assessments by microarray manufacturers and for comparisons of competing data analysis approaches. The microarray literature is saturated with statistical assessments based on spike-in experiment data. Unfortunately, the statistical assessments vary widely and are applicable only in specific cases. This has introduced confusion into the debate over best practices with regards to which platform, protocols and data analysis tools are best. Furthermore, cross-platform comparisons have proven difficult because reported concentrations are not comparable. In this article, we introduce two new spike-in experiments, present a novel statistical solution that enables cross-platform comparisons, and propose a comprehensive procedure for assessments based on spike-in experiments. The ideas are implemented in a user friendly Bioconductor package: spkTools. We demonstrated the utility of our tools by presenting the first spike-in-based comparison of the three major platforms–Affymetrix, Agilent and Illumina.

Rejoinder: Microarrays, Empirical Bayes and the Two-Groups Model

The Fisher–Neyman–Pearson theory of hypothesis testing was a triumph of mathematical elegance and practical utility. It was never designed, though, to handle 10,000 tests at once, and one can see contemporary statisticians struggling to develop theories appropriate to our new scientific environment. This paper is part of that effort: starting from just the two-groups model (2.1), it aims to show Bayesian and frequentist ideas merging into a practical framework for largescale simultaneous testing. False discovery rates, Benjamini and Hochberg’s influential contribution to modern statistical theory, is the main methodology featured in the paper, but I really was not trying to sell any specific technology as the final word. In fact, the discussants offer an attractive menu of alternatives. It is still early in the large-scale hypothesis testing story, and I expect, and hope for, major developments in both theory and practice. The central issue, as Carl Morris makes clear, is the combination of information from a collection of more or less similar sources, for example from the expression levels of different genes in a microarray study. Crucial questions revolve around the comparability and relevance of the various sources, as well as the proper choice of a null distribution. Technical issues such as the exact control of Type I errors are important as well, but, in my opinion, have played too big a role in the microarray literature. The discussions today are an appealing mixture of technical facility and big-picture thinking. They are substantial essays in their own right, and I will be able to respond here to only a few of the issues raised. I once wrote, about the jackknife, that good simple ideas are our most precious intellectual commodity. False discovery rates fall into that elite category. The two-groups model is used here to unearth the Bayesian roots of Benjamini and Hochberg’s originally frequentist construction. In a Bayesian framework it is natural

A framework for significance analysis of gene expression data using dimension reduction methods.

BackgroundThe most popular methods for significance analysis on microarray data are well suited to find genes differentially expressed across predefined categories. However, identification of features that correlate with continuous dependent variables is more difficult using these methods, and long lists of significant genes returned are not easily probed for co-regulations and dependencies. Dimension reduction methods are much used in the microarray literature for classification or for obtaining low-dimensional representations of data sets. These methods have an additional interpretation strength that is often not fully exploited when expression data are analysed. In addition, significance analysis may be performed directly on the model parameters to find genes that are important for any number of categorical or continuous responses. We introduce a general scheme for analysis of expression data that combines significance testing with the interpretative advantages of the dimension reduction methods. This approach is applicable both for explorative analysis and for classification and regression problems.ResultsThree public data sets are analysed. One is used for classification, one contains spiked-in transcripts of known concentrations, and one represents a regression problem with several measured responses. Model-based significance analysis is performed using a modified version of Hotelling's T2-test, and a false discovery rate significance level is estimated by resampling. Our results show that underlying biological phenomena and unknown relationships in the data can be detected by a simple visual interpretation of the model parameters. It is also found that measured phenotypic responses may model the expression data more accurately than if the design-parameters are used as input. For the classification data, our method finds much the same genes as the standard methods, in addition to some extra which are shown to be biologically relevant. The list of spiked-in genes is also reproduced with high accuracy.ConclusionThe dimension reduction methods are versatile tools that may also be used for significance testing. Visual inspection of model components is useful for interpretation, and the methodology is the same whether the goal is classification, prediction of responses, feature selection or exploration of a data set. The presented framework is conceptually and algorithmically simple, and a Matlab toolbox (Mathworks Inc, USA) is supplemented.

Assessing the need for sequence-based normalization in tiling microarray experiments

Increases in microarray feature density allow the construction of so-called tiling microarrays. These arrays, or sets of arrays, contain probes targeting regions of sequenced genomes at regular genomic intervals. The unbiased nature of this approach allows for the identification of novel transcribed sequences, the localization of transcription factor binding sites (ChIP-chip), and high resolution comparative genomic hybridization, among other uses. These applications are quickly growing in popularity as tiling microarrays become more affordable. To reach maximum utility, the tiling microarray platform needs be developed to the point that 1 nt resolutions are achieved and that we have confidence in individual measurements taken at this fine of resolution. Any biases in tiling array signals must be systematically removed to achieve this goal. Towards this end, we investigated the importance of probe sequence composition on the efficacy of tiling microarrays for identifying novel transcription and transcription factor binding sites. We found that intensities are highly sequence dependent and can greatly influence results. We developed three metrics for assessing this sequence dependence and use them in evaluating existing sequence-based normalizations from the tiling microarray literature. In addition, we applied three new techniques for addressing this problem; one method, adapted from similar work on GeneChip brand microarrays, is based on modeling array signal as a linear function of probe sequence, the second method extends this approach by iterative weighting and re-fitting of the model, and the third technique extrapolates the popular quantile normalization algorithm for between-array normalization to probe sequence space. These three methods perform favorably to existing strategies, based on the metrics defined here. http://tiling.gersteinlab.org/sequence_effects/

Analysing Microarray Data using the Multi-functional Immune Ontologiser

Abstract Gene expression microarrays are a prominent experimental tool in functional genomics allowing researchers to gain a deeper understanding of biological processes. To date, no such tool has been developed to allow researchers with a specialised biological research interest to distinctively identify those genes and gene functionalities associated more strongly with the research area. Based on this functional analysis capability we present a specialised multi-functional Immune Ontologiser – a software, specialised for immunologists to annotate multiple genes from microarray datasets within two new ontologies: a newly structured Immune Ontology focussed at immunology and haematology and a uniquely curated ImmunoArray-PubOntology. The Immune Ontology functionally annotates genes identifying immunology related functions enriched with upregulated or downregulated genes of interest. The ImmunoArray-PubOntology compares and contrasts gene functionality of microarray datasets, comparing genes of interest with the differential gene expression matrices published amongst immunologyrelated microarray literature. This aspect facilitates literature mining by extracting publications containing gene sets of interest in a well-structured immunological context where the literature has been categorised according to disease types. The software consists of a query-optimised database of two parts – the ImmunoGene-database and a unique Database of Immunological Microarray Publications (DIMP) to provide the user with a more detailed insight into other studies involving their genes and research groups investigating similar research areas. Using our Immune Ontologiser software to analyse tolerance array data we identify 70 interesting up-regulated genes in terms of their functionality within tolerance. Furthermore, from these 70 genes we identify 15 genes to have immunology-related functions. More interestingly, the remaining 55 genes were not previously known to be directly involved within the immunology related condition and hence we have identified target genes for future investigation. Among the 70 genes, 21 have been identified by our software to be studied within various immunology-related diseases via microarray experiments performed by other laboratories.The software and database schema is freely available at ftp://ftp.brunel.ac.uk/cspgssk. Additional material is available online at http://www.brunel.ac.uk/about/acad/health/healthres/researchareas/mi/publications/supplementary. A detailed microarray protocol is available at http://www.ebi.ac.uk/arrayexpress under the Accession Number: E-MEXP-283.

Molecular Classification of Breast Cancer: Limitations and Potential

Reverse transcription polymerase chain reaction and DNA microarrays are increasingly used in the clinic and in clinical research as prognostic or predictive tests. Results from these tests led to novel risk stratification methods and to new molecular classification of breast cancer. Some of these tools already complement existing diagnostic tests and can aid medical decision making in some situations. Better understanding of the molecular classes of breast cancer, independent of their prognostic and predictive values, may also lead to new biological insights and eventually to better therapies that are directed toward particular molecular subsets. However, there is substantially less experience with these emerging technologies than with the more established methods, the accuracy of which is often overestimated. This review discusses some of the limitations and strengths of current gene expression-based molecular classification of breast cancer. To provide context for this discussion, we also briefly examine the performance of estrogen receptor immunohistochemistry, which represents an essential part of the routine diagnostic workup for all breast cancer patients.

Quality determination and the repair of poor quality spots in array experiments

BackgroundA common feature of microarray experiments is the occurence of missing gene expression data. These missing values occur for a variety of reasons, in particular, because of the filtering of poor quality spots and the removal of undefined values when a logarithmic transformation is applied to negative background-corrected intensities. The efficiency and power of an analysis performed can be substantially reduced by having an incomplete matrix of gene intensities. Additionally, most statistical methods require a complete intensity matrix. Furthermore, biases may be introduced into analyses through missing information on some genes. Thus methods for appropriately replacing (imputing) missing data and/or weighting poor quality spots are required.ResultsWe present a likelihood-based method for imputing missing data or weighting poor quality spots that requires a number of biological or technical replicates. This likelihood-based approach assumes that the data for a given spot arising from each channel of a two-dye (two-channel) cDNA microarray comparison experiment independently come from a three-component mixture distribution – the parameters of which are estimated through use of a constrained E-M algorithm. Posterior probabilities of belonging to each component of the mixture distributions are calculated and used to decide whether imputation is required. These posterior probabilities may also be used to construct quality weights that can down-weight poor quality spots in any analysis performed afterwards. The approach is illustrated using data obtained from an experiment to observe gene expression changes with 24 hr paclitaxel (Taxol ®) treatment on a human cervical cancer derived cell line (HeLa).ConclusionAs the quality of microarray experiments affect downstream processes, it is important to have a reliable and automatic method of identifying poor quality spots and arrays. We propose a method of identifying poor quality spots, and suggest a method of repairing the arrays by either imputation or assigning quality weights to the spots. This repaired data set would be less biased and can be analysed using any of the appropriate statistical methods found in the microarray literature.