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Microarrays for an integrative genomics. Ukr Biokhim Zh Biclustering on expression data: A review. J Biomed Inform. Neuro-Fuzzy modeling for microarray cancer gene expression data. Oxford University Computing Laboratory, On the use of learning bayesian networks to analyze gene expression data: classification and gene network reconstruction. University of Amsterdam, Master Thesis Effect of normalization on significance testing for oligonucleotide microarrays.

J Biopharm Stat. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Ambry Genetics 42 reviews. BS or MS degree in a life sciences field:.

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Lab Technician. Biomarkers for the monitoring of disease activity of POC are currently lacking.

A number of published gene signatures validated using independent samples have been shown to serve as significant predictors of clinical outcome [ 12 , 13 , 14 , 15 ]. However, the development of prognostic signatures that are robust and stable e. In Section 3 , we will discuss recent examples of promising transcriptomic biomarkers for disease diagnosis and prognosis that have been identified using meta-analysis approaches. Published prognostic gene signatures derived from internal validation often show little overlap with genes identified by other study groups [ 15 ].

Potential causes of small reproducibility include differences in sample collection methods, processing protocols, and microarray platforms, patient heterogeneity, and small sample sizes [ 12 ]. Due to the difficulty of acquiring samples, particularly from human tissue and the associated costs, microarray experiments from single-institution patient cohorts are often composed of small sample sizes.

Predictive models trained on the gene signatures identified from these smaller-sized individual studies are less robust [ 15 , 20 ]. Michiels et al. Integration of multiple microarray data sets has been advocated to improve gene signature selection [ 22 ]. Increasing sample sizes increases the statistical power to obtain a more precise estimate of integration of differential gene expression and to assess the heterogeneity of the overall estimate, as well as to reduce the effects of individual study-specific biases [ 23 , 24 , 25 , 26 ].

Meta-analysis is most commonly applied for the purpose of detecting differentially-expressed DE genes [ 27 ] which may serve as a candidate gene signature or be used as features in classification models or classifiers to further refine a clinically useful gene signature [ 28 ]. Supervised classification techniques also known as prediction analysis or supervised machine learning are the most commonly used methods in microarray analysis that lead to identification of clinically-useful biomarkers i.

Classification methods for gene signature selection are beyond the scope of this article and have been reviewed elsewhere [ 29 ]. A conceptual framework by Hamid et al. Early or late stage integration of data can be used regardless of the biological question e. The overall principle of these two approaches is summarized in Figure 1.

Ramasamy et al. A systematic review of microarray meta-analysis studies in the literature has found that the criteria to include or exclude microarray studies is mostly subjective and ad hoc and remains an open question in the field [ 27 ]. Two critical pre-processing steps we will highlight here are i removing arrays with poor quality and ii determining the relationships between probes and genes. Identifying microarrays of poor quality is essential prior to integrative analysis because inclusion of poor quality studies may reduce statistical power and adversely affect the outcome of meta-analysis [ 27 , 33 ].

There are a number of quality assessment packages available for Bioconductor, including Simpleaffy [ 34 ] and affyPLM [ 35 ] for Affymetrix. The MetaQC package provides six quality control measurements to identify problematic studies across multiple platforms for further assessment of causes of lower quality to determine their exclusion from meta-analysis [ 36 , 37 ]. Another important pre-processing step is ascertaining which probes represent a given gene within and across the different microarray platforms.

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Co-inertia analysis, a multivariate analysis method that describes the common trends or co-relationships between datasets of two conditions, has been applied to determine the loss of information incurred by reducing the number of genes to the subset common to different platforms [ 41 ]. Imputation of gene expression present in some datasets, but not others, to allow these genes to be part of predictive models has been proposed [ 42 ].

If multiple probes match a single gene, selecting the probe with the highest interquartile range IQR has been recommended [ 43 ]. Genes with low mean expression across most studies are typically filtered out prior to meta-analysis. Turnbull et al. Furthermore, incorporation of a quality measure based on detection p -values estimated from Affymetrix arrays into the study-specific test statistics within a meta-analysis of two Affymetrix array studies using an effect sized model produced more biologically meaningful results than an unweighted model [ 25 , 45 ].

In the meta-analysis approach, each experiment is first analyzed separately and the results of each study are then combined. Meta-analysis methods that combine primary statistics e. Ranked lists of genes produced for each study e. The minP method takes the minimum p -value from combined studies, whereas the maxP method takes the maximum of the combined p -values.

Rhodes et al. Combined effect size to generate an estimate of the overall effect size and its confidence interval is frequently used in meta-analysis of clinical research data. Choi et al. The effect size was measured by the standardized mean difference obtained by dividing the difference in the average gene expression between the treatment and control groups by a pooled estimate of standard deviation.

The effect size was used to measure the magnitude of treatment effect in each study and a random effects model was used to incorporate inter-study variability. The choice of the statistical meta-analysis method is selected based on the biological purpose of the analysis. A gene serving as a biomarker from a meta-analysis is expected to show concordant biological effects across all or most experiments for a given condition derived from relatively homogenous sources e.

While detecting biomarkers DE in all studies seems an ideal goal, it can be too stringent when the number of samples is large, increasing the heterogeneity of experimental, platform, or biological samples [ 50 ]. Meta-analysis methods detecting DE in the majority of samples HS r are generally recommended as they provide robustness and detection of relevant signals across the majority of samples [ 33 ]. Song and Tseng [ 52 ] proposed a robust order statistic, rth ordered p-value rOP , which tests the alternative hypothesis that there are significant p -values in at least a given percentage of studies.

This method detects biomarkers DE in the majority of studies e. Several comparative studies systematically comparing meta-analysis methods for microarray data have been previously published [ 33 , 53 , 54 ]. Chang et al. They then applied four statistical criteria to the assessment of each meta-analysis method: 1 detection capability the number of DE genes detected ; 2 biological association degree of association between DE list with predefined genes from pathways related to the disease , stability randomly splitting the data and comparing results of the two-meta-analyses and robustness effect of including an outlying irrelevant study to the meta-analysis.

Among the methods based on HS A setting, the maxP performed the worst based on their four criteria and the investigators recommend that it be avoided. Rank product method had improved performance but weaker detection capability. It is important to note that differentially-expressed genes determined by combing p -values or ranks obtained by two-sided hypothesis testing may result in genes with discordant DE across two-class outcomes which can be difficult to interpret [ 27 ].

Wang et al. The objective and type of outcome types e. Methods combing effect sizes standardized mean differences or odds ratios are appropriate for combining two-class outcomes. Meta-analysis of expression studies with continuous outcomes e. To capture concordant expression patterns for multi-class outcomes, Lu et al.

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Direct integration of data sets performed on different microarray platforms may introduce undesirable batch effects due to systematic multiplicative biases [ 23 , 32 , 56 ]. For example, integrating different Affymetrix platforms is less complex to analyze by meta-analysis or cross platform normalization than datasets performed across very different platforms. Studies using low complexity datasets, mainly from the Affymetrix platform, have directly merged the studies to construct a gene signature [ 41 , 57 , 58 , 59 ].

Cross-platform transformation and normalization methods have been developed with an aim to remove the artifactual differences between data from different microarray platforms while preserving the underlying biological differences between conditions. Early attempts at cross-platform merging applied straightforward transformation methods of location and scale mean and variance to process the gene expression data from different studies.

Batch mean centering [ 56 ] is a simple transformative method that standardizes the expression of each gene to have the same center mean expression. Probe sets can be further transformed to have the same variance or distributions on different platforms [ 60 , 61 ]. While these methods are relatively easy and intuitive, the batch mean centering method has been shown to have only marginal improvement compared to uncorrected data for cross-platform integration of Illumina and Affymetrix data [ 32 ].

While this transformation has been applied for identifying meta-signatures, it has been found to be difficult to compare to other normalization methods [ 26 ]. Over the past decade, a number of more complex cross-platform normalization methods have been published and their performance has been compared in several studies [ 2 , 32 ]. Of these four programs, the authors favour DWD and XPN, while the comparative analysis of cross-platform normalization methods on clinical datasets by Turnbull et al.

We will discuss the results of these comparative analyses in more detail in the following Section 2. The Distance Weighted Discrimination method, like Support Vector Machines SVM , is a margin-based classification method that was developed to improve performance over the latter method.

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Essentially, SVM finds a hyperplane that separates the two classes i. However SVM has data pile-up problems along the margin which have been improved by modifying the margin to maximize the sum of the inverse distance in DWD [ 67 ]. DWD adjusts the microarray data by projecting the different batches onto the hyperplane, finding the batch mean and then subtracting out the plane multiplied by this mean. Combat, an empirical Bayes method, estimates parameters that represent the batch effects by pooling information across genes in each batch to shrink the batch effect parameter toward the overall mean of the batch effect estimates across genes [ 64 ].

The data are then transformed to remove the effects of the different batch effect parameters across platforms. Combat is performed using either a parametric prior method or a non-parametric method based on the prior distributions of the estimated parameters [ 68 ]. First, K-means clustering is used to find blocks of similar genes and samples across the platforms.

This approach is robust to the number of row K and column L clusters. Then, within each block the data is normalized between platforms within this block. The normalized values obtained over multiple clustering performed over repeated runs is then averaged to better capture the data structure. A comparative analysis of cross-platform normalization methods by Rudy and Valafar [ 2 ] found the DWD classification method to provide effective batch adjustment for microarray data [ 67 ] and to be the most robust to variation in treatment group sizes between the platforms with the least loss of treatment information lower underdetection , while XPN showed the greatest inter-platform concordance [ 2 ].

However, they found that DWD removed not only the platform specific systematic bias, but also relevant biological variability between samples reduced inter-sample variance , while Combat and XPN preserved this biological signal slightly increased inter-sample variance while appropriately correcting the platform specific bias reduced inter-platform variance. Although Combat and XPN have been found to perform well in previous analyses, the user must be cautious when applying this method to datasets that are unbalanced e.

One limitation of some existing cross-platform normalizing methods is that they can only be applied to two batches at a time. While cross-platform normalization steps can be chained together, the effect of these multiple normalization steps or which chaining method is still unclear [ 60 ]. Different experiments from multiple different arrays can be directly merged from the CEL files simultaneously using several packages implemented in R [ 69 ] including inSilico Merging [ 70 ], the CONOR [ 2 ], and virtualArray [ 71 ].

The virtualArray package allows cross-platform normalization using empirical Bayes methods default or the user may select one quantile discretization, normal discretization normalization, gene quantile normalization, median rank scores, quantile normalization, or mean centering [ 71 ]. This batch effect removal step can be supervised allowing the user to specify samples into groups based on platform as well as other attributes e.

Before the combined expression data undergoes cross-platform normalization, the data must be transformed to a common scale e. As with meta-analysis, low expression and low variance genes are typically filtered out. In a comparative study, Taminau et al. An additional advantage of cross-platform normalization is that it allows prediction models applied to a subset of studies to be applied across additional studies from other platforms [ 27 ]. While cross-platform normalization has been applied in multiple studies [ 72 , 73 , 74 ], it has less frequently been used in the literature compared to meta-analysis [ 2 ].

One major limitation of existing cross-platform normalization is that they require that every treatment group or sample type be represented on each platform to allow differentiation of treatment effects from platform effects. Sweeney et al. Their work analyzed publicly-available gene expression datasets from 22 independent cohorts composed of microarrays in total and applied a meta-analysis strategy implementing both effect size and p -values of differential gene expression.

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The investigators identified 82 genes differentially expressed between sepsis and inflammation and then performed a greedy forward search to determine which combination of these 82 genes produced the best improvement of area under the curve AUC in their discovery datasets. This resulted in an gene transcriptional signature that was applied to 15 independent validation cohorts and was found to improve discrimination of patients with infection from those with sterile inflammation compared to use of clinical data alone.

This gene signature requires further validation using prospective cohorts, however its excellent discriminatory power in both the discovery and validation cohorts suggests that it is likely to become a useful clinical assay in the future. Their analysis identified hepatocyte nuclear factor 4 alpha HNF4A and polypyrimidine tract binding protein 1 PTBP1 , as the most significant up- and down-regulated genes in blood samples from PD patients.

The relative abundance of HFN4A mRNA was found to correlate with disease severity in PD and the results were validated using samples obtained from two independent clinical trials. In the previously discussed cross-platform normalization approaches Section 2. It is important to account for possibly confounding e. Additional categorical and continuous variables can be easily included along with the gene expression data using regression methods such as the elastic net penalty to fit a generalized linear model GLM [ 42 ].

These models can also be readily adapted for different outcomes such as categorical, continuous, and survival times. Cho et al. Modelling confounding factors with variable selection in meta-analysis has recently been shown to improve robustness and sensitivity of DE gene detection [ 43 ] and inter-study concordance [ 78 ]. Chikina et al.