Dataset Information

Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments.

ABSTRACT:

Background

Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.

Results

We describe an analytical method (Geneva: Gene Vector Analysis) to relate genes to biological properties and to other similar experiments in a uniform way. This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments. We also present an improvement to the null model estimate by using the empirical background distribution drawn from previous experiments. We validated Geneva by rediscovering a number of previous findings, and by finding significant relationships within microarrays in the GEO repository.

Conclusion

Provided a reasonable corpus of previous experiments is available, this method is more accurate than the class label permutation model, especially for data sets with limited number of replicates. Geneva is, moreover, computationally faster because the background distributions can be precomputed. We also provide a standard evaluation data set based on 5 pairs of related experiments that should share similar functional relationships and 28 pairs of unrelated experiments from GEO. Discovering relationships amongst GEO data sets has implications for drug repositioning, and understanding relationships between diseases and drugs.

SUBMITTER: Tanner SW

PROVIDER: S-EPMC2543031 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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Publications

Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments.

Tanner Stephen W SW Agarwal Pankaj P

BMC bioinformatics 20080822

<h4>Background</h4>Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.<h4>Results</h4>We describe an analytical method (Geneva: Gene Vector Analysis) to ...[more]

PMID: 18721468

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Project description:BACKGROUND: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework. RESULTS: The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets. CONCLUSION: This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.

Project description:BackgroundMicroarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).ResultsWe hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile.ConclusionsFactors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

Project description:BackgroundThe small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework.ResultsWe present a novel Bayesian moderated-T, which we show to perform favorably in simulations, with two real, dual-channel microarray experiments and in two controlled single-channel experiments. In simulations, the new method achieved greater power while correctly estimating the true proportion of false positives, and in the analysis of two publicly-available "spike-in" experiments, the new method performed favorably compared to all tested alternatives. We also applied our method to two experimental datasets and discuss the additional biological insights as revealed by our method in contrast to the others. The R-source code for implementing our algorithm is freely available at http://eh3.uc.edu/ibmt.ConclusionWe use a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic (IBMT). The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e. t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.

Dataset Information

Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments.

Background

Results

Conclusion

Publications

Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments.

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