Multiple Microarray Datasets Research Articles

Inferring gene regulatory networks from multiple microarray datasets

Microarray gene expression data has increasingly become the common data source that can provide insights into biological processes at a system-wide level. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to a large number of genes, which makes the problem of inferring gene regulatory network an ill-posed one. On the other hand, gene expression data generated by different groups worldwide are increasingly accumulated on many species and can be accessed from public databases or individual websites, although each experiment has only a limited number of time-points. This paper proposes a novel method to combine multiple time-course microarray datasets from different conditions for inferring gene regulatory networks. The proposed method is called GNR (Gene Network Reconstruction tool) which is based on linear programming and a decomposition procedure. The method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the prediction reliability. We tested GNR using both simulated data and experimental data in yeast and Arabidopsis. The result demonstrates the effectiveness of GNR in terms of predicting new gene regulatory relationship in yeast and Arabidopsis. The software is available from http://zhangorup.aporc.org/bioinfo/grninfer/, http://digbio.missouri.edu/grninfer/ and http://intelligent.eic.osaka-sandai.ac.jp or upon request from the authors.

Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks

BackgroundGenes and proteins are organized into functional modular networks in which the network context of a gene or protein has implications for cellular function. Highly connected hub proteins, largely responsible for maintaining network connectivity, have been found to be much more likely to be essential for yeast survival.ResultsHere we investigate the properties of weighted gene co-expression networks formed from multiple microarray datasets. The constructed networks approximate scale-free topology, but this is not universal across all datasets. We show strong positive correlations between gene connectivity within the whole network and gene essentiality as well as gene sequence conservation. We demonstrate the preservation of a modular structure of the networks formed, and demonstrate that, within some of these modules, it is possible to observe a strong correlation between connectivity and essentiality or between connectivity and conservation within the modules particularly within modules containing larger numbers of essential genes.ConclusionApplication of these techniques can allow a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes.

Integrating probe-level expression changes across generations of Affymetrix arrays

There is an urgent need for bioinformatic methods that allow integrative analysis of multiple microarray data sets. While previous studies have mainly concentrated on reproducibility of gene expression levels within or between different platforms, we propose a novel meta-analytic method that takes into account the vast amount of available probe-level information to combine the expression changes across different studies. We first show that the comparability of relative expression changes and the consistency of differentially expressed genes between different Affymetrix array generations can be considerably improved by determining the expression changes at the probe-level and by considering the latest information on probe-level sequence matching instead of the probe annotations provided by the manufacturer. With the improved probe-level expression change estimates, data from different generations of Affymetrix arrays can be combined more effectively. This will allow for the full exploitation of existing results when designing and analyzing new experiments.

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes.

BackgroundThe extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods.ResultsIn contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis.ConclusionCross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance.

PAGE: Parametric Analysis of Gene Set Enrichment

BackgroundGene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate.ResultsWe developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level.ConclusionPAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.

Race and the poor prognosis basal breast tumor (BBT) phenotype in the population-based Carolina Breast Cancer Study (CBCS)

9510 Background: Gene expression arrays reveal several breast cancer (BrCa) subtypes, with the basal-like subtype (BBT) carrying a poor prognosis (Sorlie T, PNAS 2003). Multiple BrCa microarray datasets (Sorlie et al PNAS 2003; van't Veer et al, Nature 2002; Sotiriou et al, PNAS 2003) suggest that 90% of BBT are ER-negative, PR-negative, and HER-2 nonamplified (Perou C, unpublished). Thus, this “triple negative” phenotype may be used to identify and determine the frequency of BBT. Methods: Data were obtained from the CBCS, a population-based case-control study of BrCa causation among African-American (AA) and non-African American (nonAA) women. The CBCS oversampled AA and women under the age of 50. HER-2 was determined by UNC Lineberger Immunohistochemistry (IHC) Core. We determined the proportion of triple negative BrCa by race (AA vs. non-AA) and by stage, mitotic index (>10 vs. ≤10) nuclear grade and histologic grade (grade 3 vs. 1–2). Results: 657 incident BrCa were evaluated from Phase I of the CBCS, including 260 AA and 397 nonAA women. 26.2% of all BrCa were triple negative, however this phenotype was significantly more frequent in AA (33.9% vs. 21.2%, p=0.0003), premenopausal women (30.3% vs. 21.9%, p=0.02), mitotic index > 10 (45.8% vs. 11.3%, p<0.0001), and nuclear or histologic grade 3 tumors (p=0.0001). The highest prevalence of triple negative BrCa occurred among premenopausal AA women (44.3%) compared with postmenopausal AA women (24.6%) (p=0.0008). In multivariate models combining all factors, the significant predictors of the triple negative phenotype were high mitotic index (p<0.0001), high nuclear or histologic grade (p<0.0001 both), and race (p=0.03). Conclusions: The poor-prognosis BBT subtype is strongly associated with high proliferative capacity (mitotic index > 10) and nuclear pleomorphism. In a large population-based study, the triple negative phenotype characteristic of BBT is markedly more common in AA women, particularly premenopausal AA women. The high proportion of BBT likely plays a role in the poor prognosis experienced by AA women with BrCa. (supported by NCI (SPORE CA58223), NIH (GCRC MO1RR00046). No significant financial relationships to disclose.

Bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers.

The DNA microarray has revolutionized cancer research. Now, scientists can obtain a genome-wide perspective of cancer gene expression. One potential application of this technology is the discovery of novel cancer biomarkers for more accurate diagnosis and prognosis, and potentially for the earlier detection of disease or the monitoring of treatment effectiveness. Because microarray experiments generate a tremendous amount of data and because the number of laboratories generating microarray data is rapidly growing, new bioinformatics strategies that promote the maximum utilization of such data are necessary. Here, we describe a method to validate multiple microarray data sets, a Web-based cancer microarray database for biomarker discovery, and methods for integrating gene ontology annotations with microarray data to improve candidate biomarker selection.