Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

Abstract

Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

Gene Expression Profiles Reveal Potential Targets for Breast Cancer Diagnosis and Treatment

“…After quality control and normalization, the expression profile was developed, and a meta-analysis was conducted for each data collection. We performed the data integration to increase data quality [26, 27], increase the statistical calculation [28], and decrease systemic errors [29–31]. Networkanalyst web server have used for data processing and due to the different applied platform, it was preferred to do random effect model choice for analysis of datasets.…”

“…After quality control and normalization, the expression profile was developed, and a meta-analysis was conducted for each data collection. We performed the data integration to increase data quality [26,27], increase the statistical calculation [28], and decrease systemic errors [29][30][31].…”

Computational Modeling and Analysis of Microarray Data: New Horizons

“…The integration of microarray data from multiple datasets, with a view to reduction of noise and enhanced inference is directly addressed by the paper of Sirbu et al [5] who also note that additional insight from microarrays is gained on features not directly targeted by sequencing methods. The authors present an integration test case, based on public Drosophila melanogaster datasets and evaluated using an evolutionary computation framework.…”