Integrated analytics of microarray big data reveals robust gene signature

The advance of high throughput biotechnology enables the generation of large amount of biomedical data. The microarray is increasingly a popular approach for the detection of genome-wide gene expression. Microarray data have thus increased significantly in public accessible database repositories, which provide valuable big data for scientific research. To deal with the challenge of microarray big data collected in different research labs using different experimental set-ups and on different bio-samples, this paper presents a primary study to evaluate the impact of two important factors (the origin of bio-samples and the quality of microarray data) on the integrated analytics of multiple microarray data. The aim is to enable the extraction of reliable and robust gene biomarkers from microarray big data. Our work showed that in order to enhance biomarker discovery from microarray big data (i) it is necessary to treat the microarray data differently in terms of their quality, (ii) it is recommended to stratifying (i.e., sub-group) the data according to the origin of bio-samples in the analytics.