Abstract
Abstract Over the last decade, microarray studies have had a profound impact on clinical research including cancer classification, seeking biomarkers of diseases, and prognosis prediction. However, the complexity of clinical samples could lead to inconsistent results. It is because the influences of biological variance, introduced from clinical samples, are hard to estimate, such as the heterogeneous of cells in clinical samples, individual and population variance in genetics. In this research, we investigated the influence of estimating the biological variance for the conclusion of research. To break intra- and inter-individual variance in clinical studies down to three levels: technical, anatomic, and individual, we designed experiments and algorithms to investigate three forms of variances. As a case study, a group of “inter-individual variable genes” were identified to exemplify the influence of underestimated variance on the statistical and biological aspects in identification of differentially expressed genes. Our results showed that inadequate estimation of variance inevitably led to the inclusion of non-statistically significant genes into those listed as significant, thereby interfering with the correct prediction of biological functions. Our data demonstrates that an appropriate evaluation of variance is critical in selecting significant genes of differential expression. To estimate biological variance precisely, one major point is performing a large number of clinical experiments but it is also too expensive. Fortunately, largely available data could be accessed by public repositories with rapid development of microarray. Meta-analysis of substantial amounts of accumulated data, by integrating valuable information from multiple studies, is becoming more important in microarray research. However, collecting data of special interest from public microarray repositories often present major practical problems. Moreover, including low-quality data may significantly reduce meta-analysis efficiency. To obtain large reliable clinical microarray data, we constructed a microarray meta-analysis database (M2DB) for clinical studies. It is a human curated microarray database designed for easy querying, based on clinical information and for interactive retrieval of either raw or uniformly pre-processed data, along with a set of quality-control metrics. The database contains more than 10,000 previously published Affymetrix GeneChip arrays, performed using human clinical specimens. M2DB allows online querying according to a flexible combination of five clinical annotations describing disease state and sampling location. We hope that this research will promote further evolution of microarray meta-analysis. In the following, we utilized M2DB to perform meta-analysis for identifying reference gene for quantitative RT-PCR in clinical studies. The accuracy of quantitative real-time PCR (qRT-PCR) is highly dependent on reliable reference gene(s). Some housekeeping genes which are commonly used for normalization are widely recognized as inappropriate in many experimental conditions. After uniform data preprocessing and data quality control, 4,804 Affymetrix HU-133A arrays performed by clinical samples were classified into four physiological states with 13 organ/tissue types. We identified a list of reference genes for each organ/tissue types which exhibited stable expression across physiological states. Furthermore, 102 genes identified as reference gene candidates in multiple organ/tissue types were selected for further analysis. According to our results, researchers could select single or multiple reference gene(s) for normalization of qRT-PCR in clinical studies.