ISSUES IN META-ANALYSIS OF CANCERMICROARRAY STUDIES: DATA DEPOSITORY INR AND A META-ANALYSIS METHOD FORMULTI-CLASS BIOMARKER DETECTION

Systematic information integration of multiple related microarray studies has become an important issue as the technology has become significant mature and more prevalent in public health relevance over the past decade. The aggregated information provides more robust and accurate biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multiclass studies and expression pattern concordance are rarely explored. We first consider a natural extension of combining p-values from the traditional ANOVA model. Since p-values from ANOVA do not guarantee to reflect the concordant expression pattern information across studies, we propose a multi-class correlation measure (MCC) to specifically look for biomarkers of concordant inter-class patterns across a pair of studies. For both approaches, we focus on identifying biomarkers differentially expressed in all studies (i.e. ANOVA-maxP and min-MCC). The min-MCC method is further extended to identify biomarkers differentially expressed in partial studies using an optimally-weighted technique (OW-min-MCC). All methods are evaluated by simulation studies and by three meta-analysis applications to multi-tissue mouse metabolism data sets, multi-condition mouse trauma data sets and multi-malignant-condition human prostate cancer data sets. The results show complementary strength of ANOVA-based and MCC-based approaches for different biological purposes. For detecting biomarkers with concordant inter-class patterns across studies, min-MCC has better power and performance. If biomarkers with discordant inter-class patterns across studies are expected and are of biological interests, ANOVA-maxP better serves this purpose