Statistical approaches in the analysis of gene expression data derived from bone regeneration specific cDNA microarrays

Recent advances in molecular biology (e.g., cDNA microarray technology) enables the simultaneous monitoring of the expression level of thousands of genes. Due to the massive amount of complex data generated, sophisticated statistical approaches are necessary in order to properly address the experimental investigation. In this paper, we present statistical analysis of cDNA microarray data derived from bone regeneration experiments. Several interesting features from these data distinguish it from commonly used microarray experiment (i.e., separate hybridization of mRNA samples from reference and experimental tissues, selectively spotted cDNA sequences and 1060 systematically selected blank spots included in each array). Using this data set, we propose new methods for bioinformatic data normalization, as well as the modification and application of various other published methods in order to identify co-regulated gene expression patterns during the healing of a bone fracture. The proposed normalization methods perform effectively to eliminate the variations with a simple algorithm. Results from our cluster analysis revealed several clusters having distinct gene expression patterns during fracture healing. Our simulation study supports the reliability of the proposed methods.