Novel algorithm for transcriptome analysis

A growing body of evidence implicates the oocyte as a key regulator of ovarian folliculogenesis and early embryonic development. We have screened bovine cDNA microarrays (containing expressed sequence tags representing >15,000 unique genes) with Cy3- and Cy5-labeled cDNA derived from bovine oocyte samples collected at two different stages of meiotic maturation (germinal vesicle vs. metaphase II; n = 3 samples per group). Here, we present a novel data analysis approach that uses all available information from above experiments to obtain and index the transcriptome of bovine oocytes and changes in transcriptome composition in response to meiotic maturation. Signal intensities (Fg) for all housekeeping genes were omitted prior to analysis. A local threshold for gene expression was computed as background intensity (Bg) plus 2 times the standard deviation of background and foreground signals. Within each array, data were normalized by the LOWESS procedure. Subsequently, a two-stage mixed model was fitted to remove systematic variations. In the first stage, the response was the LOWESS normalized Fg with treatment as a fixed effect. In stage 2, the residuals from stage 1 were analyzed in a gene-specific model that included treatment group and spots nested within patch and array. A test for the difference between least squares means for the treatment effect was performed. A false discovery rate (FDR) adjustment on the p values for the difference was carried out. This novel algorithm was compared with approaches that ignore the FDR and the threshold described herein and stark differences obtained.