A segmental nearest neighbor normalization and gene identification method gives superior results for DNA-array analysis

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Abstract
An intuitive normalization and gene identification method is proposed. After segmentation of the entire expression range into intensity intervals, the mean and standard deviation of the logarithm of expression ratios are calculated for each interval using the nearest neighbor genes. Genes with high differential expression are excluded from these calculations. For glass arrays, normalization is performed for each interval by using the mean of the logarithm of expression ratios in the interval. For nylonplastic membranes, the average of the means of the logarithm of ratios across the intervals of higher intensities is used for normalization. Compared with other normalization methods, this method delivered the smallest normalization errors for 42 nylonplastic arrays used to analyze cultured T cells and 22 Clostridium acetobutylicum glass arrays. For identifying differentially expressed genes, upper and lower boundaries are constructed for each interval by using the standard deviation of the expression ratio logarithms. When a C. acetobutylicum pSOL1 megaplasmid-deficient strain M5 was used, this method identified more "down-regulated" pSOL1 genes with fewer misidentifications in a comparative array analysis of M5 versus the parent strain. A comparison of quantitative RT-PCR results with different gene identification methods indicates that the proposed method is superior to other methods.