Test of Significant Differences with a priori Probability in Microarray Experiments

Kyoko Toda, Seiichi Ishida, Kotoko Nakata, Rieko Matsuda, Yukari Shigemoto-Mogami, Kayoko Fujishita, Shogo Ozawa, Jun Ichi Sawada, Kazuhide Inoue, Koichi Shudo, Yuzuru Hayashi

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8 Citations (Scopus)


A traditional method for comparing two expression levels of genes in microarray experiments is the two-sample t-test. Because of the difficulty in using a large number of microarrays, an alternative method is required which can provide a reliable judgment of the comparison from a small number of replicates, even from a single pair of control and treatment. We present a method for detecting the changes in the gene expression levels under two different conditions in microarray experiments. Our method targets a single experiment for each condition, while retaining the statistical advantages of the t-test. The new proposals are: 1) standard deviation (SD) estimates of the expression levels which are an indicator for significant differences are given a priori as a function of the expression levels; 2) the limit of detection (LOD) for the expression levels is used to eliminate the majority of genes expressed at extremely low levels. The a priori SD estimates are obtained from six replicates under a fixed condition and are shown to be the approximate, but proper description of the expression uncertainty covering diverse conditions (e.g., different samples (human and rat) and different DNA chips). The LOD is defined as three times blank SD according to the IUPAC recommendation. A cell line (HL60) which will undergo macrophage differentiation on treatment with 12-O-tetradecanoylphorbol 13-acetate (TPA) is taken as an example. Our method is compared with the t-test for the data on duplicate TPA experiments and the former alone is evaluated with the data on a single TPA experiment. The errors from sample preparation and instrumental analysis are discussed.

Original languageEnglish
Pages (from-to)1529-1535
Number of pages7
Journalanalytical sciences
Issue number11
Publication statusPublished - Nov 2003
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry


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