title: Improved parameter estimation for variance-stabilizing transformation of gene-expression microarray data.
abstract: A gene-expression microarray datum is modeled as an exponential expression signal (log-normal distribution) and additive noise. Variance-stabilizing transformation based on this model is useful for improving the uniformity of variance, which is often assumed for conventional statistical analysis methods. However, the existing method of estimating transformation parameters may not be perfect because of poor management of outliers. By employing an information normalization technique, we have developed an improved parameter estimation method, which enables statistically more straightforward outlier exclusion and works well even in the case of small sample size. Validation of this method with experimental data has suggested that it is superior to the conventional method.
authors.affiliation: Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, Saitama 351-0198, Japan. minoue@brain.riken.jp
authors.forename: Masato
authors.initials: M
authors.lastname: Inoue
authors.forename: Shin-Ichi
authors.initials: S
authors.lastname: Nishimura
authors.forename: Gen
authors.initials: G
authors.lastname: Hori
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Nakahara
authors.forename: Michiko
authors.initials: M
authors.lastname: Saito
authors.forename: Yoshihiro
authors.initials: Y
authors.lastname: Yoshihara
authors.forename: Shun-Ichi
authors.initials: S
authors.lastname: Amari
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity