Improved parameter estimation for variance-stabilizing transformation of gene-expression microarray data.
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.
Inoue
Masato
M
Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, Saitama 351-0198, Japan. minoue@brain.riken.jp
Nishimura
Shin-Ichi
S
Hori
Gen
G
Nakahara
Hiroyuki
H
Saito
Michiko
M
Yoshihara
Yoshihiro
Y
Amari
Shun-Ichi
S
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