Improved parameter estimation for variance-stabilizing transformation of gene-expression microarray data

Masato Inoue*, Shin Ichi Nishimura, Gen Hori, Hiroyuki Nakahara, Michiko Saito, Yoshihiro Yoshihara, Shun Ichi Amari

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)669-679
Number of pages11
JournalJournal of Bioinformatics and Computational Biology
Issue number4
Publication statusPublished - 2004 Dec
Externally publishedYes


  • Gene-expression microarray data
  • Information normalization
  • Variance stabilization

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications


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