Accurate quantification of gene expression using fuzzy clustering approaches

Yu Ping Wang, Maheswar Gunampally, Jie Chen, Douglas Bittet, Merlin G. Butler, Wei Wen Cai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Despite the widespread application of microarray imaging for biomedical research, barriers still exist regarding its reliability and reproducibility for clinical use. A critical problem lies in accurate spot segmentation and quantification of gene expression level (mRNA) from microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots with complex shapes suck as donuts and scratches. Clustering approaches suck as k-means and mixture models were introduced to overcome this difficulty, which used the hard labeling of each pixel. In this paper, we introduce a more sophisticated fuzzy clustering based method. We show that possiblistic c-means clustering performed the best among several fuzzy clustering approaches. In addition, we compared three statistical criteria in measuring gene expression levels and show that a new unbiased statistic is able to quantify the gene expression level more accurately. The proposed algorithms have been tested on a variety of simulated and real microarray images, demonstrating their better performance.

Original languageEnglish (US)
Title of host publication5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07
DOIs
StatePublished - 2007
Externally publishedYes
Event5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07 - Tuusula, Finland
Duration: Jun 10 2007Jun 12 2007

Publication series

NameGENSIPS'07 - 5th IEEE International Workshop on Genomic Signal Processing and Statistics

Other

Other5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07
Country/TerritoryFinland
CityTuusula
Period6/10/076/12/07

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • Computer Science Applications
  • Signal Processing
  • Electrical and Electronic Engineering

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