Contamination removal methods in cDNA microarray data

Shih-Huang Chan, Wan Chi Chang, Chien Ju Lin

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

Abstract

Our objective is to detect and remedy the contaminated spots for cDNA microarray data. To check the existence of unusual spots, single linkage clustering is used to assess the background intensities. Then, K-means clustering method is applied to identify the contaminated area. We estimate the amount of contamination, for background and foreground, through the use of nonparametric spline regression and empirical cumulative distribution approach, separately. A simulation study shows that the performance of the recommended approach is promising.

Original languageEnglish
Title of host publication2006 IEEE International Workshop on Genomic Signal Processing and Statstics, GENSIPS 2006
Pages39-40
Number of pages2
DOIs
Publication statusPublished - 2006 Dec 1
Event2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006 - College Station, TX, United States
Duration: 2006 May 282006 May 30

Publication series

Name2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006

Other

Other2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006
CountryUnited States
CityCollege Station, TX
Period06-05-2806-05-30

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Contamination removal methods in cDNA microarray data'. Together they form a unique fingerprint.

Cite this