Cross-platform pathway activity transformation and classification of microarray data
Conference proceedings article
Authors/Editors
Strategic Research Themes
No matching items found.
Publication Details
Author list: Engchuan W., Meechai A., Tongsima S., Chan J.H.
Publisher: Springer
Publication year: 2015
Volume number: 331
Start page: 139
End page: 148
Number of pages: 10
ISBN: 9783319131528
ISSN: 2194-5357
eISSN: 2194-5357
Languages: English-Great Britain (EN-GB)
View in Web of Science | View on publisher site | View citing articles in Web of Science
Abstract
One of the most challenging problems in microarray study is to analyze microarray data from different platforms. This will improve the reliability of the study, as number of samples is larger and it can be applied for rare disease study, for which only a few microarray data have been published. As different microarray platforms cover different number of genes, so the integrative study of two different platforms needs to be able to deal with the missing value issue. Many works have been done for cross-platform microarray data utilization but none of them have focused on gene-set based microarray data classification. In this study, we applied the Bayesian-based method to reconstruct the expression level of the missing genes before transforming it to the gene-set activity. Two gene-set activity transformation methods; Negatively Correlated Feature Set (NCFS-i) and Analysis-of-Variance Feature Set (AFS), were used to evaluate the performance of this method using actual microarray datasets. The results show that the imputation of missing data can improve the classification performance of the cross-platform study. ฉ Springer International Publishing Switzerland 2015.
Keywords
Bayesian computation, Cross-platform analysis, Gene-set activity transformation, Microarray data, Negative correlation
Contact Information