Classification and Identification of Differential Gene Expression for Microarray Data: Improvement of the Random Forest Method

2008 ◽  
Author(s):  
Xiaoyan Wu ◽  
Zhenyu Wu ◽  
Kang Li
Keyword(s):  
Gene Expression ◽  
Random Forest ◽  
Differential Gene Expression ◽  
Microarray Data ◽  
Random Forest Method ◽  
Differential Gene ◽  
Data Improvement

scholarly journals Multiplicative background correction for spotted microarrays to improve reproducibility

2006 ◽  
Vol 87 (3) ◽  
pp. 195-206 ◽  
Author(s):  
DABAO ZHANG ◽  
MIN ZHANG ◽  
MARTIN T. WELLS
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Differential Gene Expression ◽  
Microarray Data ◽  
Background Noise ◽  
Simple Approach ◽  
Background Correction ◽  
Microarray Data Analysis ◽  
Differential Gene ◽  
Gene Expression Levels

We propose a simple approach, the multiplicative background correction, to solve a perplexing problem in spotted microarray data analysis: correcting the foreground intensities for the background noise, especially for spots with genes that are weakly expressed or not at all. The conventional approach, the additive background correction, directly subtracts the background intensities from foreground intensities. When the foreground intensities marginally dominate the background intensities, the additive background correction provides unreliable estimates of the differential gene expression levels and usually presents M–A plots with ‘fishtails’ or fans. Unreliable additive background correction makes it preferable to ignore the background noise, which may increase the number of false positives. Based on the more realistic multiplicative assumption instead of the conventional additive assumption, we propose to logarithmically transform the intensity readings before the background correction, with the logarithmic transformation symmetrizing the skewed intensity readings. This approach not only precludes the ‘fishtails’ and fans in the M–A plots, but provides highly reproducible background-corrected intensities for both strongly and weakly expressed genes. The superiority of the multiplicative background correction to the additive one as well as the no background correction is justified by publicly available self-hybridization datasets.

scholarly journals Weighted Change-Point Method for Detecting Differential Gene Expression in Breast Cancer Microarray Data

PLoS ONE ◽  
2012 ◽  
Vol 7 (1) ◽  
pp. e29860 ◽  
Author(s):  
Yao Wang ◽  
Guang Sun ◽  
Zhaohua Ji ◽  
Chong Xing ◽  
Yanchun Liang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differential Gene Expression ◽  
Microarray Data ◽  
Change Point ◽  
Point Method ◽  
Differential Gene
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