Kernel density weighted loess normalization improves the performance of detection within asymmetrical data

BMC Bioinformatics

Methodology Article

Wen-Ping Hsieh¹  Yu-Min Lin²  Tzu-Ming Chu²  Russell D Wolfinger² 

[1] Institute of Statistics, National Tsing Hua University, 300, Hsin-Chu City, Taiwan;SAS Institute Inc., 27513, Cary, NC, USA;

关键词: Kernel Density;    Kernel Density Estimation;    Quantile Normalization;    Null Gene;    Loess Normalization;   

DOI  :  10.1186/1471-2105-12-222

 received in 2010-09-24, accepted in 2011-06-01,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundNormalization of gene expression data has been studied for many years and various strategies have been formulated to deal with various types of data. Most normalization algorithms rely on the assumption that the number of up-regulated genes and the number of down-regulated genes are roughly the same. However, the well-known Golden Spike experiment presents a unique situation in which differentially regulated genes are biased toward one direction, thereby challenging the conclusions of previous bench mark studies.ResultsThis study proposes two novel approaches, KDL and KDQ, based on kernel density estimation to improve upon the basic idea of invariant set selection. The key concept is to provide various importance scores to data points on the MA plot according to their proximity to the cluster of the null genes under the assumption that null genes are more densely distributed than those that are differentially regulated. The comparison is demonstrated in the Golden Spike experiment as well as with simulation data using the ROC curves and compression rates. KDL and KDQ in combination with GCRMA provided the best performance among all approaches.ConclusionsThis study determined that methods based on invariant sets are better able to resolve the problem of asymmetry. Normalization, either before or after expression summary for probesets, improves performance to a similar degree.

【 授权许可】

CC BY   
© Hsieh et al; licensee BioMed Central Ltd. 2011