Irrelevant gene elimination for partial least squares based dimension reduction by using feature probes.
It is hard to analyse gene expression data which has only a few observations but with thousands of measured genes. Partial Least Squares based Dimension Reduction (PLSDR) is superior for handling such high dimensional problems, but irrelevant features will introduce errors into the dimension reduction process. Here, feature selection is applied to filter the data and an algorithm named PLSDRg is described by integrating PLSDR with gene elimination, which is performed by the indication of t-statistic scores on standardised probes. Experimental results on six microarray data sets show that PLSDRg is effective and reliable to improve generalisation performance of classifiers.
School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China. stamina_zeng@shu.edu.cn
Xue-Qiang
XQ
Zeng
Guo-Zheng
GZ
Li
Geng-Feng
GF
Wu
Jack Y
JY
Yang
Mary Qu
MQ
Yang
D000465:Algorithms; D016477:Artifacts; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D016018:Least-Squares Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size