Missing Values Estimation for Time Course Gene Expression Data Using the Sequential Partial Least Squares Regression Fitting

被引：0

作者：

Kim, Kyungsook ^{[1
]}

Oh, Mira ^{[2
]}

Baek, Jangsun ^{[1
]}

Son, Young Sook ^{[1
]}

机构：

[1] Chonnam Natl Univ, Dept Stat, 300 Yongbong Dong, Gwangju 500757, South Korea

[2] Gwangju Inst Sci Technol, Dept Informat & Commun, Gwangju 500712, South Korea

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2008年 / 21卷 / 02期

关键词：

Microarray; time course gene expression data; missing value estimation; partial least squares regression fitting; sequential partial least squares regression fitting;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The size of microarray gene expression data is very big and its observation process is also very complex. Thus missing values are frequently occurred. In this paper we propose the sequential partial least squares(SPLS) regression fitting method to estimate missing values for time course gene expression data that has correlations among observations over time points. The SPLS method is to combine the sequential technique with the partial least squares(PLS) regression fitting method. The usefulness of method proposed is evaluated through some simulation study for three yeast time course data.

引用

页码：275 / 290

页数：16

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