A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data

被引：6

作者：

Shaikh, Mateen ^{[1
]}

McNicholas, Paul D. ^{[1
]}

Desmond, Anthony F. ^{[1
]}

机构：

[1] Univ Guelph, Guelph, ON N1G 2W1, Canada

来源：

INTERNATIONAL JOURNAL OF BIOSTATISTICS | 2010年 / 6卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

clustering; gene expression time course data; longitudinal data; missing data; mixture models; pseudo-EM; MAXIMUM-LIKELIHOOD; MISSING VALUES; MODELS;

D O I：

10.2202/1557-4679.1223

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

A method for clustering incomplete longitudinal data, and gene expression time course data in particular, is presented. Specifically, an existing method that utilizes mixtures of multivariate Gaussian distributions with modified Cholesky-decomposed covariance structure is extended to accommodate incomplete data. Parameter estimation is carried out in a fashion that is similar to an expectation-maximization algorithm. We focus on the particular application of clustering incomplete gene expression time course data. In this application, our approach gives good clustering performance when compared to the results when there is no missing data. Possible extensions of this work are also suggested.

引用

页数：17

共 25 条

[11] Model-based clustering, discriminant analysis, and density estimation [J].