Qin Li-Xuan, Breeden Linda, Self Steven G
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.
BMC Res Notes. 2014 Jan 24;7:60. doi: 10.1186/1756-0500-7-60.
Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies.
In this paper, we applied the clustering of regression models method to data from a time course study of yeast on two genotypes, wild type and YOX1 mutant, each with two technical replicates, and compared the clustering results with K-means clustering. We identified gene clusters that have similar expression patterns in wild type yeast, two of which were missed by K-means clustering. We further identified gene clusters whose expression patterns were changed in YOX1 mutant yeast compared to wild type yeast.
The clustering of regression models method can be a valuable tool for identifying genes that are coordinately transcribed by a common mechanism.
在高通量微阵列研究中，寻找在不同样本间具有相似表达模式的基因是一个经常被问到的重要问题。传统的聚类算法，如K均值聚类和层次聚类，直接基于观测到的测量值对基因进行聚类，而没有考虑收集微阵列数据时的具体实验设计。一种新的基于模型的聚类方法，即回归模型聚类方法，考虑了微阵列研究的具体设计，并基于基因与样本协变量的关系进行聚类。它可以为来自复杂研究设计（如重复时间进程研究）的研究找到有用的基因簇。
在本文中，我们将回归模型聚类方法应用于酵母在野生型和YOX1突变体两种基因型上的时间进程研究数据，每种基因型有两个技术重复，并将聚类结果与K均值聚类进行比较。我们鉴定出在野生型酵母中具有相似表达模式的基因簇，其中两个被K均值聚类遗漏。我们进一步鉴定出与野生型酵母相比，其表达模式在YOX1突变体酵母中发生变化的基因簇。
回归模型聚类方法可以成为识别由共同机制协同转录的基因的有价值工具。
