Statistical analysis of a small set of time-ordered gene expression data using linear splines

被引:44
作者
de Hoon, MJL [1 ]
Imoto, S [1 ]
Miyano, S [1 ]
机构
[1] Univ Tokyo, Ctr Human Genome, Inst Med Sci, Minato Ku, Tokyo 1088639, Japan
关键词
D O I
10.1093/bioinformatics/18.11.1477
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: Recently, the temporal response of genes to changes in their environment has been investigated using cDNA microarray technology by measuring the gene expression levels at a small number of time points. Conventional techniques for time series analysis are not suitable for such a short series of time-ordered data. The analysis of gene expression data has therefore usually been limited to a fold-change analysis, instead of a systematic statistical approach. Methods: We use the maximum likelihood method together with Akaike's Information Criterion to fit linear splines to a small set of time-ordered gene expression data in order to infer statistically meaningful information from the measurements. The significance of measured gene expression data is assessed using Student's t-test. Results: Previous gene expression measurements of the cyanobacterium Synechocystis sp. PCC6803 were reanalyzed using linear splines. The temporal response was identified of many genes that had been missed by a fold-change analysis. Based on our statistical analysis, we found that about four gene expression measurements or more are needed at each time point.
引用
收藏
页码:1477 / 1485
页数:9
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