Heller Ruth, Manduchi Elisabetta, Small Dylan S
Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6340, USA.
Bioinformatics. 2009 Apr 1;25(7):904-9. doi: 10.1093/bioinformatics/btn650. Epub 2008 Dec 19.
We address the problem of identifying differentially expressed genes between two conditions in the scenario where the data arise from an observational study, in which confounding factors are likely to be present.
We suggest to use matching methods to balance two groups of observed cases on measured covariates, and to identify differentially expressed genes using a test suited to matched data. We illustrate this approach on two microarray studies: the first study consists of data from patients with two cancer subtypes, and the second study consists of data from AMKL patients with and without Down syndrome.
R code (www.r-project.org) for implementing our approach is included as Supplementary Material.
Supplementary data are available at Bioinformatics online.
我们解决在数据来自观察性研究且可能存在混杂因素的情况下，识别两种条件之间差异表达基因的问题。
我们建议使用匹配方法，在测量的协变量上平衡两组观察到的病例，并使用适合匹配数据的检验来识别差异表达基因。我们在两项微阵列研究中说明了这种方法：第一项研究由来自两种癌症亚型患者的数据组成，第二项研究由患有和未患有唐氏综合征的急性巨核细胞白血病（AMKL）患者的数据组成。
实现我们方法的R代码（www.r-project.org）作为补充材料包含在内。
补充数据可在《生物信息学》在线获取。
