Permutation test for incomplete paired data with application to cDNA microarray data

Donghyeon Yu, Johan Lim, Feng Liang, Kyunga Kim, Byung Soo Kim, Woncheol Jang

Research output: Contribution to journalArticlepeer-review


A paired data set is common in microarray experiments, where the data are often incompletely observed for some pairs due to various technical reasons. In microarray paired data sets, it is of main interest to detect differentially expressed genes, which are usually identified by testing the equality of means of expressions within a pair. While much attention has been paid to testing mean equality with incomplete paired data in previous literature, the existing methods commonly assume the normality of data or rely on the large sample theory. In this paper, we propose a new test based on permutations, which is free from the normality assumption and large sample theory. We consider permutation statistics with linear mixtures of paired and unpaired samples as test statistics, and propose a procedure to find the optimal mixture that minimizes the conditional variances of the test statistics, given the observations. Simulations are conducted for numerical power comparisons between the proposed permutation tests and other existing methods. We apply the proposed method to find differentially expressed genes for a colorectal cancer study.

Original languageEnglish (US)
Pages (from-to)510-521
Number of pages12
JournalComputational Statistics and Data Analysis
Issue number3
StatePublished - Mar 1 2012


  • Colorectal cancer
  • Incomplete paired data
  • Microarray data
  • Permutation test

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


Dive into the research topics of 'Permutation test for incomplete paired data with application to cDNA microarray data'. Together they form a unique fingerprint.

Cite this