Reproducible detection of disease-associated markers from gene expression data.

ABSTRACT:

Background

Detection of disease-associated markers plays a crucial role in gene screening for biological studies. Two-sample test statistics, such as the t-statistic, are widely used to rank genes based on gene expression data. However, the resultant gene ranking is often not reproducible among different data sets. Such irreproducibility may be caused by disease heterogeneity.

Results

When we divided data into two subsets, we found that the signs of the two t-statistics were often reversed. Focusing on such instability, we proposed a sign-sum statistic that counts the signs of the t-statistics for all possible subsets. The proposed method excludes genes affected by heterogeneity, thereby improving the reproducibility of gene ranking. We compared the sign-sum statistic with t

SUBMITTER: Omae K 

PROVIDER: S-EPMC4991096 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature