Physical Sciences and Mathematics Commons^™

A Mixture Model Based Approach For Estimating The Fdr In Replicated Microarray Data, Shuo Jiao, Shunpu Zhang

Shuo Jiao

One of the mostly used methods for estimating the false discovery rate (FDR) is the permutation based method. The permutation based method has the well-known granularity problem due to the discrete nature of the permuted null scores. The granularity problem may produce very unstable FDR estimates. Such instability may cause scientists to over- or under-estimate the number of false positives among the genes declared as significant, and hence result in inaccurate interpretation of biological data. In this paper, we propose a new model based method as an improvement of the permutation based FDR estimation method of SAM [1] The new …

On Correcting The Overestimation Of The Permutation Based False Discovery Rate Estimator., Shuo Jiao, Shunpu Zhang

Shuo Jiao

Motivation: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR), which is defined as the expected percentage of the number of false positive genes among the claimed significant genes. As a consequence, the accuracy of the FDR estimators will

be important for correctly controlling FDR. Xie et al. found that the standard permutation method of estimating FDR is biased and proposed to delete the predicted differentially expressed (DE) genes in the estimation of FDR for one-sample comparison. However, we notice that the formula of the FDR used …

The T-Mixture Model Approach For Detecting Differentially Expressed Genes In Microarrays, Shuo Jiao, Shunpu Zhang

Shuo Jiao

The finite mixture model approach has attracted much attention in analyzing microarray data due to its robustness to the excessive variability which is common in the microarray data. Pan (2003) proposed to use the normal mixture model method (MMM) to estimate the distribution of a test statistic and its null distribution. However, considering the fact that the test statistic is often of t-type, our studies find that the rejection region from MMM is often significantly larger than the correct rejection region, resulting an inflated type I error. This motivates us to propose the t-mixture model (TMM) approach. In this paper, …