Outlier-Based Differential Expression Analysis in Proteomics Studies

Outlier sums for differential gene expression analysis.

We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-stat...

Cancer outlier differential gene expression detection.

We study statistical methods to detect cancer genes that are over- or down-expressed in some but not all samples in a disease group. This has proven useful in cancer studies where oncogenes are activated only in a small subset of samples. We propose the outlier robust t-statistic (ORT), which is intuitively motivated from the t-statistic, the most commonly used differential gene expression dete...

Prestige centrality-based functional outlier detection in gene expression analysis

MOTIVATION Traditional gene expression analysis techniques capture an average gene expression state across sample replicates. However, the average signal across replicates will not capture activated gene networks in different states across replicates. For example, if a particular gene expression network is activated within a subset or all sample replicates, yet the activation state across the s...

Finding Outlier DMUs in Data Envelopment Analysis

Analysis of Differential Gene Expression Studies

In this chapter, we focus on the analysis of differential gene expression studies. Many microarray studies are designed to detect genes associated with different phenotypes, for example, the comparison of cancer tumors and normal cells. In some multifactor experiments, genetic networks are perturbed with various treatments to understand the effects of those treatments and their interactions wit...