Related Experiment Video
Updated: May 17, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
The distribution-based p-value for the outlier sum in differential gene expression analysis
Lin-An Chen1, Dung-Tsa Chen, Wenyaw Chan
1Institute of Statistics, National Chiao Tung University , Hsinchu , Taiwan lachen@stat.nctu.edu.tw.
Abstract:
Outlier sums were proposed by Tibshirani & Hastie (2007) and Wu (2007) for detecting outlier genes where only a small subset of disease samples shows unusually high gene expression, but they did not develop their distributional properties and formal statistical inference. In this study, a new outlier sum for detection of outlier genes is proposed, its asymptotic distribution theory is developed, and the p-value based on this outlier sum is formulated. Its analytic form is derived on the basis of the large-sample theory. We compare the proposed method with existing outlier sum methods by power comparisons. Our method is applied to DNA microarray data from samples of primary breast tumors examined by Huang et al. (2003). The results show that the proposed method is more efficient in detecting outlier genes.
Related Concept Videos
Quantifying and Rejecting Outliers: The Grubbs Test
P-value
P-value stands for the probability value. P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
Detection of Gross Error: The Q Test
What Are Outliers?
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
Outliers and Influential Points
Significance Testing: Overview
