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Detection of differentially expressed gene sets in a partially paired microarray data set.

Johan Lim¹, Jayoun Kim, Sang-cheol Kim

¹Seoul National University.

Statistical Applications in Genetics and Molecular Biology

|April 14, 2012

Summary

This study introduces a new statistic (Tp) to find sets of differentially expressed genes in partially paired data. The method effectively identifies more genes, especially for small gene sets, compared to existing techniques.

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Area of Science:

Bioinformatics
Statistical Genetics
Genomics

Background:

Partially paired data is common in microarray experiments.
Existing methods like the t3 statistic analyze differentially expressed genes using linear combinations of paired and unpaired statistics.
Previous research has explored testing strategies for partially paired data.

Purpose of the Study:

To extend the t3 statistic to a Hotelling's T2 type statistic (Tp) for detecting differentially expressed gene sets of size p.
To incorporate inter-gene correlation into false discovery rate estimation using Efron's empirical null principle.
To evaluate the performance of the Tp statistic in identifying differentially expressed genes in colorectal cancer data.

Main Methods:

Extension of the t3 statistic to the Tp statistic for gene set analysis.
Application of Efron's empirical null principle for false discovery rate estimation.
Analysis of colorectal cancer microarray data for differentially expressed gene sets of sizes p=2 and p=3.

Main Results:

The proposed Tp statistic identified additional differentially expressed genes not detected by univariate methods, particularly for gene sets of size p=2 and marginally for p=3.
The study demonstrated that the empirical null principle is robust to deviations from normal distribution assumptions in simulation studies.
The Tp statistic complements univariate procedures in detecting differentially expressed genes in partially paired microarray data.

Conclusions:

The Tp statistic is a valuable tool for detecting differentially expressed gene sets in partially paired microarray data, especially for small gene set sizes.
The incorporation of inter-gene correlation via Efron's empirical null principle improves the accuracy of differential expression analysis.
The findings suggest that the Tp statistic can enhance the discovery of biologically relevant gene sets in cancer research.