Abstract

BackgroundCurrently, most tests of differential gene expression using Affymetrix expression array data are performed using expression summary values representing each probe set on a microarray. Recently testing methods have been proposed which incorporate probe level information. We propose a new approach that uses Fisher's method of combining evidence from multiple sources of information. Specifically, we combine p-values from probe level tests of significance.ResultsThe combined p method and other competing methods were compared using three spike-in datasets (where probe sets corresponding differentially spiked transcripts are known) and array data from a biological study validated with qRT-PCR. Based on power and false discovery rates computed for the spike-in datasets, we demonstrate that the combined p method compares favorably with other methods. We find that probe level testing methods select many of the same genes as differentially expressed. We illustrate the use of the combined p method for diagnostic purposes using examples.ConclusionCombined p is a promising alternative to existing methods of testing for differential gene expression. In addition, the combined p method is particularly well suited as a diagnostic tool for exploratory analysis of microarray data.

Highlights

  • Most tests of differential gene expression using Affymetrix expression array data are performed using expression summary values representing each probe set on a microarray

  • The probe level information is combined into a summary value by probe set and array, this summary information is used to test for differential expression

  • In order to compare the performance of the methods, we use three different spike-in datasets and array data from a biological study validated with qRT-PCR

Read more

Summary

Introduction

Most tests of differential gene expression using Affymetrix expression array data are performed using expression summary values representing each probe set on a microarray. A common approach to identifying differentially expressed genes is to calculate an expression index for each probe set and array and use these expression indices as the basis for statistical testing. The probe level information is combined into a summary value by probe set and array, this summary information is used to test for differential expression. The most popular methods for calculating expression indices include Affymetrix Microarray Suite 5 (MAS5) [1], model based expression index (MBEI) [2] and robust multi-chip average (RMA) [3]. Examples of statistical tests to identify differentially expressed genes based on expression indices include the t-test, the non-

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call