A global statistical test for improved detection of gene activity

David Jg Bakewell,Ernst Wit

doi:10.1186/1752-0509-1-s1-p10

David Jg Bakewell, Ernst Wit

Open Access

https://doi.org/10.1186/1752-0509-1-s1-p10

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Abstract

Introduction Interpreting gene transcription for understanding cellular processes using DNA microarray measurements presents a number of challenges to the investigator. One of these is the relatively large number of genes (mRNA transcript identities) that are measured with relatively few independent biological samples. The small number of independent samples limits the statistical inference that can be made about the behaviour of individual genes. Consequently, the microarray end-user is faced with bewildering lists of differentially expressed genes that typically contain false positives – a direct result of the well-known 'few samples – many genes' dimensionality problem. At the same time investigators are using microarrays to understand processes involving the collective action of a number of genes, often organized as a complex or pathway [1]. To address these needs and remedy the problem of dimensionality, we consider a global, likelihood ratio (LR) test for examining the behaviour of a pre-assigned group of genes – rather than individual genes.

Highlights

Interpreting gene transcription for understanding cellular processes using DNA microarray measurements presents a number of challenges to the investigator
The P-values determined by likelihood ratio (LR) and sequential t-test methods are compared using data from Monte Carlo (MC) simulations and a published two-channel spotted microarray experiment [10]
The MC simulations generated LR and Tstatistics that enabled pathways to be identified as True Positive (Tp) and False Positive (Fp) for a particular cutoff value

Summary

Introduction

Interpreting gene transcription for understanding cellular processes using DNA microarray measurements presents a number of challenges to the investigator. At the same time investigators are using microarrays to understand processes involving the collective action of a number of genes, often organized as a complex or pathway [1] To address these needs and remedy the problem of dimensionality, we consider a global, likelihood ratio (LR) test for examining the behaviour of a pre-assigned group of genes – rather than individual genes. This poster describes the development of the LR test for detecting low levels of activity in a group of related genes or 'pathway'. It is well suited to investigating the involvement of a particular functional group of genes in explaining the difference between two or more phenotypes

Methods

Results

Concluding remark

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