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Updated: Jun 11, 2026

Microarray Analysis for Saccharomyces cerevisiae

Microarray Analysis for Saccharomyces cerevisiae

Published on: April 7, 2011

ROAST: rotation gene set tests for complex microarray experiments.

Di Wu¹, Elgene Lim, François Vaillant

¹The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, Australia.

Bioinformatics (Oxford, England)

|July 9, 2010

Summary

This summary is machine-generated.

ROAST is a new gene set test that uses rotation instead of permutation. This method offers greater statistical power and flexibility for analyzing gene expression data across various experimental designs, even with limited replication.

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Area of Science:

Bioinformatics
Computational Biology
Statistical Genetics

Background:

Gene set tests analyze differential gene expression for sets of genes, enhancing statistical power and result interpretation.
Existing permutation-based methods have limitations, including unrealistic independence assumptions or applicability only to two-group comparisons.

Purpose of the Study:

To introduce ROAST, a statistically rigorous gene set test addressing limitations of existing methods.
To provide a flexible tool applicable to diverse experimental designs, including those with correlated samples and minimal replication.

Main Methods:

ROAST employs rotation, a Monte Carlo technique for multivariate regression, instead of traditional permutation.
The method accommodates complex experimental designs expressible as linear models and allows for array weights and gene-wise correlations.
ROAST can be tuned for pathway subsets, directional regulation testing, and incorporates probe weighting for prior importance.

Main Results:

ROAST demonstrates robust power and appropriate statistical size in simulation studies compared to permutation methods.
The procedure is effective even with minimal replication due to rotation's independence from sample size.
Application to human and mouse mammary stem cell data demonstrates its utility in assessing transcriptional conservation.

Conclusions:

ROAST offers a statistically sound and versatile alternative for gene set analysis in various biological contexts.
Its implementation in the Bioconductor package limma ensures accessibility for researchers.