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Independent filtering increases detection power for high-throughput experiments.

Richard Bourgon¹, Robert Gentleman, Wolfgang Huber

¹European Bioinformatics Institute, Cambridge CB10 1SD, UK.

Proceedings of the National Academy of Sciences of the United States of America

|May 13, 2010

Summary

This summary is machine-generated.

Independent filtering enhances statistical power in high-dimensional data analysis by pre-filtering variables. This two-stage approach, using variance filtering and t-tests, boosts discovery rates by 50% in microarray studies.

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Area of Science:

Bioinformatics
Statistical Genetics
Genomics

Background:

Variable selection in high-dimensional data often uses variable-by-variable statistical testing.
Multiple testing adjustments in such approaches can lead to reduced statistical power.

Purpose of the Study:

To introduce a two-stage approach for variable selection that enhances statistical power.
To identify filter/test statistic pairs that maintain type I error control.
To demonstrate the efficacy of independent filtering in high-dimensional data analysis.

Main Methods:

A two-stage variable selection strategy involving an initial filter and subsequent statistical testing.
Evaluation of filter/test statistic pairs for independence under null and alternative hypotheses.
Application of gene-by-gene filtering by overall variance followed by a t-test on microarray data.

Main Results:

Some existing filter/test statistic pairs can compromise type I error control.
Identified filter/test pairs that preserve type I error control.
Gene-by-gene variance filtering followed by a t-test increased microarray data discoveries by 50% and established a fold-change lower bound.

Conclusions:

Independent filtering is a powerful strategy to increase experimental efficiency and discovery rates in high-dimensional data.
The combination of overall variance filtering and t-tests is effective for microarray data analysis.
Careful selection of filter/test pairs is crucial for maintaining statistical rigor.