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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Probe set algorithms: is there a rational best bet?

Jinwook Seo¹, Eric P Hoffman

¹Research Center for Genetic Medicine, Children's National Medical Center, 111 Michigan Ave NW, Washington DC 20010, USA. jseo@cnmcresearch.org

BMC Bioinformatics

|September 1, 2006

Summary

Choosing the right probe set algorithm is crucial for accurate mRNA expression profiling. Algorithm performance varies, with background assumptions, not signal calculations, driving differences in biological credibility.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Affymetrix microarrays are standard for mRNA expression profiling.
Multiple probes per transcript (probe sets) enable robust background assessment and gene expression measures.
Various computational algorithms exist to process microarray image data into normalized expression signals.

Purpose of the Study:

To summarize the debate surrounding probe set algorithms for microarray data analysis.
To illustrate how algorithm choices, including mismatch weight and normalization, impact data interpretation.
To evaluate the performance of new hybrid algorithms against traditional ones.

Main Methods:

Comparative analysis of probe set algorithms (MAS5, dCHIP, RMA, PLIER, GC-RMA, Probe Profiler PCA).
Illustration of data interpretation changes based on algorithm parameters like mismatch weight and normalization.
Utilizing an interactive power analysis tool to assess algorithm performance.

Main Results:

Algorithm choice significantly alters data interpretation, affecting biological credibility.
New hybrid algorithms like PLIER show promise in reducing false positives from poorly performing probe sets.
Variability in algorithm performance is largely attributed to assumptions about 'background' rather than 'signal' calculations.

Conclusions:

The 'best' probe set algorithm is project-dependent due to the complexity of quantifying 'background'.
Understanding algorithm assumptions regarding background is key for reliable mRNA expression profiling.
Biologists should carefully consider algorithm choice for accurate experimental interpretation.