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'maskBAD'--a package to detect and remove Affymetrix probes with binding affinity differences.

Michael Dannemann¹, Michael Lachmann, Anna Lorenc

¹Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany.

BMC Bioinformatics

|April 18, 2012

Summary

This study introduces an R package to identify and remove problematic probes on Affymetrix microarrays, improving gene expression analysis accuracy. The method effectively reduces false positives and negatives, outperforming existing techniques for reliable results.

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Area of Science:

Bioinformatics
Genomics
Microarray Analysis

Background:

Hybridization differences due to target sequence variations confound microarray gene expression analysis.
These differences can cause false positives and reduce the power to detect true expression changes.
Problematic probes affect differential expression detection and introduce artifacts, particularly in comparisons like cancer vs. healthy tissue.

Purpose of the Study:

To develop a method for detecting, characterizing, and removing problematic probes from Affymetrix microarrays.
To improve the accuracy and reliability of gene expression data analysis.
To provide a user-friendly R package for Affymetrix data preprocessing.

Main Methods:

Developed an R Bioconductor compatible package.
Utilized correlation of signal intensities from probes within probe sets to identify problematic probes.
Validated the method using mouse genomes to determine false positive and false negative rates.

Main Results:

The developed method routinely outperforms previous approaches in detecting single nucleotide polymorphisms (SNPs)/insertions-deletions (indels).
Achieved detection of 76.2% of known SNPs/indels in mouse expression data with a maximum of 5.5% false positives.
At comparable false positive rates, the new method surpassed the best previous method (72.6% detection).

Conclusions:

Detection and removal of probes with hybridization differences should be a standard step in Affymetrix data preprocessing.
The user-friendly R package enhances data quality from Affymetrix microarray experiments.
Implementing this method improves the reliability of gene expression analysis.