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Abstract:
Background: Hybridization differences caused by target sequence differences can be a confounding factor in analyzing
gene expression on microarrays, lead to false positives and reduce power to detect real expression differences. We
prepared an R Bioconductor compatible package to detect, characterize and remove such probes in Affymetrix 3’IVT and
exon-based arrays on the basis of correlation of signal intensities from probes within probe sets.
Results: Using completely mouse genomes we determined type 1 (false negatives) and type 2 (false positives) errors with
high accuracy and we show that our method routinely outperforms previous methods. When detecting 76.2% of known
SNP/indels in mouse expression data, we obtain at most 5.5% false positives. At the same level of false positives, best
previous method detected 72.6%. We also show that probes with differing binding affinity both hinder differential
expression detection and introduce artifacts in cancer-healthy tissue comparison.
Conclusions: Detection and removal of such probes should be a routine step in Affymetrix data preprocessing. We
prepared a user friendly R package, compatible with Bioconductor, that allows the filtering and improving of data from
Affymetrix microarrays experiments
