Estimation and correction of non-specific binding in a large-scale spike-in experimentReport as inadecuate




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Genome Biology

, 8:R126

First Online: 26 June 2007Received: 13 December 2006Revised: 11 May 2007Accepted: 26 June 2007

Abstract

BackgroundThe availability of a recently published large-scale spike-in microarray dataset helps us to understand the influence of probe sequence in non-specific binding NSB signal and enables the benchmarking of several models for the estimation of NSB. In a typical microarray experiment using Affymetrix whole genome chips, 30% to 50% of the probes will apparently have absent target transcripts and show only NSB signal, and these probes can have significant repercussions for normalization and the statistical analysis of the data if NSB is not estimated correctly.

ResultsWe have found that the MAS5 perfect match-mismatch PM-MM model is a poor model for estimation of NSB, and that the Naef and Zhang sequence-based models can reasonably estimate NSB. In general, using the GC robust multi-array average, which uses Naef binding affinities, to calculate NSB GC-NSB outperforms other methods for detecting differential expression. However, there is an intensity dependence of the best performing methods for generating probeset expression values. At low intensity, methods using GC-NSB outperform other methods, but at medium intensity, MAS5 PM-MM methods perform best, and at high intensity, MAS5 PM-MM and Zhang-s position-dependent nearest-neighbor PDNN methods perform best.

ConclusionA combined statistical analysis using the MAS5 PM-MM, GC-NSB and PDNN methods to generate probeset values results in an improved ability to detect differential expression and estimates of false discovery rates compared with the individual methods. Additional improvements in detecting differential expression can be achieved by a strict elimination of empty probesets before normalization. However, there are still large gaps in our understanding of the Affymetrix GeneChip technology, and additional large-scale datasets, in which the concentration of each transcript is known, need to be produced before better models of specific binding can be created.

Electronic supplementary materialThe online version of this article doi:10.1186-gb-2007-8-6-r126 contains supplementary material, which is available to authorized users.

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Author: Eugene F Schuster - Eric Blanc - Linda Partridge - Janet M Thornton

Source: https://link.springer.com/



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