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BMC Bioinformatics

, 10:449

First Online: 29 December 2009Received: 26 June 2009Accepted: 29 December 2009

Abstract

BackgroundMicroarrays depend on appropriate probe design to deliver the promise of accurate genome-wide measurement. Probe design, ideally, produces a unique probe-target match with homogeneous duplex stability over the complete set of probes. Much of microarray pre-processing is concerned with adjusting for non-ideal probes that do not report target concentration accurately. Cross-hybridizing probes non-unique, probe composition and structure, as well as platform effects such as instrument limitations, have been shown to affect the interpretation of signal. Data cleansing pipelines seldom filter specifically for these constraints, relying instead on general statistical tests to remove the most variable probes from the samples in a study. This adjusts probes contributing to ProbeSet gene values in a study-specific manner. We refer to the complete set of factors as biologically applied filter levels BaFL and have assembled an analysis pipeline for managing them consistently. The pipeline and associated experiments reported here examine the outcome of comprehensively excluding probes affected by known factors on inter-experiment target behavior consistency.

ResultsWe present here a -white box- probe filtering and intensity transformation protocol that incorporates currently understood factors affecting probe and target interactions; the method has been tested on data from the Affymetrix human GeneChip HG-U95Av2, using two independent datasets from studies of a complex lung adenocarcinoma phenotype. The protocol incorporates probe-specific effects from SNPs, cross-hybridization and low heteroduplex affinity, as well as effects from scanner sensitivity, sample batches, and includes simple statistical tests for identifying unresolved biological factors leading to sample variability. Subsequent to filtering for these factors, the consistency and reliability of the remaining measurements is shown to be markedly improved.

ConclusionsThe data cleansing protocol yields reproducible estimates of a given probe or ProbeSet-s gene-s relative expression that translates across datasets, allowing for credible cross-experiment comparisons. We provide supporting evidence for the validity of removing several large classes of probes, and for our approaches for removing outlying samples. The resulting expression profiles demonstrate consistency across the two independent datasets. Finally, we demonstrate that, given an appropriate sampling pool, the method enhances the t-test-s statistical power to discriminate significantly different means over sample classes.

AbbreviationsBaFLBiologically applied Filter Level

AffyMAPSDetectorAffymetrix MicroArray Probe SNP Detector

α alpha0.05 is the common Type I error

Batcha set of microarray chips which underwent hybridizations at the same time

DEstatistically significant difference in class expression means, for the given α 0.05

FDRFalse Discovery Rates

FWERFamily Wise Error Rates, Type II error

MMsingle base mismatch probes

Oligoarrayauxhttp:-www.bioinfo.rpi.edu-applications-hybrid-OligoArrayAux.php

φ phiLaplacian dimension projection

PMperfect match probes

ArrayInitiativea script written to generate CDFs with or without excluded probes

SVDSingle Value Decomposition

xhorizontal grid placement of probes on the microarray chip

yvertical grid placement of probes on the microarray chip.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-10-449 contains supplementary material, which is available to authorized users.

Kevin J Thompson, Hrishikesh Deshmukh contributed equally to this work.

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Autor: Kevin J Thompson - Hrishikesh Deshmukh - Jeffrey L Solka - Jennifer W Weller

Fuente: https://link.springer.com/







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