Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16737

Title: Quality control of Platinum Spike dataset by probe-level mixed models
Authors: Khamiakova, Tatsiana
Shkedy, Ziv
Amaratunga, Dhammika
Talloen, Willem
Gohlmann, Hinrich
Bijnens, Luc
Kasim, Adetayo
Issue Date: 2014
Citation: MATHEMATICAL BIOSCIENCES, 248, p. 1-10
Abstract: Benchmark datasets are important for the validation and optimization of the analysis routes. Lately, a new benchmark dataset, ‘Platinum Spike’, for the Affymetrix GeneChip experiments has been introduced. We performed a quality check of the Platinum Spike dataset by using probe-level linear mixed models. The results have shown that there are ‘empty’ probe sets detecting transcripts, spiked in at different concentrations, and, reversely, there are probe sets that do not detect transcripts, spiked in at different concentrations, even though they were designed to do so. We proposed a formal inference procedure for testing the assumption of independence of all technical replicates in the data and concluded that for almost 10% of probe sets arrays cannot be treated independently, which has strong implications for the normalization procedures and testing for the differential expression. The proposed diagnostics procedure is used to facilitate a thorough exploration of gene expression Affymetrix data beyond the preprocessing and differential expression analysis.
Notes: Khamiakova, T (reprint author), Hasselt Univ, CenStat, Agoralaan D, B-3590 Diepenbeek, Belgium. tatsiana.khamiakova@uhasselt.be
URI: http://hdl.handle.net/1942/16737
DOI: 10.1016/j.mbs.2013.11.004
ISI #: 000332435200001
ISSN: 0025-5564
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
published version1.49 MBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.