eprintid: 8502
rev_number: 80
eprint_status: archive
userid: 598
dir: disk0/00/00/85/02
datestamp: 2008-10-27 16:10:28
lastmod: 2020-02-17 04:29:35
status_changed: 2008-10-27 16:10:28
type: article
metadata_visibility: show
item_issues_count: 0
creators_name: Barenco, M
creators_name: Stark, J
creators_name: Brewer, D
creators_name: Tomescu, D
creators_name: Callard, R
creators_name: Hubank, M
title: Correction of scaling mismatches in oligonucleotide microarray data
ispublished: pub
subjects: 23700
subjects: 22300
divisions: UCL
abstract: Background: Gene expression microarray data is notoriously subject to high signal variability. Moreover, unavoidable variation in the concentration of transcripts applied to microarrays may result in poor scaling of the summarized data which can hamper analytical interpretations. This is especially relevant in a systems biology context, where systematic biases in the signals of particular genes can have severe effects on subsequent analyses. Conventionally it would be necessary to replace the mismatched arrays, but individual time points cannot be rerun and inserted because of experimental variability. It would therefore be necessary to repeat the whole time series experiment, which is both impractical and expensive. Results: We explain how scaling mismatches occur in data summarized by the popular MAS5 (GCOS; Affymetrix) algorithm, and propose a simple recursive algorithm to correct them. Its principle is to identify a set of constant genes and to use this set to rescale the microarray signals. We study the properties of the algorithm using artificially generated data and apply it to experimental data. We show that the set of constant genes it generates can be used to rescale data from other experiments, provided that the underlying system is similar to the original. We also demonstrate, using a simple example, that the method can successfully correct existing imbalancesin the data. Conclusion: The set of constant genes obtained for a given experiment can be applied to other experiments, provided the systems studied are sufficiently similar. This type of rescaling is especially relevant in systems biology applications using microarray data. © 2006 Barenco et al; licensee BioMed Central Ltd.
date: 2006-05-09
date_type: published
rae2008: 4
oa_status: green
primo: open
primo_central: open_green
article_type_text: Journal Article
elements_source: Scopus
elements_id: 78371
doi: 10.1186/1471-2105-7-251
lyricists_name: Callard, Robin
lyricists_id: RCALL65
full_text_status: public
publication: BMC Bioinformatics
volume: 7
refereed: TRUE
issn: 1471-2105
citation:        Barenco, M;    Stark, J;    Brewer, D;    Tomescu, D;    Callard, R;    Hubank, M;      (2006)    Correction of scaling mismatches in oligonucleotide microarray data.                   BMC Bioinformatics , 7       10.1186/1471-2105-7-251 <https://doi.org/10.1186/1471-2105-7-251>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/8502/1/8502.pdf