Search: onr:"swepub:oai:DiVA.org:umu-14868" >
Orthogonal projecti...
-
Bylesjö, MaxUmeå universitet,Kemiska institutionen
(author)
Orthogonal projections to latent structures as a strategy for microarray data normalization
- Article/chapterEnglish2007
Publisher, publication year, extent ...
-
2007-06-18
-
Springer Science and Business Media LLC,2007
-
printrdacarrier
Numbers
-
LIBRIS-ID:oai:DiVA.org:umu-14868
-
https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-14868URI
-
https://doi.org/10.1186/1471-2105-8-207DOI
Supplementary language notes
-
Language:English
-
Summary in:English
Part of subdatabase
Classification
-
Subject category:ref swepub-contenttype
-
Subject category:art swepub-publicationtype
Notes
-
BackgroundDuring generation of microarray data, various forms of systematic biases are frequently introduced which limits accuracy and precision of the results. In order to properly estimate biological effects, these biases must be identified and discarded.ResultsWe introduce a normalization strategy for multi-channel microarray data based on orthogonal projections to latent structures (OPLS); a multivariate regression method. The effect of applying the normalization methodology on single-channel Affymetrix data as well as dual-channel cDNA data is illustrated. We provide a parallel comparison to a wide range of commonly employed normalization methods with diverse properties and strengths based on sensitivity and specificity from external (spike-in) controls. On the illustrated data sets, the OPLS normalization strategy exhibits leading average true negative and true positive rates in comparison to other evaluated methods.ConclusionsThe OPLS methodology identifies joint variation within biological samples to enable the removal of sources of variation that are non-correlated (orthogonal) to the within-sample variation. This ensures that structured variation related to the underlying biological samples is separated from the remaining, bias-related sources of systematic variation. As a consequence, the methodology does not require any explicit knowledge regarding the presence or characteristics of certain biases. Furthermore, there is no underlying assumption that the majority of elements should be non-differentially expressed, making it applicable to specialized boutique arrays.
Subject headings and genre
Added entries (persons, corporate bodies, meetings, titles ...)
-
Eriksson, DanielUmeå universitet,Umeå Plant Science Centre (UPSC)
(author)
-
Sjödin, AndreasUmeå universitet,Institutionen för fysiologisk botanik,Umeå Plant Science Centre (UPSC)(Swepub:umu)anssjn96
(author)
-
Jansson, StefanUmeå universitet,Institutionen för fysiologisk botanik,Umeå Plant Science Centre (UPSC)(Swepub:umu)stja0001
(author)
-
Moritz, ThomasUmeå universitet,Umeå Plant Science Centre (UPSC)
(author)
-
Trygg, JohanUmeå universitet,Kemiska institutionen,Computational Life Science Cluster (CLiC)(Swepub:umu)jotr0001
(author)
-
Umeå universitetKemiska institutionen
(creator_code:org_t)
Related titles
-
In:BMC Bioinformatics: Springer Science and Business Media LLC8:2071471-2105
Internet link
Find in a library
To the university's database