de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

'maskBAD' – a package to detect and remove Affymetrix probes with binding affinity differences

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons72638

Dannemann,  Michael
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons72809

Lachmann,  Michael
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons56802

Lorenc,  Anna
Department Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

Dannemann_2012.pdf
(Verlagsversion), 519KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Dannemann, M., Lachmann, M., & Lorenc, A. (2012). 'maskBAD' – a package to detect and remove Affymetrix probes with binding affinity differences. BMC Bioinformatics, 13: 56. doi:10.1186/1471-2105-13-56.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-0CAE-0
Zusammenfassung
Background: Hybridization differences caused by target sequence differences can be a confounding factor in analyzing gene expression on microarrays, lead to false positives and reduce power to detect real expression differences. We prepared an R Bioconductor compatible package to detect, characterize and remove such probes in Affymetrix 3’IVT and exon-based arrays on the basis of correlation of signal intensities from probes within probe sets. Results: Using completely mouse genomes we determined type 1 (false negatives) and type 2 (false positives) errors with high accuracy and we show that our method routinely outperforms previous methods. When detecting 76.2% of known SNP/indels in mouse expression data, we obtain at most 5.5% false positives. At the same level of false positives, best previous method detected 72.6%. We also show that probes with differing binding affinity both hinder differential expression detection and introduce artifacts in cancer-healthy tissue comparison. Conclusions: Detection and removal of such probes should be a routine step in Affymetrix data preprocessing. We prepared a user friendly R package, compatible with Bioconductor, that allows the filtering and improving of data from Affymetrix microarrays experiments