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WeightNameValue
1000 Titel
  • Identification of sample annotation errors in gene expression datasets
1000 Autor/in
  1. Lohr, Miriam |
  2. Hellwig, Birte |
  3. Edlund, Karolina |
  4. Mattsson, Johanna S. M. |
  5. Botling, Johan |
  6. Schmidt, Marcus |
  7. Micke, Patrick |
  8. Rahnenführer, Jörg |
  9. Hengstler, Jan G. |
1000 Erscheinungsjahr 2015
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2015-11-25
1000 Erschienen in
1000 Quellenangabe
  • 89(12): 2265-2272
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2015
1000 Lizenz
1000 Verlagsversion
  • http://dx.doi.org/10.1007/s00204-015-1632-4 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4673097/ |
1000 Ergänzendes Material
  • https://link.springer.com/article/10.1007%2Fs00204-015-1632-4#SupplementaryMaterial |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • The comprehensive transcriptomic analysis of clinically annotated human tissue has found widespread use in oncology, cell biology, immunology, and toxicology. In cancer research, microarray-based gene expression profiling has successfully been applied to subclassify disease entities, predict therapy response, and identify cellu- lar mechanisms. Public accessibility of raw data, together with corresponding information on clinicopathological parameters, offers the opportunity to reuse previously analyzed data and to gain statistical power by combining multiple datasets. However, results and conclusions obviously depend on the reliability of the available information. Here, we propose gene expression-based methods for identifying sample misannotations in public transcriptomic datasets. Sample mix-up can be detected by a classifier that differentiates between samples from male and female patients. Correlation analysis identifies multiple measurements of material from the same sample. The analysis of 45 datasets (including 4913 patients) revealed that erroneous sample annotation, affecting 40 % of the analyzed datasets, may be a more widespread phenomenon than previously thought. Removal of erroneously labelled samples may influence the results of the statistical evaluation in some datasets. Our methods may help to identify individual datasets that contain numerous discrepancies and could be routinely included into the statistical analysis of clinical gene expression data.
1000 Sacherschließung
lokal Misannotation
lokal Quality control
lokal Gene expression
lokal Microarray
lokal Male–female classifier
1000 Fachgruppe
  1. Medizin |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/TG9ociwgTWlyaWFt|https://frl.publisso.de/adhoc/creator/SGVsbHdpZywgQmlydGU=|https://frl.publisso.de/adhoc/creator/RWRsdW5kLCBLYXJvbGluYQ==|https://frl.publisso.de/adhoc/creator/TWF0dHNzb24sIEpvaGFubmEgUy4gTS4=|https://frl.publisso.de/adhoc/creator/Qm90bGluZywgSm9oYW4=|https://frl.publisso.de/adhoc/creator/U2NobWlkdCwgTWFyY3Vz|https://frl.publisso.de/adhoc/creator/TWlja2UsIFBhdHJpY2s=|https://frl.publisso.de/adhoc/creator/UmFobmVuZsO8aHJlciwgSsO2cmc=|http://d-nb.info/gnd/175862257
1000 Förderer
  1. German Research Foundation (DFG) |
  2. Swedish Cancer Society |
1000 Fördernummer
  1. RA 870/4-1; RA 870/5-1
  2. -
1000 Förderprogramm
  1. -
  2. -
1000 Dateien
  1. Identification of sample annotation errors in gene expression datasets
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer German Research Foundation (DFG) |
    1000 Förderprogramm -
    1000 Fördernummer RA 870/4-1; RA 870/5-1
  2. 1000 joinedFunding-child
    1000 Förderer Swedish Cancer Society |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6404728.rdf
1000 Erstellt am 2017-09-27T14:22:31.274+0200
1000 Erstellt von 122
1000 beschreibt frl:6404728
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet Mon May 28 09:39:38 CEST 2018
1000 Objekt bearb. Mon May 28 09:39:37 CEST 2018
1000 Vgl. frl:6404728
1000 Oai Id
  1. oai:frl.publisso.de:frl:6404728 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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