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Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. (Bioinformatik / Bioinformatics)ORCID-id: 0000-0001-6427-0315
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2019 (engelsk)Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, nr 18, s. 3357-3364Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Motivation: Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential.

Results: We have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses.

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Oxford University Press, 2019. Vol. 35, nr 18, s. 3357-3364
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Bioinformatik
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URN: urn:nbn:se:his:diva-16769DOI: 10.1093/bioinformatics/btz066ISI: 000487327500019PubMedID: 30715209Scopus ID: 2-s2.0-85072349088OAI: oai:DiVA.org:his-16769DiVA, id: diva2:1304220
Tilgjengelig fra: 2019-04-11 Laget: 2019-04-11 Sist oppdatert: 2019-10-11bibliografisk kontrollert

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