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Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.ORCID iD: 0000-0002-0364-2709
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Univ Skovde, Syst Biol Res Ctr, Sch Biosci, Div Biol & Bioinformat, Skovde, Sweden.
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2019 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 18, p. 3357-3364Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
OXFORD UNIV PRESS , 2019. Vol. 35, no 18, p. 3357-3364
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Bioinformatics (Computational Biology)
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URN: urn:nbn:se:uu:diva-395689DOI: 10.1093/bioinformatics/btz066ISI: 000487327500019PubMedID: 30715209OAI: oai:DiVA.org:uu-395689DiVA, id: diva2:1365280
Funder
EU, European Research Council, 640275Swedish Cancer SocietySwedish Research CouncilSwedish Childhood Cancer FoundationRagnar Söderbergs stiftelseAvailable from: 2019-10-24 Created: 2019-10-24 Last updated: 2019-10-24Bibliographically approved

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Weishaupt, HolgerJohansson, PatrikSundström, AndersNelander, SvenJohansson Swartling, Fredrik K.
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