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FC1000: normalized gene expression changes of systematically perturbed human cells
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab. Walter & Eliza Hall Inst Med Res, Bioinformat Div, Melbourne, Vic 3052, Australia..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
2017 (English)In: Statistical Applications in Genetics and Molecular Biology, ISSN 1544-6115, E-ISSN 1544-6115, Vol. 16, no 4, p. 217-242Article in journal (Refereed) Published
Abstract [en]

The systematic study of transcriptional responses to genetic and chemical perturbations in human cells is still in its early stages. The largest available dataset to date is the newly released L1000 compendium. With its 1.3 million gene expression profiles of treated human cells it offers many opportunities for biomedical data mining, but also data normalization challenges of new dimensions. We developed a novel and practical approach to obtain accurate estimates of fold change response profiles from L1000, based on the RUV (Remove Unwanted Variation) statistical framework. Extending RUV to a big data setting, we propose an estimation procedure, in which an underlying RUV model is tuned by feedback through dataset specific statistical measures, reflecting p-value distributions and internal gene knockdown controls. Applying these metrics-termed evaluation endpoints - to disjoint data splits and integrating the results to select an optimal normalization, the procedure reduces bias and noise in the L1000 data, which in turn broadens the potential of this resource for pharmacological and functional genomic analyses. Our pipeline and normalization results are distributed as an R package (nelanderlab.org/FC1000.html).

Place, publisher, year, edition, pages
2017. Vol. 16, no 4, p. 217-242
Keyword [en]
gene expression, normalization, p-value inflation, remove unwanted variation
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:uu:diva-335706DOI: 10.1515/sagmb-2016-0072ISI: 000408884100001PubMedID: 28862994OAI: oai:DiVA.org:uu-335706DiVA, id: diva2:1163845
Funder
eSSENCE - An eScience CollaborationSwedish Research Council, 2014-03314Swedish Cancer Society, CAN 2011/1198, CAN 2014/579AstraZeneca, BD15-0088Swedish Childhood Cancer Foundation, PR2014-0143
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-01-12Bibliographically approved

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