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dc.contributor.authorGiles, C.
dc.contributor.authorAlbrecht, M.
dc.contributor.authorLam, V.
dc.contributor.authorTakechi, Ryu
dc.contributor.authorMamo, J.
dc.date.accessioned2017-01-30T12:28:25Z
dc.date.available2017-01-30T12:28:25Z
dc.date.created2016-07-28T19:30:18Z
dc.date.issued2016
dc.identifier.citationGiles, C. and Albrecht, M. and Lam, V. and Takechi, R. and Mamo, J. 2016. Biostatistical analysis of quantitative immunofluorescence microscopy images. Journal of Microscopy. 264 (3): pp. 321-333.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/21941
dc.identifier.doi10.1111/jmi.12446
dc.description.abstract

Semiquantitative immunofluorescence microscopy has become a key methodology in biomedical research. Typical statistical workflows are considered in the context of avoiding pseudo-replication and marginalising experimental error. However, immunofluorescence microscopy naturally generates hierarchically structured data that can be leveraged to improve statistical power and enrich biological interpretation. Herein, we describe a robust distribution fitting procedure and compare several statistical tests, outlining their potential advantages/disadvantages in the context of biological interpretation. Further, we describe tractable procedures for power analysis that incorporates the underlying distribution, sample size and number of images captured per sample. The procedures outlined have significant potential for increasing understanding of biological processes and decreasing both ethical and financial burden through experimental optimization.

dc.publisherWiley-Blackwell
dc.titleBiostatistical analysis of quantitative immunofluorescence microscopy images
dc.typeJournal Article
dcterms.source.titleJournal of Microscopy
curtin.departmentSchool of Public Health
curtin.accessStatusFulltext not available


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