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dc.contributor.authorGontar, A.en
dc.contributor.authorBottema, M.en
dc.contributor.authorBinder, B.en
dc.contributor.authorTronnolone, H.en
dc.identifier.citationRoyal Society Open Science, 2018; 5(10):180820-1-180820-12en
dc.description.abstractPseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstrating that CSPs capture differences in morphology, while CSPs are found to provide greater discriminatory power than spatial indices previously used to quantify pseudohyphal growth. The analysis demonstrates that CSPs provide a promising avenue for analysing morphology in high-throughput assays.en
dc.description.statementofresponsibilityAmelia Gontar, Murk J. Bottema, Benjamin J. Binder and Hayden Tronnoloneen
dc.publisherRoyal Societyen
dc.rights© 2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.en
dc.subjectClustered shape primitives; dimorphic yeast; pseudohyphal growth; shape characterizationen
dc.titleCharacterizing the shape patterns of dimorphic yeast pseudohyphaeen
dc.typeJournal articleen
pubs.library.collectionMathematical Sciences publicationsen
dc.identifier.orcidBinder, B. [0000-0002-1812-6715]en
dc.identifier.orcidTronnolone, H. [0000-0003-4532-2030]en
Appears in Collections:Mathematical Sciences publications

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