Abstract
Pseudohyphal 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.
Highlights
Yeasts are commonly known as single-celled fungi that typically grow as unconnected cells and may reproduce by budding according to a regulated pattern
Yeasts grow in colonies of unconnected cells; dimorphic yeasts, including Saccharomyces cerevisiae, are able to alter their growth pattern in response to external stimuli to produce chains of elongated cells
We show that yeast colony morphology may be accurately and automatically quantified using clustered shape primitives (CSPs)
Summary
Yeasts are commonly known as single-celled fungi that typically grow as unconnected cells and may reproduce by budding according to a regulated pattern. Such approaches are able to provide a suitably accurate classification but rely on the specification of a large number of features, most of which may not be useful or are unrelated to yeast morphology, while other important features may be overlooked In contrast to this approach, methods have recently been developed for automatically learning the shape patterns in complex three-dimensional binary arrays [15,16]. No need for a predetermined list of features To demonstrate that this approach accurately captures changes in colony morphology, we use CSPs to classify two-dimensional binary images of S. cerevisiae colonies that are known to have different growth patterns. The classification results demonstrate that CSPs provide a new avenue for analysing morphology in high-throughput assays from genome-wide deletion mutant libraries and provide a rigorous framework for identifying changes in growth pattern
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