Comparison of quantitative methods for cell‐shape analysis

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Abstract
Summary: Morphology is an important large‐scale manifestation of the global organizational and physiological state of cells, and is commonly used as a qualitative or quantitative measure of the outcome of various assays. Here we evaluate several different basic representations of cell shape – binary masks, distance maps and polygonal outlines – and different subsequent encodings of those representations – Fourier and Zernike decompositions, and the principal and independent components analyses – to determine which are best at capturing biologically important shape variation. We find that principal components analysis of two‐dimensional shapes represented as outlines provide measures of morphology which are quantitative, biologically meaningful, human interpretable and work well across a range of cell types and parameter settings.