title: Comparison of quantitative methods for cell-shape analysis.
abstract: 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.
authors.affiliation: Program in Biomedical Informatics, and Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA.
authors.forename: Z
authors.initials: Z
authors.lastname: Pincus
authors.forename: J A
authors.initials: JA
authors.lastname: Theriot
mesh_terms: D000818:Animals; D016934:Caulobacter / Q000166:cytology*; D003585:Cell Biology; D048429:Cell Size; D002478:Cells, Cultured / Q000166:cytology*