Shape component analysis
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DescriptionStructure-preserving dimension reduction on biological shape spaces
Homepagehttps://github.com/ccdlcmu/shape_component_analysis_Matlab
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Shape component analysis: structure-preserving dimension reduction on biological shape spaces
Hao-Chih Lee; Tao Liao; Yongjie Jessica Zhang; Ge Yang
Motivation: Quantitative shape analysis is required by a wide range of biological studies across diverse scales, ranging from molecules to cells and organisms. In particular, high-throughput and systems-level studies of biological structures and functions have started to produce large volumes of complex high-dimensional shape data. Analysis and understanding of high-dimensional biological shape data require dimension-reduction techniques.
Results: We have developed a technique for non-linear dimension reduction of 2D and 3D biological shape representations on their Riemannian spaces. A key feature of this technique is that it preserves distances between different shapes in an embedded low-dimensional shape space. We demonstrate an application of this technique by combining it with non-linear mean-shift clustering on the Riemannian spaces for unsupervised clustering of shapes of cellular organelles and proteins.
Availability and implementation: Source code and data for reproducing results of this article are freely available at https://github.com/ccdlcmu/shape_component_analysis_Matlab . The implementation was made in MATLAB and supported on MS Windows, Linux and Mac OS.
Contact: geyang@andrew.cmu.edu
DOI: https://doi.org/10.1093/bioinformatics/btv648
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