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T. Y.- C. Tsai

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Cell Biology· 1

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Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Organism:human· 1 Assay:imaging· 1 Approach:variational autoencoder· 1

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A. Hadjitheodorou· 1 shared C. K. Chan· 1 shared J. A. Theriot· 1 shared

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A. Hadjitheodorou

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Cell BiologybioRxivPreprintJun 26, 2020

Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells

C. K. Chan, A. Hadjitheodorou, T. Y.- C. Tsai, J. A. Theriot

This paper compares PCA and VAE-GAN for analyzing cell shape in neutrophils, finding that VAE-GAN more efficiently captures shape variation while maintaining interpretability.

DOI 10.1101/2020.06.26.174474

humanimaging

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