LAUSR

One application of deep learning in analysis of cell biological microscopy data is developing meaningful quantitative representations of cellular and/or molecular phenotypic signatures. Because image orientation has no relevance for shape and morphology, encoding orientation within such representations confounds downstream analyses. This study presents O2-VAE, a neural method for learning orientation-invariant, image-based shape representations. The authors demonstrate that O2-VAE is not sensitive to image orientation for applications of cell/organelle shape phenotyping. Specifically, O2-VAE was verified on diverse experimental systems, ranging from simulations to human induced pluripotent stem cells, for downstream analyses that include clustering, dimensionality reduction, and/or outlier detection. This article opens the door for design and evaluation of orientation-invariant representations that may enable more effective deep learning–driven phenotyping. This preprint has been assigned the following badges: New Methods, Open Software, Cross-Validation. Read the preprint on bioRxiv ( Burgess et al., 2022 ): https://doi.org/10.1101/2022.12.08.519671 .