New methods for computational decomposition of whole-mount in situ images enable effective curation of a large, highly redundant collection of Xenopus images

PLoS Computational Biology
Ilya PatrushevMichael J Gilchrist

Abstract

The precise anatomical location of gene expression is an essential component of the study of gene function. For most model organisms this task is usually undertaken via visual inspection of gene expression images by interested researchers. Computational analysis of gene expression has been developed in several model organisms, notably in Drosophila which exhibits a uniform shape and outline in the early stages of development. Here we address the challenge of computational analysis of gene expression in Xenopus, where the range of developmental stages of interest encompasses a wide range of embryo size and shape. Embryos may have different orientation across images, and, in addition, embryos have a pigmented epidermis that can mask or confuse underlying gene expression. Here we report the development of a set of computational tools capable of processing large image sets with variable characteristics. These tools efficiently separate the Xenopus embryo from the background, separately identify both histochemically stained and naturally pigmented regions within the embryo, and can sort images from the same gene and developmental stage according to similarity of gene expression patterns without information about relative orientation...Continue Reading

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Citations

Jun 4, 2020·Integrative and Comparative Biology·Erica L WestermanChristopher P J Sanford

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Key Resources (RRID) Mentioned

SCR_003280
SCR_014924

Software Mentioned

scipy
OpenCV
Xenbase
images
numpy
sklearn
FastICA
analyse
XenMARK
select

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