Overview
The retina itself isn't a uniform tissue - it has over 10 major cell types. A wide variety of neural cell types, with distinct roles in interpreting and transmitting the visual signal to the brain, support the cones and rods which convert light into the signal. And the RPE and vasculature support the high energetic needs of the retina while the clear lens and cornea shape the light onto it.
scEiaD is a meta-atlas that allows researchers to better understand the retina by compiling 1.1 million single cell eye and body tissue transcriptomes across 45 studies, 37 publications, and 4 species. It has deep metadata mining, rigorous quality control analysis, differential gene expression testing, and deep learning based batch effect correction in a unified bioinformatic framework. This allows researchers to analyze the universe of ocular single cell expression information in one location.
Resources
The article covering of the data creation and benchmarking of version 0.74 data is now publised at (now **not** on plae, but the codebase and principles are the same) GigaScience!
Vinay S Swamy and others, Building the mega single-cell transcriptome ocular meta-atlas, GigaScience, Volume 10, Issue 10, October 2021, giab061, https://doi.org/10.1093/gigascience/giab061
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Questions?
Please reach out to David McGaughey at mcgaugheyd@mail.nei.gov if you have questions about the scEiaD dataset or the PLAE application.
