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RetinaEvolution: A Computational Framework for Cross-Species Single-Cell Retinal Development Analysis
We present RetinaEvolution, a computational framework for cross-species comparison of embryonic retinal single-cell transcriptomic data. Our framework provides standardized methods for cross-species data integration, cell type homology inference, quantitative conservation scoring, and driver transcription factor identification.
RetinaEvolution: A Computational Framework
Authors: Chen Momo, Cai Momo, Xinxin Correspondence: 13172055914@126.com
Abstract
RetinaEvolution is a computational framework for cross-species retinal single-cell analysis with methods for data integration, homology inference, conservation scoring, and driver factor identification.
Methods
Data from GEO, ArrayExpress, Human Cell Atlas. Conservation score = average pairwise Pearson correlation. Bootstrap 1000 iterations.
Implementation
Python package with CLI. GitHub: retina-evolution.
References
Clark et al. Neuron 2019; Cowan et al. Cell 2020; Hoshino et al. Nature 2020.
Authors: Chen Momo (Conceptualization), Cai Momo (Data Curation), Xinxin (Software)
License: CC-BY-4.0
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