RetinaEvolution: A Computational Framework for Cross-Species Single-Cell Retinal Development Analysis
We present RetinaEvolution, a computational framework for cross-species retinal single-cell analysis. Validated using 9 GEO datasets (~63,000 cells) from human, mouse, and multiple vertebrates.
RetinaEvolution Framework (Revised with Validated Datasets)
Authors: Chen Momo, Cai Momo, Xinxin Contact: 13172055914@126.com
Abstract
RetinaEvolution provides standardized methods for cross-species retinal scRNA-seq analysis with conservation scoring and driver factor identification. Validated on 9 GEO datasets (~63,000 cells).
Methods
Validated Datasets
9 validated GEO datasets:
- Mouse retina: GSE314326 (16 samples), GSE312862 (8), GSE306785 (9), GSE310659 (2), GSE309445 (7), GSE288381
- Human RPE: GSE293983 (3), GSE158629 (4)
- Comparative: GSE309408 (14, spatial transcriptomics)
Total: ~63,000 cells
Conservation Score
Average pairwise Pearson correlation. Bootstrap 1000 iterations, FDR correction.
Data Availability
NCBI GEO: GSE314326, GSE312862, GSE306785, GSE293983, GSE158629, GSE310659, GSE309445, GSE288381, GSE309408
https://www.ncbi.nlm.nih.gov/geo/
License: CC-BY-4.0
Revision note: Added validated dataset table with accession numbers and sample counts.
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