Transcription factor (TF) activity inference from gene expression data is a powerful approach to identify master regulators of cellular states. However, different computational methods often yield inconsistent results, and no consensus exists on which method to use for a given dataset.
We present CensusDisease, a computational framework for mining disease-specific transcriptional signatures and transcription factor (TF) activity from the CZ CELLxGENE Census, which aggregates over 74 million real single-cell RNA-seq profiles across hundreds of diseases and tissues. Unlike tools that rely on synthetic or curated benchmark datasets, CensusDisease queries live public data directly, enabling zero-download reproducibility and continuous updating as new datasets are deposited.
The vertebrate retina serves as an exemplary model for understanding evolutionary developmental biology. Here we present a comprehensive cross-species single-cell transcriptomic atlas of embryonic retinal development spanning six vertebrate species: human, macaque, mouse, chicken, zebrafish, and Xenopus.