Boolean implication analysis of single-cell data predicts retinal cell type markers.

ABSTRACT:

Background

The retina is a complex tissue containing multiple cell types that are essential for vision. Understanding the gene expression patterns of various retinal cell types has potential applications in regenerative medicine. Retinal organoids (optic vesicles) derived from pluripotent stem cells have begun to yield insights into the transcriptomics of developing retinal cell types in humans through single cell RNA-sequencing studies. Previous methods of gene reporting have relied upon techniques in vivo using microarray data, or correlational and dimension reduction methods for analyzing single cell RNA-sequencing data computationally. We aimed to develop a state-of-the-art Boolean method that filtered out noise, could be applied to a wide variety of datasets and lent insight i

SUBMITTER: Subramanian R 

PROVIDER: S-EPMC9482279 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature