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Boolean Implication Analysis of Single-cell Data Predicts Retinal Cell Type Markers by Rohan Subramanian; Debashis Sahoo is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Boolean Implication Analysis of Single-cell Data Predicts Retinal Cell Type Markers about?

## 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 into gene expression over differentiation. ## Results Here, we present a bioinformatic approach using Boolean implication to discover genes which are retinal cell type-specific or involved in retinal cell fate. We apply this approach to previously published retina and retinal organoid datasets and improve upon previously published correlational methods. Our method improves the prediction acc

Who reads Boolean Implication Analysis of Single-cell Data Predicts Retinal Cell Type Markers?

Author

Rohan Subramanian; Debashis Sahoo

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)