| Title: |
Mining single-cell data for cell type–disease associations |
| Authors: |
Chen, Kevin G; Farley, Kathryn O; Lassmann, Timo |
| Contributors: |
Feilman Foundation; Stan Perron Charitable Foundation |
| Source: |
NAR Genomics and Bioinformatics ; volume 6, issue 4 ; ISSN 2631-9268 |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2024 |
| Description: |
A robust understanding of the cellular mechanisms underlying diseases sets the foundation for the effective design of drugs and other interventions. The wealth of existing single-cell atlases offers the opportunity to uncover high-resolution information on expression patterns across various cell types and time points. To better understand the associations between cell types and diseases, we leveraged previously developed tools to construct a standardized analysis pipeline and systematically explored associations across four single-cell datasets, spanning a range of tissue types, cell types and developmental time periods. We utilized a set of existing tools to identify co-expression modules and temporal patterns per cell type and then investigated these modules for known disease and phenotype enrichments. Our pipeline reveals known and novel putative cell type–disease associations across all investigated datasets. In addition, we found that automatically discovered gene co-expression modules and temporal clusters are enriched for drug targets, suggesting that our analysis could be used to identify novel therapeutic targets. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/nargab/lqae180 |
| Availability: |
https://doi.org/10.1093/nargab/lqae180; https://academic.oup.com/nargab/article-pdf/6/4/lqae180/61231835/lqae180.pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.3D772FCD |
| Database: |
BASE |