Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.16.537094v1?rss=1
Authors: Hou, W., Ji, Z.
Abstract:
Cell type annotation is an essential step in single-cell RNA-seq analysis. However, it is a time-consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high-quality reference datasets and the development of additional pipelines. We demonstrate that GPT-4, a highly potent large language model, can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single-cell RNA-seq analysis pipelines. Evaluated across hundreds of tissue types and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations, and has the potential to considerably reduce the effort and expertise needed in cell type annotation.
Copy rights belong to original authors. Visit the link for more info
Podcast created by Paper Player, LLC
