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Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis

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-q...

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Autores principales: Hou, Wenpin, Ji, Zhicheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153208/
https://www.ncbi.nlm.nih.gov/pubmed/37131626
http://dx.doi.org/10.1101/2023.04.16.537094
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author Hou, Wenpin
Ji, Zhicheng
author_facet Hou, Wenpin
Ji, Zhicheng
author_sort Hou, Wenpin
collection PubMed
description 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.
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spelling pubmed-101532082023-05-03 Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis Hou, Wenpin Ji, Zhicheng bioRxiv Article 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. Cold Spring Harbor Laboratory 2023-04-21 /pmc/articles/PMC10153208/ /pubmed/37131626 http://dx.doi.org/10.1101/2023.04.16.537094 Text en https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Hou, Wenpin
Ji, Zhicheng
Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis
title Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis
title_full Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis
title_fullStr Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis
title_full_unstemmed Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis
title_short Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis
title_sort reference-free and cost-effective automated cell type annotation with gpt-4 in single-cell rna-seq analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153208/
https://www.ncbi.nlm.nih.gov/pubmed/37131626
http://dx.doi.org/10.1101/2023.04.16.537094
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