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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jul 31, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis.

Wenpin Hou¹, Zhicheng Ji²

¹Department of Biostatistics, The Mailman School of Public Health, Columbia University, New York City, NY, USA.

Biorxiv : the Preprint Server for Biology

|May 3, 2023

Summary

Large language models like GPT-4 can now automatically and accurately annotate cell types in single-cell RNA sequencing (scRNA-seq) data, reducing manual effort. An R package, GPTCelltype, simplifies this process for researchers.

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Last Updated: Jul 31, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Cell type annotation is crucial for single-cell RNA sequencing (scRNA-seq) analysis but is often manual, time-consuming, and requires specialized expertise.
Existing automated methods necessitate high-quality reference datasets and complex pipeline development.

Approach:

This study evaluates the efficacy of GPT-4, a powerful large language model, for automated cell type annotation using marker gene information from standard scRNA-seq pipelines.
GPT-4's performance was assessed across diverse tissue and cell types.

Key Points:

GPT-4 accurately annotates cell types by leveraging marker gene data.
Annotations generated by GPT-4 show high concordance with manual annotations.
The model demonstrates potential to significantly decrease the effort and expertise required for cell type annotation.

Conclusions:

GPT-4 offers a robust and efficient solution for automated cell type annotation in scRNA-seq data.
The development of GPTCelltype, an open-source R package, further streamlines the application of GPT-4 for cell type annotation, making it accessible to a wider research community.