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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 microarray-based...

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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

Transformers for single-cell RNA sequencing: a survey.

Tianxing Hu¹, Zhi Wei¹

¹Department of Computer Science, New Jersey Institute of Technology, Guttenberg Information Technologies Center (GITC), Suite 4100, University Heights, Newark, New Jersey 07102, USA.

Briefings in Bioinformatics

|June 29, 2026

Summary

This summary is machine-generated.

Transformers show promise for analyzing complex single-cell RNA sequencing (scRNA-seq) data, overcoming limitations of traditional methods. This survey details their application, performance, and future directions in biomedical research.

Keywords:

deep learning self-attention single-cell RNA sequencing transfer learning transformer

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Last Updated: Jun 30, 2026

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Published on: January 10, 2019

Transcriptome Analysis of Single Cells

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Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

Published on: December 3, 2019

Area of Science:

Computational biology
Genomics
Artificial intelligence in medicine

Background:

Single-cell RNA sequencing (scRNA-seq) generates high-dimensional, sparse data challenging for conventional analysis.
Transformers, a deep learning architecture, offer advanced capabilities for complex biological data.
Existing methods struggle with scRNA-seq data characteristics like sparseness and batch effects.

Purpose of the Study:

To provide a comprehensive overview of Transformer applications in scRNA-seq data analysis.
To systematically analyze Transformer models for specific and multiple downstream tasks (foundation models).
To examine Transformer models regarding performance, efficiency, interpretability, and scalability.

Main Methods:

Systematic review and analysis of Transformer architectures applied to scRNA-seq.
Categorization of Transformers into task-specific and foundation models.
Evaluation of models based on performance, computational efficiency, interpretability, and scalability.

Main Results:

Transformers significantly improve model performance in scRNA-seq analysis due to self-attention and transfer learning.
Analysis covers both specialized Transformers and versatile foundation models.
Identified key considerations for model selection and future research directions.

Conclusions:

Transformers represent a powerful tool for advancing scRNA-seq data analysis.
This survey offers a practical resource for researchers, addressing current challenges and guiding future development.
Future research should explore Transformers beyond scRNA-seq and across other omics layers.