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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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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

scSwinFormer: A Transformer-Based Cell-Type Annotation Method for scRNA-Seq Data Using Smooth Gene Embedding and

Hengyu Qin¹, Xiumin Shi¹, Han Zhou¹

¹School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.

Journal of Chemical Information and Modeling

|August 5, 2024

Summary

A new Transformer-based deep learning method, scSwinFormer, accurately annotates cell types in large-scale single-cell RNA sequencing (scRNA-seq) data. This approach effectively models gene dependencies, outperforming existing methods for robust biological system analysis.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell omics provides detailed insights into cellular heterogeneity.
Accurate cell-type identification is crucial for single-cell RNA sequencing (scRNA-seq) data analysis.
Existing scRNA-seq annotation methods struggle with high-dimensional, sparse data and long-term gene dependencies.

Purpose of the Study:

To develop a novel deep learning method for accurate cell-type annotation of large-scale scRNA-seq data.
To address limitations in current methods regarding data modeling and gene dependency capture.

Main Methods:

Developed scSwinFormer, a Transformer-based deep learning model for scRNA-seq data.
Utilized a smooth gene embedding module for sequence modeling.
Employed a self-attention module to capture gene dependencies.
Incorporated a Cell Token to synthesize global data information.

Main Results:

ScSwinFormer demonstrated superior performance in cell-type annotation compared to state-of-the-art methods.
The model achieved high accuracy on multiple real-world scRNA-seq datasets.
Evaluations included both external and benchmark dataset experiments.

Conclusions:

ScSwinFormer offers an effective solution for accurate cell-type annotation in large-scale scRNA-seq data.
The model's architecture successfully addresses challenges posed by data sparsity and gene dependencies.
This advancement facilitates a more detailed understanding of biological systems through improved scRNA-seq analysis.