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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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scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process

Sijing An¹, Jinhui Shi¹, Runyan Liu¹

¹Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China.

Bioinformatics (Oxford, England)

|April 11, 2024

Summary

Single-cell Deep Adaptive Clustering (scDAC) accurately identifies cell types in single-cell RNA sequencing data. This novel method couples Autoencoder and Dirichlet Process Mixture Model for robust and adaptive clustering.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Existing clustering methods struggle with accurate, adaptive cluster number determination in large-scale scRNA-seq datasets.

Purpose of the Study:

To develop a novel computational model for adaptive clustering of scRNA-seq data.
To accurately determine the number of cell types or subtypes directly from scRNA-seq data.

Main Methods:

Proposed the single-cell Deep Adaptive Clustering (scDAC) model.
Coupled Autoencoder (AE) and Dirichlet Process Mixture Model (DPMM) for joint optimization.
Validated scDAC on multiple scRNA-seq datasets against 15 existing methods.

Main Results:

scDAC adaptively identifies accurate cell type numbers, outperforming 15 widely used clustering methods.
Demonstrated robust performance across diverse scRNA-seq datasets and varying hyperparameters.
Successfully revealed cellular heterogeneity with high accuracy.

Conclusions:

scDAC offers a robust and accurate solution for adaptive clustering of scRNA-seq data.
The model's ability to determine intrinsic biological cluster numbers advances single-cell data analysis.
scDAC provides a valuable tool for discovering cell types and subtypes.