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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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Related Experiment Video

Updated: Jun 13, 2025

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

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Automatically Detecting Anchor Cells and Clustering for scRNA-Seq Data Using scTSNN.

Qiaoming Liu, Dandan Zhang, Dong Wang

IEEE Journal of Biomedical and Health Informatics

|September 16, 2024

Summary

We developed scTSNN, a novel clustering method for single-cell RNA sequencing data. It accurately identifies cell types and structures, outperforming existing methods in accuracy and robustness.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) advances cell heterogeneity studies.
Unsupervised clustering methods aim to identify cell types and structures automatically.
Discovering true cell types in unsupervised scRNA-seq remains a challenge.

Purpose of the Study:

To propose scTSNN, a tensor shared nearest neighbor anchor clustering method for scRNA-seq data.
To improve the accuracy and robustness of cell type identification and structure detection.
To demonstrate the utility of scTSNN in cell pseudotime inference and rare cell identification.

Main Methods:

scTSNN utilizes tensor affinity learning to capture local-global topological structures.
A density-based shared nearest neighbor approach automatically identifies anchor cells.
Non-anchor cells are then partitioned to achieve final clustering results.

Main Results:

scTSNN accurately detects complex structures in synthetic and real scRNA-seq datasets.
The method demonstrates superior performance in accuracy and robustness compared to state-of-the-art techniques.
Case studies on mammalian and cancer cells highlight the value of identified anchor cells.

Conclusions:

scTSNN offers a robust and accurate solution for unsupervised clustering of scRNA-seq data.
The identified anchor cells provide valuable insights for downstream analyses like pseudotime inference and rare cell detection.
scTSNN holds significant application and research value in single-cell data analysis.