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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: Aug 16, 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

LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell

Qiaoming Liu¹, Yingjian Liang^2,3, Dong Wang¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

Frontiers in Genetics

|December 19, 2022

Summary

This summary is machine-generated.

A new algorithm, linear fast semi-supervised clustering (LFSC), identifies cell types in complex tissues using single-cell RNA sequencing. LFSC improves accuracy and efficiency for disease research.

Keywords:

anchor graph bulk RNA-seq clustering data integration single-cell RNA-seq

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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Genomics
Bioinformatics
Immunology

Background:

Identifying cell types in complex tissues is crucial for understanding cellular heterogeneity in diseases.
Single-cell transcriptomics provides high-resolution data for cell type identification.
Existing methods may lack efficiency or accuracy in complex datasets.

Purpose of the Study:

To introduce a novel algorithm, linear fast semi-supervised clustering (LFSC), for accurate cell type identification from single-cell transcriptomes.
To leverage bulk RNA sequencing data for generating reference samples to guide cell type identification.
To improve the efficiency and robustness of cell type clustering in complex biological samples.

Main Methods:

Developed a linear fast semi-supervised clustering (LFSC) algorithm.
Constructed an anchor graph to represent relationships between reference samples and individual cells.
Applied a connectivity constraint to the learned graph to preserve cluster structure.
Ensured the algorithm's complexity is linear with data size for improved performance.

Main Results:

LFSC demonstrated superior clustering accuracy and robustness compared to existing baseline methods on real single-cell RNA sequencing datasets.
The algorithm successfully identified cell types with high precision and reliability.
The linear complexity of LFSC significantly enhances computational effectiveness and efficiency.

Conclusions:

LFSC is an effective and efficient tool for cell type identification in complex tissues using single-cell transcriptomics.
The algorithm's performance surpasses current methods, offering advancements in disease research.
Application in liver cancer revealed LFSC's capability to discover novel cell types and cancer biomarkers.