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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: Oct 13, 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

Effectively Clustering Single Cell RNA Sequencing Data by Sparse Representation.

Rui-Yi Li, Zhiye Wang, Jihong Guan

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|November 17, 2021

Summary

This summary is machine-generated.

This study introduces SPARC, a novel clustering method for single-cell RNA sequencing (scRNA-seq) data. SPARC utilizes a new similarity metric to effectively capture cellular relationships and improve clustering accuracy.

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Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Clustering analysis is crucial for single-cell RNA sequencing (scRNA-seq) data interpretation.
Existing methods often overlook global cellular relationships, limiting the capture of latent structures.
There is a need for advanced clustering techniques that consider comprehensive cell-cell interactions.

Purpose of the Study:

To propose SPARC, a novel clustering method for scRNA-seq data.
To introduce a new similarity metric based on sparse representation coefficients.
To enhance parameter selection with an integrated outlier detection method.

Main Methods:

Developed SPARC, a clustering algorithm for scRNA-seq data.
Implemented a novel similarity metric leveraging sparse representation coefficients.
Integrated an outlier detection mechanism for parameter optimization.

Main Results:

SPARC demonstrated state-of-the-art performance across twelve real scRNA-seq datasets.
The sparse representation-based similarity metric proved more effective than traditional metrics.
SPARC identified high-quality cell clusters with superior accuracy compared to existing methods.

Conclusions:

SPARC offers a novel and effective approach for clustering scRNA-seq data.
The method significantly improves the accuracy of cellular structure analysis.
This study provides a new paradigm for mining complex biological data from scRNA-seq experiments.