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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: Jul 8, 2025

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

scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data.

Zhi-Hua Du¹, Wei-Lin Hu¹, Jian-Qiang Li¹

¹College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China.

Communications Biology

|December 14, 2023

Summary

This summary is machine-generated.

The scPML model improves single-cell RNA sequencing (scRNA-seq) analysis by integrating gene signaling pathways for robust cell type annotation. This approach enhances the characterization of cellular heterogeneity across diverse datasets and species.

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Last Updated: Jul 8, 2025

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

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Published on: May 22, 2017

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

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

Published on: March 12, 2021

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell technologies reveal cellular heterogeneity crucial for medicine and disease research.
Accurate cell type annotation is vital for single-cell omics studies.
Current supervised learning methods struggle with dataset variability and complex gene associations.

Purpose of the Study:

To develop a novel model for robust and generalizable cell type annotation.
To address limitations in existing single-cell data analysis methods.
To improve the characterization of cellular heterogeneity and gene regulatory mechanisms.

Main Methods:

Proposed the scPML model incorporating gene signaling pathway data.
Utilized pathway information to partition cellular genetic features.
Developed methods to characterize cell-cell interaction maps.

Main Results:

scPML demonstrated superior performance in cell type annotation.
The model effectively detected unknown cell types.
Achieved high accuracy across different species, platforms, and tissues.

Conclusions:

scPML offers a more robust and generalizable approach to cell type annotation.
Integrating gene signaling pathways enhances the understanding of cellular heterogeneity.
The model advances single-cell omics research by improving data analysis accuracy.