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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: Jan 13, 2026

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

scANMF: Prior Knowledge and Graph-Regularized NMF for Accurate Cell Type Annotation in scRNA-seq.

Weilai Chi¹, Ying Zheng¹, Huaying Fang^2,3

¹School of Mathematical Sciences, Capital Normal University, Beijing 100048, China.

International Journal of Molecular Sciences

|January 10, 2026

Summary

Accurate cell-type annotation in single-cell RNA sequencing (scRNA-seq) is improved by scANMF, a novel framework integrating multiple data types. This robust method enhances annotation accuracy even with limited or noisy prior biological information.

Keywords:

cell-type annotation graph regularization non-negative matrix factorization prior knowledge integration robustness to noise semi-supervised learning single-cell RNA sequencing

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Last Updated: Jan 13, 2026

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

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Published on: May 22, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) offers high-resolution cellular heterogeneity insights.
Accurate cell-type annotation is hindered by data sparsity, noise, and cross-platform/species variability.
Existing tools often rely on single prior knowledge sources, limiting performance with incomplete data.

Purpose of the Study:

To develop a robust framework for accurate cell-type annotation in scRNA-seq data.
To integrate diverse prior knowledge sources for improved annotation performance.
To address limitations of existing annotation tools, especially with restricted prior information.

Main Methods:

Introduced scANMF, a prior- and graph-regularized non-negative matrix factorization framework.
Integrated marker-gene information, partial label supervision, and local manifold structure.
Factorized expression matrices into interpretable gene-factor and cell-factor representations.

Main Results:

Achieved high annotation accuracy across within-dataset, cross-platform, and cross-species evaluations.
Demonstrated stability under varying label sparsity and marker-gene noise.
Showcased robustness to hyperparameter choices and complementary contributions from different priors.

Conclusions:

scANMF provides a practical and robust framework for scRNA-seq data annotation.
The method excels particularly when high-quality prior knowledge is limited.
Integration of multiple priors significantly enhances annotation accuracy and stability.