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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: Feb 21, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Linnorm: improved statistical analysis for single cell RNA-seq expression data.

Shun H Yip^1,2,3, Panwen Wang², Jean-Pierre A Kocher²

¹Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.

Nucleic Acids Research

|October 6, 2017

Summary

This summary is machine-generated.

Linnorm, a new method for single-cell RNA sequencing (scRNA-seq) data, effectively removes technical noise while preserving biological variations. This improves downstream analyses like subtype discovery and clustering.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates high-dimensional data with inherent technical noise.
Accurate normalization is crucial for reliable interpretation of scRNA-seq data and downstream analyses.
Existing normalization methods face challenges in balancing noise removal and preservation of biological heterogeneity.

Purpose of the Study:

To introduce Linnorm, a novel normalization and transformation method for scRNA-seq data.
To evaluate Linnorm's performance in removing technical noise and preserving biological variations.
To demonstrate Linnorm's ability to enhance existing statistical and differential gene expression analyses.

Main Methods:

Development of the Linnorm normalization and transformation algorithm.
Application of Linnorm to real-world scRNA-seq datasets.
Comparative analysis of Linnorm against established methods (NODES, SAMstrt, SCnorm, scran, DESeq, TMM) for normalization.
Benchmarking Linnorm against differential gene expression (DEG) analysis tools (BASiCS, NODES, SAMstrt, Seurat, DESeq2).

Main Results:

Linnorm demonstrates superior speed and effectiveness in removing technical noise from scRNA-seq data.
The method excels at preserving crucial cell heterogeneity, a key biological variation.
Linnorm significantly improves performance in downstream analyses, including novel subtype discovery, pseudo-temporal ordering, and clustering.
Linnorm outperforms existing DEG analysis methods in controlling false positive rates and enhancing accuracy.

Conclusions:

Linnorm offers a robust solution for scRNA-seq data normalization, addressing limitations of current approaches.
The method's ability to remove technical noise while preserving biological signals enhances the reliability of scRNA-seq data analysis.
Linnorm provides a valuable tool for advancing discoveries in cell biology and disease research through improved data interpretation.