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Updated: Jan 22, 2026
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
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lncDIFF: a novel quasi-likelihood method for differential expression analysis of non-coding RNA
Qian Li1, Xiaoqing Yu2, Ritu Chaudhary3
1Health Informatics Institute, University of South Florida, Tampa, FL, 33612, USA.
BMC Genomics
|July 4, 2019
Summary
A new tool, lncDIFF, effectively identifies differentially expressed long non-coding RNAs (lncRNAs) in cancer studies. It outperforms existing methods for low abundance RNA sequencing data, improving biomarker discovery.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Long non-coding RNAs (lncRNAs) are crucial in cancer research for diagnostic and prognostic biomarkers.
- Standard RNA sequencing analysis tools struggle with the low abundance of lncRNA genes.
Purpose of the Study:
- To develop a novel bioinformatics tool, lncDIFF, for accurate differential expression analysis of lncRNAs.
- To address the limitations of existing tools in analyzing low-abundance lncRNA data.
Main Methods:
- Investigated statistical distribution of normalized counts for low-expression lncRNAs and mRNAs.
- Developed lncDIFF using a generalized linear model with zero-inflated Exponential quasi-likelihood.
- Employed likelihood ratio tests for differential expression detection.
Main Results:
- lncDIFF demonstrated superior power and lower false discovery rates compared to DESeq2, edgeR, limma, zinbwave, DEsingle, and ShrinkBayes in simulations.
- lncDIFF identified novel lncRNA genes with prognostic value in head and neck squamous cell carcinoma data.
- The method is compatible with standard RNA-Seq preprocessing and normalization pipelines.
Conclusions:
- lncDIFF is a powerful tool for differential analysis of low-abundance non-coding RNA expression data.
- The tool enhances the discovery of lncRNA biomarkers in cancer research.
- lncDIFF is available as an R package.
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