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Updated: Nov 27, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
A Method Based on Differential Entropy-Like Function for Detecting Differentially Expressed Genes Across Multiple
Zhuo Wang1, Shuilin Jin1, Chiping Zhang1
1Department of Mathematics, Harbin Institute of Technology, Harbin 150006, China.
Abstract:
The advancement of high-throughput RNA sequencing has uncovered the profound truth in biology, ranging from the study of differential expressed genes to the identification of different genomic phenotype across multiple conditions. However, lack of biological replicates and low expressed data are still obstacles to measuring differentially expressed genes effectively. We present an algorithm based on differential entropy-like function (DEF) to test for the differential expression across time-course data or multi-sample data with few biological replicates. Compared with limma, edgeR, DESeq2, and baySeq, DEF maintains equivalent or better performance on the real data of two conditions. Moreover, DEF is well suited for predicting the genes that show the greatest differences across multiple conditions such as time-course data and identifies various biologically relevant genes.
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