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Open AccessArticle

A Method Based on Differential Entropy-Like Function for Detecting Differentially Expressed Genes Across Multiple Conditions in RNA-Seq Studies

by Zhuo Wang, Shuilin Jin *,† and Chiping Zhang *,†
Department of Mathematics, Harbin Institute of Technology, Harbin 150006, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2019, 21(3), 242; https://doi.org/10.3390/e21030242
Received: 4 January 2019 / Revised: 27 February 2019 / Accepted: 27 February 2019 / Published: 4 March 2019
(This article belongs to the Section Entropy and Biology)
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. View Full-Text
Keywords: differential entropy-like function; differential expressed genes; multiple condition data; time-course data differential entropy-like function; differential expressed genes; multiple condition data; time-course data
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Wang, Z.; Jin, S.; Zhang, C. A Method Based on Differential Entropy-Like Function for Detecting Differentially Expressed Genes Across Multiple Conditions in RNA-Seq Studies. Entropy 2019, 21, 242.

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