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

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Published on: September 18, 2021

A Population Proportion approach for ranking differentially expressed genes.

Mugdha Gadgil¹

¹Chemical Engineering and Process Development, National Chemical Laboratory, Pune, India . mc.gadgil@ncl.res.in

BMC Bioinformatics

|September 20, 2008

Summary

A new method, Population Proportion Ranking Method (PPRM), identifies genes with differential expression in subsets of samples, outperforming existing methods in complex biological variability scenarios.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

DNA microarrays analyze gene expression differences across sample classes.
Current methods often fail to detect genes differentially expressed in only a subset of samples.
Biological variability necessitates methods sensitive to sub-class expression changes.

Purpose of the Study:

Introduce a novel method, Population Proportion Ranking Method (PPRM), for identifying differentially expressed genes.
Address limitations of existing methods in detecting genes expressed in sample subsets.
Quantify gene expression variability using inter-sample ratios.

Main Methods:

PPRM quantifies variability using inter-sample ratios.
It identifies genes with specified expression differences in some samples and low intra-class variability.
The method was tested on simulated and real-world cancer gene expression data.

Main Results:

PPRM performed comparably or superiorly to t test, PPST, COPA, OS, ORT, and MOST on simulated data.
It excelled in identifying genes differentially expressed in sample subsets, outperforming t test, PPST, COPA, and OS.
PPRM demonstrated superior performance in recognizing non-differentially expressed genes with high variability.

Conclusions:

PPRM is effective in identifying genes with differential expression in subsets of samples.
The method offers improved sensitivity and specificity compared to traditional approaches, especially with biological variability.
PPRM shows promise for accurate classification of independent samples using identified predictor genes.