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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data.

Hunyong Cho¹, Yixiang Qu¹, Chuwen Liu¹

¹Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United States.

Briefings in Bioinformatics

|September 22, 2023

Summary

The logistic-beta (LB) test offers the best sensitivity for analyzing microbial gene expression (metatranscriptomics) data, outperforming other methods. This evaluation provides guidance for selecting robust statistical tools for microbiome research.

Keywords:

benchmark differential expression early childhood caries logistic-beta metagenomics metatranscriptomics

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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

Area of Science:

Microbiome research
Statistical genomics
Bioinformatics

Background:

Human microbiome function is crucial, but statistical methods for microbial gene expression (metatranscriptomics) are underdeveloped.
Existing differential expression analysis methods are often unsuitable for metatranscriptomics data and lack rigorous evaluation.

Purpose of the Study:

To comprehensively evaluate and benchmark 10 differential analysis methods for metatranscriptomics data.
To provide practical guidance for selecting appropriate statistical methods for metatranscriptomics analyses.

Main Methods:

Evaluated 10 methods (log-normal, logistic-beta, MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis, two-part Kruskal-Wallis) using simulated and real data.
Performance metrics included type I error, false discovery rate, and sensitivity.
Data sources included supragingival biofilm microbiome data from early childhood caries (ECC) and inflammatory bowel disease studies.

Main Results:

The logistic-beta (LB) test demonstrated the highest sensitivity across sample sizes and controlled type I error effectively.
MAST exhibited inflated type I error.
In the ECC study, LB and log-normal tests identified genes C8PHV7 and C8PEV7 from Campylobacter gracilis as strongly associated with childhood dental disease.

Conclusions:

The logistic-beta test is a highly sensitive and reliable method for metatranscriptomics differential expression analysis.
This study offers essential guidance for researchers to choose optimal methods, enhancing the accuracy of microbiome gene expression studies.
Accurate method selection minimizes false positives and maximizes the detection of true biological signals.