Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

Abstract

RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq data, this article focuses on the impact of both the replicate number and library size in such analyses. While the main drawback of previous relevant studies is the lack of generality, we conducted both an analysis of a two-condition experiment (with eight biological replicates per condition) to compare the results with previous benchmark studies, and a meta-analysis of 17 experiments with up to 18 biological conditions, eight biological replicates and 100 million (M) reads per sample. As a global trend, we concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact. Our study also provides new insights for practitioners aiming to enhance their experimental designs. For instance, by analyzing both the sensitivity and specificity of the DE analysis, we showed that the optimal threshold to control the false discovery rate (FDR) is approximately 2−r, where r is the replicate number. Furthermore, we showed that the false positive rate (FPR) is rather well controlled by all three studied R packages: DESeq, DESeq2, and edgeR. We also analyzed the impact of both the replicate number and library size on gene ontology (GO) enrichment analysis. Interestingly, we concluded that increases in the replicate number and library size tend to enhance the sensitivity and specificity, respectively, of the GO analysis. Finally, we recommend to RNA-Seq practitioners the production of a pilot data set to strictly analyze the power of their experimental design, or the use of a public data set, which should be similar to the data set they will obtain. For individuals working on tomato research, on the basis of the meta-analysis, we recommend at least four biological replicates per condition and 20 M reads per sample to be almost sure of obtaining about 1000 DE genes if they exist.

Characterization and expression profiling of microRNAs in response to plant feeding in two host-plant strains of the lepidopteran pest Spodoptera frugiperda

“…Since the first use of RNA-Seq to analyze transcriptomic data [ 42 ], we know more on the parameters necessary to optimize detection of differentially expressed genes. Lamarre et al recently showed that the read depth in part compensates for the number of replicates to increase the ratio of differentially expressed genes detected [ 43 ]: with 20 million reads (for 20,000 DE genes - corresponding to a coverage of 1000 reads per gene) and 2 replicates, 85% of DE genes are found, and 8 replicates are needed to reach a ratio of 100%, while with 2.5 million reads (~ coverage 125 reads/gene) and 2 replicates, only 15% of DE genes are detected and the use of 8 replicates increases the ratio of DE genes to only 60%. In our miR differential expression analysis, we performed only two biological replicates.…”

An endophyte from salt-adapted Pokkali rice confers salt-tolerance to a salt-sensitive rice variety and targets a unique pattern of genes in its new host

“…Further, biological coefficient of variation (BCV) was calculated using the "estimateGLMCommonDisp" function. Finally, differential expression p-values were computed using the readCount files of the two treatments using the EdgeR "ExactTest" method (https://bioconductor.org/packages/release/bioc/html/edgeR.html) 88 which can handle data with small sample size 89 . The "ExactTest" method is the quantile-adjusted conditional maximum likelihood (qCML) method, which is applicable for single factor analysis.…”

SlGID1a Is a Putative Candidate Gene for qtph1.1, a Major-Effect Quantitative Trait Locus Controlling Tomato Plant Height

“…Twelve libraries were constructed using the TruSeq RNA Library Prep Kit (Illumina Inc.) and sequenced on an Illumina platform by Beijing Nuohe Zhiyuan Company. DEGs were analyzed using edgeR (version 3.8.6) with the exact test method described by Lamarre et al (2018). The versions of tomato reference genome and annotation database were SL2.50 and ITAG release 2.40 respectively.…”