RNA-seq analyses: Benchmarking differential expression analyses tools reveals the effect of higher number of replicates on performance.

Salifu, S. P. P.; Nyarko, H. N.; Doughan, A.; Msatsi, H. K.; Mensah, I.; Bukari, A.-R. A.

doi:10.1101/2020.06.10.144063

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bioRxiv · 10.1101/2020.06.10.144063

RNA-seq analyses: Benchmarking differential expression analyses tools reveals the effect of higher number of replicates on performance.

Salifu, S. P. P.·Nyarko, H. N.·Doughan, A.·Msatsi, H. K.·Mensah, I.·Bukari, A.-R. A.

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Abstract

The introduction of several differential gene expression analysis tools has made it difficult for researchers to settle on a particular tool for RNA-seq analysis. This coupled with the appropriate determination of biological replicates to give an optimum representation of the study population and make biological sense. To address these challenges, we performed a survey of 8 tools used for differential expression in RNA-seq analysis. We simulated 39 different datasets (from 10 to 200 replicates, at an interval of 5) using compcodeR with a maximum of 100 replicates. Our goal was to determine the effect of varying the number of replicates on the performance (F1-score, recall and precision) of the tools. EBSeq and edgeR-glmRT recorded the highest (0.9385) and lowest (0.6505) average F1-score across all replicates, respectively. We also performed a pairwise comparison of all the tools to determine their concordance with each other in identifying differentially expressed genes. We found the greatest concordance to be between limma voom treat and limma voom ebayes. Finally, we recommend employing edgeR-glmRT for RNA-seq experiments involving 10-50 replicates and edgeR-glmQLF for studies with 55 to 200 replicates. Author summaryDownstream analysis of RNA-seq data in R often poses several challenges to researchers as it is a daunting task to choose a specific differential expression analysis tool over another. Researchers also find it challenging to determine the number (replicates) of samples to use in order to give comparable and accurate results. In this paper, we surveyed eight differential expression analysis tools using different number of replicates of simulated RNA-seq count data. We measured the performance of each tool and based on the recorded F1-scores, recall and precision, we made the following recommendations; consider edgeR-glmRT and edgeR-glmQLF for replicates of 10-50 and 55-200 respectively.

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Salifu, S. P. P., Nyarko, H. N., Doughan, A., Msatsi, H. K., Mensah, I., Bukari, A.-R. A.. 2020-06-10. RNA-seq analyses: Benchmarking differential expression analyses tools reveals the effect of higher number of replicates on performance.. https://doi.org/10.1101/2020.06.10.144063

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