On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

Abstract

Background: In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets. Result: The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined. Conclusion: For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.

Transcriptomic, proteomic and ultrastructural studies on salinity-tolerant Aedes aegypti in the context of rising sea levels and arboviral disease epidemiology

“…aegypti is characterized by differences in the comparative transcriptomics profiles of gut, anal papilla and carcass, notably for cuticle and cuticle-associated proteins, as well as signalling pathway proteins and other effector molecules. RNA-seq analysis on large pools of mosquito structures under two different biological conditions has yielded important information in other comparative transcriptomic studies [ 75 , 76 ] and is cost effective [ 77 ]. However, the use of biological replicates and/or RT-qPCR can better demonstrate changes in the expression of specific transcripts and their statistical significance.…”

“…aegypti [ 76 ]. Such pooling can retain statistical power while minimizing the cost of RNA-seq experiments [ 77 ]. Extracted RNA was sent in RNAstable® tubes (Biomatrica, CA, USA) to Macrogen (Seoul, South Korea) for cDNA library preparation and DNA sequencing.…”

Transcriptome-wide analysis reveals insight into tumor suppressor functions of 1B3, a novel synthetic miR-193a-3p mimic

“…Adequate sample pooling has been shown to sufficiently control biological variation and to enable reliable exploratory analysis. 29 , 30…”

High-Resolution Transcriptome Atlas and Improved Genome Assembly of Common Buckwheat, Fagopyrum esculentum

“…For the transcriptome map, each sample was collected in two biological replicates. Each replicate was a pool of organs/tissues taken from at least 5 plants, in order to decrease the variation of the expression levels not related to the difference between samples but to the within-group variation (see e.g., Takele Assefa et al, 2020 ). RNA quality was checked using a Bioanalyzer2100 (Agilent, United States); samples with RIN > 7 were employed for subsequent analysis.…”