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

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Rna Sequencing ◽  
Statistical Power ◽  
Sample Pooling

An amendment to this paper has been published and can be accessed via the original article.

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

2020 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Statistical Power ◽  
Dynamic Range ◽  
Substantial Reduction ◽  
Sequencing Depth ◽  
Rna Seq ◽  
Sample Pooling ◽  
Pooling Strategies ◽  
The Cost

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. Results: The data generating model in pooled experiments is defined mathematically to evaluate the 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.

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

2020 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Statistical Power ◽  
Dynamic Range ◽  
Substantial Reduction ◽  
Sequencing Depth ◽  
Data Generation ◽  
Rna Seq ◽  
Sample Pooling ◽  
Pooling Strategies

Abstract 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 data. Mathematical descriptions of the data generating mechanism in pooled experiments are used to reinforce our interpretations from the empirical and simulation studies. 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. 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 biological samples, an adequate pooling strategy is effective in maintaining the power of testing for DGE for low to medium abundance levels, along with a substantial reduction of the total cost of the experiment.

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

2020 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Statistical Power ◽  
Dynamic Range ◽  
Substantial Reduction ◽  
Sequencing Depth ◽  
Data Generation ◽  
Rna Seq ◽  
Sample Pooling ◽  
Pooling Strategies

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. Results: The data generating model in pooled experiments is defined mathematically to evaluate the 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 biological samples, an adequate pooling strategy is effective in maintaining the power of testing for 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 cost and maintain power.

scholarly journals The optimal discovery procedure for significance analysis of general gene expression studies

2019 ◽  
Author(s):  
Andrew J. Bass ◽  
John D. Storey
Keyword(s):  
Rna Sequencing ◽  
Statistical Power ◽  
Enrichment Analysis ◽  
R Package ◽  
Testing Procedure ◽  
Biological Data ◽  
Testing Procedures ◽  
Significance Analysis ◽  
Study Designs ◽  
Optimal Discovery Procedure

Analysis of biological data often involves the simultaneous testing of thousands of genes. This requires two key steps: the ranking of genes and the selection of important genes based on a significance threshold. One such testing procedure, called the "optimal discovery procedure" (ODP), leverages information across different tests to provide an optimal ranking of genes. This approach can lead to substantial improvements in statistical power compared to other methods. However, current applications of the ODP have only been established for simple study designs using microarray technology. Here we extend this work to the analysis of complex study designs and RNA sequencing studies. We then apply our extended framework to a static RNA sequencing study, a longitudinal and an independent sampling time-series study, and an independent sampling dose-response study. We find that our method shows improved performance compared to other testing procedures, finding more differentially expressed genes and increasing power for enrichment analysis. Thus the extended ODP enables a superior significance analysis of genomic studies. The algorithm is implemented in our freely available R package called edge.

scholarly journals Powerful eQTL mapping through low coverage RNA sequencing

2021 ◽  
Author(s):  
Tommer Schwarz ◽  
Toni Boltz ◽  
Kangcheng Hou ◽  
Merel Bot ◽  
Chenda Duan ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Statistical Power ◽  
Large Scale ◽  
Sequencing Depth ◽  
Rna Seq ◽  
High Coverage ◽  
Regulatory Variants ◽  
Fold Reduction ◽  
Low Coverage ◽  
Number Of Individuals

Mapping genetic variants that regulate gene expression (eQTLs) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-Seq limits sample size, sequencing depth, and therefore, discovery power. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-Seq of whole blood tissue across 1490 individuals at low-coverage (5.9 million reads/sample) and show that the effective power is higher than an RNA-Seq study of 570 individuals at high-coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-Seq data to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power. Our study suggests that lowering coverage while increasing the number of individuals is an effective approach to increase discovery power in RNA-Seq studies.

scholarly journals Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Igor Mandric ◽  
Tommer Schwarz ◽  
Arunabha Majumdar ◽  
Kangcheng Hou ◽  
Leah Briscoe ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Statistical Power ◽  
Eqtl Analysis ◽  
Optimized Design ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific

Abstract Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.

scholarly journals Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

2019 ◽  
Author(s):  
Igor Mandric ◽  
Tommer Schwarz ◽  
Arunabha Majumdar ◽  
Richard Perez ◽  
Meena Subramaniam ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Statistical Power ◽  
Cost Savings ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
High Coverage ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific

AbstractSingle-cell RNA-sequencing (scRNA-Seq) is a compelling approach to simultaneously measure cellular composition and state which is impossible with bulk profiling approaches. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power.

scholarly journals Prime-seq, efficient and powerful bulk RNA-sequencing

2021 ◽  
Author(s):  
Aleksandar Janjic ◽  
Lucas Esteban Wange ◽  
Johannes Walter Bagnoli ◽  
Johanna Geuder ◽  
Phong Nguyen ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Statistical Power ◽  
Gene Expression Analysis ◽  
Measurement Accuracy ◽  
Rna Isolation ◽  
Rna Seq ◽  
Specialized Equipment ◽  
Library Complexity ◽  
Cost Efficient ◽  
Generation Sequencing

With the advent of Next Generation Sequencing, RNA-sequencing (RNA-seq) has become the major method for quantitative gene expression analysis. Reducing library costs by early barcoding has propelled single-cell RNA-seq, but has not yet caught on for bulk RNA-seq. Here, we optimized and validated a bulk RNA-seq method we call prime-seq. We show that with respect to library complexity, measurement accuracy, and statistical power it performs equivalent to TruSeq, a standard bulk RNA-seq method, but is four-fold more cost-efficient due to almost 50-fold cheaper library costs. We also validate a direct RNA isolation step that further improves cost and time-efficiency, show that intronic reads are derived from RNA, validate that prime-seq performs optimal with only 1,000 cells as input, and calculate that prime-seq is the most cost-efficient bulk RNA-seq method currently available. We discuss why many labs would profit from a cost-efficient early barcoding RNA-seq protocol and argue that prime-seq is well suited for setting up such a protocol as it is well validated, well documented, and requires no specialized equipment.

Power-Enhanced Funnel Plots for Meta-Analysis

2020 ◽  
Vol 228 (1) ◽  
pp. 43-49 ◽  
Author(s):  
Michael Kossmeier ◽  
Ulrich S. Tran ◽  
Martin Voracek
Keyword(s):  
Statistical Power ◽  
Meta Analysis ◽  
Relevant Information ◽  
Selective Reporting ◽  
Graphical Display ◽  
Funnel Plot ◽  
Evidential Value ◽  
Graphical Displays ◽  
Funnel Plots ◽  
Meta Analyses

Abstract. Currently, dedicated graphical displays to depict study-level statistical power in the context of meta-analysis are unavailable. Here, we introduce the sunset (power-enhanced) funnel plot to visualize this relevant information for assessing the credibility, or evidential value, of a set of studies. The sunset funnel plot highlights the statistical power of primary studies to detect an underlying true effect of interest in the well-known funnel display with color-coded power regions and a second power axis. This graphical display allows meta-analysts to incorporate power considerations into classic funnel plot assessments of small-study effects. Nominally significant, but low-powered, studies might be seen as less credible and as more likely being affected by selective reporting. We exemplify the application of the sunset funnel plot with two published meta-analyses from medicine and psychology. Software to create this variation of the funnel plot is provided via a tailored R function. In conclusion, the sunset (power-enhanced) funnel plot is a novel and useful graphical display to critically examine and to present study-level power in the context of meta-analysis.

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