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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

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Feasibility of sample size calculation for RNA-seq studies.

Alicia Poplawski¹, Harald Binder¹

¹Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz, Langenbeckstr, Mainz, Germany.

Briefings in Bioinformatics

|January 20, 2017

Summary

Calculating sample size for RNA sequencing (RNA-seq) studies is challenging. Our evaluation found existing tools vary widely and perform poorly with small fold changes, recommending pilot data for reliable RNA-seq sample size estimation.

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Area of Science:

Genomics
Bioinformatics
Biostatistics

Background:

Sample size calculation is critical for robust study design but lacks established methods for RNA sequencing (RNA-seq).
Reliable RNA-seq sample size determination requires careful consideration of biological heterogeneity and fold changes.
Existing RNA-seq sample size tools need evaluation for practical feasibility and performance.

Purpose of the Study:

To evaluate the performance of existing RNA sequencing (RNA-seq) sample size calculation tools.
To determine if real pilot data is necessary for accurate sample size estimations in RNA-seq studies.
To identify RNA-seq sample size tools that perform well under varying biological heterogeneity and fold change conditions.

Main Methods:

Systematic literature search to identify RNA sequencing (RNA-seq) sample size tools.
Development of simulation scenarios based on real RNA sequencing data.
Evaluation of six selected RNA-seq sample size tools using simulated data with different biological heterogeneity and fold change levels.

Main Results:

Significant variability was observed in the sample size recommendations from the six evaluated RNA-seq tools.
Tool performance was strongly influenced by the magnitude of fold changes (FCs) between experimental conditions.
All evaluated tools demonstrated poor performance for small fold changes, indicating limitations in their applicability.
Some tools showed potential utility when pilot data closely matched study conditions and larger fold changes were expected.

Conclusions:

Current RNA sequencing (RNA-seq) sample size tools offer inconsistent results and are unreliable for small fold changes.
The use of real pilot data is recommended for more dependable RNA-seq sample size calculations, especially when anticipating larger fold changes.
Further development and validation of RNA-seq sample size methodologies are needed to address biological heterogeneity and varying effect sizes effectively.