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ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs

Mark A van de Wiel12*, Maarten Neerincx3, Tineke E Buffart3, Daoud Sie4 and Henk MW Verheul3

Author Affiliations

1 Department of Epidemiology and Biostatistics, VU University medical center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands

2 Department of Mathematics, VU University, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands

3 Department of Medical Oncology, VU University medical center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands

4 Department of Pathology, VU University medical center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands

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BMC Bioinformatics 2014, 15:116  doi:10.1186/1471-2105-15-116

Published: 26 April 2014

Abstract

Background

Complex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of random effects and multi-parameter inference. Moreover, when sample sizes are small, inference is likely to be too liberal when, in a Bayesian setting, applying a non-appropriate prior or to lack power when not carefully borrowing information across features.

Results

We show on microRNA sequencing data from a clinical cancer study how our software ShrinkBayes tackles the aforementioned challenges. In addition, we illustrate its comparatively good performance on multi-parameter inference for groups using a data-based simulation. Finally, in the small sample size setting, we demonstrate its high power and improved FDR estimation by use of Gaussian mixture priors that include a point mass.

Conclusion

ShrinkBayes is a versatile software package for the analysis of count-based sequencing data, which is particularly useful for studies with small sample sizes or complex designs.

Keywords:
Differential expression; Shrinkage; Sequencing; Bayesian analysis