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


CONCLUSIONS
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.
Department of Epidemiology and Biostatistics, VU University medical center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands. mark.vdwiel@vumc.nl.
Mark A
MA
van de Wiel
Maarten
M
Neerincx
Tineke E
TE
Buffart
Daoud
D
Sie
Henk M W
HM
Verheul
D001499:Bayes Theorem; D003110:Colonic Neoplasms / Q000235:genetics; D006801:Humans; D035683:MicroRNAs / Q000737:chemistry; D018401:Sample Size; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
D035683:MicroRNAs