S. Tarazona, D. Gómez-Cabrero, J. A. Westerhuis, A. Conesa
Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. In this study, we evaluate the quality parameters of such omic data sets that are critical for statistical power calculations.
We introduce the novel MultiPower method to estimate the optimal sample size in a multi-omics experiment and to assess the final statistical power of each omic dataset. MultiPower R package supports different data types, allow for equal or different sample size per omic, and incorporates practical tools to facilitate informed design decisions in multi-omic experiments. To illustrate MultiPower usage, we apply it to two multi-omic data sets with different characteristics to assess the statistical power provided by the available sample sizes.
Keywords: multi-omic experiments, statistical power, optimal sample size
Scheduled
BIO-2 Biostatistics
September 6, 2019 3:30 PM
I3L8. Georgina Blanes building