Year: 2020
Type: article
Abstract: Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.
Source: Nature Communications
Institutions: Universitat Politècnica de València, University of Florida, Universidad Pública de Navarra (UPNA), King's College London, Science for Life Laboratory, Karolinska Institutet, Complejo Hospitalario de Navarra, Navarrabiomed, Ludwig-Maximilians-Universität München, Center for Integrated Protein Science Munich, King Abdullah University of Science and Technology, North-West University, University of Amsterdam, Institute of Food and Agricultural Sciences
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,380
OpenAlex ID: W3036926163