MiSDEED: a synthetic data engine for microbiome study power analysis and study design

Philippe Chlenski; Melody Hsu; Itsik Pe'er

doi:10.1093/bioadv/vbac043

MiSDEED: a synthetic data engine for microbiome study power analysis and study design

Bioinform Adv. 2022 Jun 16;2(1):vbac043. doi: 10.1093/bioadv/vbac043. eCollection 2022.

Authors

Philippe Chlenski¹, Melody Hsu¹, Itsik Pe'er^{1

2

3}

Affiliations

¹ Department of Computer Science, Columbia University, New York, NY 10027, USA.
² Department of Systems Biology, Columbia University, New York, NY 10027, USA.
³ Data Science Institute, Columbia University, New York, NY 10027, USA.

Abstract

Summary: MiSDEED (Microbial Synthetic Data Engine for Experimental Design) is a command-line tool for generating synthetic longitudinal multinode data from simulated microbial environments. It generates relative-abundance timecourses under perturbations for an arbitrary number of time points, samples, locations and data types. All simulation parameters are exposed to the user to facilitate rapid power analysis and aid in study design. Users who want additional flexibility may also use MiSDEED as a Python package.

Availability and implementation: MiSDEED is written in Python and is freely available at https://github.com/pchlenski/misdeed.