Cancerinsilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer

Thomas D. Sherman, Luciane T. Kagohara, Raymon Cao, Raymond Cheng, Matthew Satriano, Michael Considine, Gabriel Krigsfeld, Ruchira Ranaweera, Yong Tang, Sandra A. Jablonski, Genevieve Stein-O’Brien, Daria A. Gaykalova, Louis M. Weiner, Christine H. Chung, Elana J. Fertig

Research output: Contribution to journalArticlepeer-review

Abstract

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/.

Original languageEnglish (US)
Article numbere1006935
JournalPLoS computational biology
Volume15
Issue number4
DOIs
StatePublished - 2019

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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