by 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
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/
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