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

Authors:
Luciane T Kagohara
Luciane T Kagohara
Johns Hopkins University School of Medicine
Raymond Cheng
Raymond Cheng
University of Washington
United States
Michael Considine
Michael Considine
School of Plant Biology
Gabriel Krigsfeld
Gabriel Krigsfeld
University of Pennsylvania
United States
Ruchira Ranaweera
Ruchira Ranaweera
Robert Wood Johnson Medical School
United States

PLoS Comput Biol 2018 Jun 19;14(4):e1006935. Epub 2019 Apr 19.

Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD United States of America.

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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http://dx.doi.org/10.1371/journal.pcbi.1006935DOI Listing
June 2018
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References

(Supplied by CrossRef)
Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications
Y Liang et al.
Brief. Bioinform 2017
Survival and Death Signals Can Predict Tumor Response to Therapy After Oncogene Inactivation
PT Tran et al.
Sci. Transl. Med 2011

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