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 Tsukamoto Kagohara, Raymon Cao, Raymond Cheng, Matthew Satriano, Michael Considine, Gabriel Krigsfeld, Ruchira Ranaweera, Yong Tang, Sandra A. Jablonski, Genevieve Stein-O'Brien, Daria Gaykalova, Louis M. Weiner, Christine H. Chung, Elana Fertig

Research output: Contribution to journalArticle

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)
Pages (from-to)e1006935
JournalPLoS computational biology
Volume14
Issue number4
DOIs
StatePublished - Jun 1 2018

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Statistical Modeling
Bioinformatics
Gene Expression Data
Gene expression
Mathematical Modeling
gene expression
Benchmarking
cancer
Cancer
Computational Biology
Gene Expression
bioinformatics
neoplasms
Cell
modeling
Neoplasms
molecular dynamics
cells
Molecular Dynamics Simulation
Mathematical models

ASJC Scopus subject areas

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

Cite this

CancerInSilico : An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer. / Sherman, Thomas D.; Tsukamoto Kagohara, Luciane; Cao, Raymon; Cheng, Raymond; Satriano, Matthew; Considine, Michael; Krigsfeld, Gabriel; Ranaweera, Ruchira; Tang, Yong; Jablonski, Sandra A.; Stein-O'Brien, Genevieve; Gaykalova, Daria; Weiner, Louis M.; Chung, Christine H.; Fertig, Elana.

In: PLoS computational biology, Vol. 14, No. 4, 01.06.2018, p. e1006935.

Research output: Contribution to journalArticle

Sherman, TD, Tsukamoto Kagohara, L, Cao, R, Cheng, R, Satriano, M, Considine, M, Krigsfeld, G, Ranaweera, R, Tang, Y, Jablonski, SA, Stein-O'Brien, G, Gaykalova, D, Weiner, LM, Chung, CH & Fertig, E 2018, 'CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer', PLoS computational biology, vol. 14, no. 4, pp. e1006935. https://doi.org/10.1371/journal.pcbi.1006935
Sherman, Thomas D. ; Tsukamoto Kagohara, Luciane ; Cao, Raymon ; Cheng, Raymond ; Satriano, Matthew ; Considine, Michael ; Krigsfeld, Gabriel ; Ranaweera, Ruchira ; Tang, Yong ; Jablonski, Sandra A. ; Stein-O'Brien, Genevieve ; Gaykalova, Daria ; Weiner, Louis M. ; Chung, Christine H. ; Fertig, Elana. / CancerInSilico : An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer. In: PLoS computational biology. 2018 ; Vol. 14, No. 4. pp. e1006935.
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