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

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 perfor...

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Bibliographic Details
Published in:PLoS computational biology 2019-04, Vol.14 (4), p.e1006935-e1006935
Main Authors: Sherman, Thomas D, Kagohara, Luciane T, Cao, Raymon, Cheng, Raymond, Satriano, Matthew, Considine, Michael, Krigsfeld, Gabriel, Ranaweera, Ruchira, Tang, Yong, Jablonski, Sandra A, Stein-O'Brien, Genevieve, Gaykalova, Daria A, Weiner, Louis M, Chung, Christine H, Fertig, Elana J
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Applied mathematics
Bioinformatics
Biology and Life Sciences
Cancer
Cancer research
Cell culture
Cell cycle
Computational biology
Computational Biology - methods
Computer and Information Sciences
Computer Simulation
Datasets
Engineering and Technology
Gene Expression
Genetic aspects
Genomes
Genomics
Ground truth
Humans
Mathematical analysis
Mathematical models
Methods
Multiplication & division
Neoplasms - pathology
Oncology
Physical Sciences
R (Programming language)
Research and Analysis Methods
Software
Statistical models
Supervision
Technology application
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Summary: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/.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1006935