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• W2940375021 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/." @default.
• W2940375021 created "2019-04-25" @default.
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• W2940375021 date "2019-04-19" @default.
• W2940375021 modified "2023-10-17" @default.
• W2940375021 title "CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer" @default.
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• W2940375021 doi "https://doi.org/10.1371/journal.pcbi.1006935" @default.
• W2940375021 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6504085" @default.
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• W2940375021 hasPublicationYear "2019" @default.
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