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Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico
release_pxh7k2p6r5a5taoyyopehnuwbu
by
Thomas D Sherman,
Luciane Tsukamoto Kagohara,
Raymon Cao,
Raymond Cheng,
Matthew Satriano,
Michael Considine,
Gabriel Krigsfeld,
Ruchira Ranaweera,
Yong Tang,
Sandra Jablonski,
Genevieve Stein-O'Brien,
Daria Gaykalova
(+3 others)
Released
as a post
by Cold Spring Harbor Laboratory.
2018
Abstract
Motivation: 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. Results: 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 cell-based mathematical model, implemented for an off-lattice, cell-center Monte Carlo mathematical model. We also adapt this model to simulate the impact of growth suppression by targeted therapeutics in cancer and benchmark simulations against bulk in vitro experimental data. Sensitivity to parameters is evaluated and used to predict the relative impact of variation in cellular growth parameters and cell types on tumor heterogeneity in therapeutic response. Availability and Implementation: CancerInSilico is implemented in an R/Bioconductor package by the same name. Applications presented are available from https://github.com/FertigLab/CancerInSilico-Figures.
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Date 2018-05-23
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Date 2018-05-23
DOI
10.1101/328807
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