RT Journal Article
SR Electronic
T1 Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 328807
DO 10.1101/328807
A1 Thomas D Sherman
A1 Luciane T Kagohara
A1 Raymon Cao
A1 Raymond Cheng
A1 Matthew Satriano
A1 Michael Considine
A1 Gabriel Krigsfeld
A1 Ruchira Ranaweera
A1 Yong Tang
A1 Sandra A Jablonski
A1 Genevieve Stein-O’Brien
A1 Daria A Gaykalova
A1 Louis M Weiner
A1 Christine H Chung
A1 Elana J Fertig
YR 2018
UL http://biorxiv.org/content/early/2018/06/28/328807.abstract
AB 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.