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

• Published in: PLoS computational biology Vol. 14; no. 4; p. 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: Journal Article
• Language: English
• Published: United States Public Library of Science 19.04.2019; Public Library of Science (PLoS)
• 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; Performance assessment; Physical Sciences; R (Programming language); Research and Analysis Methods; Software; Statistical models; Supervision; Technology application; Transcription; United States > US; Baltimore Maryland
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006935

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/.