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/.
| Original language | English (US) |
|---|---|
| Pages (from-to) | e1006935 |
| Journal | PLoS computational biology |
| Volume | 14 |
| Issue number | 4 |
| DOIs | |
| State | Published - Jun 1 2018 |
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ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Modeling and Simulation
- Ecology
- Molecular Biology
- Genetics
- Cellular and Molecular Neuroscience
- Computational Theory and Mathematics
Cite this
CancerInSilico : An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer. / Sherman, Thomas D.; Tsukamoto Kagohara, Luciane; Cao, Raymon; Cheng, Raymond; Satriano, Matthew; Considine, Michael; Krigsfeld, Gabriel; Ranaweera, Ruchira; Tang, Yong; Jablonski, Sandra A.; Stein-O'Brien, Genevieve; Gaykalova, Daria; Weiner, Louis M.; Chung, Christine H.; Fertig, Elana.
In: PLoS computational biology, Vol. 14, No. 4, 01.06.2018, p. e1006935.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - CancerInSilico
T2 - An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer
AU - Sherman, Thomas D.
AU - Tsukamoto Kagohara, Luciane
AU - Cao, Raymon
AU - Cheng, Raymond
AU - Satriano, Matthew
AU - Considine, Michael
AU - Krigsfeld, Gabriel
AU - Ranaweera, Ruchira
AU - Tang, Yong
AU - Jablonski, Sandra A.
AU - Stein-O'Brien, Genevieve
AU - Gaykalova, Daria
AU - Weiner, Louis M.
AU - Chung, Christine H.
AU - Fertig, Elana
PY - 2018/6/1
Y1 - 2018/6/1
N2 - 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/.
AB - 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/.
UR - http://www.scopus.com/inward/record.url?scp=85065776237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065776237&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1006935
DO - 10.1371/journal.pcbi.1006935
M3 - Article
C2 - 31002670
AN - SCOPUS:85065776237
VL - 14
SP - e1006935
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 4
ER -