Package: OncoSimulR 4.9.0

Ramon Diaz-Uriarte

OncoSimulR: Forward Genetic Simulation of Cancer Progression with Epistasis

Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Fitness (including just birth, just death, or both birth and death) can also be a function of the relative and absolute frequencies of other genotypes (i.e., frequency-dependent fitness). Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulating multi-species scenarios and therapeutic interventions, including adaptive therapy, is also possible. Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, additive, NK, Ising, and Eggbox models) and plotting them.

Authors:Ramon Diaz-Uriarte [aut, cre], Sergio Sanchez-Carrillo [aut], Juan Antonio Miguel Gonzalez [aut], Alberto Gonzalez Klein [aut], Javier Mu\~noz Haro [aut], Javier Lopez Cano [aut], Niklas Endres [ctb], Mark Taylor [ctb], Arash Partow [ctb], Sophie Brouillet [ctb], Sebastian Matuszewski [ctb], Harry Annoni [ctb], Luca Ferretti [ctb], Guillaume Achaz [ctb], Tymoteusz Wolodzko [ctb], Guillermo Gorines Cordero [ctb], Ivan Lorca Alonso [ctb], Francisco Mu\~noz Lopez [ctb], David Roncero Moro\~no [ctb], Alvaro Quevedo [ctb], Pablo Perez [ctb], Cristina Devesa [ctb], Alejandro Herrador [ctb], Holger Froehlich [ctb], Florian Markowetz [ctb], Achim Tresch [ctb], Theresa Niederberger [ctb], Christian Bender [ctb], Matthias Maneck [ctb], Claudio Lottaz [ctb], Tim Beissbarth [ctb], Sara Dorado Alfaro [ctb], Miguel Hernandez del Valle [ctb], Alvaro Huertas Garcia [ctb], Diego Ma\~nanes Cayero [ctb], Alejandro Martin Mu\~noz [ctb], Marta Couce Iglesias [ctb], Silvia Garcia Cobos [ctb], Carlos Madariaga Aramendi [ctb], Ana Rodriguez Ronchel [ctb], Lucia Sanchez Garcia [ctb], Yolanda Benitez Quesada [ctb], Asier Fernandez Pato [ctb], Esperanza Lopez Lopez [ctb], Alberto Manuel Parra Perez [ctb], Jorge Garcia Calleja [ctb], Ana del Ramo Galian [ctb], Alejandro de los Reyes Benitez [ctb], Guillermo Garcia Hoyos [ctb], Rosalia Palomino Cabrera [ctb], Rafael Barrero Rodriguez [ctb], Silvia Talavera Marcos [ctb]

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OncoSimulR.pdf |OncoSimulR.html
OncoSimulR/json (API)
NEWS

# Install 'OncoSimulR' in R:
install.packages('OncoSimulR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/rdiaz02/oncosimul/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • atex2b - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • atex4 - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • atex5 - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • benchmark_1 - Summary results from some benchmarks reported in the vignette.
  • benchmark_1_0.05 - Summary results from some benchmarks reported in the vignette.
  • benchmark_2 - Summary results from some benchmarks reported in the vignette.
  • benchmark_3 - Summary results from some benchmarks reported in the vignette.
  • ex_missing_drivers_b11 - An example where there are intermediate missing drivers.
  • ex_missing_drivers_b12 - An example where there are intermediate missing drivers.
  • examplePosets - Example posets
  • examplesFitnessEffects - Examples of fitness effects
  • mcfLs - McfLs simulation from the vignette
  • osi - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • osi_with_ints - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • s_3_a - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • s_3_b - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • simT2 - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • simT3 - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • simul_period_1 - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • smyelo3v57 - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • uvex2 - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • uvex3 - Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.
  • woAntibS - Runs from simulations of frequency-dependent examples shown in the vignette.

On BioConductor:OncoSimulR-4.7.8(bioc 3.20)OncoSimulR-4.6.1(bioc 3.19)

biologicalquestionsomaticmutation

6.92 score 6 stars 66 scripts 195 downloads 3 mentions 28 exports 68 dependencies

Last updated 23 days agofrom:3291e0a696. Checks:OK: 1 NOTE: 7 ERROR: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-win-x86_64NOTEOct 30 2024
R-4.5-linux-x86_64NOTEOct 30 2024
R-4.4-win-x86_64NOTEOct 30 2024
R-4.4-mac-x86_64NOTEOct 30 2024
R-4.4-mac-aarch64NOTEOct 30 2024
R-4.3-win-x86_64NOTEOct 30 2024
R-4.3-mac-x86_64NOTEOct 30 2024
R-4.3-mac-aarch64ERROROct 30 2024

Exports:allFitnessEffectsallMutatorEffectscreateInterventionscreateRulescreateUserVarsdiversityLODdiversityPOMevalAllGenotypesevalAllGenotypesFitAndMutevalAllGenotypesMutevalGenotypeevalGenotypeFitAndMutevalGenotypeMutLODMagellan_statsoncoSimulIndivoncoSimulPoponcoSimulSampleOncoSimulWide2LongplotClonePhylogplotFitnessLandscapeplotPosetPOMrfitnesssampledGenotypessamplePopsimOGraphto_Magellan

Dependencies:abindbackportsBiocGenericsbootbroomcarcarDataclicolorspacecowplotcpp11data.tableDerivdoBydplyrfansifarverFormulagenericsggplot2ggrepelgluegraphgtablegtoolsigraphisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6RColorBrewerRcppRcppEigenRgraphvizrlangscalessmatrSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression.

Rendered fromOncoSimulR.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2024-07-30
Started: 2016-08-19

Readme and manuals

Help Manual

Help pageTopics
Create fitness and mutation effects specification from restrictions, epistasis, and order effects.allFitnessEffects allMutatorEffects
Summary results from some benchmarks reported in the vignette.benchmark_1 benchmark_1_0.05 benchmark_2 benchmark_3
Function that checks and creates an specification for interventions.adapt_interventions_to_cpp check_double_id check_what_happens createInterventions transform_intervention verify_interventions
Functions that check and create specifications for user variables and rules.adapt_rules_to_cpp check_acttion check_double_rule_id check_same_name createRules createUserVars transform_rule verify_rules verify_user_vars
Evaluate fitness/mutator effects of one or all possible genotypes.evalAllGenotypes evalAllGenotypesFitAndMut evalAllGenotypesMut evalGenotype evalGenotypeFitAndMut evalGenotypeMut
An example where there are intermediate missing drivers.ex_missing_drivers_b11 ex_missing_drivers_b12
Example posetsexamplePosets
Examples of fitness effectsexamplesFitnessEffects
Runs from simulations of frequency-dependent examples shown in the vignette.woAntibS
mcfLs simulation from the vignettemcfLs
Simulate tumor progression for one or more individuals, optionally returning just a sample in time.oncoSimulIndiv oncoSimulPop oncoSimulSample print.oncosimul print.oncosimulpop summary.oncosimul summary.oncosimulpop
Convert the 'pops.by.time' component of an 'oncosimul' object into "long" format.OncoSimulWide2Long
Plot fitnessEffects objects.plot.fitnessEffects
Plot simulated tumor progression data.plot.oncosimul plot.oncosimulpop
Plot a parent-child relationship of the clones.plotClonePhylog
Plot a fitness landscape.plot.evalAllGenotypes plot.evalAllGenotypesMut plot.genotype_fitness_matrix plotFitnessLandscape
Plot a poset.plotPoset
Obtain Lines of Descent and Paths of the Maximum and their diversity from simulations.diversityLOD diversityPOM LOD LOD.oncosimul2 LOD.oncosimulpop POM POM.oncosimul2 POM.oncosimulpop
Posetposet
Generate random fitness.rfitness
Obtain a sample from a population of simulations.print.sampledGenotypes sampledGenotypes samplePop
Simulate oncogenetic/CBN/XMPN DAGs.simOGraph
Create output for MAGELLAN and obtain MAGELLAN statistics.Magellan_stats to_Magellan
Runs from simulations of interventions examples shown in the vignette. Most, but not all, are from intervention examples.atex2b atex4 atex5 osi osi_with_ints simT2 simT3 simul_period_1 smyelo3v57 s_3_a s_3_b uvex2 uvex3