Package: MOSim 2.8.0

Sonia Tarazona

MOSim: Multi-Omics Simulation (MOSim)

MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups.

Authors:Carolina Monzó [aut], Carlos Martínez [aut], Sonia Tarazona [cre, aut]

MOSim_2.8.0.tar.gz
MOSim_2.8.0.zip(r-4.7-x86_64)MOSim_2.8.0.zip(r-4.6-x86_64)MOSim_2.8.0.zip(r-4.5-x86_64)
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MOSim_2.8.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MOSim/json (API)

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

Bug tracker:https://github.com/conesalab/mosim/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On BioConductor:MOSim-2.9.0(bioc 3.24)MOSim-2.8.0(bioc 3.23)

softwaretimecourseexperimentaldesignrnaseqcpp

6.76 score 12 stars 10 scripts 394 downloads 4 mentions 28 exports 186 dependencies

Last updated from:00c58b76aa (on RELEASE_3_23). Checks:1 ERROR, 11 WARNING, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
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source / vignettesOK580
linux-release-arm64WARNING680
linux-release-x86_64WARNING763
macos-release-arm64WARNING514
macos-release-x86_64WARNING876
macos-oldrel-arm64WARNING539
macos-oldrel-x86_64WARNING814
windows-develWARNING838
windows-releaseWARNING713
windows-oldrelWARNING674
wasm-releaseOK203

Exports:calculate_mean_per_list_dfcheck_patternsdiscretizeexperimentalDesignmake_association_dataframemake_cluster_patternsmatch_gene_regulatormatch_gene_regulator_clustermosimomicDataomicResultsomicSettingsomicSimplotProfilerandom_unif_intervalsc_mosimsc_omicDatasc_omicResultssc_omicSettingssc_param_estimationshuffle_group_matrixsimulate_coexpressionsparsim_create_simulation_parametersparsim_estimate_intensitysparsim_estimate_library_sizesparsim_estimate_parameter_from_datasparsim_estimate_variabilitysparsim_simulation

Dependencies:abindaskpassassortheadbase64encbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularBiostringsbitopsblusterbslibcachemcaToolscliclustercodetoolscommonmarkcowplotcpp11crayoncrosstalkcurldata.tableDelayedArraydeldirdigestdotCall64dplyrdqrngedgeRevaluatefarverfastDummiesfastmapfastmatchfitdistrplusFNNfontawesomeformatRfsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomicRangesggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolshereHiddenMarkovhighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglambda.rlaterlatticelazyevallifecyclelimmalistenvlmtestlocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemetapodmimeminiUInlmeopensslotelparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppRollRcppTOMLreshape2reticulateRhtslibrlangrmarkdownROCRrprojrootRsamtoolsRSpectrarsvdRtsneS4ArraysS4VectorsS7sassScaledMatrixscalesscattermorescransctransformscuttleSeqinfoSeuratSeuratObjectshinySignacSingleCellExperimentsitmosnowsourcetoolsspspamSparseArraysparseMatrixStatsspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstatmodstringistringrSummarizedExperimentsurvivalsystensortibbletidyrtidyselecttinytexUCSC.utilsutf8uwotvctrsviridisLitewithrxfunxtableXVectoryamlzoo

Simulating Single-Cell Multi-Omics Data with MOSim
Introduction | Installation | Simulating Single-Cell Multi-Omics Data | Data Preparation | Providing Custom Data | Running the Simulation: sc_mosim | Default sc_mosim Simulation | Customizing the sc_mosim Simulation | Working with Simulation Results | The sc_mosim Simulation Object | Retrieving Simulation Settings | Accessing the Count Data Matrices | How to cite MOSim
Rendered fromscMOSim.Rmdusingknitr::rmarkdown

Last update: 2025-04-02
Started: 2023-07-21

Simulating bulk Multi-Omics Data with MOSim
mosim | Abstract | Introduction | Getting started | MOSim input parameters | Running the simulation: | Provided STATegra dataset | Providing custom data: omicData | Changing omic settings: omicSim | Working with simulation results | The simulation object | Retrieving the simulation settings: omicSettings | Accessing the count data matrices: omicResults | Obtaining the experimental design matrix: experimentalDesign | Plotting results: plotProfile | Advanced use cases | Negative binomial variance | Transforming regulator data to 1/0 format | Special simulation case: Transcription Factors | How to cite MOSim
Rendered fromMOSim.Rmdusingknitr::rmarkdown

Last update: 2025-03-27
Started: 2017-05-24

Readme and manuals

Help Manual

Help pageTopics
Data to showcase scRNA and scATAC-seq associationassociationList
calculate_mean_per_list_dfcalculate_mean_per_list_df
check_patternscheck_patterns
Discretize ChIP-Seq counts to simulate a binary datasetdiscretize
Retrieves the experimental designexperimentalDesign
Check if a variable is declared.is.declared
make_association_dataframemake_association_dataframe
make_cluster_patternsmake_cluster_patterns
match_gene_regulatormatch_gene_regulator
match_gene_regulator_clustermatch_gene_regulator_cluster
mosimmosim
Set customized data for an omic.omicData
Retrieves the simulated data.omicResults
Retrieves the settings used in a simulationomicSettings
Set the simulation settings for an omic.omicSim
Generate a plot of a feature's profile for one or two omics.plotProfile
random_unif_interval Function to call the C code This function is a copy of the `random_unif_interval` function from the `SPARSim` package (v0.9.5), originally developed by Giacomo Baruzzo, Ilaria Patuzzi, Barbara Di Camillo (2020). The original package is licensed under the GPL-3 license.random_unif_interval
Default datasampleData
sc_mosimsc_mosim
sc_omicDatasc_omicData
sc_omicResultssc_omicResults
sc_omicSettingssc_omicSettings
sc_param_estimationsc_param_estimation
Data to test scMOSimscatac
Data to test scMOSimscrna
shuffle_group_matrix, Reorder cell type-specific expression matrix during co-expression simulation. Copied from ACORDE (https://github.com/ConesaLab/acorde) to facilitate stability and running within our scripts This function is a slightly modified copy of the `shuffle_group_matrix` function from the `Acorde` package (v1.0.0), originally developed by Arzalluz-Luque A, Salguero P, Tarazona S, Conesa A. (2022). acorde unravels functionally interpretable networks of isoform co-usage from single cell data. Nature communications 1828. DOI: 10.1038/s41467-022-29497-w. The original package is licensed under the GPL-3 license.shuffle_group_matrix
simulate coexpressionsimulate_coexpression
Simulate technical variabilitysimulate_hyper
Create SPARSim simulation parametersparsim_create_simulation_parameter
Estimate SPARSIm "intensity" parametersparsim_estimate_intensity
Estimate SPARSim "library size" parametersparsim_estimate_library_size
Estimate SPARSim simulation parameter from a given count tablesparsim_estimate_parameter_from_data
Estimate SPARSim "variability" parametersparsim_estimate_variability
Function to simulate a raw count tablesparsim_simulation
Data to extract human TFTF_human