Package: scMultiSim 1.9.0

Hechen Li

scMultiSim: Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions

scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.

Authors:Hechen Li [aut, cre], Xiuwei Zhang [aut], Ziqi Zhang [aut], Michael Squires [aut]

scMultiSim_1.9.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
scMultiSim/json (API)

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

Bug tracker:https://github.com/zhanglabgt/scmultisim/issues

Pkgdown/docs site:https://zhanglabgt.github.io

Datasets:
  • dens_nonzero - This is the density function of log(x+1), where x is the non-zero values for ATAC-SEQ data
  • GRN_params_100 - 100_gene_GRN is a matrix of GRN params consisting of 100 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene ID
  • GRN_params_1139 - GRN_params_1139 is a matrix of GRN params consisting of 1139 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene ID

On BioConductor:scMultiSim-1.9.0(bioc 3.24)scMultiSim-1.8.0(bioc 3.23)

singlecelltranscriptomicsgeneexpressionsequencingexperimentaldesign

7.50 score 68 stars 29 scripts 26 exports 87 dependencies

Last updated from:792432a118. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING199
linux-devel-x86_64NOTE373
source / vignettesOK641
linux-release-x86_64NOTE346
macos-release-arm64NOTE169
macos-oldrel-arm64NOTE262
windows-develNOTE245
windows-releaseNOTE250
windows-oldrelNOTE247
wasm-releaseOK189

Exports:add_expr_noiseadd_outlierscci_cell_type_paramsdivide_batchesgen_cluttergene_corr_ccigene_corr_regulatorGet_1region_ATAC_correlationGet_ATAC_correlationPhyla1Phyla3Phyla5plot_cell_locplot_gene_module_cor_heatmapplot_gridplot_grnplot_phylaplot_rna_velocityplot_tsnerun_shinyscmultisim_helpsim_examplesim_example_spatialsim_true_countsTrue2ObservedATACTrue2ObservedCounts

Dependencies:abindapeassertthatBHBiobaseBiocGenericsBiocParallelbitopscaToolscliclusterGenerationcodacodetoolscombinatcommonmarkcpp11crayonDelayedArrayDEoptimdigestdoParalleldplyrexpmfarverfastmatchforeachformatRfutile.loggerfutile.optionsgenericsGenomicRangesggplot2gluegplotsgtablegtoolsigraphIRangesisobanditeratorsKernelKnnKernSmoothlabelinglambda.rlatticelifecyclelitedownmagrittrmapsmarkdownMASSMatrixMatrixGenericsmatrixStatsmnormtnlmenumDerivoptimParallelphangornphytoolspillarpkgconfigquadprogR6RColorBrewerRcppRcppArmadillorlangRtsneS4ArraysS4VectorsS7scalesscatterplot3dSeqinfosnowSparseArraySummarizedExperimenttibbletidyselectutf8vctrsviridisLitewithrxfunXVectorzeallot

Simulating Spatial Cell-Cell Interactions
Simulating Spatial Cell-Cell Interactions | Speeding up the Simulation | Spatial layouts | Built-in layouts | Custom layouts | Spatial domains | Spatially variable genes | Long-distance Cell-Cell Interactions | Session Information
Rendered fromspatialCCI.Rmdusingknitr::knitr

Last update: 2024-09-26
Started: 2024-09-26

Getting Started
Installation | Running Simulation | The Shiny app | Add technical noise and batch effect | Visualize the results
Rendered fromworkflow.Rmdusingknitr::knitr

Last update: 2024-09-02
Started: 2024-08-29

Simulating Multimodal Single-cell Datasets
Simulating True Counts | GRN and Differentiation Tree | Omitting the GRN | Running the Simulation | Accessing the Results | Visualizing the Results | Adding Technical Variation and Batch Effect | Adding technical noise | Adding batch effects | Adjusting Parameters | The Shiny App | Deciding Number of CIFs: num.cifs | Discrete Cell Population: discrete.cif | Adjusting the Effect of Cell Population: diff.cif.fraction | Adjusting the Inherent Cell Heterogeneity: cif.mean and cif.sigma | Adjusting the Intrinsic Noise: intinsic.noise | Adjust the effect of chromatin accessibility: atac.effect | Simulating Dynamic GRN | Session Information
Rendered frombasics.Rmdusingknitr::knitr

Last update: 2024-09-02
Started: 2023-05-18

Parameter Guide
Options: General | rand.seed | threads | speed.up | Options: Genes | GRN | num.genes | unregulated.gene.ratio | giv.mean, giv.sd, giv.prob | dynamic.GRN | hge.prop, hge.mean, hge.sd | hge.range | hge.max.var | Options: Cells | num.cells | tree | discrete.cif | discrete.min.pop.size, discrete.min.pop.index | discrete.pop.size | Options: CIF | num.cifs | diff.cif.fraction | cif.center, cif.sigma | use.impulse | Options: Simulation - ATAC | atac.effect | region.distrib | atac.p_zero | riv.mean, riv.sd, riv.prob | Customization | mod.cif.giv | ext.cif.giv | Optins: Simulation | vary | bimod | scale.s | intrinsic.noise | Options: Simulation - RNA Velocity | do.velocity | beta | d | num.cycles | cycle.len | Options: Simulation - Spatial Cell-Cell Interaction | grid.size | layout | step.size | params | cell.type.interaction | cell.type.lr.pairs | max.neighbors | radius | start.layer
Rendered fromoptions.Rmdusingknitr::knitr

Last update: 2024-09-02
Started: 2024-08-29

Readme and manuals

Help Manual

Help pageTopics
Add experimental noise to true countsadd_expr_noise
Add outliers to the observed countsadd_outliers
Generate cell-type level CCI parameterscci_cell_type_params
this is the density function of log(x+1), where x is the non-zero values for ATAC-SEQ datadens_nonzero
Divide batches for observed countsdivide_batches
generate a clutter of cells by growing from the centergen_clutter
Plot the ligand-receptor correlation summarygene_corr_cci
Print the correlations between targets of each regulatorgene_corr_regulator
This function gets the average correlation rna seq counts and region effect on genes for genes which are only associated with 1 chromatin regionGet_1region_ATAC_correlation
This function gets the average correlation rna seq counts and chromatin region effect on genesGet_ATAC_correlation
100_gene_GRN is a matrix of GRN params consisting of 100 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene IDGRN_params_100
GRN_params_1139 is a matrix of GRN params consisting of 1139 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene IDGRN_params_1139
Creating a linear example treePhyla1
Creating an example tree with 3 tipsPhyla3
Creating an example tree with 5 tipsPhyla5
Plot cell locationsplot_cell_loc
Plot the gene module correlation heatmapplot_gene_module_cor_heatmap
Plot the CCI gridplot_grid
Plot the GRN networkplot_grn
Plot a R phylogenic treeplot_phyla
Plot RNA velocity as arrows on tSNE plotplot_rna_velocity
Plot t-SNE visualization of a data matrixplot_tsne
Launch the Shiny App to configure the simulationrun_shiny
Show detailed documentations of scMultiSim's parametersscmultisim_help
Simulate a small example dataset with 200 cells and the 100-gene GRNsim_example
Simulate a small example dataset with 200 cells and the 100-gene GRN, with CCI enabledsim_example_spatial
Simulate true scRNA and scATAC counts from the parameterssim_true_counts
The class for spatial grids.SpatialGrid spatialGrid-class
Simulate observed ATAC-seq matrix given technical noise and the true countsTrue2ObservedATAC
Simulate observed count matrix given technical biases and the true countsTrue2ObservedCounts