Function reference • scMultiSim

Simulation

Functions for simulating single-cell data.

sim_true_counts(): Simulate true scRNA and scATAC counts from the parameters

add_expr_noise(): Add experimental noise to true counts

divide_batches(): Divide batches for observed counts

add_outliers(): Add outliers to the observed counts

Visualization

Functions for visualizing the results.

plot_cell_loc(): Plot cell locations

plot_gene_module_cor_heatmap(): Plot the gene module correlation heatmap

plot_grid(): Plot the CCI grid

plot_grn(): Plot the GRN network

plot_phyla(): Plot a R phylogenic tree

plot_rna_velocity(): Plot RNA velocity as arrows on tSNE plot

plot_tsne(): Plot t-SNE visualization of a data matrix

gene_corr_cci(): Plot the ligand-receptor correlation summary

gene_corr_regulator(): Print the correlations between targets of each regulator

Help

Functions for getting help.

run_shiny(): Launch the Shiny App to configure the simulation

scmultisim_help(): Show detailed documentations of scMultiSim's parameters

Utilities

Utility functions that can be useful for simulating data.

cci_cell_type_params(): Generate cell-type level CCI parameters

gen_clutter(): generate a clutter of cells by growing from the center

Data

Default data provided by scMultiSim

Phyla1(): Creating a linear example tree

Phyla3(): Creating an example tree with 3 tips

Phyla5(): Creating an example tree with 5 tips

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

dens_nonzero: this is the density function of log(x+1), where x is the non-zero values for ATAC-SEQ data

gene_len_pool: a pool of gene lengths to sample from

len2nfrag: from transcript length to number of fragments (for the nonUMI protocol)

param_realdata.zeisel.imputed: distribution of kinetic parameters learned from the Zeisel UMI cortex datasets

Internal helpers

Internal helper functions, but can be useful for advanced customization.

Get_1region_ATAC_correlation(): This function gets the average correlation rna seq counts and region effect on genes for genes which are only associated with 1 chromatin region

Get_ATAC_correlation(): This function gets the average correlation rna seq counts and chromatin region effect on genes

True2ObservedATAC(): Simulate observed ATAC-seq matrix given technical noise and the true counts

True2ObservedCounts(): Simulate observed count matrix given technical biases and the true counts

sim_example_200_cells(): Simulate a small example dataset with 200 cells and the 100-gene GRN

sim_example_200_cells_spatial(): Simulate a small example dataset with 200 cells and the 100-gene GRN, with CCI enabled