4. Parameter Guide

rand.seed

integer (default: 0)

scMultiSim should produce the same result if all other parameters are the same.

threads

integer (default: 1)

Use multithreading only when generating the CIF matrix. It will not speed up the simulation a lot, thus not recommended.

speed.up

logical (default: FALSE)

Enable experimental speed-up mode. It is recommended to enable this option, and it will be the default in the future. Currently, it is disabled for reproducibility.

GRN

A data frame with 3 columns as below. Supply NA to disable the GRN effect. (required)

If num.genes presents, the gene IDs should not exceed this number. The gene IDs should start from 1 and should not ship any intermidiate numbers.

Two sample datasets GRN_params_100 and GRN_params_1000 from Dibaeinia, P., & Sinha, S. (2020) are provided for testing and inspection.

num.genes

integer (default: NULL)

If a GRN is supplied, override the total number of genes. It should be larger than the largest gene ID in the GRN. Otherwise, the number of genes will be determined by N_genes * (1 + r_u), where r_u is unregulated.gene.ratio.

If GRN is disabled, this option specifies the total number of genes.

unregulated.gene.ratio

number > 0 (default: 0.1)

Ratio of unreulated to regulated genes. When a GRN is supplied with N genes, scMultiSim will simulate N * r_u extra (unregulated) genes.

giv.mean, giv.sd, giv.prob

(default: 0, 1, 0.3)

The parameters used to sample the GIV matrix. With probability giv.prob, the value is sampled from N(giv.mean, giv.sd). Otherwise the value is 0.

dynamic.GRN

list (default: NULL)

Enables dynamic (cell-specific GRN). Run scmultisim_help("dynamic.GRN") to see more explaination.

hge.prop, hge.mean, hge.sd

(default: 0, 5, 1)

Treat some random genes as highly-expressed (house-keeping) genes. A proportion of hge.prop genes will have expression scaled by a multiplier sampled from N(hge.mean, hge.sd).

hge.range

integer (default: 1)

When selecting highly-expressed genes, only choose genes with ID > hge.range.

hge.max.var

number (default: 500)

When selecting highly-expressed genes, only choose genes with variation < hge.max.var.

num.cells

integer (default: 1000)

The number of cells to be simulated.

tree

phylo (default: Phyla5())

The cell differential tree, which will be used to generate cell trajectories (if discrete.cif = T) or clusters (if discrete.cif = F). In discrete population mode, only the tree tips will be used. Three demo trees, Phyla5(), Phyla3() and Phyla1(), are provided.

discrete.cif

logical (default: FALSE)

Whether the cell population is discrete (continuous otherwise).

discrete.min.pop.size, discrete.min.pop.index

integer, integer (default: 70, 1)

In discrete population mode, specify one cluster to have the smallest cell population. The cluster will contain discrete.min.pop.size cells. discrete.min.pop.index should be a valid cluster index (tree tip number).

discrete.pop.size

integer vector (default: NA); e.g. c(200, 250, 300)

Manually specify the size of each cluster.

num.cifs

integer (default: 50)

Total number of differential and non-differential CIFs, which can be viewed as latent representation of cells.

diff.cif.fraction

number (default: 0.9)

Fraction of differential CIFs. Differential CIFs encode the cell type information, while non-differential CIFs are randomly sampled for each cell.

cif.center, cif.sigma

(default: 1, 0.1)

The distribution used to sample CIF values.

use.impulse

logical (default: FALSE)

In continuous population mode, when sampling CIFs along the tree, use the impulse model rather than the default gaussian random walk.

atac.effect

number ∈ [0, 1] (default: 0.5)

The influence of chromatin accessability data on gene expression.

region.distrib

vector of length 3, should sum to 1 (default: c(0.1, 0.5, 0.4))

The probability that a gene is regulated by 0, 1, 2 consecutive regions, respectively.

atac.p_zero

number ∈ [0, 1] (default: 0.8)

The proportion of zeros we see in the simulated scATAC-seq data.

riv.mean, riv.sd, riv.prob

(default: 0, 1, 0.3)

The parameters used to sample the RIV (Region Identity Vectors). With probability riv.prob, the value is sampled from N(riv.mean, riv.sd). Otherwise the value is 0.

mod.cif.giv

function (default: NULL)

Modify the generated CIF and GIV. The function takes four arguments: the kinetic parameter index (1=kon, 2=koff, 3=s), the current CIF matrix, the GIV matrix, and the cell metadata dataframe. It should return a list of two elements: the modified CIF matrix and the modified GIV matrix.

sim_true_counts(list(
    # ...
    mod.cif.giv = function(i, cif, giv, meta) {
        # modify cif and giv
        return(list(cif, giv))
    }
))

ext.cif.giv

function (default: NULL)

Add extra CIF and GIV. The function takes one argument, the kinetic parameter index (1=kon, 2=koff, 3=s). It should return a list of two elements: the extra CIF matrix (n_extra_cif x n_cells) and the GIV matrix (n_genes x n_extra_cif). Return NULL for no extra CIF and GIV.”

sim_true_counts(list(
    # ...
    ext.cif.giv = function(i) {
        # add extra cif and giv
        return(list(extra_cif, extra_giv))
    }
))

vary

character (default: "s")

Can be "all", "kon", "koff", "s", "except_kon", "except_koff", "except_s". It specifies which kinetic parameters to vary across cells, i.e. which kinetic parameters have differential CIFs sampled from the tree.

bimod

number (default: 0)

A number between 0 and 1, which adjust the bimodality of the gene expression distribution.

scale.s

number (default: 1)

Manually scale the final s parameter, thus the gene expression. When discrete.cif = T, it can be a vector specifying the scale.s for each cluster. In this case, you can use smaller value for cell types known to be small (like naive cells).

intrinsic.noise

number (default: 1)

A number between 0 and 1, which specify the weight of the random sample from the Beta-Poisson distribution.

       0 <----------------------> 1
Theoritical mean          Random sample from
                      Beta-Poisson distribution

do.velocity

logical (default: FALSE)

When set to TRUE, simulate using the full kinetic model and generate RNA velocity data. Otherwise, the Beta-Poission model will be used.

beta

number (default: 0.4)

The splicing rate of each gene in the kinetic model.

d

number (default: 1)

The degradation rate of each gene in the kinetic model.

num.cycles

number (default: 3)

The number of cycles run before sampling the gene expression of a cell.

cycle.len

number (default: 1)

In velocity mode, a multiplier for the cell cycle length. It is multiplied by the expected time to transition from k_on to k_off and back to form the the length of a cycle.

grid.size

integer

Manually specify the width and height of the grid.

layout

“enhanced”, “layers”, “islands”, or a function (default: "enhanced")

Specify the layout of the cell types. scMultiSim provides three built-in layouts: "enhanced", "layers", and "islands".

If set to "islands", you can specify which cell types are the islands, e.g. "islands:1,2".

If using a custom function, it should take two arguments: function (grid_size, cell_types) - grid_size: (integer) The width and height of the grid. - cell_types: (integer vector) Each cell’s cell type.

It should return a n_cell x 2 matrix, where each row is the x and y coordinates of a cell.

step.size

number

If using continuous population, use this step size to further divide the cell types on the tree. For example, if the tree only has one branch a -> b and the branch length is 1 while the step size is 0.34, there will be totally three cell types: a_b_1, a_b_2, a_b_3.

params

data.frame

The spatial effect between a ligand and a receptor gene. It should be a data frame similar to the GRN parameter, i.e. with columns receptor, ligand, and effect.

Example:

cci = list(
  params = data.frame(
    target    = c(2,   6,   10,   8, 20,  30),
    regulator = c(101, 102, 103, 104, 105, 106),
    effect    = 20
  )
)

cell.type.interaction

“random” or a matrix

Specify which cell types can communicate using which ligand-receptor pair. It should be a 3d n_cell_types x n_cell_types x n_ligand_pair numeric matrix. The value at (i, j, k) is 1 if there exist CCI of LR-pair k between cell type i and cell type j.

This matrix can be generated using the cci_cell_type_params() function. It can fill the matrix randomly, or return an empty matrix for you to fill manually. If you want to fill it randomly, you can simply supply "random" for this option.

cell.type.lr.pairs

integer vector

If cell.type.interaction is "random", specify how many LR pairs should be enabled between each cell type pair. Should be a range, e.g. 4:6. The actual number of LR pairs will be uniformly sampled from this range.

max.neighbors

integer

The number of interacting cells for each cell. If the cell’s available neighbor count is not large enough, the actual interacting cells may be smaller than this value.

radius

number (default: 1), or “gaussian:sigma”

Which cells should be considered as neighbors. The interacting cells are those within these neighbors.

When it is a number, it controls the maximum distance between two cells for them to interact.

When it is a string, it should be in the format gaussian:sigma, for example, gaussian:1.2. In this case, the probability of two cells interacting is proportional to the distance with a Gaussian kernel applied.

start.layer

integer

From which layer (time step) the simulation should start. If set to 1, the simulation will start with one cell in the grid and add one more cell in each following layer. If set to num_cells, the simulation will start from all cells available in the grid and only continues for a few static layers, which will greatly speed up the simulation.