Simulating Spatial Cell-Cell Interactions
scMultiSim can simulate spatial cell-cell interactions.
To do so, we need to provide the cci option as a list.
The following code will print more instructions on how to use the cci option.
library(scMultiSim)
scmultisim_help("cci")
##
## To enable simulating cell-cell interaction, the value should be a list including
## the following names:
##
## - params: (data.frame)
## The spatial effect between neighbor cells.
## It should be a data frame similar to the GRN parameter.
## - step.size: (number, optional)
## 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 1 -> 2
## and the branch length is 1 while the step size is 0.34, there will be totally
## three cell types: 1_2_1, 1_2_2, 1_2_3.
## - cell.type.interaction: ("random" or a matrix)
## The interaction level between different cell types.
## They act as factors multiplied to the ligand effect.
## Supply the string "random" to let scMultiSim generate these factors randomly.
## Otherwise, use cci_cell_type_params() to generate the template data structure.
## See the help of this method for more info.
## - max.neighbors: (integer from 1 to 4, optional)
## Constraint the maxinum number of neighbors with CCI for each cell.
## The neighbors with CCI will be randomly sampled.
##
## NULL
Now, we prepare a ligand-receptor interaction database.
This is pretty similar to the GRN network: it is a data frame with three columns,
specifying target, regulator, and effect, respectively.
The target and regulator columns should contain the IDs of the target and regulator genes.
In the following example, we have two ligand-receptor pairs interacting between two neighboring cells.
lig_params <- data.frame(
target = c(101, 102),
regulator = c(103, 104),
effect = c(5.2, 5.9)
)
We can now simulate the spatial cell-cell interactions.
In scMultiSim, the CCI network is cell-type based, which means that between each cell type pair,
we can have a different CCI network sampled from the database defined above.
Here, we set the step.size to 0.5, so the differentiation tree is divided into segments of length 0.5,
each segment is treated as a cell type in CCI.
We set cell.type.interaction to random, so the CCI network between each cell type pair is randomly sampled from the database.
Here, we use only 100 cells to speed up the simulation. Feel free to try a larger number of cells when running this vignette locally.
data(GRN_params_100)
set.seed(42)
options_ <- list(
GRN = GRN_params_100,
num.genes = 120,
num.cells = 80,
num.cifs = 20,
cif.sigma = 0.2,
tree = Phyla3(),
intrinsic.noise = 0.5,
cci = list(
params = lig_params,
max.neighbors = 4,
grid.size = 13,
cell.type.interaction = "random",
step.size = 0.5
)
)
results <- sim_true_counts(options_)
## CCI simulation is enabled.
## Get CIF...Done
## Get params...Done
## Simulating...50..
## Time spent: 2.15 mins
The results$cell_meta will contain the cell type information used in CCI.
We can plot the cell spatial locations using plot_cell_loc().
The arrows indicate cell-cell interactions between two cells (for the first ligand-receptor pair).
plot_cell_loc(results)
The cell locations are available in results$cci_locs.
head(results$cci_locs)
## x y
## cell1 6 6
## cell2 5 3
## cell3 5 7
## cell4 9 3
## cell5 9 4
## cell6 10 4
Session Information
sessionInfo()
## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dplyr_1.1.4 scMultiSim_1.1.0 knitr_1.46
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.4 generics_0.1.3
## [3] SparseArray_1.5.0 lattice_0.22-6
## [5] digest_0.6.35 magrittr_2.0.3
## [7] evaluate_0.23 grid_4.4.0
## [9] iterators_1.0.14 foreach_1.5.2
## [11] jsonlite_1.8.8 Matrix_1.7-0
## [13] ape_5.8 GenomeInfoDb_1.41.0
## [15] httr_1.4.7 fansi_1.0.6
## [17] UCSC.utils_1.1.0 scales_1.3.0
## [19] codetools_0.2-20 abind_1.4-5
## [21] cli_3.6.2 rlang_1.1.3
## [23] crayon_1.5.2 XVector_0.45.0
## [25] Biobase_2.65.0 commonmark_1.9.1
## [27] munsell_0.5.1 yaml_2.3.8
## [29] withr_3.0.0 DelayedArray_0.31.0
## [31] Rtsne_0.17 S4Arrays_1.5.0
## [33] tools_4.4.0 parallel_4.4.0
## [35] zeallot_0.1.0 colorspace_2.1-0
## [37] ggplot2_3.5.1 GenomeInfoDbData_1.2.12
## [39] SummarizedExperiment_1.35.0 BiocGenerics_0.51.0
## [41] assertthat_0.2.1 mime_0.12
## [43] vctrs_0.6.5 R6_2.5.1
## [45] matrixStats_1.3.0 stats4_4.4.0
## [47] lifecycle_1.0.4 zlibbioc_1.51.0
## [49] S4Vectors_0.43.0 IRanges_2.39.0
## [51] MASS_7.3-60.2 pkgconfig_2.0.3
## [53] pillar_1.9.0 gtable_0.3.5
## [55] glue_1.7.0 Rcpp_1.0.12
## [57] highr_0.10 xfun_0.43
## [59] tibble_3.2.1 GenomicRanges_1.57.0
## [61] tidyselect_1.2.1 KernelKnn_1.1.5
## [63] MatrixGenerics_1.17.0 farver_2.1.1
## [65] nlme_3.1-164 labeling_0.4.3
## [67] compiler_4.4.0 markdown_1.12