---
title: "Simulating Spatial Cell-Cell Interactions"
output:
  BiocStyle::html_document:
  toc: true
  toc_depth: 2
vignette: >
  %\VignetteEngine{knitr::knitr}
  %\VignetteIndexEntry{Simulating Spatial Cell-Cell Interactions}
  %\usepackage[UTF-8]{inputenc}
---
```{r "setup", include=FALSE}
require("knitr")
opts_chunk$set(fig.width=4, fig.height=3)
```


# 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.

```{r help-cci}
library(scMultiSim)

scmultisim_help("cci")
```

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.

```{r cci-network}
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.

```{r}
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_)
```

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).

```{r plot-cell-loc, fig.width=4.5, fig.height=4}
plot_cell_loc(results)
```

The cell locations are available in `results$cci_locs`.

```{r print-cell-loc}
head(results$cci_locs)
```

# Session Information

```{r session-info}
sessionInfo()
```