---
title: "Spatial Mixed-Effects Modelling with spicy"
date: "`r BiocStyle::doc_date()`"
params:
  test: FALSE
author:
- name: Nicolas Canete
  affiliation:  
  - &WIMR Westmead Institute for Medical Research, University of Sydney, Australia
  email: nicolas.canete@sydney.edu.au
- name: Ellis Patrick
  affiliation:
  - &WIMR Westmead Institute for Medical Research, University of Sydney, Australia
  - School of Mathematics and Statistics, University of Sydney, Australia
package: "`r BiocStyle::pkg_ver('spicyR')`"
vignette: >
  %\VignetteIndexEntry{"Introduction to spicy"}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output: 
  BiocStyle::html_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(BiocStyle)
```


```{r warning=FALSE, message=FALSE}
# load required packages
library(spicyR)
library(ggplot2)
```
 
# Installation

```{r, eval = FALSE}
if (!require("BiocManager"))
    install.packages("BiocManager")
BiocManager::install("spicyR")
```


# Overview
This guide will provide a step-by-step guide on how mixed effects models can be 
applied to multiple segmented and labelled images to identify how the 
localisation of different cell types can change across different conditions. 
Here, the subject is modelled as a random effect, and the different conditions 
are modelled as a fixed effect.

# Example data
Here, we use a subset of the Damond et al 2019 imaging mass cytometry dataset. We will compare 
the spatial distributions of cells in the pancreatic islets of individuals with early onset diabetes and healthy controls. 

`diabetesData` is a `SegmentedCells` object containing single-cell data of 160 images 
from 8 subjects, with 20 images per subjects.

`cellSummary()` returns a `DataFrame` object providing the location (`x` and `y`) 
and cell type (`cellType`) of each cell and the image it belongs to (`imageID`).

`imagePheno()` returns a `tibble` object providing the corresponding subject 
(`subject`) and condition (`condition`) for each image.






```{r}
data("diabetesData")
diabetesData
cellSummary(diabetesData)
imagePheno(diabetesData)
```

In this data set, cell types include immune cell types (B cells, naive T cells,
T Helper cells, T cytotoxic cells, neutrophils, macrophages) and pancreatic islet
cells (alpha, beta, gamma, delta).

# Mixed Effects Modelling

To investigate changes in colocalisation between two different cell types, we 
measure the level of colocalisation between two cell types by modelling with the 
`Lcross()` function in the `spatstat` package. Specifically, the mean difference 
between the obtained function and the theoretical function is used as a measure
for the level of colocalisation. Differences of this statistic between two 
conditions is modelled using a weighted mixed effects model, with condition as 
the fixed effect and subject as the random effect.
spicyTestBootstrap
## Testing for change in colocalisation for a specific pair of cells

Firstly, we can see whether one cell type tends to be around another cell type 
in one condition compared to the other. This can be done using the `spicy()` 
function, where we include `condition`, and `subject`. In this example, we want 
to see whether or not Delta cells (`to`) tend to be found around Beta cells (`from`)
in onset diabetes images compared to non-diabetic images.

```{r message=FALSE}
spicyTestPair <- spicy(diabetesData, 
                       condition = "stage", 
                       subject = "case", 
                       from = "beta", 
                       to = "delta")
spicyTestPair
topPairs(spicyTestPair)
```

We obtain a `spicy` object which details the results of the mixed effects 
modelling performed. As the `coefficient` in `spicyTest` is positive, we find 
that Th cells cells are more likely to be found around beta cells in the onset
diabetes group compared to the non-diabetic control.

## Test for change in colocalisation for all pairwise cell combinations

Here, we can perform what we did above for all pairwise combinations of cell 
types by excluding the `from` and `to` parameters from `spicy()`.

```{r eval=FALSE}
spicyTest <- spicy(diabetesData, 
                   condition = "stage", 
                   subject = "case")
```

```{r}
data("spicyTest")
```


```{r}
spicyTest
topPairs(spicyTest)  
```

Again, we obtain a `spicy` object which outlines the result of the mixed effects 
models performed for each pairwise combination if cell types.

We can represent this as a heatmap using the `spatialMEMMultiPlot()` function by 
providing it the `spicy` object obtained.
```{r}
signifPlot(spicyTest, 
           breaks=c(-3, 3, 0.5),
           marksToPlot = c("alpha", "beta", "gamma", "delta", 
                           "B", "naiveTc", "Th", "Tc", "neutrophil", "macrophage"))
```

## Bootstrapping with spicy
There are multiple ways for calculating p-values for mixed effects models. We 
have also implemented a bootstrapping approach. All that is needed is a choice 
for the number of resamples used in the bootstrap which can be set with the 
`nsim` parameter in `spicy()`.

```{r}
data("spicyTestBootstrap")
```


```{r eval=FALSE, warning=FALSE, message=FALSE}
spicyTestBootstrap <- spicy(diabetesData, 
                            condition = "stage", 
                            subject = "case",
                            from = "beta",
                            to = "Tc",
                            nsim = 1000)
```

```{r}
spicyTestBootstrap

topPairs(spicyTestBootstrap)  
```
Indeed, we get improved statistical power compared to the previous method.


# sessionInfo()

```{r}
sessionInfo()
```