---
title: "Introduction to Clustering of Local Indicators of Spatial Assocation (LISA) curves"
date: "`r BiocStyle::doc_date()`"
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
  email: ellis.patrick@sydney.edu.au
package: "`r BiocStyle::pkg_ver('spicyR')`"
vignette: >
  %\VignetteIndexEntry{"Inroduction to lisaClust"}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
output: 
  BiocStyle::html_document
editor_options: 
  markdown: 
    wrap: 72
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE, message = FALSE, warning = FALSE
)
library(BiocStyle)
```

# Installation

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

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

# Overview

Clustering local indicators of spatial association (LISA) functions is a
methodology for identifying consistent spatial organisation of multiple
cell-types in an unsupervised way. This can be used to enable the
characterization of interactions between multiple cell-types
simultaneously and can complement traditional pairwise analysis. In our
implementation our LISA curves are a localised summary of an L-function
from a Poisson point process model. Our framework `lisaClust` can be
used to provide a high-level summary of cell-type colocalization in
high-parameter spatial cytometry data, facilitating the identification
of distinct tissue compartments or identification of complex cellular
microenvironments.

# Quick start

## Generate toy data

TO illustrate our `lisaClust` framework, here we consider a very simple
toy example where two cell-types are completely separated spatially. We
simulate data for two different images.

```{r eval=T}
set.seed(51773)
x <- round(c(runif(200),runif(200)+1,runif(200)+2,runif(200)+3,
           runif(200)+3,runif(200)+2,runif(200)+1,runif(200)),4)*100
y <- round(c(runif(200),runif(200)+1,runif(200)+2,runif(200)+3,
             runif(200),runif(200)+1,runif(200)+2,runif(200)+3),4)*100
cellType <- factor(paste('c',rep(rep(c(1:2),rep(200,2)),4),sep = ''))
imageID <- rep(c('s1', 's2'),c(800,800))

cells <- data.frame(x, y, cellType, imageID)

ggplot(cells, aes(x,y, colour = cellType)) + geom_point() + facet_wrap(~imageID) + theme_minimal()


```

## Create Single Cell Experiment object

First we store our data in a `SingleCellExperiment` object.

```{r}

SCE <- SingleCellExperiment(colData = cells)
SCE
```

## Running lisaCLust

We can then use the convenience function `lisaClust` to simultaneously
calculate local indicators of spatial association (LISA) functions using
the `lisa` function and perform k-means clustering. The number of
clusters can be specified with the `k =` parameter. In the example
below, we've chosen `k = 2`, resulting in a total of 2 clusters.

These clusters are stored in `colData` of the `SingleCellExperiment`
object, as a new column with the column name `regions`.

```{r}
SCE <- lisaClust(SCE, k = 2)
colData(SCE) |> head()
```

## Plot identified regions

`lisaClust` also provides the convenient `hatchingPlot` function to
visualise the different regions that have been demarcated by the
clustering. `hatchingPlot` outputs a `ggplot` object where the regions
are marked by different hatching patterns. In a real biological dataset,
this allows us to plot both regions and cell-types on the same
visualization.

In the example below, we can visualise our stimulated data where our 2
cell types have been separated neatly into 2 distinct regions based on
which cell type each region is dominated by. `region_2` is dominated by
the red cell type `c1`, and `region_1` is dominated by the blue cell
type `c2`.

```{r}
hatchingPlot(SCE, useImages = c('s1','s2'))
```
## Using other clustering methods.

While the `lisaClust` function is convenient, we have not implemented an exhaustive
suite of clustering methods as it is very easy to do this yourself. There are 
just two simple steps.

### Generate LISA curves

We can calculate local indicators of spatial association (LISA) functions 
using the `lisa` function. Here the LISA curves are a 
localised summary of an L-function from a Poisson point process model. The radii 
that will be calculated over can be set with `Rs`.

```{r}
lisaCurves <- lisa(SCE, Rs = c(20, 50, 100))
```

### Perform some clustering

The LISA curves can then be used to cluster the cells. Here we use k-means 
clustering, other clustering methods like SOM could be used. We can store these 
cell clusters or cell "regions" in our `SingleCellExperiment` object using the 
`cellAnnotation() <-` function.

```{r}
# Custom clustering algorithm
kM <- kmeans(lisaCurves,2)

# Storing clusters into colData
colData(SCE)$custom_region <- paste('region',kM$cluster,sep = '_')
colData(SCE) |> head()
```




# Damond et al. islet data.

Next, we apply our `lisaClust` framework to three images of pancreatic
islets from *A Map of Human Type 1 Diabetes Progression by Imaging Mass
Cytometry* by Damond et al. (2019).

## Read in data

We will start by reading in the data and storing it as a
`SingleCellExperiment` object. Here the data is in a format consistent with
that outputted by CellProfiler.

```{r}
isletFile <- system.file("extdata","isletCells.txt.gz", package = "spicyR")
cells <- read.table(isletFile, header = TRUE)
damonSCE <- SingleCellExperiment(assay = list(intensities = t(cells[,grepl(names(cells), pattern = "Intensity_")])),
                                 colData = cells[,!grepl(names(cells), pattern = "Intensity_")]
                                 )

```

## Cluster cell-types

This data does not include annotation of the cell-types of each cell.
Here we extract the marker intensities from the `SingleCellExperiment` object
using `assay()`. We then perform k-means clustering with 10
clusters and store these cell-type clusters in our `SingleCellExperiment`
object using `colData() <-`.

```{r}
markers <- t(assay(damonSCE, "intensities"))
kM <- kmeans(markers,10)
colData(damonSCE)$cluster <- paste('cluster', kM$cluster, sep = '')
colData(damonSCE)[, c("ImageNumber", "cluster")] |> head()
```

## Generate LISA curves

As before, we can perform k-means clustering on the local
indicators of spatial association (LISA) functions using the `lisaClust`
function, remembering to specify the `imageID`, `cellType`, and `spatialCoords` 
columns in `colData`.

```{r}

damonSCE <- lisaClust(damonSCE, 
                      k = 2, 
                      Rs = c(10,20,50), 
                      imageID = "ImageNumber", 
                      cellType = "cluster",
                      spatialCoords = c("Location_Center_X", "Location_Center_Y"))

```

These regions are stored in `colData` and can be extracted.

```{r}
colData(damonSCE)[, c("ImageNumber", "region")] |>
  head(20)
```

## Examine cell type enrichment

`lisaClust` also provides a convenient function, `regionMap`, for examining which 
cell types are located in which regions. In this example, we use this to check
which cell types appear more frequently in each region than expected by chance.

Here, we clearly see that clusters 2, 5, 1, and 8 are highly concentrated in 
region 1, whilst all other clusters are thinly spread out across region 2.

We can further segregate these cells by increasing the number of clusters, ie.
increasing the parameter `k = ` in the `lisaClust()` function, but for the purposes
of demonstration, let's take a look at the `hatchingPlot` of these regions.

```{r}
regionMap(damonSCE, 
          imageID = "ImageNumber", 
          cellType = "cluster",
          spatialCoords = c("Location_Center_X", "Location_Center_Y"),
          type = "bubble")
```

## Plot identified regions

Finally, we can use `hatchingPlot` to construct a `ggplot` object where
the regions are marked by different hatching patterns. This allows us to
visualize the two regions and ten cell-types simultaneously.

```{r}
hatchingPlot(damonSCE, 
             cellType = "cluster",
             spatialCoords = c("Location_Center_X", "Location_Center_Y"))
```

# sessionInfo()

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