---
title: "2. Reconstruction and analysis of pancreatic islets from IMC data"
author: 
  - name: "Samuel Gunz"
    affiliation:
      - &DMLS Department of Molecular Life Sciences, University of Zurich, Switzerland
      - &SIB SIB Swiss Institute of Bioinformatics, University of Zurich, Switzerland
    email: "samuel.gunz@uzh.ch"
  - name: Mark D. Robinson
    affiliation:
      - *DMLS
      - *SIB
package: "`r BiocStyle::Biocpkg('sosta')`"
output:
  BiocStyle::html_document
abstract: >
  In this vignette we use `sosta` to reconstruct pancreatic islets of different diabetic stages from IMC data [@damondMapHumanType2019]. Based on the reconstruction we calculate structure metrics. Finally, we show how to do staticstical comparison of the metrics accounting for the correlation structure of this dataset.
vignette: >
  %\VignetteIndexEntry{Reconstruction and analysis of pancreatic islets from IMC data}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
bibliography: sosta.bib
editor_options: 
  chunk_output_type: console
---

```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)
set.seed(8048)
```

# Installation

The `r BiocStyle::Biocpkg('sosta')` package can be installed from Bioconductor as follows:

```{r installation, include = TRUE, eval=FALSE}
if (!requireNamespace("BiocManager")) {
    install.packages("BiocManager")
}
BiocManager::install("sosta")
```

# Setup

For this vignette, we will need several additional packages:

```{r setup, message=FALSE}
library("dplyr")
library("ExperimentHub")
library("ggplot2")
library("lme4")
library("lmerTest")
library("sf")
library("SpatialExperiment")
library("sosta")
library("tidyr")
theme_set(theme_bw())
```

# Introduction

In this vignette, we will use an imaging mass cytometry (IMC) dataset of pancreatic islets from human donors at different stages of type 1 diabetes (T1D) and healthy controls [@damondMapHumanType2019]. Note that we will only use a subset of the patients.

```{r loading, echo=TRUE, message=FALSE}
# load experiment hub
eh <- ExperimentHub()
oid <- names(eh[eh$title == "Damond_2019_Pancreas - sce - v1 - full"])

# Load single cell experiment object
spe <- eh[[oid]]
# Convert to spatial experiment object
spe <- toSpatialExperiment(spe,
    sample_id = "image_name",
    spatialCoordsNames = c("cell_x", "cell_y")
)
```


First, we plot the data for illustration. As we have multiple images per patient, we will subset to a few slides. As can be seen, the dimensions of the field of view are different for each image.

```{r}
df <- cbind(
    colData(spe[, spe$image_name %in% c("E04", "E03", "G01", "J02")]),
    spatialCoords(spe[, spe$image_name %in% c("E04", "E03", "G01", "J02")])
)
df |>
    as.data.frame() |>
    ggplot(aes(x = cell_x, y = cell_y, color = cell_category)) +
    geom_point(size = 0.25) +
    facet_wrap(~image_name, ncol = 2) +
    coord_equal()
```

The goal is to reconstruct / segment and quantify the pancreatic islets.

# Reconstruction of pancreatic islets

## Reconstruction of pancreatic islets for one image

In this example, we will reconstruct the islets based on the point pattern *density* of the islet cells. We will start with estimating the parameters that we use for reconstruction afterwards. For one image this can be illustrated as follows.

```{r}
shapeIntensityImage(
    spe,
    marks = "cell_category",
    imageCol = "image_name",
    imageId = "G01",
    markSelect = "islet"
)
```

We see the density (pixel-level) image on the left and a histogram of the intensity values on the right. The estimated threshold is roughly the mean between the two modes of the (truncated) pixel intensity distribution.

Note that the above calculation was done for one image. The function `estimateReconstructionParametersSPE` returns two plots with the estimated parameters for each image. The parameter `bndw` is the bandwidth parameter that is used for estimating the intensity profile of the point pattern. The parameter `thres` is the estimated parameter for the density threshold for reconstruction. We subset 20 images to speed up computation.

```{r}
n <- estimateReconstructionParametersSPE(
    spe,
    marks = "cell_category",
    imageCol = "image_name",
    markSelect = "islet",
    nImages = 20,
    plotHist = TRUE
)
```

We can inspect the relationship of the estimated bandwidth and threshold. 

```{r}
n |>
    ggplot(aes(x = bndw, y = thres)) +
    geom_point()
```


We note that the estimated bandwidth varies more than the estimated threshold. We will use the mean of the two estimated vectors as our parameters.

```{r}
(thresSPE <- mean(n$thres))
(bndwSPE <- mean(n$bndw))
```

## Reconstruction of pancreatic islets for all images

The function `reconstructShapeDensitySPE` reconstructs the islets for all images in the `spe` object. We use the estimated parameters from above. For computational reasons, we will subset to 10 images per patient for the rest of the vignette.

```{r, eval=TRUE}
# Sample 15 images per patient
sel <- colData(spe) |>
    as.data.frame() |>
    group_by(patient_id) |>
    select(image_name) |>
    sample_n(size = 10, replace = FALSE) |>
    pull(image_name)

# Select sampled images
spe <- spe[, spe$image_name %in% sel]

# Run on all images
allIslets <- reconstructShapeDensitySPE(
    spe,
    marks = "cell_category",
    imageCol = "image_name",
    markSelect = "islet",
    bndw = bndwSPE,
    thres = thresSPE,
    nCores = 1
)
```

The result is a (simple feature collection)[https://r-spatial.github.io/sf/articles/sf1.html]. This contains the polygons (`<GEOMETRY>` column), a structure identifier (`structID`) and the image identifier (`image_name`). Let's add some patient metadata to the object.

```{r}
colsKeep <- c(
    "patient_stage", "tissue_slide", "tissue_region",
    "patient_id", "patient_disease_duration",
    "patient_age", "patient_gender", "sample_id"
)

patientData <- colData(spe) |>
    as_tibble() |>
    group_by(image_name) |>
    select(all_of(colsKeep)) |>
    unique()

allIslets <- allIslets |>
    dplyr::left_join(patientData, by = "image_name")
```

We can now inspect the number of structures found per patient or image.

```{r}
allIslets |>
    st_drop_geometry() |>
    group_by(patient_id) |>
    summarise(n = n()) |>
    ungroup()
```

# Calculation of metrics

## Structure metrics

Now that we have islet structures for all images, we can now use the function `totalShapeMetrics` to calculate a set of metrics related to the shape of the islets.

```{r}
isletMetrics <- totalShapeMetrics(allIslets)
```

The result is a simple feature collection with polygons. We will add some patient level information to the simple feature collection for plotting afterwards.


```{r}
# specify factor levels
lv <- c("Non-diabetic", "Onset", "Long-duration")

allIslets <- allIslets |>
    cbind(t(isletMetrics)) |>
    mutate(patient_stage = factor(patient_stage, levels = lv))
```

# Investigate metrics

## Plot structure metrics

We use PCA to get an overview of the different features. Each dot represents one structure.

```{r}
library(ggfortify)

autoplot(
    prcomp(t(isletMetrics), scale. = TRUE),
    x = 1,
    y = 2,
    data = allIslets,
    color = "patient_stage",
    size = 2,
    # shape = 'type',
    loadings = TRUE,
    loadings.colour = "steelblue3",
    loadings.label = TRUE,
    loadings.label.size = 3,
    loadings.label.repel = TRUE,
    loadings.label.colour = "black"
) +
    scale_color_brewer(palette = "Dark2") +
    theme_bw() +
    coord_fixed()
```

We can use boxplots to investigate differences of shape metrics between patient stages. We will subset to a few metrics that are not colinear in the PCA plot. Note that the boxplots don't reveal patient specific effects.

```{r, fig.width=8, fig.height=6}
allIslets |>
    sf::st_drop_geometry() |>
    select(patient_stage, rownames(isletMetrics)) |>
    pivot_longer(-patient_stage) |>
    filter(name %in% c("Area", "Compactness", "Curl")) |>
    ggplot(aes(x = patient_stage, y = value, fill = patient_stage)) +
    geom_boxplot() +
    facet_wrap(~name, scales = "free") +
    scale_fill_brewer(palette = "Dark2") +
    scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
    guides(fill = "none")
```

## Testing using mixed effects models

As the individual structure level metrics are not independent we have to account for dependence between measurements. This dependence can lie on the level of the patient and the slide as we have repeated measurements for each level.

To account for this, we will use mixed linear models with random effects for the patient and the individual slides (image name). We will use the `r BiocStyle::CRANpkg('lme4')` package for fitting linear mixed effects models [@batesFittingLinearMixedEffects2015] and `r BiocStyle::CRANpkg('lmerTest')` for p-value calculation [@kuznetsovaLmerTestPackageTests2017]. 

To see differences between the Area of the islets between conditions, we can test as follows.

```{r}
mod <- lmer(Area ~ patient_stage + (1 | patient_id) + (1 | image_name), data = allIslets)
```


```{r}
summary(mod)
```

As we can see from `summary(mod)` there is a signification difference in islet area of long-duration patients with respect to non-diabetic patients, accounting for correlation on the patient and image level. Please note that this was only run on a subset of the data.

# Session Info

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

# References