knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(BiocStyle)
# load required packages
library(spicyR)
library(ggplot2)

Installation

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.

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.

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().

spicyTest <- spicy(diabetesData, 
                   condition = "stage", 
                   subject = "case")
data("spicyTest")
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.

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().

data("spicyTestBootstrap")
spicyTestBootstrap <- spicy(diabetesData, 
                            condition = "stage", 
                            subject = "case",
                            from = "beta",
                            to = "Tc",
                            nsim = 1000)
spicyTestBootstrap

topPairs(spicyTestBootstrap)  

Indeed, we get improved statistical power compared to the previous method.

sessionInfo()

sessionInfo()


ellispatrick/spicyR documentation built on Sept. 10, 2021, 11:06 a.m.