08_SpatialDeconvolution.qmd

---
title: "Spatial decon"
author:
  - Jamie Soul
format:
  html:
    self-contained: true
    theme: litera
    toc: true
editor: visual
code-block-bg: true
code-block-border-left: "#31BAE9"
---

# Spatial Deconvolution

## Load libraries

```{r}
#| output: false
library(NanoStringNCTools)
library(GeomxTools)
library(GeoMxWorkflows)
library(tidyverse)
library(SpatialDecon)
library(Seurat)
library(ComplexHeatmap)
library(SingleR)
library(celldex)
library(cowplot)
library(ggsci)
library(RColorBrewer)

source("src/utilityFunctions.R")

set.seed(123)
```

## Load the normalised data

```{r}
target_spatialData <- readRDS("results/normalisedSpatialData.RDS")
```

## Calculate background scores

```{r}
bg <- derive_GeoMx_background(norm = target_spatialData@assayData$q_norm,
                             probepool = fData(target_spatialData)$Module,
                             negnames = "NegProbe-WTX")


```

## Deconvolute using safeTME

```{r}
res <- runspatialdecon(object = target_spatialData,
                      norm_elt = "q_norm",
                      raw_elt = "exprs",
                      X = safeTME,
                      align_genes = TRUE,cellmerges=safeTME.matches)

dat <- t(res$beta)
colnames(dat) <- pData(target_spatialData)$Annotation

dat <- dat[ rowSums(dat)>0,]


columns <- c("segment", "Disease")
annotationColours <- mapply(function(column,colourSet) makeColours(pData(target_spatialData)[, column],colourSet),columns,c("nrc","Set2"),SIMPLIFY = FALSE)
names(annotationColours) <- columns

column_ha = HeatmapAnnotation( df = pData(target_spatialData)[, c("segment", "Disease")],col=annotationColours,show_legend = FALSE,annotation_name_gp= gpar(fontsize = 18))

col_fun = circlize::colorRamp2(c(-1, 0, 20), c("green", "white", "red"))
p <- Heatmap(dat, name="beta" ,show_column_names = FALSE, top_annotation=column_ha,col = col_fun, row_names_gp = grid::gpar(fontsize = 18))

saveRDS(p,"results/spatialDeconBroadHeatmap.RDS")

png("figures/Deconv/EndothelialCellTypeHeatmap_broad.png",width = 5,height=7,res=600,units="in")
p
dev.off()
p


```

## Deconvolute using skin specific profile

```{r}
skin <- download_profile_matrix(species = "Human",
                                       age_group = "Adult", 
                                       matrixname = "Skin_HCA")

res <- runspatialdecon(object = target_spatialData,
                      norm_elt = "q_norm",
                      raw_elt = "exprs",
                      X = skin,
                      align_genes = TRUE)

dat <- t(res$beta)
colnames(dat) <- pData(target_spatialData)$Annotation

dat <- dat[ rowSums(dat)>0,]
dat <- dat[c("Keratinocytes.1","T.cells"),]
rownames(dat) <- c("KRT","T Cells")

pData(target_spatialData)$Staining <- pData(target_spatialData)$segment

columns <- c("Staining", "Disease")
annotationColours <- mapply(function(column,colourSet) makeColours(pData(target_spatialData)[, column],colourSet),columns,c("nrc","Set2"),SIMPLIFY = FALSE)
names(annotationColours) <- columns

column_ha = HeatmapAnnotation( df = pData(target_spatialData)[, c("Staining", "Disease")],col=annotationColours,show_legend = FALSE,annotation_name_gp= gpar(fontsize = 15))

col_fun = circlize::colorRamp2(c(-1, 0, max(dat)), c("green", "white", "red"))
p <- Heatmap(dat, name="beta" ,show_column_names = FALSE, top_annotation=column_ha,col = col_fun, row_names_gp = grid::gpar(fontsize = 16))

saveRDS(p,"results/Fig1E_spatialDeconSkinHeatmap.RDS")

png("figures/Deconv/EndothelialCellTypeHeatmap_broad.png",width = 5,height=7,res=600,units="in")
p
dev.off()
p
```