params <-
list(test = FALSE)

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE
)
library(BiocStyle)

## ----eval = FALSE-------------------------------------------------------------
#  if (!require("BiocManager")) {
#    install.packages("BiocManager")
#  }
#  BiocManager::install("spicyR")

## ----warning=FALSE, message=FALSE---------------------------------------------
# load required packages
library(spicyR)
library(ggplot2)
library(SpatialExperiment)
library(SpatialDatasets)
library(imcRtools)
library(dplyr)
library(survival)

## ----warning=FALSE, message=FALSE---------------------------------------------
kerenSPE <- SpatialDatasets::spe_Keren_2018()

## -----------------------------------------------------------------------------
spicyTestPair <- spicy(
  kerenSPE,
  condition = "tumour_type",
  from = "CD8_T_cell",
  to = "Neutrophils"
)

topPairs(spicyTestPair)

## -----------------------------------------------------------------------------
spicyTest <- spicy(
  kerenSPE,
  condition = "tumour_type"
)

topPairs(spicyTest)

## -----------------------------------------------------------------------------
bind(spicyTest)[1:5, 1:5]

## -----------------------------------------------------------------------------
signifPlot(
  spicyTest,
  breaks = c(-3, 3, 1),
  marksToPlot = c("Macrophages", "DC_or_Mono", "dn_T_CD3", "Neutrophils",
                  "CD8_T_cell", "Keratin_Tumour")
)

## -----------------------------------------------------------------------------
spicyBoxPlot(results = spicyTest, 
             # from = "Macrophages",
             # to = "dn_T_CD3"
             rank = 1)

## -----------------------------------------------------------------------------
kerenSPE <- imcRtools::buildSpatialGraph(kerenSPE, 
                                         img_id = "imageID", 
                                         type = "knn", k = 20,
                                        coords = c("x", "y"))

pairAbundances <- convPairs(kerenSPE,
                  colPair = "knn_interaction_graph")

head(pairAbundances["B_cell__B_cell"])

## -----------------------------------------------------------------------------
spicyTestColPairs <- spicy(
  kerenSPE,
  condition = "tumour_type",
  alternateResult = pairAbundances,
  weights = FALSE
)

topPairs(spicyTestColPairs)

## -----------------------------------------------------------------------------
signifPlot(
  spicyTestColPairs,
  breaks = c(-3, 3, 1),
  marksToPlot = c("Macrophages", "dn_T_CD3", "CD4_T_cell", 
                  "B_cell", "DC_or_Mono", "Neutrophils", "CD8_T_cell")
)

## -----------------------------------------------------------------------------
kerenSPE$event = 1 - kerenSPE$Censored
kerenSPE$survival = Surv(kerenSPE$`Survival_days_capped*`, kerenSPE$event)

## -----------------------------------------------------------------------------
# Running survival analysis
spicySurvival = spicy(kerenSPE,
                      condition = "survival")

# top 10 significant pairs
head(spicySurvival$survivalResults, 10)

## -----------------------------------------------------------------------------
# filter SPE object to obtain image 24 data
kerenSubset = kerenSPE[, colData(kerenSPE)$imageID == "24"]

pairwiseAssoc = getPairwise(kerenSubset, 
                            sigma = NULL, 
                            Rs = 100) |>
  as.data.frame()

pairwiseAssoc[["Keratin_Tumour__Neutrophils"]]

## -----------------------------------------------------------------------------
pairwiseAssoc = getPairwise(kerenSubset, 
                            sigma = 20, 
                            Rs = 100) |>
  as.data.frame()

pairwiseAssoc[["Keratin_Tumour__Neutrophils"]]

## -----------------------------------------------------------------------------
# obtain colData for image 24
cData = colData(kerenSPE) |> as.data.frame() |> 
          dplyr::filter(imageID == "24")

# obtain cells present in image 24
coords = spatialCoords(kerenSPE) |> as.data.frame()
coords$cellID = rownames(coords)
coords = coords |> dplyr::filter(cellID %in% cData$CellID)

cData$X = coords$x
cData$Y = coords$y

cData = cData |> 
  dplyr::mutate(cellTypeNew = ifelse(cellType %in% c("Keratin_Tumour", "Neutrophils"), 
                                     cellType, "Other"))

 pal = setNames(c("#d6b11c", "#850f07"), 
                c("Keratin_Tumour", "Neutrophils"))

ggplot() +
    stat_density_2d(data = cData, aes(x = X, y = Y, fill = after_stat(density)), 
                    geom = "raster", 
                    contour = FALSE) +
    geom_point(data = cData |> filter(cellType != "Other"),
               aes(x = X, y = Y, colour = cellTypeNew), size = 1) +
    scale_color_manual(values = pal) +
    scale_fill_distiller(palette = "Blues", direction = 1) +
    theme_classic() +
    labs(title = "image ID: 24")

## ----eval=FALSE---------------------------------------------------------------
#  spicyMixedTest <- spicy(
#    diabetesData,
#    condition = "stage",
#    subject = "case"
#  )

## -----------------------------------------------------------------------------
sessionInfo()