params <-
list(test = FALSE)

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

## ----warning=FALSE, message=FALSE---------------------------------------------
# load required packages
library(simpleSeg)
library(ggplot2)
library(EBImage)
library(cytomapper)

## ---- eval = FALSE------------------------------------------------------------
#  # Install the package from Bioconductor
#  if (!requireNamespace("BiocManager", quietly = TRUE)) {
#      install.packages("BiocManager")
#  }
#  
#  BiocManager::install("simpleSeg")

## -----------------------------------------------------------------------------

# Get path to image directory
pathToImages <- system.file("extdata", package = "simpleSeg")

# Get directories of images

imageDirs <- dir(pathToImages, "Point", full.names = TRUE)
names(imageDirs) <- dir(pathToImages, "Point", full.names = FALSE)





# Get files in each directory
files <- files <- lapply(
  imageDirs,
  list.files,
  pattern = "tif",
  full.names = TRUE
)

# Read files with readImage from EBImage
images <- lapply(files, EBImage::readImage, as.is = TRUE)

# Convert to cytoImageList
images <- cytomapper::CytoImageList(images)
mcols(images)$imageID <- names(images)




## -----------------------------------------------------------------------------

masks <- simpleSeg::simpleSeg(images,
                              nucleus = "HH3",
                              transform = "sqrt")

## -----------------------------------------------------------------------------
# Visualise segmentation performance one way.
EBImage::display(colorLabels(masks[[1]]))


## -----------------------------------------------------------------------------

# Visualise segmentation performance another way.
cytomapper::plotPixels(image = images[1], 
                       mask = masks[1], 
                       img_id = "imageID", 
                       colour_by = c("PanKRT", "GLUT1", "HH3", "CD3", "CD20"), 
                       display = "single",
                       colour = list(HH3 = c("black","blue"), 
                                     CD3 = c("black","purple"),
                                     CD20 = c("black","green"),
                                     GLUT1 = c("black", "red"),
                                     PanKRT = c("black", "yellow")),
                       bcg = list(HH3 = c(0, 1, 1.5), 
                                     CD3 = c(0, 1, 1.5),
                                     CD20 = c(0, 1, 1.5),
                                     GLUT1 = c(0, 1, 1.5),
                                     PanKRT = c(0, 1, 1.5)),
                       legend = NULL)



## ----parallel example---------------------------------------------------------
masks <- simpleSeg::simpleSeg(images,
                              nucleus = "HH3",
                              cores = 1)

## ---- out.width = "400px"-----------------------------------------------------
cellSCE <- cytomapper::measureObjects(masks, images, img_id = "imageID")

## -----------------------------------------------------------------------------
# Transform and normalise the marker expression of each cell type.
# Use a square root transform, then trimmed the 99 quantile 
cellSCE <- normalizeCells(cellSCE,
                          assayIn = "counts",
                          assayOut = "norm",
                          imageID = "imageID",
                          transformation = "sqrt",
                          method = c("trim99", "minMax"))

## ---- fig.width=5, fig.height=5-----------------------------------------------

# Extract marker data and bind with information about images
df <- as.data.frame(cbind(colData(cellSCE), t(assay(cellSCE, "counts"))))

# Plots densities of PanKRT for each image.
ggplot(df, aes(x = PanKRT, colour = imageID)) + 
  geom_density() + 
  labs(x = "PanKRT expression") +
  theme_minimal()


## ---- fig.width=5, fig.height=5-----------------------------------------------

# Extract normalised marker information.
df <- as.data.frame(cbind(colData(cellSCE), t(assay(cellSCE, "norm"))))

# Plots densities of normalised PanKRT for each image.
ggplot(df, aes(x = PanKRT, colour = imageID)) + 
  geom_density() + 
  labs(x = "PanKRT expression") +
  theme_minimal()






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