Spectre - simple spatial analysis

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<- Back to Spectre home page

Introduction

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Here we provide instructions for analysing spatial data after cell segmentation as part of Spectre’s simple segmentation and spatial analysis workflow. This workflow is split into 3x scripts, which can be run after performing cell segmentation:

1. Extract cellular data from ROIs and masks
2. Cellular analysis
3. Spatial analysis

Setup

---

If you go to https://github.com/ImmuneDynamics/Spectre you can download the repository, including the analysis workflow scripts.

You can find the simple spatial analysis workflow scripts in this folder.

You will need a folder called ‘data’ containing a folder called ‘ROIs’. Each ROI should be a folder with a unique name, and the TIFF files for that ROI should be in the corresponding folder.

Within the ‘data’ folder, you will need a folder called ‘masks’. Each of your mask types should be stored here – with the ROI name, and then some pattern that can separate which type of mask it is.

You will also need a ‘metadata’ folder containing a CSV file called ‘sample.metadata’. In this file you should include a column called ‘ROI’ with the name of each ROI, and then as many other columns as you like containing metadata for each ROI. We recommend including ‘Sample’ (e.g. if multiple ROIs are taken from one sample, you can note this here), ‘Group’ (the experiment group each ROI belongs to), and ‘Batch’ (the sample preparation/acquisition batch for the ROI – this could be some kind of reference, or a date).

Script 1. Extract cellular data

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Load libraries

…

###################################################################################
### Spectre: spatial 1 - add masks and extract cellular data
###################################################################################

First, load the Spectre package and associated packages.

    ### Load libraries

    library('Spectre')

    Spectre::package.check(type = 'spatial')
    Spectre::package.load(type = 'spatial')

Next, set directories.

    ### Set PrimaryDirectory

    dirname(rstudioapi::getActiveDocumentContext()$path)
    setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
    getwd()
    PrimaryDirectory <- getwd()
    PrimaryDirectory

    ### Set InputDirectory (ROI TIFFs)

    setwd(PrimaryDirectory)
    setwd("../data/ROIs/")
    InputDirectory <- getwd()
    InputDirectory

    ### Set MaskDirectory (ROI mask TIFFs)

    setwd(PrimaryDirectory)
    setwd("../data/masks")
    MaskDirectory <- getwd()
    MaskDirectory

    ### Create output directory

    setwd(PrimaryDirectory)
    dir.create("Output 1 - add masks")
    setwd("Output 1 - add masks")
    OutputDirectory <- getwd()
    OutputDirectory

## Warning in dir.create("Output 1 - add masks"): 'Output 1 - add masks' already
## exists

## [1] "/Users/thomasa/OneDrive - The University of Sydney (Staff)/Library/Github (public)/ImmuneDynamics.github.io/spectre/protocols/Spatial-simple/Results/Output 1 - add masks"

Check ROIs and TIFFs

###################################################################################
### Check ROIs and TIFFs
###################################################################################

### Initialise the spatial data object with channel TIFF files

    setwd(InputDirectory)

    rois <- list.dirs(full.names = FALSE, recursive = FALSE)
    as.matrix(rois)

##      [,1]
## [1,] "ROI002"
## [2,] "ROI004"
## [3,] "ROI006"
## [4,] "ROI008"
## [5,] "ROI010"
## [6,] "ROI012"

    ### Check channel names

    tiff.list <- list()

    for(i in rois){
      setwd(InputDirectory)
      setwd(i)
      tiff.list[[i]] <- list.files(getwd())
    }

    t(as.data.frame(tiff.list))

##        [,1]               [,2]              [,3]             [,4]
## ROI002 "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff"
## ROI004 "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff"
## ROI006 "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff"
## ROI008 "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff"
## ROI010 "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff"
## ROI012 "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff"
##        [,5]              [,6]              [,7]              [,8]
## ROI002 "CD45_Sm152.tiff" "CD8a_Dy162.tiff" "DNA1_Ir191.tiff" "DNA3_Ir193.tiff"
## ROI004 "CD45_Sm152.tiff" "CD8a_Dy162.tiff" "DNA1_Ir191.tiff" "DNA3_Ir193.tiff"
## ROI006 "CD45_Sm152.tiff" "CD8a_Dy162.tiff" "DNA1_Ir191.tiff" "DNA3_Ir193.tiff"
## ROI008 "CD45_Sm152.tiff" "CD8a_Dy162.tiff" "DNA1_Ir191.tiff" "DNA3_Ir193.tiff"
## ROI010 "CD45_Sm152.tiff" "CD8a_Dy162.tiff" "DNA1_Ir191.tiff" "DNA3_Ir193.tiff"
## ROI012 "CD45_Sm152.tiff" "CD8a_Dy162.tiff" "DNA1_Ir191.tiff" "DNA3_Ir193.tiff"

Read in TIFF files

###################################################################################
### Read in TIFF files and create spatial objects
################################################################################### 

    ### Read in ROI channel TIFFs

    setwd(InputDirectory)
    spatial.dat <- read.spatial.files(dir = InputDirectory)

As the files are read in, you will see feedback like this:

## Reading TIFFs from:/Users/thomasa/OneDrive - The University of Sydney (Staff)/Library/Github (public)/Spectre/workflows/Spatial - simple/data/ROIs

##   ...with extent correction

##   ...with y-axis orientation flipping

## Reading ROI: ROI002

##   -- reading single band in TIFF:CD11b_Sm149.tiff

##   -- reading single band in TIFF:CD20_Dy161.tiff

##   -- reading single band in TIFF:CD3_Er170.tiff

##   -- reading single band in TIFF:CD4_Gd156.tiff

##   -- reading single band in TIFF:CD45_Sm152.tiff

##   -- reading single band in TIFF:CD8a_Dy162.tiff

##   -- reading single band in TIFF:DNA1_Ir191.tiff

##   -- reading single band in TIFF:DNA3_Ir193.tiff

    ### Check results

    str(spatial.dat, 3)

## List of 6
##  $ ROI002:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  : list()
##   .. ..@ DATA   : list()
##  $ ROI004:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  : list()
##   .. ..@ DATA   : list()
##  $ ROI006:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  : list()
##   .. ..@ DATA   : list()
##  $ ROI008:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  : list()
##   .. ..@ DATA   : list()
##  $ ROI010:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  : list()
##   .. ..@ DATA   : list()
##  $ ROI012:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  : list()
##   .. ..@ DATA   : list()

    spatial.dat[[1]]@RASTERS

## class      : RasterStack
## dimensions : 501, 500, 250500, 8  (nrow, ncol, ncell, nlayers)
## resolution : 1, 1  (x, y)
## extent     : 0, 500, 0, 501  (xmin, xmax, ymin, ymax)
## crs        : NA
## names      : CD11b_Sm149, CD20_Dy161, CD3_Er170, CD4_Gd156, CD45_Sm152, CD8a_Dy162, DNA1_Ir191, DNA3_Ir193
## min values :           0,          0,         0,         0,          0,          0,          0,          0
## max values :          33,        779,        41,        40,        734,        109,       1670,       3007

Read in masks files

###################################################################################
### Read in masks files
###################################################################################

    ### Define cell mask extension for different mask types

        setwd(MaskDirectory)

        all.masks <- list.files(pattern = '.tif')
        as.matrix(all.masks)

##       [,1]
##  [1,] "ROI002_Cell_mask.tiff"
##  [2,] "ROI002_Cytoplasm_mask.tiff"
##  [3,] "ROI002_Nuclei_mask.tiff"
##  [4,] "ROI004_Cell_mask.tiff"
##  [5,] "ROI004_Cytoplasm_mask.tiff"
##  [6,] "ROI004_Nuclei_mask.tiff"
##  [7,] "ROI006_Cell_mask.tiff"
##  [8,] "ROI006_Cytoplasm_mask.tiff"
##  [9,] "ROI006_Nuclei_mask.tiff"
## [10,] "ROI008_Cell_mask.tiff"
## [11,] "ROI008_Cytoplasm_mask.tiff"
## [12,] "ROI008_Nuclei_mask.tiff"
## [13,] "ROI010_Cell_mask.tiff"
## [14,] "ROI010_Cytoplasm_mask.tiff"
## [15,] "ROI010_Nuclei_mask.tiff"
## [16,] "ROI012_Cell_mask.tiff"
## [17,] "ROI012_Cytoplasm_mask.tiff"
## [18,] "ROI012_Nuclei_mask.tiff"

        mask.types <- list('cell.mask' = '_Cell_mask.tiff')

        mask.types

## $cell.mask
## [1] "_Cell_mask.tiff"

    ### Read in masks

        for(i in names(mask.types)){
              spatial.dat <- do.add.masks(dat = spatial.dat,
                                          mask.dir = MaskDirectory,
                                          mask.pattern = mask.types[[i]],
                                          mask.label = i)
        }

        str(spatial.dat, 3)

## List of 6
##  $ ROI002:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI004:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI006:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI008:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI010:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI012:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()

        str(spatial.dat[[1]]@MASKS, 3)

## List of 1
##  $ cell.mask:List of 1
##   ..$ maskraster:Formal class 'RasterLayer' [package "raster"] with 12 slots

Rename rasters (if required)

###################################################################################
### Rename rasters (if required)
###################################################################################

Here you can check the consistency of the raster names, and if required, adjust those names.

    ### Check channel names

        channel.names <- list()

        for(i in names(spatial.dat)){
          channel.names[[i]] <- names(spatial.dat[[i]]@RASTERS)
        }

        t(as.data.frame(channel.names))

##        [,1]          [,2]         [,3]        [,4]        [,5]
## ROI002 "CD11b_Sm149" "CD20_Dy161" "CD3_Er170" "CD4_Gd156" "CD45_Sm152"
## ROI004 "CD11b_Sm149" "CD20_Dy161" "CD3_Er170" "CD4_Gd156" "CD45_Sm152"
## ROI006 "CD11b_Sm149" "CD20_Dy161" "CD3_Er170" "CD4_Gd156" "CD45_Sm152"
## ROI008 "CD11b_Sm149" "CD20_Dy161" "CD3_Er170" "CD4_Gd156" "CD45_Sm152"
## ROI010 "CD11b_Sm149" "CD20_Dy161" "CD3_Er170" "CD4_Gd156" "CD45_Sm152"
## ROI012 "CD11b_Sm149" "CD20_Dy161" "CD3_Er170" "CD4_Gd156" "CD45_Sm152"
##        [,6]         [,7]         [,8]
## ROI002 "CD8a_Dy162" "DNA1_Ir191" "DNA3_Ir193"
## ROI004 "CD8a_Dy162" "DNA1_Ir191" "DNA3_Ir193"
## ROI006 "CD8a_Dy162" "DNA1_Ir191" "DNA3_Ir193"
## ROI008 "CD8a_Dy162" "DNA1_Ir191" "DNA3_Ir193"
## ROI010 "CD8a_Dy162" "DNA1_Ir191" "DNA3_Ir193"
## ROI012 "CD8a_Dy162" "DNA1_Ir191" "DNA3_Ir193"

 ### List of corrections (first entry is the 'correct' one)

        # corrections <- list(c('CD4','Cd4'),
        #                     c('CD8','CD8a')
        #                     )

    ### Replace the 'incorrect' names

        # for(i in names(spatial.dat)){
        #   # i <- names(spatial.dat)[[1]]
        #
        #   for(a in c(1:length(corrections))){
        #     # a <- 1
        #
        #     trg <- which(names(spatial.dat[[i]]@RASTERS) == corrections[[a]][2])
        #     if(length(trg) != 0){
        #       names(spatial.dat[[i]]@RASTERS)[trg] <- corrections[[a]][1]
        #     }
        #   }
        # }

    ### Check channel names

        # channel.names <- list()
        #
        # for(i in names(spatial.dat)){
        #   channel.names[[i]] <- names(spatial.dat[[i]]@RASTERS)
        # }
        #
        # t(as.data.frame(channel.names))      

Generate outlines

###################################################################################
### Generate polygons and outlines
###################################################################################

    ### Generate polygons and outlines

        for(i in names(mask.types)){
          spatial.dat <- do.create.outlines(dat = spatial.dat, mask.name = i)
        }

    ### Checks

        str(spatial.dat, 3)

## List of 6
##  $ ROI002:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI004:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI006:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI008:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI010:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()
##  $ ROI012:Formal class 'spatial' [package "Spectre"] with 3 slots
##   .. ..@ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
##   .. ..@ MASKS  :List of 1
##   .. ..@ DATA   : list()

        str(spatial.dat[[1]]@MASKS, 2)

## List of 1
##  $ cell.mask:List of 4
##   ..$ maskraster:Formal class 'RasterLayer' [package "raster"] with 12 slots
##   ..$ polygons  :Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
##   ..$ outlines  :'data.frame':   93518 obs. of  7 variables:
##   ..$ centroids :Formal class 'SpatialPoints' [package "sp"] with 3 slots

Mask QC plots

###################################################################################
### Mask QC plots
###################################################################################      

Here you can choose a ‘base’ raster to use for the spatial plot, and which mask you would like to plot.

    ### Mask plot setup

        setwd(OutputDirectory)
        dir.create('Plots - cell masks')
        setwd('Plots - cell masks')

        as.matrix(names(spatial.dat[[1]]@RASTERS))

##      [,1]
## [1,] "CD11b_Sm149"
## [2,] "CD20_Dy161"
## [3,] "CD3_Er170"
## [4,] "CD4_Gd156"
## [5,] "CD45_Sm152"
## [6,] "CD8a_Dy162"
## [7,] "DNA1_Ir191"
## [8,] "DNA3_Ir193"

        base <- 'DNA1_Ir191'
        base

## [1] "DNA1_Ir191"

        as.matrix(names(spatial.dat[[1]]@MASKS))

##      [,1]
## [1,] "cell.mask"

        mask <- 'cell.mask'
        mask    

## [1] "cell.mask"

    ### Create plots

        for(i in names(spatial.dat)){
          make.spatial.plot(dat = spatial.dat,
                            image.roi = i,
                            image.channel = base,
                            mask.outlines = mask)
        }

You will see plots that look like this for each ROI, allowing you to assess the suitability of the mask.

    ### Create plots

        for(i in names(spatial.dat)[1]){
          make.spatial.plot(dat = spatial.dat,
                            image.roi = i,
                            image.channel = base,
                            mask.outlines = mask)
        }

Calculate cellular data

###################################################################################
### Calculate cellular data and plot
###################################################################################   

    ### Calculate cellular data for each cell mask (this step may take some time)

        spatial.dat <- do.extract(spatial.dat, 'cell.mask')
        str(spatial.dat, 3)

        spatial.dat[[1]]@DATA

    ### Factor plot setup

        setwd(OutputDirectory)
        dir.create('Plots - factors')
        setwd('Plots - factors')

        as.matrix(names(spatial.dat))

##      [,1]
## [1,] "ROI002"
## [2,] "ROI004"
## [3,] "ROI006"
## [4,] "ROI008"
## [5,] "ROI010"
## [6,] "ROI012"

        plot.rois <- names(spatial.dat)[c(1:2)]
        plot.rois

## [1] "ROI002" "ROI004"

        as.matrix(names(spatial.dat[[1]]@RASTERS))

##      [,1]
## [1,] "CD11b_Sm149"
## [2,] "CD20_Dy161"
## [3,] "CD3_Er170"
## [4,] "CD4_Gd156"
## [5,] "CD45_Sm152"
## [6,] "CD8a_Dy162"
## [7,] "DNA1_Ir191"
## [8,] "DNA3_Ir193"

        base <- 'DNA1_Ir191'
        base

## [1] "DNA1_Ir191"

        plot.factors <- names(spatial.dat[[1]]@MASKS)[-which('cell.mask' == names(spatial.dat[[1]]@MASKS))]
        plot.factors

## character(0)

        plot.exp <- names(spatial.dat[[1]]@RASTERS)
        plot.exp

## [1] "CD11b_Sm149" "CD20_Dy161"  "CD3_Er170"   "CD4_Gd156"   "CD45_Sm152"
## [6] "CD8a_Dy162"  "DNA1_Ir191"  "DNA3_Ir193"

    ### Make factor plots

        for(i in plot.rois){

          setwd(OutputDirectory)
          setwd('Plots - factors')
          dir.create(i)
          setwd(i)

          for(a in plot.factors){
            make.spatial.plot(dat = spatial.dat,
                              image.roi = i,
                              image.channel = base,
                              mask.outlines = mask,
                              cell.dat = 'CellData',
                              cell.col = a,
                              cell.col.type = 'factor',
                              title = paste0(a, ' - ', i))
          }
        }

    ### Make exp plots

        for(i in plot.rois){

          setwd(OutputDirectory)
          setwd('Plots - factors')
          dir.create(i)
          setwd(i)

          for(a in plot.exp){
            make.spatial.plot(dat = spatial.dat,
                              image.roi = i,
                              image.channel = base,
                              mask.outlines = mask,
                              cell.dat = 'CellData',
                              cell.col = a,
                              title = paste0(a, ' - ', i))
          }
        }

Extract cellular data and annotate

###################################################################################
### Extract cellular data and annotate
###################################################################################  

    ### Extract cellular data

        cell.dat <- do.pull.data(spatial.dat, 'CellData')
        cell.dat

##           ROI   ID         x          y   Area CD11b_Sm149 CD20_Dy161 CD3_Er170
##     1: ROI002    1 276.24140 218.403834 100982   0.3623715  6.8142638 0.4266008
##     2: ROI002    2 222.54000   3.200000     50   0.2800000  1.9000000 0.4000000
##     3: ROI002    3 247.44643   3.660714     56   0.3928571  0.4464286 0.1428571
##     4: ROI002    4 333.27941   4.250000     68   0.3529412  1.2205882 0.4558823
##     5: ROI002    5 388.37755   4.785714     49   0.1428571  0.5714286 0.1632653
##    ---
## 15564: ROI012 1423 216.41892 494.986486     37   0.2972973  0.2972973 0.2432432
## 15565: ROI012 1424  59.82099 495.919753     81   0.3333333  1.0000000 0.2592593
## 15566: ROI012 1425 432.20732 495.731707     82   0.6829268  0.8780488 0.2560976
## 15567: ROI012 1426 397.91463 495.548780     41   0.3170732  0.5121951 0.4146341
## 15568: ROI012 1427 420.92105 496.578947     38   0.2105263  1.1578947 2.6842105
##        CD4_Gd156 CD45_Sm152 CD8a_Dy162 DNA1_Ir191 DNA3_Ir193
##     1: 0.6018696  3.1577609  1.5598127   42.90354   79.08650
##     2: 0.6000000  1.5800000  4.0999999   44.28000   84.58000
##     3: 0.2678571  0.6607143  3.0357144   29.41072   55.87500
##     4: 1.0441177  2.3529413  3.3823528   29.25000   54.79412
##     5: 0.1224490  0.3265306  2.4489796   43.75510   81.75510
##    ---
## 15564: 0.6216216  1.0270270  0.3243243   40.29730   76.18919
## 15565: 0.7283950  0.6790124  1.8148148   29.44444   55.50617
## 15566: 1.0487804  2.0975609  0.9878049   42.02439   77.53658
## 15567: 0.9756098  3.1219511  1.2439024   34.85366   63.07317
## 15568: 0.9210526 10.2368422  4.3421054   33.28947   60.76316

Area calculation

###################################################################################
### Area calculation
###################################################################################       

        area.totals <- do.calculate.area(spatial.dat)
        area.totals

##       ROI    Total
## 1: ROI002 500.4998
## 2: ROI004 500.4998
## 3: ROI006 500.0000
## 4: ROI008 500.4998
## 5: ROI010 500.4998
## 6: ROI012 500.4998

Save data

###################################################################################
### Save data
###################################################################################  

    ### Output QS and CSV file

        setwd(OutputDirectory)
        dir.create('Data')
        setwd('Data')

        qsave(spatial.dat, "spatial.dat.qs")
        fwrite(cell.dat, 'cell.dat.csv')
        fwrite(area.totals, 'area.totals.csv')

    ### Pull cellular data and write FCS file from each ROI independently

        setwd(OutputDirectory)
        dir.create('FCS files')
        setwd('FCS files')

        for(i in names(spatial.dat)){

          ## Extract data and setup cols

              tmp <- list()
              tmp[[i]] <- spatial.dat[[i]]

              cell.dat <- do.pull.data(tmp, 'CellData')
              cell.dat <- do.asinh(cell.dat, names(spatial.dat[[i]]@RASTERS), cofactor = 1)

          ### Invert y axis

              all.neg <- function(test) -1*abs(test)

              y_invert <- cell.dat[['y']]
              y_invert <- all.neg(y_invert)
              cell.dat[['y_invert']] <- y_invert

          ### Write FCS files

              write.files(cell.dat, i, write.csv = FALSE, write.fcs = TRUE)
              rm(cell.dat)
              rm(i)
        }

Script 2. Cellular analysis

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Coming soon!

Script 3. Spatial analysis

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Coming soon!