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Introduction

Here we provide a brief script in R that requires minimal input to convert TIFF files (plus masks) into FCS and CSV files. FCS can then be used for subsequent analysis in FlowJo.


Setup

1. Install R, RStudio, and Spectre: instructions on this page.

2. Go to https://github.com/ImmuneDynamics/Spectre and download the repository


3. Once it's downloaded, you can explore the contents to find the 'TIFF to FCS.R' file. 


4. Copy this 'TIFF to FCS.R' file into a folder, and create a folder in the same location called 'ROIs'. You can then copy your ROI folders into that location. Each ROI folder should contain one TIFF file per channel.


5. You will also need a folder containing 'masks'. You can have multiple masks types present, but each mask needs to contain the name of the ROI (e.g. 'ROI002'), and some name that clarifies the mask type (e.g. '_Cell_mask.tiff').



Setting up the run in RStudio

Open RStudio and then open the 'TIFF to FCS.R' file. If you aren't familiar with interacting with R/RStudio, check out out quick tutorial on this page.


In the first large section of code we can set up our run and our preferences.

###################################################################################
### Spectre: TIFF to FCS
###################################################################################


Load packages and set directories

x

###################################################################################
### Spectre: spatial 1 - add masks and extract cellular data
###################################################################################
    ### Load libraries
    
        library('Spectre')
        Spectre::package.check(type = 'spatial')
        Spectre::package.load(type = 'spatial')
    ### Set PrimaryDirectory
    
        dirname(rstudioapi::getActiveDocumentContext()$path)            # Finds the directory where this script is located
        setwd(dirname(rstudioapi::getActiveDocumentContext()$path))     # Sets the working directory to where the script is located
        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


Check TIFFs

x

###################################################################################
### 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"
        tiff.list <- list()
    ### Check channel names
    
        for(i in rois){
          setwd(InputDirectory)
          setwd(i)
          tiff.list[[i]] <- list.files(getwd())
        }
        t(as.data.frame(tiff.list))
       [,1]               [,2]                 [,3]                  [,4]               [,5]              [,6]             [,7]             [,8]              [,9]              [,10]             [,11]                  [,12]            
ROI002 "140Ce_Ce140.tiff" "80ArAr_ArAr80.tiff" "alphaSMA_Pr141.tiff" "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff" "CD45_Sm152.tiff" "CD68_Tb159.tiff" "CD8a_Dy162.tiff" "CollagenI_Tm169.tiff" "DNA1_Ir191.tiff"
ROI004 "140Ce_Ce140.tiff" "80ArAr_ArAr80.tiff" "alphaSMA_Pr141.tiff" "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff" "CD45_Sm152.tiff" "CD68_Tb159.tiff" "CD8a_Dy162.tiff" "CollagenI_Tm169.tiff" "DNA1_Ir191.tiff"
ROI006 "140Ce_Ce140.tiff" "80ArAr_ArAr80.tiff" "alphaSMA_Pr141.tiff" "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff" "CD45_Sm152.tiff" "CD68_Tb159.tiff" "CD8a_Dy162.tiff" "CollagenI_Tm169.tiff" "DNA1_Ir191.tiff"
ROI008 "140Ce_Ce140.tiff" "80ArAr_ArAr80.tiff" "alphaSMA_Pr141.tiff" "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff" "CD45_Sm152.tiff" "CD68_Tb159.tiff" "CD8a_Dy162.tiff" "CollagenI_Tm169.tiff" "DNA1_Ir191.tiff"
ROI010 "140Ce_Ce140.tiff" "80ArAr_ArAr80.tiff" "alphaSMA_Pr141.tiff" "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff" "CD45_Sm152.tiff" "CD68_Tb159.tiff" "CD8a_Dy162.tiff" "CollagenI_Tm169.tiff" "DNA1_Ir191.tiff"
ROI012 "140Ce_Ce140.tiff" "80ArAr_ArAr80.tiff" "alphaSMA_Pr141.tiff" "CD11b_Sm149.tiff" "CD20_Dy161.tiff" "CD3_Er170.tiff" "CD4_Gd156.tiff" "CD45_Sm152.tiff" "CD68_Tb159.tiff" "CD8a_Dy162.tiff" "CollagenI_Tm169.tiff" "DNA1_Ir191.tiff"
       [,13]             [,14]                  [,15]           
ROI002 "DNA3_Ir193.tiff" "HistoneH3_Yb176.tiff" "VIM_Nd143.tiff"
ROI004 "DNA3_Ir193.tiff" "HistoneH3_Yb176.tiff" "VIM_Nd143.tiff"
ROI006 "DNA3_Ir193.tiff" "HistoneH3_Yb176.tiff" "VIM_Nd143.tiff"
ROI008 "DNA3_Ir193.tiff" "HistoneH3_Yb176.tiff" "VIM_Nd143.tiff"
ROI010 "DNA3_Ir193.tiff" "HistoneH3_Yb176.tiff" "VIM_Nd143.tiff"
ROI012 "DNA3_Ir193.tiff" "HistoneH3_Yb176.tiff" "VIM_Nd143.tiff"


Import channel TIFFs

x

###################################################################################
### Read in TIFF files and create spatial objects
###################################################################################   
    ### Read in ROI channel TIFFs
    
        setwd(InputDirectory)
        spatial.dat <- read.spatial.files(rois = rois, roi.loc = getwd())
    ### Check results
    
        str(spatial.dat, 3)
List of 6
 $ ROI002:List of 1
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
 $ ROI004:List of 1
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
 $ ROI006:List of 1
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
 $ ROI008:List of 1
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
 $ ROI010:List of 1
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
 $ ROI012:List of 1
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
        spatial.dat[[1]]$RASTERS
class      : RasterStack 
dimensions : 501, 500, 250500, 15  (nrow, ncol, ncell, nlayers)
resolution : 1, 1  (x, y)
extent     : 0, 500, 0, 501  (xmin, xmax, ymin, ymax)
crs        : NA 
names      : X140Ce_Ce140f, X80ArAr_ArAr80f, alphaSMA_Pr141f, CD11b_Sm149f, CD20_Dy161f, CD3_Er170f, CD4_Gd156f, CD45_Sm152f, CD68_Tb159f, CD8a_Dy162f, CollagenI_Tm169f, DNA1_Ir191f, DNA3_Ir193f, HistoneH3_Yb176f, VIM_Nd143f 
min values :             0,            3399,               0,            0,           0,          0,          0,           0,           0,           0,                0,           0,           0,                0,          0 
max values :           443,            5297,            1231,           33,         779,         41,         40,         734,        1223,         109,              183,        1670,        3007,             3167,       7764


Import masks

x

###################################################################################
### 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" 
    ### Import CELL masks
        
        as.matrix(all.masks)
        cell.mask.ext <- '_Cell_mask.tiff'
        
        cell.masks <- list.files(pattern = cell.mask.ext)
        as.matrix(cell.masks)
     [,1]                   
[1,] "ROI002_Cell_mask.tiff"
[2,] "ROI004_Cell_mask.tiff"
[3,] "ROI006_Cell_mask.tiff"
[4,] "ROI008_Cell_mask.tiff"
[5,] "ROI010_Cell_mask.tiff"
[6,] "ROI012_Cell_mask.tiff"
        spatial.dat <- do.add.masks(spatial.dat = spatial.dat,
                                    mask.loc = getwd(),
                                    masks = cell.masks,
                                    mask.label = 'cell.mask',
                                    mask.ext = cell.mask.ext)
Adding masks to cell.mask
  -- processing ROI002
     ... correcting extent
     ... flipping Y
  -- processing ROI004
     ... correcting extent
     ... flipping Y
  -- processing ROI006
     ... correcting extent
     ... flipping Y
  -- processing ROI008
     ... correcting extent
     ... flipping Y
  -- processing ROI010
     ... correcting extent
     ... flipping Y
  -- processing ROI012
     ... correcting extent
     ... flipping Y
Returning spatial data object with added masks
    ### Review data
        
        str(spatial.dat, 3)
List of 6
 $ ROI002:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 1
 $ ROI004:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 1
 $ ROI006:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 1
 $ ROI008:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 1
 $ ROI010:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 1
 $ ROI012:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 1
        spatial.dat[[1]]$MASKS
$cell.mask
$cell.mask$maskraster
class      : RasterLayer 
dimensions : 501, 500, 250500  (nrow, ncol, ncell)
resolution : 1, 1  (x, y)
extent     : 0, 500, 0, 501  (xmin, xmax, ymin, ymax)
crs        : NA 
source     : memory
names      : cell.mask 
values     : 0, 65535  (min, max)


Create polygons for cell

x

###################################################################################
### Generate polygons and outlines
###################################################################################
    ### Generate polygons and outlines
    
        spatial.dat <- do.create.outlines(spatial.dat, 'cell.mask')
Creating polygons, outlines, and centroids using 'stars' method.
Processing masks for ROI ROI002
although coordinates are longitude/latitude, st_union assumes that they are planar
although coordinates are longitude/latitude, st_union assumes that they are planar
although coordinates are longitude/latitude, st_union assumes that they are planar

...

... polygons complete
Regions defined for each Polygons
... outlines complete
... centroids complete
Returning spatial data
    ### Checks
    
        str(spatial.dat, 3)
List of 6
 $ ROI002:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
 $ ROI004:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
 $ ROI006:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
 $ ROI008:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
 $ ROI010:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
 $ ROI012:List of 2
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
        spatial.dat[[1]]$MASKS
$cell.mask
$cell.mask$maskraster
class      : RasterLayer 
dimensions : 501, 500, 250500  (nrow, ncol, ncell)
resolution : 1, 1  (x, y)
extent     : 0, 500, 0, 501  (xmin, xmax, ymin, ymax)
crs        : NA 
source     : memory
names      : cell.mask 
values     : 0, 65535  (min, max)


$cell.mask$polygons
class       : SpatialPolygonsDataFrame 
features    : 2750 
extent      : 0, 500, 0, 501  (xmin, xmax, ymin, ymax)
crs         : NA 
variables   : 1
names       : cell.mask 
min values  :         0 
max values  :     65535 

$cell.mask$outlines
    long lat order  hole piece id group
1     19 103     1 FALSE     1  1   1.1
2     19 104     2 FALSE     1  1   1.1
3     20 104     3 FALSE     1  1   1.1


Mask QC plots

x

###################################################################################
### Mask QC plots
###################################################################################      
    ### Mask plots
        
        setwd(OutputDirectory)
        dir.create('Plots - cell masks')
        setwd('Plots - cell masks')
		as.matrix(names(spatial.dat[[1]]$RASTERS))

        base <- 'DNA1_Ir191'
        mask <- 'cell.mask'
        for(i in names(spatial.dat)){
          make.spatial.plot(spatial.dat = spatial.dat, 
                            image.roi = i, 
                            image.channel = base, 
                            mask.outlines = 'cell.mask')
        }


Calculate 'per cell' data from masks

x

###################################################################################
### Extract 'per cell' data
################################################################################### 
    ### Extract cellular data for each cell mask
        
        spatial.dat <- do.extract(spatial.dat, 'cell.mask')
Processing ROI002
... X140Ce_Ce140f
|==============================================================================================================| 100%
... X80ArAr_ArAr80f
|==============================================================================================================| 100%
... alphaSMA_Pr141f
|==============================================================================================================| 100%

...
        str(spatial.dat, 3)
List of 6
 $ ROI002:List of 3
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
  ..$ DATA   :List of 1
  .. ..$ CellData:Classes ‘data.table’ and 'data.frame':	2750 obs. of  19 variables:
  .. .. ..- attr(*, ".internal.selfref")=<externalptr> 
 $ ROI004:List of 3
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
  ..$ DATA   :List of 1
  .. ..$ CellData:Classes ‘data.table’ and 'data.frame':	4205 obs. of  19 variables:
  .. .. ..- attr(*, ".internal.selfref")=<externalptr> 
 $ ROI006:List of 3
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
  ..$ DATA   :List of 1
  .. ..$ CellData:Classes ‘data.table’ and 'data.frame':	4200 obs. of  19 variables:
  .. .. ..- attr(*, ".internal.selfref")=<externalptr> 
 $ ROI008:List of 3
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
  ..$ DATA   :List of 1
  .. ..$ CellData:Classes ‘data.table’ and 'data.frame':	1685 obs. of  19 variables:
  .. .. ..- attr(*, ".internal.selfref")=<externalptr> 
 $ ROI010:List of 3
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
  ..$ DATA   :List of 1
  .. ..$ CellData:Classes ‘data.table’ and 'data.frame':	1301 obs. of  19 variables:
  .. .. ..- attr(*, ".internal.selfref")=<externalptr> 
 $ ROI012:List of 3
  ..$ RASTERS:Formal class 'RasterStack' [package "raster"] with 11 slots
  ..$ MASKS  :List of 1
  .. ..$ cell.mask:List of 4
  ..$ DATA   :List of 1
  .. ..$ CellData:Classes ‘data.table’ and 'data.frame':	1427 obs. of  19 variables:
  .. .. ..- attr(*, ".internal.selfref")=<externalptr>


Save spatial data object

x

###################################################################################
### Save spatial data object as qs file
###################################################################################         
    ### Output
        
        setwd(OutputDirectory)
        dir.create('QS file')
        setwd('QS file')
        
        qsave(spatial.dat, "spatial.dat.qs")    


Write FCS files

x

###################################################################################
### Write FCS files
###################################################################################       
    ### Pull cellular data
        
        cell.dat <- do.pull.data(spatial.dat, 'CellData')
        cell.dat
         ROI   ID         x          y   Area X140Ce_Ce140f X80ArAr_ArAr80f alphaSMA_Pr141f CD11b_Sm149f CD20_Dy161f	...
    1: ROI002    1 276.24140 218.403834 100982    0.11077221        4291.782        9.292290    0.3623715   6.8142638	...
    2: ROI002    2 222.54000   3.200000     50    0.06000000        4143.240        8.840000    0.2800000   1.9000000 	... 
    3: ROI002    3 247.44643   3.660714     56    0.03571429        4150.036        3.392857    0.3928571   0.4464286 	... 
    4: ROI002    4 333.27941   4.250000     68    0.08823530        4184.838        5.720588    0.3529412   1.2205882 	... 
    5: ROI002    5 388.37755   4.785714     49    0.08163265        4123.775        2.102041    0.1428571   0.5714286 	... 
   ---                                                                                                               
15564: ROI012 1423 216.41892 494.986486     37    0.05405406        4016.324       64.027023    0.2972973   0.2972973	... 
15565: ROI012 1424  59.82099 495.919753     81    0.16049382        4092.963        5.938272    0.3333333   1.0000000	... 
15566: ROI012 1425 432.20732 495.731707     82    0.08536585        4042.378        9.524390    0.6829268   0.8780488	... 
15567: ROI012 1426 397.91463 495.548780     41    0.00000000        4051.805        8.341463    0.3170732   0.5121951	... 
15568: ROI012 1427 420.92105 496.578947     38    0.13157895        4051.763        4.315790    0.2105263   1.1578947 	...
        as.matrix(names(cell.dat))
      [,1]              
 [1,] "ROI"             
 [2,] "ID"              
 [3,] "x"               
 [4,] "y"               
 [5,] "Area"            
 [6,] "X140Ce_Ce140f"   
 [7,] "X80ArAr_ArAr80f" 
 [8,] "alphaSMA_Pr141f" 
 [9,] "CD11b_Sm149f"    
[10,] "CD20_Dy161f"     
[11,] "CD3_Er170f"      
[12,] "CD4_Gd156f"      
[13,] "CD45_Sm152f"     
[14,] "CD68_Tb159f"     
[15,] "CD8a_Dy162f"     
[16,] "CollagenI_Tm169f"
[17,] "DNA1_Ir191f"     
[18,] "DNA3_Ir193f"     
[19,] "HistoneH3_Yb176f"
[20,] "VIM_Nd143f" 
        cellular.cols <- names(cell.dat)[c(6:20)]         
		as.matrix(cellular.cols)
      [,1]              
 [1,] "X140Ce_Ce140f"   
 [2,] "X80ArAr_ArAr80f" 
 [3,] "alphaSMA_Pr141f" 
 [4,] "CD11b_Sm149f"    
 [5,] "CD20_Dy161f"     
 [6,] "CD3_Er170f"      
 [7,] "CD4_Gd156f"      
 [8,] "CD45_Sm152f"     
 [9,] "CD68_Tb159f"     
[10,] "CD8a_Dy162f"     
[11,] "CollagenI_Tm169f"
[12,] "DNA1_Ir191f"     
[13,] "DNA3_Ir193f"     
[14,] "HistoneH3_Yb176f"
[15,] "VIM_Nd143f"
    ### Arcsinh transformation
    
        cell.dat <- do.asinh(cell.dat, cellular.cols, 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
        
        cell.dat
    ### Write FCS files  
        
        setwd(OutputDirectory)
        dir.create('FCS files')
        setwd('FCS files')
        
        write.files(cell.dat, 'spatial_all', write.csv = FALSE, write.fcs = TRUE)
        write.files(cell.dat, 'spatial', divide.by = 'ROI', write.csv = FALSE, write.fcs = TRUE)


Further plots

x

###################################################################################
### Further plots
###################################################################################       
    ### Plot settings
        
        as.matrix(names(spatial.dat))
        
        plot.rois <- c("ROI002", "ROI004")
        plot.rois
[1] "ROI002" "ROI004"
        as.matrix(names(spatial.dat[[1]]$RASTERS))
        exp.plot <- names(spatial.dat[[1]]$RASTERS)[1:15]
        exp.plot
 [1] "X140Ce_Ce140f"    "X80ArAr_ArAr80f"  "alphaSMA_Pr141f"  "CD11b_Sm149f"     "CD20_Dy161f"      "CD3_Er170f"      
 [7] "CD4_Gd156f"       "CD45_Sm152f"      "CD68_Tb159f"      "CD8a_Dy162f"      "CollagenI_Tm169f" "DNA1_Ir191f"     
[13] "DNA3_Ir193f"      "HistoneH3_Yb176f" "VIM_Nd143f"     
        as.matrix(names(spatial.dat[[1]]$RASTERS))
        factor.plot <- names(spatial.dat[[1]]$RASTERS)[0]
        factor.plot
character(0)
    ### Expression data point plots
        
        setwd(OutputDirectory)
        dir.create('Plots - expression')
        setwd('Plots - expression')
        
        for(i in plot.rois){
          for(a in exp.plot){
            make.spatial.plot(spatial.dat = spatial.dat, 
                              image.roi = i, 
                              image.channel = a, 
                              mask.outlines = 'cell.mask')
          }
        }

    ### Factor data point plots
        
        setwd(OutputDirectory)
        dir.create('Plots - data point expression')
        setwd('Plots - data point expression')
        
        for(i in plot.rois){
          for(a in exp.plot){
            make.spatial.plot(spatial.dat = spatial.dat, 
                              image.roi = i, 
                              image.channel = a, 
                              mask.outlines = 'cell.mask', 
                              cell.dat = 'CellData', 
                              cell.col = a)
          }
          
          for(a in factor.plot){
            make.spatial.plot(spatial.dat = spatial.dat, 
                              image.roi = i, 
                              image.channel = a, 
                              mask.outlines = 'cell.mask', 
                              cell.dat = 'CellData', 
                              cell.col = a, 
                              cell.col.type = 'factor')
          }
        }




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