First we load the SPIAT library.

library(SPIAT)

Reading in data

format_image_to_spe() is the main function to read in data to SPIAT. format_image_to_spe() creates a SpatialExperiment object which is used in all subsequent functions. The key data points of interest for SPIAT are cell coordinates, marker intensities and cell phenotypes for each cell.

format_image_to_spe() has specific options to read in data generated from inForm, HALO, CODEX and cellprofiler. However, we advise pre-formatting the data before input to SPIAT so that accepted by the ‘general’ option (shown below). This is due to often inconsistencies in the column names or data formats across different versions or as a result of different user options when using the other platforms.

Reading in data through the ‘general’ option (RECOMMENDED)

Format “general” allows you to input a matrix of intensities (intensity_matrix), and a vector of phenotypes, which should be in the same order in which they appear in the intensity_matrix. They must be of the form of marker combinations (e.g. “CD3,CD8”), as opposed to cell names (e.g. “cytotoxic T cells”), as SPIAT does matching with the marker names. phenotypes is an optional parameter and can be set as NULL if no phenotypes are available. The user also needs to provide separate vectors with the X and Y coordinates of the cells (coord_x and coord_y). The cells must be in the same order as in the intensity_matrix. If you have Xmin, Xmax,Ymin and Ymax columns in the raw data, we advise calculating the average to obtain a single X and Y coordinate, which you can then use as input to coord_x and coord_y.

Specifically, if intensity_matrix is available, please make sure the colnames of the intensity matrix are the cell IDs as some SPIAT functions (like identify_bordering_cells()) require the constructed image object to have cell IDs as rownames of the colData and colnames of the intensity matrix. If intensity_matrix is NULL, the function will automatically assign IDs to the cells.

Here we use some dummy data to illustrate how to read “general” format.

# Construct a dummy marker intensity matrix
## rows are markers, columns are cells
intensity_matrix <- matrix(c(14.557, 0.169, 1.655, 0.054,
                             17.588, 0.229, 1.188, 2.074, 
                             21.262, 4.206,  5.924, 0.021), nrow = 4, ncol = 3)
# define marker names as rownames
rownames(intensity_matrix) <- c("DAPI", "CD3", "CD4", "AMACR")
# define cell IDs as colnames
colnames(intensity_matrix) <- c("Cell_1", "Cell_2", "Cell_3") 

# Construct a dummy metadata (phenotypes, x/y coordinates)
# the order of the elements in these vectors correspond to the cell order 
# in `intensity matrix`
phenotypes <- c("OTHER",  "AMACR", "CD3,CD4")
coord_x <- c(82, 171, 184)
coord_y <- c(30, 22, 38)

general_format_image <- format_image_to_spe(format = "general", 
                                            intensity_matrix = intensity_matrix,
                                            phenotypes = phenotypes, 
                                            coord_x = coord_x,coord_y = coord_y)

The formatted image now contains phenotypes, locations, and marker intensity information of 3 cells. Note that if users want to define cell IDs, the cell IDs should be defined as the colnames of the intensity matrix. The order of the rows of the metadata should correspond to the order of the colnames of the intensity matrix. The function will automatically assign rownames to the spatialCoords() and colData() of the image (now as a spatialExperiment object).

Use the following code to inspect the formatted SpatialExperiment object.

# phenotypes and cell properties (if available)
colData(general_format_image)

## DataFrame with 3 rows and 3 columns
##            Cell.ID   Phenotype   sample_id
##        <character> <character> <character>
## Cell_1      Cell_1       OTHER    sample01
## Cell_2      Cell_2       AMACR    sample01
## Cell_3      Cell_3     CD3,CD4    sample01

# cell coordinates
spatialCoords(general_format_image)

##      Cell.X.Position Cell.Y.Position
## [1,]              82              30
## [2,]             171              22
## [3,]             184              38

# marker intensities
assay(general_format_image)

##       Cell_1 Cell_2 Cell_3
## DAPI  14.557 17.588 21.262
## CD3    0.169  0.229  4.206
## CD4    1.655  1.188  5.924
## AMACR  0.054  2.074  0.021

Reading in data pre-formatted by other software

If you prefer to use data directly generated from inForm, HALO, CODEX or cellprofiler, these can be specified by format param in format_image_to_spe(). We will show examples for the inForm and HALO formats.

For reading in input generated with CODEX or cellprofiler see the documentations (?format_image_to_spe).

Reading in data from inForm

To read in data from inForm, you need the table file generated by inForm containing the cell IDs, cell locations, phenotypes (if available) and marker intensities. You also need to extract a vector of marker names and marker locations (“Nucleus”, “Cytoplasm”, or “Membrane”). format_image_to_spe() uses the “Cell X Position” and “Cell Y Position” columns and the “Phenotype” column in the inForm raw data. The phenotype of a cell can be a single marker, for example, “CD3”, or a combination of markers, such as “CD3,CD4”. As a convention, SPIAT assumes that cells marked as “OTHER” in “inForm” refer to cells positive for DAPI but no other marker. The phenotypes must be based on the markers (e.g. CD3,CD4), rather than names of cells (e.g. cytotoxic T cells). The names of the cells (e.g. cytotoxic T cells) can be added later using the define_celltypes() function. The following cell properties columns are also required to be present in the inForm input file: Entire Cell Area (pixels), Nucleus Area (pixels), Nucleus Compactness, Nucleus Axis Ratio, and Entire Cell Axis Ratio. If not present in the raw data, these can be columns with NAs.

To read in inForm data, you need to specify the following parameters:

format: “inForm”
path: path to the raw inForm image data file
markers: names of markers used in the OPAL staining. These must be in the same order as the marker columns in the input file, and must match the marker names used in the input file. One of the markers must be “DAPI”.
locations: locations of the markers in cells, either “Nucleus”, “Cytoplasm” or “Membrane.” These must be in the same order as markers. The locations are used to auto-detect the intensity (and dye) columns.

A small example of inForm input is included in SPIAT containing dummy marker intensity values and all the other required columns (see below). This example file is just for demonstrating importing a raw data file, later in the Inspecting the SpaitalExperiment object section we will load a larger preformatted dataset. Users are welcome to use this formatting option (format = 'inForm') if it is closer to the format of their files.

raw_inform_data <- system.file("extdata", "tiny_inform.txt.gz", package = "SPIAT")
markers <- c("DAPI", "CD3", "PD-L1", "CD4", "CD8", "AMACR")
locations <- c("Nucleus","Cytoplasm", "Membrane","Cytoplasm","Cytoplasm",
               "Cytoplasm") # The order is the same as `markers`.
formatted_image <- format_image_to_spe(format="inForm", path=raw_inform_data, 
                                       markers=markers, locations=locations)

Alternatively, rather than specifying the locations, you can also specify the specific intensity columns with the parameter intensity_columns_interest as shown below.

raw_inform_data <- system.file("extdata", "tiny_inform.txt.gz", package = "SPIAT")
markers <- c("DAPI", "CD3", "PD-L1", "CD4", "CD8", "AMACR")
intensity_columns_interest <- c(
  "Nucleus DAPI (DAPI) Mean (Normalized Counts, Total Weighting)",
  "Cytoplasm CD3 (Opal 520) Mean (Normalized Counts, Total Weighting)", 
  "Membrane PD-L1 (Opal 540) Mean (Normalized Counts, Total Weighting)",
  "Cytoplasm CD4 (Opal 620) Mean (Normalized Counts, Total Weighting)",
  "Cytoplasm CD8 (Opal 650) Mean (Normalized Counts, Total Weighting)", 
  "Cytoplasm AMACR (Opal 690) Mean (Normalized Counts, Total Weighting)"
  ) # The order is the same as `markers`.
formatted_image <- format_inform_to_spe(path=raw_inform_data, markers=markers,
                     intensity_columns_interest=intensity_columns_interest)
class(formatted_image) # The formatted image is a SpatialExperiment object

## [1] "SpatialExperiment"
## attr(,"package")
## [1] "SpatialExperiment"

dim(colData(formatted_image))

## [1] 9 7

dim(assay(formatted_image))

## [1] 6 9

Reading in data from HALO

To read in data from HALO, you need the table file generated by HALO. The biggest difference between inForm and HALO formats is the coding of the cell phenotypes. While inForm encodes phenotypes as the combination of positive markers (e.g. “CD3,CD4”), HALO uses a binary system where 1 means the cell is positive for the marker and 0 otherwise.

format_image_to_spe() for “HALO” format collapses HALO encoded phenotypes into an inForm-like format to create the Phenotype column. For example, if HALO has assigned a cell a marker status of 1 for CD3 and 1 for CD4, SPIAT will give it the Phenotype “CD3,CD4”. Cells that have a marker status of 1 for DAPI but no other marker are given the phenotype “OTHER”.

format_image_to_spe() takes the average of the HALO X min and X max columns for each cell to create the Cell.X.Position column. It takes the average of the Y min and Y max to create the Cell.Y.Position column.

To read in HALO data, you need to specify the following parameters:

format: “HALO”
path: path to the raw HALO image data file
markers: names of markers used in the OPAL staining. These must be in the same order as the marker columns in the input file, and must match the marker names used in the input file. One of the markers must be DAPI.
locations: locations of the markers in cells, either “Nucleus”, “Cytoplasm” or “Membrane.” These must be in the order of the markers. The locations are used to auto-detect the intensity (and dye) columns.
intensity_columns_interest use if locations is not specified. Vector with the names of the columns with the level of each marker. Column names must match the order of the markers parameter.
dye_columns_interest Use if locations is not specified. Vector of names of the columns with the marker status (i.e. those indicating 1 or 0 for whether the cell is positive or negative for the marker). Column names must match the order of the markers parameter.

Users can specify the locations to auto-detect the columns as shown above for inForm. Alternatively, if users want to specify the columns instead, you can do so with intensity_columns_interest, as shown in the example below. Note that then you also must specify dye_columns_interest. The following cell properties columns are also required to be present in the HALO input file: Cell Area, Nucleus Area, Cytoplasm Area. If these are not present in the user’s data, we recommend adding these columns with NA values.

raw_halo_data <- system.file("extdata", "tiny_halo.csv.gz", package = "SPIAT")
markers <- c("DAPI", "CD3", "PD-L1", "CD4", "CD8", "AMACR")
intensity_columns_interest <- c("Dye 1 Nucleus Intensity",
                                "Dye 2 Cytoplasm Intensity",
                                "Dye 3 Membrane Intensity",
                                "Dye 4 Cytoplasm Intensity",
                                "Dye 5 Cytoplasm Intensity",
                                "Dye 6 Cytoplasm Intensity")
dye_columns_interest <- c("Dye 1 Positive Nucleus",
                          "Dye 2 Positive Cytoplasm",
                          "Dye 3 Positive Membrane",
                          "Dye 4 Positive Cytoplasm",
                          "Dye 5 Positive Cytoplasm",
                          "Dye 6 Positive Cytoplasm")
formatted_image <- format_halo_to_spe(
  path=raw_halo_data, markers=markers,
  intensity_columns_interest=intensity_columns_interest,
  dye_columns_interest=dye_columns_interest)
class(formatted_image) # The formatted image is a SpatialExperiment object

## [1] "SpatialExperiment"
## attr(,"package")
## [1] "SpatialExperiment"

dim(colData(formatted_image))

## [1] 10  5

dim(assay(formatted_image))

## [1]  6 10

Inspecting the SpaitalExperiment object

Structure of a SPIAT SpatialExperiment object

In this vignette we will use an inForm data file that’s already been formatted for SPIAT with format_image_to_spe(), which we can load with data.

data("simulated_image")

This is in SpatialExperiment format.

class(simulated_image)

## [1] "SpatialExperiment"
## attr(,"package")
## [1] "SpatialExperiment"

This example data has 5 markers and 4951 cells.

dim(simulated_image)

## [1]    5 4951

assay() stores the intensity level of every marker (rows) for every cell (columns).

# take a look at first 5 columns
assay(simulated_image)[, 1:5]

##                      Cell_1       Cell_2      Cell_3       Cell_4       Cell_5
## Tumour_marker  4.466925e-01 1.196802e-04 0.235435887 1.125552e-01 1.600443e-02
## Immune_marker1 1.143640e-05 4.360881e-19 0.120582510 2.031554e-13 1.685832e-01
## Immune_marker2 1.311175e-15 5.678623e-02 0.115769761 5.840184e-12 9.025254e-05
## Immune_marker3 6.342341e-09 2.862823e-06 0.053107792 6.289501e-04 4.912962e-13
## Immune_marker4 2.543406e-04 4.702311e-04 0.005878394 4.582812e-03 2.470984e-03

colData() stores the phenotype and cell properties. Note that the sample_id column was added by SpatialExperiment data structure and can be ignored here.

# take a look at first 5 rows
colData(simulated_image)[1:5, ]

## DataFrame with 5 rows and 2 columns
##          Phenotype   sample_id
##        <character> <character>
## Cell_1       OTHER    sample01
## Cell_2       OTHER    sample01
## Cell_3       OTHER    sample01
## Cell_4       OTHER    sample01
## Cell_5       OTHER    sample01

spatialCoords() stores cell coordinates.

# take a look at first 5 rows
spatialCoords(simulated_image)[1:5, ]

##        Cell.X.Position Cell.Y.Position
## Cell_1       139.77484       86.704079
## Cell_2        77.86721       80.096527
## Cell_3        84.44626       19.238638
## Cell_4       110.19857        5.656004
## Cell_5       167.89558      171.926407

We can check what phenotypes are there.

unique(simulated_image$Phenotype)

## [1] "OTHER"                                       
## [2] "Immune_marker1,Immune_marker2"               
## [3] "Tumour_marker"                               
## [4] "Immune_marker1,Immune_marker2,Immune_marker4"
## [5] "Immune_marker1,Immune_marker3"

The phenotypes in this example data can be interpreted as follows:

Tumour_marker = cancer cells
Immune_marker1,Immune_marker2 = immune cell type 1
Immune_marker1,Immune_marker3 = immune cell type 2
Immune_marker1,Immune_marker2,Immune_marker4 = immune cell type 3
OTHER = other cell types

Nomenclature

In SPIAT We define as markers proteins whose levels where queried by OPAL, CODEX or other platforms.

Examples of markers are “AMACR” for prostate cancer cells, “panCK” for epithelial tumour cells, “CD3” for T cells or “CD20” for B cells.

The combination of markers results in a specific cell phenotype. For example, a cell positive for both “CD3” and “CD4” markers has the “CD3,CD4” cell phenotype. The phenotype has to be strictly formatted in such way where each positive marker has to be separated by a comma, with no space in between, and the order of the positive markers has to be the same as the order in assay().

Finally, we define a cell type as a name assigned by the user to a cell phenotype. For example, a user can name “CD3,CD4” cells as “helper T cells”. We would refer to “helper T cells” therefore as a cell type.

Splitting images

In the case of large images, or images where there are two independent tissue sections, it is recommended to split images into sections defined by the user. This can be performed with image_splitter() after format_image_to_spe().

split_image <- image_splitter(simulated_image, number_of_splits=3, plot = FALSE)

Predicting cell phenotypes

SPIAT can predict cell phenotypes using marker intensity levels with predict_phenotypes(). This can be used to check the phenotypes that have been assigned by inForm and HALO. It can also potentially be used to automate the manual phenotyping performed with inForm/HALO. The underlying algorithm is based on the density distribution of marker intensities. We have found this algorithm to perform best in OPAL data. Further phenotyping methods for other data formats are under development.

This algorithm does not take into account cell shape or size, so if these are required for phenotyping, using HALO or inForm or a machine-learning based method is recommended.

predict_phenotypes() produces a density plot that shows the cutoff for calling a cell positive for a marker. If the dataset includes phenotypes obtained through another software, this function prints to the console the concordance between SPIAT’s prediction and pre-defined phenotypes as the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) phenotype assignments. It returns a table containing the phenotypes predicted by SPIAT and the actual phenotypes from inForm/HALO (if available).

predicted_image <- predict_phenotypes(spe_object = simulated_image,
                                      thresholds = NULL,
                                      tumour_marker = "Tumour_marker",
                                      baseline_markers = c("Immune_marker1", 
                                                           "Immune_marker2", 
                                                           "Immune_marker3", 
                                                           "Immune_marker4"),
                                      reference_phenotypes = TRUE)

## [1] "Tumour_marker"
## [1] "Immune_marker1"
## [1] "Immune_marker2"
## [1] "Immune_marker3"
## [1] "Immune_marker4"

We can use marker_prediction_plot() to plot the predicted cell phenotypes and the phenotypes generated from the platforms for comparison.

marker_prediction_plot(predicted_image, marker="Immune_marker1")

The plot shows Immune_marker1+ cells in the tissue. On the left are the Immune_marker1+ cells defined by the simulated image and on the right are the Immune_marker1+ cells predicted using SPIAT. Since we know that the simulated phenotypes are the truth, we leave the phenotypes as they are.

The next example shows how to replace the original phenotypes with the predicted ones. Note that for this tutorial, we still use the original phenotypes.

predicted_image2 <- predict_phenotypes(spe_object = simulated_image,
                                      thresholds = NULL,
                                      tumour_marker = "Tumour_marker",
                                      baseline_markers = c("Immune_marker1", 
                                                           "Immune_marker2", 
                                                           "Immune_marker3", 
                                                           "Immune_marker4"),
                                      reference_phenotypes = FALSE)

Specifying cell types

SPIAT can define cell types with the define_celltypes() function. By default the new column for cell types is called Cell.Type. The cell types can be defined based on Phenotype column, as well as other columns.

formatted_image <- define_celltypes(
    simulated_image, 
    categories = c("Tumour_marker","Immune_marker1,Immune_marker2", 
                   "Immune_marker1,Immune_marker3", 
                   "Immune_marker1,Immune_marker2,Immune_marker4", "OTHER"), 
    category_colname = "Phenotype", 
    names = c("Tumour", "Immune1", "Immune2", "Immune3", "Others"),
    new_colname = "Cell.Type")

You can access the vignettes for other modules of SPIAT here:

Reproducibility

sessionInfo()

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] SPIAT_1.9.0                 SpatialExperiment_1.17.0   
##  [3] SingleCellExperiment_1.29.1 SummarizedExperiment_1.37.0
##  [5] Biobase_2.67.0              GenomicRanges_1.59.1       
##  [7] GenomeInfoDb_1.43.2         IRanges_2.41.2             
##  [9] S4Vectors_0.45.2            BiocGenerics_0.53.3        
## [11] generics_0.1.3              MatrixGenerics_1.19.0      
## [13] matrixStats_1.4.1           BiocStyle_2.35.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1        viridisLite_0.4.2       dplyr_1.1.4            
##  [4] farver_2.1.2            fastmap_1.2.0           pracma_2.4.4           
##  [7] spatstat.geom_3.3-4     spatstat.explore_3.3-3  RANN_2.6.2             
## [10] digest_0.6.37           lifecycle_1.0.4         spatstat.data_3.1-4    
## [13] dbscan_1.2-0            magrittr_2.0.3          compiler_4.4.2         
## [16] rlang_1.1.4             sass_0.4.9              tools_4.4.2            
## [19] yaml_2.3.10             knitr_1.49              S4Arrays_1.7.1         
## [22] labeling_0.4.3          bit_4.5.0.1             DelayedArray_0.33.3    
## [25] plyr_1.8.9              abind_1.4-8             withr_3.0.2            
## [28] sys_3.4.3               grid_4.4.2              polyclip_1.10-7        
## [31] colorspace_2.1-1        ggplot2_3.5.1           gtools_3.9.5           
## [34] scales_1.3.0            spatstat.utils_3.1-1    cli_3.6.3              
## [37] rmarkdown_2.29          crayon_1.5.3            tzdb_0.4.0             
## [40] httr_1.4.7              reshape2_1.4.4          rjson_0.2.23           
## [43] cachem_1.1.0            stringr_1.5.1           zlibbioc_1.52.0        
## [46] parallel_4.4.2          BiocManager_1.30.25     XVector_0.47.0         
## [49] vctrs_0.6.5             Matrix_1.7-1            jsonlite_1.8.9         
## [52] bit64_4.5.2             tensor_1.5              maketools_1.3.1        
## [55] magick_2.8.5            spatstat.univar_3.1-1   jquerylib_0.1.4        
## [58] goftest_1.2-3           glue_1.8.0              spatstat.random_3.3-2  
## [61] stringi_1.8.4           gtable_0.3.6            deldir_2.0-4           
## [64] UCSC.utils_1.3.0        munsell_0.5.1           tibble_3.2.1           
## [67] pillar_1.10.0           htmltools_0.5.8.1       GenomeInfoDbData_1.2.13
## [70] R6_2.5.1                vroom_1.6.5             evaluate_1.0.1         
## [73] lattice_0.22-6          apcluster_1.4.13        mmand_1.6.3            
## [76] bslib_0.8.0             Rcpp_1.0.13-1           gridExtra_2.3          
## [79] SparseArray_1.7.2       nlme_3.1-166            spatstat.sparse_3.1-0  
## [82] xfun_0.49               buildtools_1.0.0        pkgconfig_2.0.3

Author Contributions

AT, YF, TY, ML, JZ, VO, MD are authors of the package code. MD and YF wrote the vignette. AT, YF and TY designed the package.

Reading in data and data formatting in SPIAT