Reading in data and data formatting in SPIAT
2024-03-20
Package
SPIAT 1.4.2
First we load the SPIAT library.
library(SPIAT)1 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.
1.1 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 0.0211.2 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).
1.2.1 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 filemarkers: 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 asmarkers. 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 7dim(assay(formatted_image))## [1] 6 91.2.2 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 filemarkers: 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 themarkers. The locations are used to auto-detect the intensity (and dye) columns.intensity_columns_interestuse iflocationsis not specified. Vector with the names of the columns with the level of each marker. Column names must match the order of themarkersparameter.dye_columns_interestUse 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 themarkersparameter.
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 5dim(assay(formatted_image))## [1] 6 102 Inspecting the SpaitalExperiment object
2.1 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 4951assay() 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-03colData() 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 sample01spatialCoords() 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 171.926407We 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
2.2 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.
2.3 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)2.4 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)2.5 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")3 You can access the vignettes for other modules of SPIAT here:
4 Reproducibility
sessionInfo()## R version 4.3.3 (2024-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
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## attached base packages:
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## [1] SPIAT_1.4.2 SpatialExperiment_1.12.0
## [3] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
## [5] Biobase_2.62.0 GenomicRanges_1.54.1
## [7] GenomeInfoDb_1.38.8 IRanges_2.36.0
## [9] S4Vectors_0.40.2 BiocGenerics_0.48.1
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## [7] RCurl_1.98-1.14 pracma_2.4.4 spatstat.geom_3.2-9
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