Contents

1 Installation

sosta can be loaded from Bioconductor and installed as follows

if (!requireNamespace("BiocManager")) {
    install.packages("BiocManager")
}
BiocManager::install("sosta")

2 Setup

library("sosta")
library("dplyr")
library("tidyr")
library("ggplot2")
library("ggspavis")
library("imcdatasets")

3 Introduction

In this vignette we will use data from the package imcdatasets. The dataset contains imaging mass cytometry (IMC) data of pancreatic islets of human donors at different stages of type 1 diabetes (T1D) and healthy controls (Damond et al. 2019). Note that we will only use a subset of the images full_dataset = FALSE.

First we plot the data for illustration. As we have multiple images per patient we will subset one patient and a few slides.

plotSpots(
    spe[, spe[["patient_id"]] == 6126 &
        spe[["image_name"]] %in% c("E02", "E03", "E04")],
    annotate = "cell_category",
    sample_id = "image_number",
    in_tissue = NULL,
    y_reverse = FALSE
) + facet_wrap(~image_name)

The goal is to reconstruct / segment and quantify the pancreatic islets.

4 Reconstruction of pancreatic islets

4.1 Reconstruction of pancreatic islets for one image

In this example we will reconstruct the islets based on the point pattern density of the islet cells. We will start with estimating the parameters that we use for reconstruction afterwards. For one image this can be illustrated as follows.

shapeIntensityImage(
    spe,
    marks = "cell_category",
    image_col = "image_name",
    image_id = "E04",
    mark_select = "islet"
)

We see the density (pixel) image on the left and a histogram of the intensity values on the right. The estimated threshold is roughly the mean between the two modes of the (truncated) pixel intensity distribution.

This was done for one image. The function estimateReconstructionParametersSPE returns two plots with the estimated parameters for each image. The parameter bndw is the bandwidth parameter that is used for estimating the intensity profile of the point pattern. The parameter thres is the estimated parameter for the density threshold for reconstruction. We subset 20 images to speed up computation.

n <- estimateReconstructionParametersSPE(
    spe,
    marks = "cell_category",
    image_col = "image_name",
    mark_select = "islet",
    nimages = 20,
    plot_hist = TRUE
)

We will use the mean of the two estimated vectors as our parameters.

(thres_spe <- mean(n$thres))
#> [1] 0.003463875
(bndw_spe <- mean(n$bndw))
#> [1] 12.82132

We cam now use the function reconstructShapeDensity to segment the islet of one image. The result is a sf polygon (Pebesma 2018).

islet <- reconstructShapeDensityImage(
    spe,
    marks = "cell_category",
    image_col = "image_name",
    image_id = "E04",
    mark_select = "islet",
    bndw = bndw_spe,
    dim = 500,
    thres = thres_spe
)
#> hardcopy

We can plot both the points and the estimated islets polygon.

plotSpots(
    spe[, spe[["image_name"]] %in% c("E04")],
    annotate = "cell_category",
    sample_id = "image_number",
    in_tissue = NULL,
    y_reverse = FALSE,
) +
    geom_sf(
        data = islet,
        fill = NA,
        color = "darkblue",
        inherit.aes = FALSE, # this is important
        linewidth = 0.75
    )
#> Coordinate system already present. Adding new coordinate system, which will
#> replace the existing one.

If no arguments are given, the function reconstructShapeDensityImage estimates the reconstruction parameters internally.

islet_2 <- reconstructShapeDensityImage(
    spe,
    marks = "cell_category",
    image_col = "image_name",
    image_id = "E04",
    mark_select = "islet",
    dim = 500
)
#> hardcopy
plotSpots(
    spe[, spe[["image_name"]] %in% c("E04")],
    annotate = "cell_category",
    sample_id = "image_number",
    in_tissue = NULL,
    y_reverse = FALSE,
) +
    geom_sf(
        data = islet_2,
        fill = NA,
        color = "darkblue",
        inherit.aes = FALSE,
        linewidth = 0.75
    )
#> Coordinate system already present. Adding new coordinate system, which will
#> replace the existing one.

4.2 Reconstruction of pancreatic islets for all images

The function reconstructShapeDensitySPE reconstructs the islets for all images in the spe object. We use the estimated parameters from above.

all_islets <- reconstructShapeDensitySPE(
    spe,
    marks = "cell_category",
    image_col = "image_name",
    mark_select = "islet",
    bndw = bndw_spe,
    thres = thres_spe,
    ncores = 2
)

5 Calculation of structure metrics

As we have islets for all images, we now use the function totalShapeMetrics to calculate a set of metrics related to the shape of the islets.

islet_shape_metrics <- totalShapeMetrics(all_islets)

The result is a simple feature collection with polygons. We will add some patient level information to the simple feature collection for plotting afterwards.

patient_data <- colData(spe) |>
    as_tibble() |>
    group_by(image_name) |>
    select(all_of(
        c(
            "patient_stage",
            "tissue_slide",
            "tissue_region",
            "patient_id",
            "patient_disease_duration",
            "patient_age",
            "patient_gender",
            "patient_ethnicity",
            "patient_BMI",
            "sample_id"
        )
    )) |>
    unique()
#> Adding missing grouping variables: `image_name`
all_islets <- dplyr::left_join(all_islets, patient_data, by = "image_name")
all_islets <- cbind(all_islets, t(islet_shape_metrics))

5.1 Plot structure metrics

We use PCA to get an overview of the different features. Each dot represent one structure.

library(ggfortify)

autoplot(
    prcomp(t(islet_shape_metrics), scale. = TRUE),
    x = 1,
    y = 2,
    data = all_islets,
    color = "patient_stage",
    size = 2,
    # shape = 'type',
    loadings = TRUE,
    loadings.colour = "steelblue3",
    loadings.label = TRUE,
    loadings.label.size = 3,
    loadings.label.repel = TRUE,
    loadings.label.colour = "black"
) +
    scale_color_brewer(palette = "Dark2") +
    theme_bw() +
    coord_fixed()

We can use boxplots to investigate differences of shape metrics between patient stages.

all_islets |>
    sf::st_drop_geometry() |>
    select(patient_stage, rownames(islet_shape_metrics)) |>
    pivot_longer(-patient_stage) |>
    ggplot(aes(x = patient_stage, y = value, fill = patient_stage)) +
    geom_boxplot() +
    facet_wrap(~name, scales = "free") +
    scale_fill_brewer(palette = "Dark2") +
    scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
    guides(fill = "none") +
    theme_bw()

sessionInfo()
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              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: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] ggfortify_0.4.17            imcdatasets_1.15.0         
#>  [3] cytomapper_1.19.0           EBImage_4.49.0             
#>  [5] SpatialExperiment_1.17.0    SingleCellExperiment_1.29.1
#>  [7] SummarizedExperiment_1.37.0 Biobase_2.67.0             
#>  [9] GenomicRanges_1.59.1        GenomeInfoDb_1.43.2        
#> [11] IRanges_2.41.2              S4Vectors_0.45.2           
#> [13] BiocGenerics_0.53.3         generics_0.1.3             
#> [15] MatrixGenerics_1.19.1       matrixStats_1.5.0          
#> [17] ggspavis_1.13.0             ggplot2_3.5.1              
#> [19] tidyr_1.3.1                 dplyr_1.1.4                
#> [21] sosta_0.99.3                BiocStyle_2.35.0           
#> 
#> loaded via a namespace (and not attached):
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#>   [7] farver_2.1.2            rmarkdown_2.29          vctrs_0.6.5            
#>  [10] memoise_2.0.1           spatstat.explore_3.3-4  RCurl_1.98-1.16        
#>  [13] terra_1.8-10            svgPanZoom_0.3.4        tinytex_0.54           
#>  [16] htmltools_0.5.8.1       S4Arrays_1.7.1          curl_6.1.0             
#>  [19] AnnotationHub_3.15.0    raster_3.6-31           Rhdf5lib_1.29.0        
#>  [22] SparseArray_1.7.4       rhdf5_2.51.2            sass_0.4.9             
#>  [25] KernSmooth_2.23-26      bslib_0.8.0             htmlwidgets_1.6.4      
#>  [28] cachem_1.1.0            mime_0.12               lifecycle_1.0.4        
#>  [31] ggside_0.3.1            pkgconfig_2.0.3         Matrix_1.7-1           
#>  [34] R6_2.5.1                fastmap_1.2.0           GenomeInfoDbData_1.2.13
#>  [37] shiny_1.10.0            digest_0.6.37           colorspace_2.1-1       
#>  [40] AnnotationDbi_1.69.0    patchwork_1.3.0         tensor_1.5             
#>  [43] ExperimentHub_2.15.0    RSQLite_2.3.9           labeling_0.4.3         
#>  [46] filelock_1.0.3          spatstat.sparse_3.1-0   nnls_1.6               
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#>  [55] withr_3.0.2             tiff_0.1-12             BiocParallel_1.41.0    
#>  [58] viridis_0.6.5           DBI_1.2.3               HDF5Array_1.35.7       
#>  [61] rappdirs_0.3.3          DelayedArray_0.33.4     rjson_0.2.23           
#>  [64] classInt_0.4-11         tools_4.5.0             units_0.8-5            
#>  [67] vipor_0.4.7             beeswarm_0.4.0          httpuv_1.6.15          
#>  [70] goftest_1.2-3           glue_1.8.0              nlme_3.1-166           
#>  [73] rhdf5filters_1.19.0     promises_1.3.2          grid_4.5.0             
#>  [76] sf_1.0-19               gtable_0.3.6            spatstat.data_3.1-4    
#>  [79] class_7.3-23            sp_2.1-4                XVector_0.47.2         
#>  [82] spatstat.geom_3.3-5     stringr_1.5.1           BiocVersion_3.21.1     
#>  [85] ggrepel_0.9.6           pillar_1.10.1           later_1.4.1            
#>  [88] BiocFileCache_2.15.1    lattice_0.22-6          bit_4.5.0.1            
#>  [91] deldir_2.0-4            tidyselect_1.2.1        locfit_1.5-9.10        
#>  [94] Biostrings_2.75.3       knitr_1.49              gridExtra_2.3          
#>  [97] bookdown_0.42           svglite_2.1.3           xfun_0.50              
#> [100] shinydashboard_0.7.2    smoothr_1.0.1           stringi_1.8.4          
#> [103] UCSC.utils_1.3.1        fftwtools_0.9-11        yaml_2.3.10            
#> [106] evaluate_1.0.3          codetools_0.2-20        tibble_3.2.1           
#> [109] BiocManager_1.30.25     cli_3.6.3               xtable_1.8-4           
#> [112] systemfonts_1.2.0       munsell_0.5.1           jquerylib_0.1.4        
#> [115] Rcpp_1.0.14             spatstat.random_3.3-2   dbplyr_2.5.0           
#> [118] png_0.1-8               spatstat.univar_3.1-1   parallel_4.5.0         
#> [121] blob_1.2.4              jpeg_0.1-10             bitops_1.0-9           
#> [124] viridisLite_0.4.2       scales_1.3.0            e1071_1.7-16           
#> [127] purrr_1.0.2             crayon_1.5.3            rlang_1.1.5            
#> [130] KEGGREST_1.47.0

References

Damond, Nicolas, Stefanie Engler, Vito R. T. Zanotelli, Denis Schapiro, Clive H. Wasserfall, Irina Kusmartseva, Harry S. Nick, et al. 2019. “A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry.” Cell Metabolism 29 (3): 755–768.e5. https://doi.org/10.1016/j.cmet.2018.11.014.
Pebesma, Edzer. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal 10 (1): 439. https://doi.org/10.32614/RJ-2018-009.