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Giotto Image Tools

1. Overview

Giotto uses giottoLargeImage to represent images and raster information. This is an S4 class built based on the terra SpatRaster.

An older class called giottoImage based on the magick package also exists, but is currently being phased out. Ideally, giottoLargeImages will also be renamed to giottoImage afterwards, simplifying the naming scheme.

The object structure of giottoLargeImage

giottoLargeImage
\- name                (object name)
\- raster_object       (terra raster object)
\- extent              (current spatial extent)
\- overall_extent      (spatial extent of original image - experimental)
\- scale_factor        (pixels per coordinate unit - experimental)
\- resolution          (coordinate units covered per pixel)
\- max_intensity       (approximate maximum intensity value)
\- min_intensity       (approximate minimum intensity value)
\- max_window          (value to set as maximum intensity in color scaling)
\- colors              (vector of color mappings provided as hex codes)
\- is_int              (whether values are integers)
\- file_path           (filepath to the image)
\- OS_platform         (operating system)

2. Sampling

Spatial images are often extremely large files. The high detail (resolution), large spatial region captured, and precision of recorded values (bitdepth) often results in files in the 10s of gigabytes. This makes the full size images difficult to work with. One way to get around this is to perform regular sampling of the image, touching only the values of the original image that are needed generate a downscaled representation. This is implemented in terra as spatSample().

When plotting, Giotto optimizes the speed of this sampling by striking a balance between terra::crop() and terra::spatSample() to try to prevent sampling from unnecessary regions, but also avoid large crop operations on the fullsize image, which can be very costly. This is done through plot_auto_largeImage_resample()

giottoLargeImages can also be resampled to other image formats (magick and EBImage) in addition to data.table and array using GiottoClass:::.spatraster_sample_values()

These two functions are experimental and they will be exported using more common function names in the future.

3 Color scaling

distGiottoImage() can be used to look at the values present within a giottoLargeImage. It is often the case that the values recorded within an image do not map to the full set of values allowed by its bitdepth. Giotto plots images by guessing the bitdepth based on the estimated maximum value detected. This mapping may not always be optimal.

library(GiottoClass)
gimg <- GiottoData::loadSubObjectMini("giottoLargeImage", idx = 2)
gimg <- GiottoClass:::.update_giotto_image(gimg) # update older images that are missing slots

plot(gimg)

distGiottoImage(giottoLargeImage = gimg)

Since there are few values beyond 70, try setting max_window to 70 in so that the color mapping better represents the available information.

gimg@max_window <- 70
plot(gimg)

# plot(gimg, max_intensity = 150) # can be used without setting the slot

The colormap to use for an image can also be edited. The default for a greyscale image is a monochrome black to white. Other color scales can be supplied. For monochrome colorscales: getMonochromeColors()

gimg@colors <- getMonochromeColors("green")
plot(gimg)

viridis_colors <- hcl.colors(256, palette = "viridis")
plot(gimg, col = viridis_colors) # can be used without setting the slot

TODOs:

  • provide accessors for these slots
  • make ggplot-based plotting also obey these settings
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
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## time zone: UTC
## tzcode source: system (glibc)
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## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
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## other attached packages:
## [1] GiottoClass_0.4.5
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.9                  SparseArray_1.6.0          
##  [3] gtools_3.9.5                lattice_0.22-6             
##  [5] digest_0.6.37               magrittr_2.0.3             
##  [7] evaluate_1.0.1              grid_4.4.2                 
##  [9] fastmap_1.2.0               jsonlite_1.8.9             
## [11] Matrix_1.7-1                backports_1.5.0            
## [13] GenomeInfoDb_1.42.1         GiottoData_0.2.16          
## [15] httr_1.4.7                  SingleCellExperiment_1.28.1
## [17] UCSC.utils_1.2.0            codetools_0.2-20           
## [19] textshaping_0.4.1           jquerylib_0.1.4            
## [21] abind_1.4-8                 cli_3.6.3                  
## [23] rlang_1.1.4                 crayon_1.5.3               
## [25] XVector_0.46.0              Biobase_2.66.0             
## [27] cachem_1.1.0                DelayedArray_0.32.0        
## [29] yaml_2.3.10                 S4Arrays_1.6.0             
## [31] tools_4.4.2                 GiottoUtils_0.2.2          
## [33] checkmate_2.3.2             SpatialExperiment_1.16.0   
## [35] GenomeInfoDbData_1.2.13     SummarizedExperiment_1.36.0
## [37] BiocGenerics_0.52.0         R6_2.5.1                   
## [39] magick_2.8.5                matrixStats_1.4.1          
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## [47] IRanges_2.40.1              ragg_1.3.3                 
## [49] desc_1.4.3                  pkgdown_2.1.1              
## [51] terra_1.8-5                 bslib_0.8.0                
## [53] data.table_1.16.4           Rcpp_1.0.13-1              
## [55] systemfonts_1.1.0           xfun_0.49                  
## [57] GenomicRanges_1.58.0        MatrixGenerics_1.18.0      
## [59] knitr_1.49                  rjson_0.2.23               
## [61] htmltools_0.5.8.1           rmarkdown_2.29             
## [63] compiler_4.4.2