Giotto Object Creation - Piecewise

Source: vignettes/obj_create_mod.Rmd 
# Ensure Giotto Suite is installed.
if(!"Giotto" %in% installed.packages()) {
  pak::pkg_install("drieslab/Giotto")
}
# Ensure the Python environment for Giotto has been installed.
genv_exists <- Giotto::checkGiottoEnvironment()
if(!genv_exists){
  # The following command need only be run once to install the Giotto environment.
  Giotto::installGiottoEnvironment()
}

# Ensure Giotto Suite is installed.
if(!"GiottoData" %in% installed.packages()) {
  pak::pkg_install("drieslab/GiottoData")
}

library(Giotto)

Piecewise creation is most convenient when assembling a fully custom giotto analysis object. This method requires first loading the data in as Giotto-compatible subobjects, after which they can be directly appended to an empty giotto object.

For a more complete introduction to the data and Giotto subobjects used here, see the point data Giotto object creation tutorial

1 Abbreviated data loading steps 

# function to get a filepath from GiottoData
mini_viz_raw <- function(x) {
  system.file(
    package = "GiottoData",
    file.path("Mini_datasets", "Vizgen", "Raw", x)
  )
}

mini_viz_poly_path <- mini_viz_raw(file.path("cell_boundaries", "z0_polygons.gz"))
mini_viz_tx_path <- mini_viz_raw("vizgen_transcripts.gz")
poly_dt <- data.table::fread(mini_viz_poly_path)
viz_gpoly <- createGiottoPolygon(poly_dt)
tx_dt <- data.table::fread(mini_viz_tx_path)
tx_dt[, global_y := -global_y] # flip values to match polys 
viz_gpoints <- createGiottoPoints(tx_dt)

2 Create a giotto object through appending subobjects 

g <- giotto() # initialize empty gobject
g <- setGiotto(g, viz_gpoly)
g <- setGiotto(g, viz_gpoints)
force(g)
An object of class giotto 
>Active spat_unit:  cell 
>Active feat_type:  rna 
dimensions    : 559, 498 (features, cells)
[SUBCELLULAR INFO]
polygons      : cell 
features      : rna 
[AGGREGATE INFO]


Use objHistory() to see steps and params used

This is essentially the same object as the one created through createGiottoObjectSubcellular() as shown in the previously mentioned tutorial.

Appending additional information in this manner can be used both when objects are created from scratch using giotto() and when they are created through more guided approaches such as createGiottoObject() and technology-specific convenience functions. This method of object creation and modification can be helpful for editing the object, adding additional modalities, or setting up objects for custom workflows.

3 Session Info 

R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS 15.0.1

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] Giotto_4.1.5      GiottoClass_0.4.3

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1            viridisLite_0.4.2           dplyr_1.1.4                
 [4] GiottoVisuals_0.2.7         R.utils_2.12.3              fastmap_1.2.0              
 [7] SingleCellExperiment_1.26.0 lazyeval_0.2.2              digest_0.6.37              
[10] lifecycle_1.0.4             terra_1.7-78                magrittr_2.0.3             
[13] compiler_4.4.1              rlang_1.1.4                 tools_4.4.1                
[16] yaml_2.3.10                 igraph_2.1.1                utf8_1.2.4                 
[19] data.table_1.16.2           knitr_1.48                  S4Arrays_1.4.0             
[22] htmlwidgets_1.6.4           reticulate_1.39.0           DelayedArray_0.30.0        
[25] abind_1.4-8                 withr_3.0.1                 purrr_1.0.2                
[28] BiocGenerics_0.50.0         R.oo_1.26.0                 grid_4.4.1                 
[31] stats4_4.4.1                fansi_1.0.6                 colorspace_2.1-1           
[34] ggplot2_3.5.1               scales_1.3.0                gtools_3.9.5               
[37] SummarizedExperiment_1.34.0 cli_3.6.3                   rmarkdown_2.28             
[40] crayon_1.5.3                generics_0.1.3              rstudioapi_0.16.0          
[43] httr_1.4.7                  rjson_0.2.21                zlibbioc_1.50.0            
[46] parallel_4.4.1              XVector_0.44.0              matrixStats_1.4.1          
[49] vctrs_0.6.5                 Matrix_1.7-0                jsonlite_1.8.9             
[52] IRanges_2.38.0              S4Vectors_0.42.0            ggrepel_0.9.6              
[55] scattermore_1.2             magick_2.8.5                GiottoUtils_0.2.1          
[58] plotly_4.10.4               tidyr_1.3.1                 glue_1.8.0                 
[61] codetools_0.2-20            cowplot_1.1.3               gtable_0.3.5               
[64] GenomeInfoDb_1.40.0         GenomicRanges_1.56.0        UCSC.utils_1.0.0           
[67] munsell_0.5.1               tibble_3.2.1                pillar_1.9.0               
[70] htmltools_0.5.8.1           GenomeInfoDbData_1.2.12     R6_2.5.1                   
[73] evaluate_1.0.0              lattice_0.22-6              Biobase_2.64.0             
[76] png_0.1-8                   R.methodsS3_1.8.2           backports_1.5.0            
[79] SpatialExperiment_1.14.0    Rcpp_1.0.13                 SparseArray_1.4.1          
[82] checkmate_2.3.2             colorRamp2_0.1.0            xfun_0.47                  
[85] MatrixGenerics_1.16.0       pkgconfig_2.0.3