Create a simulated Giotto object

Source: vignettes/create_simulated_object.Rmd

1 Set up Giotto Environment 

# 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()
}

2 Install the sCCIgen package 

remotes::install_github("songxiaoyu/sCCIgen")

3 Load the sCCIgen package 

library(sCCIgen)

4 Run the interactive app 

run_interactive_sCCIgen()

4.1 Select the reference dataset to use

The simulator requires to learn the expression and/or spatial patterns from a previous dataset. Select the dataset to use as reference. If you haven’t downloaded the dataset, files you can download them in this first step.

4.2 Create the parameter file

Navigate through the questions to select the properties of the simulated dataset.

4.3 Run the simulation

Choose what output you desire to obtain. By default, the simulator will create a directory with the expression and spatial information. Optionally, you can also create a local Giotto object. To follow this tutorial select the option to create a Giotto object.

5 Import the simulated data to Giotto

5.1 Read the expression matrix 

expression <- data.table::fread("output_files/output_count_1.tsv")
expression_matrix <- as.matrix(expression[,-1])
rownames(expression_matrix) <- expression$GeneName

5.2 Read the metadata and spatial locations 

metadata <- data.table::fread("output_files/output_meta_1.tsv")
spatial_locs <- metadata[, c("x.loc", "y.loc", "Cell")]
colnames(spatial_locs) <- c("sdimx", "sdimy", "cell_ID")

5.3 Create the Giotto instructions 

library(Giotto)

results_folder <- "path/to/results"
python_path <- NULL

instructions <- createGiottoInstructions(save_dir = results_folder,
                                         save_plot = TRUE,
                                         show_plot = FALSE,
                                         return_plot = FALSE,
                                         python_path = python_path)

5.4 Create the Giotto object 

giotto_object <- createGiottoObject(expression = expression_matrix,
                                    spatial_locs = spatial_locs,
                                    instructions = instructions)

5.5 Add metadata 

annotation <- metadata$annotation
region <- metadata$region

giotto_object <- addCellMetadata(giotto_object,
                                 new_metadata = annotation)

giotto_object <- addCellMetadata(giotto_object,
                                 new_metadata = region)

5.6 Plot the object 

spatPlot2D(giotto_object,
           cell_color = "annotation",
           point_size = 1)

spatPlot2D(giotto_object,
           cell_color = "region",
           point_size = 1)

giotto_object <- addStatistics(giotto_object,
                               expression_values = "raw")

spatPlot2D(giotto_object,
           cell_color = "nr_feats",
           point_size = 2,
           color_as_factor = FALSE)

6 Session Info 

R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS 15.0

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-x86_64/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_4.1.3      GiottoClass_0.4.0 shiny_1.9.1       sCCIgen_1.0      

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          rstudioapi_0.16.0          
  [3] jsonlite_1.8.9              magrittr_2.0.3             
  [5] spatstat.utils_3.1-0        magick_2.8.5               
  [7] farver_2.1.2                rmarkdown_2.28             
  [9] ragg_1.3.3                  fs_1.6.4                   
 [11] zlibbioc_1.50.0             vctrs_0.6.5                
 [13] memoise_2.0.1               spatstat.explore_3.3-2     
 [15] GiottoUtils_0.1.12          terra_1.7-78               
 [17] htmltools_0.5.8.1           S4Arrays_1.4.1             
 [19] raster_3.6-26               SparseArray_1.4.8          
 [21] sass_0.4.9                  shinyFiles_0.9.3           
 [23] KernSmooth_2.23-24          bslib_0.8.0                
 [25] htmlwidgets_1.6.4           fontawesome_0.5.2          
 [27] plotly_4.10.4               cachem_1.1.0               
 [29] igraph_2.0.3                mime_0.12                  
 [31] lifecycle_1.0.4             iterators_1.0.14           
 [33] pkgconfig_2.0.3             Matrix_1.7-0               
 [35] R6_2.5.1                    fastmap_1.2.0              
 [37] GenomeInfoDbData_1.2.12     MatrixGenerics_1.16.0      
 [39] digest_0.6.37               colorspace_2.1-1           
 [41] S4Vectors_0.42.1            tensor_1.5                 
 [43] textshaping_0.4.0           GenomicRanges_1.56.1       
 [45] spatstat.linnet_3.2-2       labeling_0.4.3             
 [47] fansi_1.0.6                 spatstat.sparse_3.1-0      
 [49] httr_1.4.7                  polyclip_1.10-7            
 [51] abind_1.4-8                 mgcv_1.9-1                 
 [53] compiler_4.4.1              proxy_0.4-27               
 [55] withr_3.0.1                 bit64_4.5.2                
 [57] doParallel_1.0.17           backports_1.5.0            
 [59] DBI_1.2.3                   spatstat.model_3.3-2       
 [61] R.utils_2.12.3              DelayedArray_0.30.1        
 [63] classInt_0.4-10             rjson_0.2.23               
 [65] gtools_3.9.5                units_0.8-5                
 [67] GiottoVisuals_0.2.5         tools_4.4.1                
 [69] httpuv_1.6.15               goftest_1.2-3              
 [71] R.oo_1.26.0                 glue_1.7.0                 
 [73] dbscan_1.2-0                nlme_3.1-166               
 [75] promises_1.3.0              sf_1.0-17                  
 [77] grid_4.4.1                  checkmate_2.3.2            
 [79] generics_0.1.3              gtable_0.3.5               
 [81] spatstat.data_3.1-2         tzdb_0.4.0                 
 [83] class_7.3-22                R.methodsS3_1.8.2          
 [85] tidyr_1.3.1                 data.table_1.16.0          
 [87] hms_1.1.3                   sp_2.1-4                   
 [89] utf8_1.2.4                  XVector_0.44.0             
 [91] BiocGenerics_0.50.0         spatstat.geom_3.3-3        
 [93] ggrepel_0.9.6               foreach_1.5.2              
 [95] pillar_1.9.0                vroom_1.6.5                
 [97] later_1.3.2                 splines_4.4.1              
 [99] dplyr_1.1.4                 lattice_0.22-6             
[101] bit_4.5.0                   deldir_2.0-4               
[103] tidyselect_1.2.1            SingleCellExperiment_1.26.0
[105] miniUI_0.1.1.1              knitr_1.48                 
[107] IRanges_2.38.1              SummarizedExperiment_1.34.0
[109] scattermore_1.2             stats4_4.4.1               
[111] xfun_0.47                   Biobase_2.64.0             
[113] matrixStats_1.4.1           UCSC.utils_1.0.0           
[115] lazyeval_0.2.2              yaml_2.3.10                
[117] evaluate_1.0.0              codetools_0.2-20           
[119] spatstat_3.2-1              tibble_3.2.1               
[121] colorRamp2_0.1.0            cli_3.6.3                  
[123] rpart_4.1.23                systemfonts_1.1.0          
[125] xtable_1.8-4                reticulate_1.39.0          
[127] munsell_0.5.1               jquerylib_0.1.4            
[129] Rcpp_1.0.13                 GenomeInfoDb_1.40.1        
[131] spatstat.random_3.3-2       png_0.1-8                  
[133] spatstat.univar_3.0-1       parallel_4.4.1             
[135] ggplot2_3.5.1               readr_2.1.5                
[137] SpatialExperiment_1.14.0    viridisLite_0.4.2          
[139] e1071_1.7-16                scales_1.3.0               
[141] purrr_1.0.2                 crayon_1.5.3               
[143] rlang_1.1.4                 cowplot_1.1.3