Create a simulated Giotto object
Source:vignettes/create_simulated_object.Rmd
create_simulated_object.Rmd1 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()
}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.
5 Import the simulated data to Giotto
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