# 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()
}
library(Giotto)The example dataset used here is from 10X link
link <- "https://cf.10xgenomics.com/samples/cell-exp/8.0.1/Human_PBMCs_Next_GEM_Flex_GEM-X_Flex_Comparison/Human_PBMCs_Next_GEM_Flex_GEM-X_Flex_Comparison_count_filtered_feature_bc_matrix.h5"
# Specify path from which data may be retrieved/stored
data_path <- "/path/to/data/"
save_path <- file.path(data_path, "pbmc_matrix.h5")
utils::download.file(link, destfile = save_path, method = "wget")Read the expression matrix, which is provided from 10X as an h5 format. get10Xmatrix_h5() is a convenience function from Giotto that will import this format as a Matrix dgCMatrix sparse matrix.
pbmc_mat <- get10Xmatrix_h5(save_path)Single cell datasets should be loaded in through createGiottoObject(). During the load, a set of dummy spatial locations that can be safely ignored will also be generated.
pbmc <- createGiottoObject(expression = pbmc_mat)
force(pbmc)An object of class giotto
>Active spat_unit: cell
>Active feat_type: rna
dimensions : 15146, 8703 (features, cells)
[SUBCELLULAR INFO]
[AGGREGATE INFO]
expression -----------------------
[cell][rna] Gene Expression
spatial locations ----------------
[cell] raw
Use objHistory() to see steps and params usedR 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 fastmap_1.2.0 SingleCellExperiment_1.26.0
[7] lazyeval_0.2.2 digest_0.6.37 lifecycle_1.0.4
[10] terra_1.7-78 magrittr_2.0.3 compiler_4.4.1
[13] rlang_1.1.4 tools_4.4.1 igraph_2.1.1
[16] utf8_1.2.4 yaml_2.3.10 data.table_1.16.2
[19] knitr_1.48 S4Arrays_1.4.0 htmlwidgets_1.6.4
[22] bit_4.5.0 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 grid_4.4.1 stats4_4.4.1
[31] fansi_1.0.6 colorspace_2.1-1 progressr_0.14.0
[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 bit64_4.5.2
[55] ggrepel_0.9.6 scattermore_1.2 hdf5r_1.3.10
[58] magick_2.8.5 GiottoUtils_0.2.1 plotly_4.10.4
[61] tidyr_1.3.1 glue_1.8.0 codetools_0.2-20
[64] cowplot_1.1.3 gtable_0.3.5 GenomeInfoDb_1.40.0
[67] GenomicRanges_1.56.0 UCSC.utils_1.0.0 munsell_0.5.1
[70] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.8.1
[73] GenomeInfoDbData_1.2.12 R6_2.5.1 evaluate_1.0.0
[76] lattice_0.22-6 Biobase_2.64.0 png_0.1-8
[79] backports_1.5.0 SpatialExperiment_1.14.0 Rcpp_1.0.13
[82] SparseArray_1.4.1 checkmate_2.3.2 colorRamp2_0.1.0
[85] xfun_0.47 MatrixGenerics_1.16.0 pkgconfig_2.0.3