Independently of the technology that you used, there are some common steps on every analysis such as the filtering of cells or spots, and normalization. We have wrapped up these common steps into a single function, processGiotto().
After processGiotto, there are different downstream analysis that you can perform with Giotto such as dimension reduction, clustering, cell type annotation, etc. Check the individual technologies examples and the tutorials section for more details.
Depending on the technology used for sequencing your sample, you might need to run some specific steps for reading the expression matrix, spatial locations (when using a spatial dataset), images, fields of view, or even sub-cellular information. Look at the different technologies examples available in the Giotto website for more details.
For running this example, we will use two mini datasets available at the GiottoData package. A mini visium dataset with spots resolution, and a mini vizgen dataset with cellular and sub-cellular resolution.
# 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)
# 1. set working directory
results_folder <- "path/to/results"
# Optional: Specify a path to a Python executable within a conda or miniconda
# environment. If set to NULL (default), the Python executable within the previously
# installed Giotto environment will be used.
python_path <- NULL # alternatively, "/local/python/path/python" if desired.
visium_object <- GiottoData::loadGiottoMini("visium",
python_path = python_path)
vizgen_object <- GiottoData::loadGiottoMini("vizgen",
python_path = python_path)
instructions(visium_object, "save_plot") <- TRUE
instructions(visium_object, "save_dir") <- results_folder
instructions(visium_object, "show_plot") <- FALSE
instructions(visium_object, "return_plot") <- FALSE
instructions(vizgen_object, "save_plot") <- TRUE
instructions(vizgen_object, "save_dir") <- results_folder
instructions(vizgen_object, "show_plot") <- FALSE
instructions(vizgen_object, "return_plot") <- FALSE
spatPlot2D(gobject = visium_object,
show_image = TRUE)
spatPlot2D(gobject = vizgen_object,
show_image = TRUE)
visium_object <- processGiotto(visium_object,
filter_params = list(expression_threshold = 1,
feat_det_in_min_cells = 1,
min_det_feats_per_cell = 50),
norm_params = list(scalefactor = 6000),
adjust_params = list(covariate_columns = "leiden_clus"))
vizgen_object <- processGiotto(gobject = vizgen_object,
filter_params = list(expression_threshold = 1,
feat_det_in_min_cells = 1,
min_det_feats_per_cell = 1),
adjust_params = NULL)
plotUMAP(gobject = visium_object,
cell_color = "leiden_clus",
show_NN_network = TRUE,
point_size = 2.5)
vizgen_object <- runPCA(vizgen_object)
vizgen_object <- runUMAP(vizgen_object)
vizgen_object <- createNearestNetwork(vizgen_object)
vizgen_object <- doLeidenCluster(vizgen_object)
plotUMAP(gobject = vizgen_object,
cell_color = "leiden_clus",
show_NN_network = TRUE,
point_size = 2.5)
plotTSNE(gobject = visium_object,
cell_color = "leiden_clus",
show_NN_network = FALSE,
point_size = 2.5)
vizgen_object <- runtSNE(vizgen_object)
plotTSNE(gobject = vizgen_object,
cell_color = "leiden_clus",
show_NN_network = TRUE,
point_size = 2.5)
spatPlot2D(gobject = visium_object,
cell_color = "leiden_clus",
show_image = TRUE,
point_size = 4)
spatPlot2D(gobject = vizgen_object,
cell_color = "leiden_clus",
show_image = TRUE,
point_size = 3)
4.4.0 (2024-04-24)
R version : x86_64-apple-darwin20
Platform: macOS Sonoma 14.6.1
Running under
: default
Matrix products: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
LAPACK
:
locale1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
[
: America/New_York
time zone: internal
tzcode source
:
attached base packages1] stats graphics grDevices utils datasets methods base
[
:
other attached packages1] Giotto_4.1.1 GiottoClass_0.3.5
[
namespace (and not attached):
loaded via a [1] colorRamp2_0.1.0 deldir_2.0-4 rlang_1.1.4
4] magrittr_2.0.3 GiottoUtils_0.1.11 matrixStats_1.3.0
[7] compiler_4.4.0 png_0.1-8 systemfonts_1.1.0
[10] vctrs_0.6.5 reshape2_1.4.4 stringr_1.5.1
[13] pkgconfig_2.0.3 SpatialExperiment_1.14.0 crayon_1.5.3
[16] fastmap_1.2.0 backports_1.5.0 magick_2.8.4
[19] XVector_0.44.0 labeling_0.4.3 utf8_1.2.4
[22] rmarkdown_2.28 UCSC.utils_1.0.0 ragg_1.3.2
[25] purrr_1.0.2 xfun_0.47 beachmat_2.20.0
[28] zlibbioc_1.50.0 GenomeInfoDb_1.40.1 jsonlite_1.8.8
[31] DelayedArray_0.30.1 BiocParallel_1.38.0 terra_1.7-78
[34] irlba_2.3.5.1 parallel_4.4.0 R6_2.5.1
[37] stringi_1.8.4 RColorBrewer_1.1-3 limma_3.60.4
[40] reticulate_1.38.0 GenomicRanges_1.56.1 scattermore_1.2
[43] Rcpp_1.0.13 SummarizedExperiment_1.34.0 knitr_1.48
[46] R.utils_2.12.3 FNN_1.1.4 IRanges_2.38.1
[49] Matrix_1.7-0 igraph_2.0.3 tidyselect_1.2.1
[52] rstudioapi_0.16.0 abind_1.4-5 yaml_2.3.10
[55] codetools_0.2-20 lattice_0.22-6 tibble_3.2.1
[58] plyr_1.8.9 Biobase_2.64.0 withr_3.0.1
[61] Rtsne_0.17 evaluate_0.24.0 pillar_1.9.0
[64] MatrixGenerics_1.16.0 checkmate_2.3.2 stats4_4.4.0
[67] plotly_4.10.4 generics_0.1.3 dbscan_1.2-0
[70] sp_2.1-4 S4Vectors_0.42.1 ggplot2_3.5.1
[73] munsell_0.5.1 scales_1.3.0 GiottoData_0.2.13
[76] gtools_3.9.5 glue_1.7.0 lazyeval_0.2.2
[79] tools_4.4.0 GiottoVisuals_0.2.5 data.table_1.16.0
[82] ScaledMatrix_1.12.0 cowplot_1.1.3 grid_4.4.0
[85] tidyr_1.3.1 colorspace_2.1-1 SingleCellExperiment_1.26.0
[88] GenomeInfoDbData_1.2.12 BiocSingular_1.20.0 rsvd_1.0.5
[91] cli_3.6.3 textshaping_0.4.0 fansi_1.0.6
[94] S4Arrays_1.4.1 viridisLite_0.4.2 dplyr_1.1.4
[97] uwot_0.2.2 gtable_0.3.5 R.methodsS3_1.8.2
[100] digest_0.6.37 BiocGenerics_0.50.0 SparseArray_1.4.8
[103] ggrepel_0.9.5 rjson_0.2.22 htmlwidgets_1.6.4
[106] farver_2.1.2 htmltools_0.5.8.1 R.oo_1.26.0
[109] lifecycle_1.0.4 httr_1.4.7 statmod_1.5.0 [