Spatially Informed clusters
Source:vignettes/spatial_informed_clusters.Rmd
spatial_informed_clusters.Rmd1 Setup and load example dataset
# Ensure Giotto Suite is installed
if(!"Giotto" %in% installed.packages()) {
pak::pkg_install("drieslab/Giotto")
}
# Ensure Giotto Data is installed
if(!"GiottoData" %in% installed.packages()) {
pak::pkg_install("drieslab/GiottoData")
}
library(Giotto)
# Ensure the Python environment for Giotto has been installed
genv_exists <- checkGiottoEnvironment()
if(!genv_exists){
# The following command need only be run once to install the Giotto environment
installGiottoEnvironment()
}
# load the object
g <- GiottoData::loadGiottoMini("visium")2 Get the spatial genes
Get the top genes per spatial co-expression module. For more information about how to calculate these genes, see the tutorial for Detection of spatial co-expression modules.
spatial_genes <- c("Hlf", "Amotl1", "Adarb1", "Rab3c",
"Prkcd", "Prph", "Tcf7l2", "Bok",
"Ptpn4", "Rgs16", "Cnp", "Bcas1",
"Qdpr", "Plekhb1", "Cryab", "Mobp",
"Stx1a", "Arpp19", "Ttc9b", "Fam163b",
"Igfbp6", "Ngef", "Lamp5", "Dkk3",
"Dkkl1", "Tbr1", "Cabp7", "Gria1",
"Cpne6", "Bhlhe22", "Lct", "1700001C02Rik",
"Crym", "Wfs1", "Scn3b", "Spink8",
"Syn2", "Nptxr", "Ppp1r1a", "Kcnip2",
"Nptx1", "Cplx2", "Snca", "2010300C02Rik",
"Itpka", "Hpca")3 Re-calculate the clustering
Use the spatial genes to calculate again the principal components, umap, network and clustering.
g <- runPCA(gobject = g,
feats_to_use = spatial_genes,
name = "custom_pca")
g <- runUMAP(g,
dim_reduction_name = "custom_pca",
dimensions_to_use = 1:20,
name = "custom_umap")
g <- createNearestNetwork(gobject = g,
dim_reduction_name = "custom_pca",
dimensions_to_use = 1:20,
k = 5,
name = "custom_NN")
g <- doLeidenCluster(gobject = g,
network_name = "custom_NN",
resolution = 0.15,
n_iterations = 1000,
name = "custom_leiden")4 Visualize
Plot the spatial distribution of the Leiden clusters calculated based on the spatial genes.
spatPlot2D(g,
cell_color = "custom_leiden",
point_size = 7)
Plot the UMAP and color the spots using the Leiden clusters calculated based on the spatial genes.
plotUMAP(gobject = g,
cell_color = "custom_leiden")
5 Session Info
R version 4.4.2 (2024-10-31)
Platform: x86_64-apple-darwin20
Running under: macOS Sequoia 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-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_4.1.4 GiottoClass_0.4.3
loaded via a namespace (and not attached):
[1] colorRamp2_0.1.0 rlang_1.1.4
[3] magrittr_2.0.3 GiottoUtils_0.2.0
[5] matrixStats_1.4.1 compiler_4.4.2
[7] systemfonts_1.1.0 png_0.1-8
[9] vctrs_0.6.5 pkgconfig_2.0.3
[11] SpatialExperiment_1.14.0 crayon_1.5.3
[13] fastmap_1.2.0 backports_1.5.0
[15] magick_2.8.5 XVector_0.44.0
[17] labeling_0.4.3 utf8_1.2.4
[19] rmarkdown_2.28 UCSC.utils_1.0.0
[21] ragg_1.3.3 purrr_1.0.2
[23] xfun_0.47 zlibbioc_1.50.0
[25] beachmat_2.20.0 GenomeInfoDb_1.40.1
[27] jsonlite_1.8.9 DelayedArray_0.30.1
[29] BiocParallel_1.38.0 terra_1.7-78
[31] irlba_2.3.5.1 parallel_4.4.2
[33] R6_2.5.1 RColorBrewer_1.1-3
[35] reticulate_1.39.0 GenomicRanges_1.56.1
[37] scattermore_1.2 Rcpp_1.0.13
[39] SummarizedExperiment_1.34.0 knitr_1.48
[41] R.utils_2.12.3 IRanges_2.38.1
[43] FNN_1.1.4.1 Matrix_1.7-1
[45] igraph_2.0.3 tidyselect_1.2.1
[47] rstudioapi_0.16.0 abind_1.4-8
[49] yaml_2.3.10 codetools_0.2-20
[51] lattice_0.22-6 tibble_3.2.1
[53] Biobase_2.64.0 withr_3.0.1
[55] evaluate_1.0.0 pillar_1.9.0
[57] MatrixGenerics_1.16.0 checkmate_2.3.2
[59] stats4_4.4.2 plotly_4.10.4
[61] generics_0.1.3 dbscan_1.2-0
[63] S4Vectors_0.42.1 ggplot2_3.5.1
[65] munsell_0.5.1 scales_1.3.0
[67] GiottoData_0.2.15 gtools_3.9.5
[69] glue_1.8.0 lazyeval_0.2.2
[71] tools_4.4.2 GiottoVisuals_0.2.7
[73] data.table_1.16.0 ScaledMatrix_1.12.0
[75] cowplot_1.1.3 grid_4.4.2
[77] tidyr_1.3.1 colorspace_2.1-1
[79] SingleCellExperiment_1.26.0 GenomeInfoDbData_1.2.12
[81] BiocSingular_1.20.0 cli_3.6.3
[83] rsvd_1.0.5 textshaping_0.4.0
[85] fansi_1.0.6 S4Arrays_1.4.1
[87] viridisLite_0.4.2 dplyr_1.1.4
[89] uwot_0.2.2 gtable_0.3.5
[91] R.methodsS3_1.8.2 digest_0.6.37
[93] BiocGenerics_0.50.0 SparseArray_1.4.8
[95] ggrepel_0.9.6 rjson_0.2.23
[97] htmlwidgets_1.6.4 farver_2.1.2
[99] htmltools_0.5.8.1 R.oo_1.26.0
[101] lifecycle_1.0.4 httr_1.4.7