Detection of spatial co-expression modules
Source:vignettes/spatial_coexpression_modules.Rmd
spatial_coexpression_modules.RmdOnce you have found Finding genes or features that follow spatial patterns in their expression, you can group them to find clusters with similar expression patterns that create co-expression modules.
1 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")3 Calculate pairwise distances between genes.
spat_cor_netw_DT <- detectSpatialCorFeats(
g,
method = "network",
spatial_network_name = "spatial_network",
subset_feats = ext_spatial_genes)4 Cluster the top spatial genes into 5 clusters
spat_cor_netw_DT <- clusterSpatialCorFeats(spat_cor_netw_DT,
name = "spat_netw_clus",
k = 5)5 Plot the correlation of the top spatial genes with their assigned cluster.
heatmSpatialCorFeats(g,
spatCorObject = spat_cor_netw_DT,
use_clus_name = "spat_netw_clus",
heatmap_legend_param = list(title = NULL))
5.1 Rank spatial correlated clusters and show genes for selected clusters
netw_ranks <- rankSpatialCorGroups(g,
spatCorObject = spat_cor_netw_DT,
use_clus_name = "spat_netw_clus")
5.2 Create the metagene enrichment score per co-expression cluster
cluster_genes_DT <- showSpatialCorFeats(spat_cor_netw_DT,
use_clus_name = "spat_netw_clus",
show_top_feats = 1)
cluster_genes <- cluster_genes_DT$clus
names(cluster_genes) <- cluster_genes_DT$feat_ID
g <- createMetafeats(g,
feat_clusters = cluster_genes,
name = "cluster_metagene")Plot the spatial distribution of the metagene enrichment scores of each spatial co-expression cluster.
spatCellPlot(g,
spat_enr_names = "cluster_metagene",
cell_annotation_values = netw_ranks$clusters,
point_size = 2,
cow_n_col = 2)
6 Get the top genes per spatial co-expression module
Run this code for downstream analysis, such as calculation of spatially informed clusters or calculation of spatial domains with HMRF.
In a real-size dataset, usually you want to extract the top ~30 genes per co-expression module, but this number is variable depending on the number of features available in your dataset.
coexpr_dt <- data.table::data.table(
genes = names(spat_cor_netw_DT$cor_clusters$spat_netw_clus),
cluster = spat_cor_netw_DT$cor_clusters$spat_netw_clus)
data.table::setorder(coexpr_dt, cluster)
top10_coexpr_dt <- coexpr_dt[, head(.SD, 10) , by = cluster]
spatial_genes <- top10_coexpr_dt$genes7 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
loaded via a namespace (and not attached):
[1] colorRamp2_0.1.0 rlang_1.1.4
[3] magrittr_2.0.3 clue_0.3-65
[5] GetoptLong_1.0.5 GiottoUtils_0.2.0
[7] matrixStats_1.4.1 compiler_4.4.1
[9] png_0.1-8 systemfonts_1.1.0
[11] vctrs_0.6.5 shape_1.4.6.1
[13] pkgconfig_2.0.3 SpatialExperiment_1.14.0
[15] crayon_1.5.3 fastmap_1.2.0
[17] backports_1.5.0 magick_2.8.5
[19] XVector_0.44.0 labeling_0.4.3
[21] utf8_1.2.4 rmarkdown_2.28
[23] UCSC.utils_1.0.0 ragg_1.3.3
[25] purrr_1.0.2 xfun_0.47
[27] zlibbioc_1.50.0 GenomeInfoDb_1.40.1
[29] jsonlite_1.8.9 DelayedArray_0.30.1
[31] terra_1.7-78 cluster_2.1.6
[33] parallel_4.4.1 R6_2.5.1
[35] RColorBrewer_1.1-3 reticulate_1.39.0
[37] GenomicRanges_1.56.1 scattermore_1.2
[39] Rcpp_1.0.13 SummarizedExperiment_1.34.0
[41] iterators_1.0.14 knitr_1.48
[43] R.utils_2.12.3 IRanges_2.38.1
[45] Matrix_1.7-0 igraph_2.0.3
[47] tidyselect_1.2.1 rstudioapi_0.16.0
[49] abind_1.4-8 yaml_2.3.10
[51] doParallel_1.0.17 codetools_0.2-20
[53] lattice_0.22-6 tibble_3.2.1
[55] Biobase_2.64.0 withr_3.0.1
[57] evaluate_1.0.0 circlize_0.4.16
[59] pillar_1.9.0 MatrixGenerics_1.16.0
[61] checkmate_2.3.2 foreach_1.5.2
[63] stats4_4.4.1 plotly_4.10.4
[65] generics_0.1.3 dbscan_1.2-0
[67] S4Vectors_0.42.1 ggplot2_3.5.1
[69] munsell_0.5.1 scales_1.3.0
[71] GiottoData_0.2.15 gtools_3.9.5
[73] glue_1.8.0 lazyeval_0.2.2
[75] tools_4.4.1 GiottoVisuals_0.2.5
[77] data.table_1.16.0 Cairo_1.6-2
[79] cowplot_1.1.3 grid_4.4.1
[81] tidyr_1.3.1 colorspace_2.1-1
[83] SingleCellExperiment_1.26.0 GenomeInfoDbData_1.2.12
[85] cli_3.6.3 textshaping_0.4.0
[87] fansi_1.0.6 S4Arrays_1.4.1
[89] viridisLite_0.4.2 ComplexHeatmap_2.20.0
[91] dplyr_1.1.4 gtable_0.3.5
[93] R.methodsS3_1.8.2 digest_0.6.37
[95] BiocGenerics_0.50.0 SparseArray_1.4.8
[97] ggrepel_0.9.6 rjson_0.2.23
[99] htmlwidgets_1.6.4 farver_2.1.2
[101] htmltools_0.5.8.1 R.oo_1.26.0
[103] lifecycle_1.0.4 httr_1.4.7
[105] GlobalOptions_0.1.2