Merge branch 'suite' of https://github.com/drieslab/Giotto_website in…

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drieslab · Oct 25, 2024 · 075b168 · 075b168
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diff --git a/_pkgdown.yml b/_pkgdown.yml
@@ -96,6 +96,10 @@ navbar:
             href: articles/networks.html
           - text: Detection of spatial genes
             href: articles/spatial_genes.html
+          - text: Detection of spatial co-expression modules
+            href: articles/spatial_coexpression_modules.html
+          - text: Spatial domain analysis using HMRF
+            href: articles/hmrf.html
       - text: -------
       - text: Spatial manipulation
         menu:
@@ -364,6 +368,8 @@ articles:
   - dimension_reduction
   - networks
   - spatial_genes
+  - spatial_coexpression_modules
+  - hmrf
 - title: Seq-Scope
   navbar: ~
   contents:

diff --git a/.../spatial_genes/3-heatmSpatialCorFeats.png → ...ession_modules/1-heatmSpatialCorFeats.png b/.../spatial_genes/3-heatmSpatialCorFeats.png → ...ession_modules/1-heatmSpatialCorFeats.png
diff --git a/.../spatial_genes/4-rankSpatialCorGroups.png → ...ession_modules/2-rankSpatialCorGroups.png b/.../spatial_genes/4-rankSpatialCorGroups.png → ...ession_modules/2-rankSpatialCorGroups.png
diff --git a/...images/spatial_genes/5-spatCellPlot2D.png → ...coexpression_modules/3-spatCellPlot2D.png b/...images/spatial_genes/5-spatCellPlot2D.png → ...coexpression_modules/3-spatCellPlot2D.png
diff --git a/vignettes/spatial_coexpression_modules.Rmd b/vignettes/spatial_coexpression_modules.Rmd
@@ -0,0 +1,205 @@
+---
+title: "Detection of spatial co-expression modules"
+output: 
+  html_document:
+    number_sections: true
+    toc: true
+pkgdown:
+  as_is: true
+vignette: >
+  %\VignetteIndexEntry{Detection of spatial co-expression modules}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+Once 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.
+
+# Setup and load example dataset
+
+```{r, eval=FALSE}
+# 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()
+}
+```
+
+```{r, eval=FALSE}
+# load the object
+g <- GiottoData::loadGiottoMini("visium")
+```
+
+# Select the top spatial genes
+
+```{r, eval=FALSE}
+ext_spatial_genes <- ranktest[1:100,]$feats
+```
+
+# Calculate pairwise distances between genes.
+
+```{r, eval=FALSE}
+spat_cor_netw_DT <- detectSpatialCorFeats(
+    g,
+    method = "network",
+    spatial_network_name = "spatial_network",
+    subset_feats = ext_spatial_genes)
+```
+
+# Cluster the top spatial genes into 5 clusters
+
+```{r, eval=FALSE}
+spat_cor_netw_DT <- clusterSpatialCorFeats(spat_cor_netw_DT, 
+                                           name = "spat_netw_clus", 
+                                           k = 5)
+```
+
+# Plot the correlation of the top spatial genes with their assigned cluster.
+
+```{r, eval=FALSE}
+heatmSpatialCorFeats(g,
+                     spatCorObject = spat_cor_netw_DT,
+                     use_clus_name = "spat_netw_clus",
+                     heatmap_legend_param = list(title = NULL))
+```
+
+```{r, echo=FALSE, out.width="80%", fig.align='center'}
+knitr::include_graphics("images/spatial_coexpression_modules/1-heatmSpatialCorFeats.png")
+```
+
+## Rank spatial correlated clusters and show genes for selected clusters
+
+```{r, eval=FALSE}
+netw_ranks <- rankSpatialCorGroups(g,
+                                   spatCorObject = spat_cor_netw_DT, 
+                                   use_clus_name = "spat_netw_clus")
+```
+
+```{r, echo=FALSE, out.width="70%", fig.align='center'}
+knitr::include_graphics("images/spatial_coexpression_modules/2-rankSpatialCorGroups.png")
+```
+
+## Create the metagene enrichment score per co-expression cluster
+
+```{r, eval=FALSE}
+cluster_genes_DT <- showSpatialCorFeats(spat_cor_netw_DT, 
+                                        use_clus_name = "spat_netw_clus", 
+                                        show_top_feats = 1)
+
+cluster_genes <- cluster_genes_DT$clus 
+names(cluster_genes) <- cluster_genes_DT$feat_ID
+
+g <- createMetafeats(g, 
+                     feat_clusters = cluster_genes, 
+                     name = "cluster_metagene")
+```
+
+Plot the spatial distribution of the metagene enrichment scores of each spatial co-expression cluster.
+
+```{r, eval=FALSE}
+spatCellPlot(g,
+             spat_enr_names = "cluster_metagene",
+             cell_annotation_values = netw_ranks$clusters,
+             point_size = 2, 
+             cow_n_col = 2)
+```
+
+```{r, echo=FALSE, out.width="100%", fig.align='center'}
+knitr::include_graphics("images/spatial_coexpression_modules/3-spatCellPlot2D.png")
+```
+
+# Session Info
+
+```{r, eval=FALSE}
+sessionInfo()
+```
+
+```{r, eval=FALSE}
+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
+```
diff --git a/vignettes/spatial_genes.Rmd b/vignettes/spatial_genes.Rmd
@@ -64,7 +64,7 @@ spatPlot2D(g,
 knitr::include_graphics("images/spatial_genes/1-spatPlot2D.png")
 ```
 
-## Rank binarization
+# Rank binarization
 
 Rank the genes on the spatial dataset depending on whether they exhibit a spatial pattern location or not.
 
@@ -96,91 +96,6 @@ spatFeatPlot2D(g,
 knitr::include_graphics("images/spatial_genes/2-spatFeatPlot2D.png")
 ```
 
-# Spatial co-expression modules
-
-## Select the top spatial genes
-
-```{r, eval=FALSE}
-ext_spatial_genes <- ranktest[1:100,]$feats
-```
-
-## Calculate pairwise distances between genes.
-
-```{r, eval=FALSE}
-spat_cor_netw_DT <- detectSpatialCorFeats(g,
-    method = "network",
-    spatial_network_name = "spatial_network",
-    subset_feats = ext_spatial_genes)
-```
-
-## Cluster the top spatial genes into 5 clusters
-
-```{r, eval=FALSE}
-spat_cor_netw_DT <- clusterSpatialCorFeats(spat_cor_netw_DT, 
-    name = "spat_netw_clus", 
-    k = 5
-)
-```
-
-## Plot the correlation of the top spatial genes with their assigned cluster.
-
-```{r, eval=FALSE}
-heatmSpatialCorFeats(g,
-    spatCorObject = spat_cor_netw_DT,
-    use_clus_name = "spat_netw_clus",
-    heatmap_legend_param = list(title = NULL)
-)
-```
-
-```{r, echo=FALSE, out.width="80%", fig.align='center'}
-knitr::include_graphics("images/spatial_genes/3-heatmSpatialCorFeats.png")
-```
-
-## Rank spatial correlated clusters and show genes for selected clusters
-
-```{r, eval=FALSE}
-netw_ranks <- rankSpatialCorGroups(g,
-    spatCorObject = spat_cor_netw_DT, 
-    use_clus_name = "spat_netw_clus"
-)
-```
-
-```{r, echo=FALSE, out.width="70%", fig.align='center'}
-knitr::include_graphics("images/spatial_genes/4-rankSpatialCorGroups.png")
-```
-
-## Create the metagene enrichment score per co-expression cluster
-
-```{r, eval=FALSE}
-cluster_genes_DT <- showSpatialCorFeats(spat_cor_netw_DT, 
-    use_clus_name = "spat_netw_clus", 
-    show_top_feats = 1
-)
-
-cluster_genes <- cluster_genes_DT$clus 
-names(cluster_genes) <- cluster_genes_DT$feat_ID
-
-g <- createMetafeats(g, 
-    feat_clusters = cluster_genes, 
-    name = "cluster_metagene"
-)
-```
-
-Plot the spatial distribution of the metagene enrichment scores of each spatial co-expression cluster.
-
-```{r, eval=FALSE}
-spatCellPlot(g,
-    spat_enr_names = "cluster_metagene",
-    cell_annotation_values = netw_ranks$clusters,
-    point_size = 2, 
-    cow_n_col = 2
-)
-```
-
-```{r, echo=FALSE, out.width="100%", fig.align='center'}
-knitr::include_graphics("images/spatial_genes/5-spatCellPlot2D.png")
-```
-
 # Session Info
 
 ```{r, eval=FALSE}