add mini tutorials

_pkgdown.yml

@@ -88,10 +88,14 @@ navbar:
         menu:
           - text: Process Giotto convenience function
             href: articles/process_giotto.html
+          - text: Detection of Highly Variable Features
+            href: articles/hvf.html
           - text: Dimension reduction
             href: articles/dimension_reduction.html
           - text: Spatial and feature networks
             href: articles/networks.html
+          - text: Detection of spatial genes
+            href: articles/spatial_genes.html
       - text: -------
       - text: Spatial manipulation
         menu:
@@ -353,8 +357,10 @@ articles:
   navbar: ~
   contents:
   - process_giotto
+  - hvf
   - dimension_reduction
   - networks
+  - spatial_genes
 - title: Seq-Scope
   navbar: ~
   contents:

vignettes/hvf.Rmd

@@ -0,0 +1,168 @@
+---
+title: "Detection of Highly Variable Features"
+output: 
+  html_document:
+    number_sections: true
+    toc: true
+pkgdown:
+  as_is: true
+vignette: >
+  %\VignetteIndexEntry{Detection of Highly Variable Features}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+Calculating Highly Variable Features (HVF) is necessary to identify genes (or features) that display significant variability across the spots. 
+
+# 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")
+```
+
+There are a few methods to choose from depending on the underlying distribution of the data:
+
+## Loess regression 
+
+It is used when the relationship between mean expression and variance is non-linear or can be described by a non-parametric model.
+
+```{r, eval=FALSE}
+g <- calculateHVF(gobject = g, 
+                  method = "cov_loess",
+                  save_plot = TRUE)
+```
+
+```{r, echo=FALSE, out.width="60%", fig.align='center'}
+knitr::include_graphics("images/hvf/1-HVFplot.png")
+```
+
+
+## Pearson residuals 
+
+They are used for variance stabilization (to account for technical noise) and highlighting overdispersed genes.
+
+```{r, eval=FALSE}
+g <- calculateHVF(gobject = g, 
+                  method = "var_p_resid",
+                  save_plot = TRUE)
+```
+
+```{r, echo=FALSE, out.width="60%", fig.align='center'}
+knitr::include_graphics("images/hvf/2-HVFplot.png")
+```
+
+## Binned (covariance groups) 
+
+They are used when gene expression variability differs across expression levels or spatial regions, without assuming a specific relationship between mean expression and variance. This is the default method in the calculateHVF() function.
+
+```{r, eval=FALSE}
+g <- calculateHVF(gobject = g, 
+                  method = "cov_groups",
+                  save_plot = TRUE)
+```
+
+```{r, echo=FALSE, out.width="60%", fig.align='center'}
+knitr::include_graphics("images/hvf/3-HVFplot.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] tidyselect_1.2.1            viridisLite_0.4.2          
+ [3] farver_2.1.2                dplyr_1.1.4                
+ [5] GiottoVisuals_0.2.5         R.utils_2.12.3             
+ [7] fastmap_1.2.0               SingleCellExperiment_1.26.0
+ [9] lazyeval_0.2.2              digest_0.6.37              
+[11] lifecycle_1.0.4             terra_1.7-78               
+[13] magrittr_2.0.3              compiler_4.4.1             
+[15] rlang_1.1.4                 tools_4.4.1                
+[17] igraph_2.0.3                utf8_1.2.4                 
+[19] yaml_2.3.10                 data.table_1.16.0          
+[21] knitr_1.48                  labeling_0.4.3             
+[23] S4Arrays_1.4.1              htmlwidgets_1.6.4          
+[25] reticulate_1.39.0           DelayedArray_0.30.1        
+[27] RColorBrewer_1.1-3          abind_1.4-8                
+[29] withr_3.0.1                 purrr_1.0.2                
+[31] BiocGenerics_0.50.0         R.oo_1.26.0                
+[33] grid_4.4.1                  stats4_4.4.1               
+[35] fansi_1.0.6                 future_1.34.0              
+[37] colorspace_2.1-1            ggplot2_3.5.1              
+[39] globals_0.16.3              scales_1.3.0               
+[41] gtools_3.9.5                SummarizedExperiment_1.34.0
+[43] cli_3.6.3                   rmarkdown_2.28             
+[45] crayon_1.5.3                ragg_1.3.3                 
+[47] generics_0.1.3              future.apply_1.11.2        
+[49] rstudioapi_0.16.0           httr_1.4.7                 
+[51] rjson_0.2.23                zlibbioc_1.50.0            
+[53] parallel_4.4.1              XVector_0.44.0             
+[55] matrixStats_1.4.1           vctrs_0.6.5                
+[57] Matrix_1.7-0                jsonlite_1.8.9             
+[59] GiottoData_0.2.15           IRanges_2.38.1             
+[61] S4Vectors_0.42.1            ggrepel_0.9.6              
+[63] scattermore_1.2             listenv_0.9.1              
+[65] systemfonts_1.1.0           magick_2.8.5               
+[67] GiottoUtils_0.2.0           plotly_4.10.4              
+[69] tidyr_1.3.1                 parallelly_1.38.0          
+[71] glue_1.8.0                  codetools_0.2-20           
+[73] cowplot_1.1.3               gtable_0.3.5               
+[75] GenomeInfoDb_1.40.1         GenomicRanges_1.56.1       
+[77] UCSC.utils_1.0.0            munsell_0.5.1              
+[79] tibble_3.2.1                pillar_1.9.0               
+[81] htmltools_0.5.8.1           GenomeInfoDbData_1.2.12    
+[83] R6_2.5.1                    textshaping_0.4.0          
+[85] evaluate_1.0.0              lattice_0.22-6             
+[87] Biobase_2.64.0              png_0.1-8                  
+[89] R.methodsS3_1.8.2           backports_1.5.0            
+[91] SpatialExperiment_1.14.0    Rcpp_1.0.13                
+[93] SparseArray_1.4.8           checkmate_2.3.2            
+[95] colorRamp2_0.1.0            xfun_0.47                  
+[97] MatrixGenerics_1.16.0       pkgconfig_2.0.3 
+```