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Giotto Visium Brain

Introduction to Giotto Visium Platform

Overview

10X Genomics recently launched the Giotto Visium platform, a cutting-edge tool for acquiring spatial gene expression data using Visium Spatial Gene Expression slides. This innovative technology allows researchers to analyze gene expression patterns while preserving spatial information, which is crucial for understanding the intricate organization and functionality of tissues, especially in complex structures like the brain.

Dataset Description

The data used in this tutorial originates from a Visium Spatial Gene Expression slide of the adult mouse. This dataset is available on the 10X Genomics support site and can be downloaded from the following link: V1_Adult_Mouse_Brain. Ensure that the necessary files are properly organized in the data_path directory.

Part 1: Setup and Installation

Package Installation

Before proceeding, ensure the required R packages are installed:

# Ensure Giotto Suite is installed
if (!"Giotto" %in% installed.packages()) {
  devtools::install_github("drieslab/Giotto@suite")
}

# Ensure GiottoData is installed
if (!"GiottoData" %in% installed.packages()) {
  devtools::install_github("drieslab/GiottoData")
}

# Check and install Python environment for Giotto
library(Giotto)

genv_exists <- checkGiottoEnvironment()
if (!genv_exists) {
  installGiottoEnvironment()  # Run this command once to install Giotto environment
}

# Install additional required packages
if (!"KODAMA" %in% installed.packages()) {
  devtools::install_github("tkcaccia/KODAMA")
}
if (!"KODAMAextra" %in% installed.packages()) {
  devtools::install_github("tkcaccia/KODAMAextra")
}

Part 2: Setup and Installation

Package Installation

Before proceeding, ensure the required R packages are installed:

# Ensure Giotto Suite is installed
if (!"Giotto" %in% installed.packages()) {
  devtools::install_github("drieslab/Giotto@suite")
}

# Ensure GiottoData is installed
if (!"GiottoData" %in% installed.packages()) {
  devtools::install_github("drieslab/GiottoData")
}

# Check and install Python environment for Giotto
library(Giotto)

genv_exists <- checkGiottoEnvironment()
if (!genv_exists) {
  installGiottoEnvironment()  # Run this command once to install Giotto environment
}

# Install additional required packages
if (!"KODAMA" %in% installed.packages()) {
  devtools::install_github("tkcaccia/KODAMA")
}
if (!"KODAMAextra" %in% installed.packages()) {
  devtools::install_github("tkcaccia/KODAMAextra")
}

library(KODAMA)
library(KODAMAextra)
library(Giotto)
library(GiottoData)

Part 3: Data Preparation and Analysis

Setting Up the Environment

Set Working Directory and Giotto Instructions

# Set working directory
results_folder <- '../result'

# Create Giotto instructions
my_python_path <- NULL  # Use the default Python environment for Giotto
instrs <- createGiottoInstructions(save_dir = results_folder,
                                   save_plot = TRUE,
                                   show_plot = FALSE,
                                   python_path = my_python_path)

###Loading and Preparing Data Create Giotto Visium Object

# Provide path to Visium data folder
data_path <- '../DATA/data_path'

# Create Giotto Visium object
visium_brain <- createGiottoVisiumObject(visium_dir = data_path,
                                         expr_data = 'raw',
                                         png_name = 'tissue_lowres_image.png',
                                         gene_column_index = 2,
                                         instructions = instrs)

Part 4: Exploratory Data Analysis

Visualizing Data and Quality Control

# Show associated images with Giotto object
showGiottoImageNames(visium_brain)
Image type: largeImage 

--> Name: image 
# Check metadata
pDataDT(visium_brain)
                 cell_ID in_tissue array_row array_col
                  <char>     <int>     <int>     <int>
   1: AAACAACGAATAGTTC-1         0         0        16
   2: AAACAAGTATCTCCCA-1         1        50       102
   3: AAACAATCTACTAGCA-1         1         3        43
   4: AAACACCAATAACTGC-1         1        59        19
   5: AAACAGAGCGACTCCT-1         1        14        94
  ---                                                 
4988: TTGTTTCACATCCAGG-1         1        58        42
4989: TTGTTTCATTAGTCTA-1         1        60        30
4990: TTGTTTCCATACAACT-1         1        45        27
4991: TTGTTTGTATTACACG-1         0        73        41
4992: TTGTTTGTGTAAATTC-1         1         7        51
# Visualize spatial plot
spatPlot2D(gobject = visium_brain, cell_color = 'in_tissue', point_size = 2,
           cell_color_code = c('0' = 'lightgrey', '1' = 'blue'),
           show_image = TRUE, image_name = 'image', show_plot= TRUE, return_plot = FALSE, save_plot = TRUE)

Version Author Date
ca0df73 Stefano Cacciatore 2024-07-16

Part 5: Data Processing and Analysis

Data Filtering and Normalization

# Subset spots covered by tissue
metadata <- pDataDT(visium_brain)
in_tissue_barcodes <- metadata[in_tissue == 1]$cell_ID
visium_brain <- subsetGiotto(visium_brain, cell_ids = in_tissue_barcodes)

# Filter data
visium_brain <- filterGiotto(gobject = visium_brain,
                             expression_threshold = 1,
                             feat_det_in_min_cells = 50,
                             min_det_feats_per_cell = 1000,
                             expression_values = c('raw'),
                             verbose = TRUE)

Feature type:  rna 
Number of cells removed:  4  out of  2702 
Number of feats removed:  7311  out of  22125 
# Normalize data
visium_brain <- normalizeGiotto(gobject = visium_brain, scalefactor = 6000, verbose = TRUE)

## add gene & cell statistics
visium_brain <- addStatistics(gobject = visium_brain)

## visualize
spatPlot2D(gobject = visium_brain, show_image = T, point_alpha = 0.7,
           cell_color = 'nr_feats', color_as_factor = F,save_plot = TRUE, show_plot= TRUE, return_plot = FALSE)

Version Author Date
ca0df73 Stefano Cacciatore 2024-07-16
visium_brain <- calculateHVF(gobject = visium_brain, save_plot = TRUE, show_plot= TRUE, return_plot = FALSE)

Version Author Date
ca0df73 Stefano Cacciatore 2024-07-16

Part 6: Advanced Analysis and Visualization

Dimensionality Reduction and Clustering

# Perform PCA
gene_metadata <- fDataDT(visium_brain)
featgenes <- gene_metadata[hvf == 'yes' & perc_cells > 3 & mean_expr_det > 0.4]$feat_ID
visium_brain <- runPCA(gobject = visium_brain,
                       feats_to_use = featgenes)

# Perform KODAMA analysis and visualization
visium_brain <- RunKODAMAmatrix(visium_brain, f.par.pls = 50, FUN = "PLS", n.cores = 4)
socket cluster with 4 nodes on host 'localhost'
================================================================================[1] "Finished parallel computation"

[1] "Calculation of dissimilarity matrix..."
================================================================================
visium_brain <- RunKODAMAvisualization(visium_brain, method = "UMAP")

visium_brain <- createNearestNetwork(gobject = visium_brain,dim_reduction_to_use = "KODAMA", dim_reduction_name="KODAMA",dimensions_to_use = 1:2, k = 15)


# Perform clustering and visualize results
visium_brain <- doLeidenCluster(gobject = visium_brain, resolution = 0.6, n_iterations = 1000,network_name = "sNN.KODAMA")


spatDimPlot(gobject = visium_brain, dim_reduction_to_use ="KODAMA", dim_reduction_name="KODAMA",cell_color = 'leiden_clus',
            dim_point_size = 2, spat_point_size = 2.5, show_plot= TRUE, return_plot = FALSE, save_plot = TRUE)

Version Author Date
ca0df73 Stefano Cacciatore 2024-07-16

This tutorial provides a comprehensive guide to using the Giotto Visium platform for spatial gene expression analysis, covering setup, data handling, advanced analysis techniques, and visualization. Each section aims to help you effectively leverage this powerful tool for your research endeavors.


sessionInfo()
R version 4.3.3 (2024-02-29 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: Africa/Johannesburg
tzcode source: internal

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] GiottoData_0.2.13 KODAMAextra_1.0   e1071_1.7-14      doParallel_1.0.17
 [5] iterators_1.0.14  foreach_1.5.2     KODAMA_3.1        umap_0.2.10.0    
 [9] Rtsne_0.17        minerva_1.5.10    Giotto_4.0.8      GiottoClass_0.3.1

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          rstudioapi_0.16.0          
  [3] jsonlite_1.8.8              magrittr_2.0.3             
  [5] magick_2.8.3                farver_2.1.2               
  [7] rmarkdown_2.27              fs_1.6.4                   
  [9] zlibbioc_1.48.2             ragg_1.3.2                 
 [11] vctrs_0.6.5                 GiottoUtils_0.1.8          
 [13] RCurl_1.98-1.14             askpass_1.2.0              
 [15] terra_1.7-78                htmltools_0.5.8.1          
 [17] S4Arrays_1.2.1              SparseArray_1.2.4          
 [19] parallelly_1.37.1           sass_0.4.9                 
 [21] bslib_0.7.0                 htmlwidgets_1.6.4          
 [23] plyr_1.8.9                  plotly_4.10.4              
 [25] cachem_1.1.0                whisker_0.4.1              
 [27] igraph_2.0.3                lifecycle_1.0.4            
 [29] pkgconfig_2.0.3             rsvd_1.0.5                 
 [31] Matrix_1.6-5                R6_2.5.1                   
 [33] fastmap_1.2.0               future_1.33.2              
 [35] GenomeInfoDbData_1.2.11     MatrixGenerics_1.14.0      
 [37] digest_0.6.36               colorspace_2.1-0           
 [39] S4Vectors_0.40.2            rprojroot_2.0.4            
 [41] irlba_2.3.5.1               RSpectra_0.16-1            
 [43] textshaping_0.4.0           GenomicRanges_1.54.1       
 [45] beachmat_2.18.1             labeling_0.4.3             
 [47] fansi_1.0.6                 httr_1.4.7                 
 [49] abind_1.4-5                 compiler_4.3.3             
 [51] proxy_0.4-27                withr_3.0.0                
 [53] backports_1.5.0             BiocParallel_1.36.0        
 [55] highr_0.11                  R.utils_2.12.3             
 [57] openssl_2.2.0               rappdirs_0.3.3             
 [59] DelayedArray_0.28.0         rjson_0.2.21               
 [61] gtools_3.9.5                GiottoVisuals_0.2.3        
 [63] tools_4.3.3                 httpuv_1.6.15              
 [65] future.apply_1.11.2         R.oo_1.26.0                
 [67] glue_1.7.0                  dbscan_1.2-0               
 [69] promises_1.3.0              grid_4.3.3                 
 [71] checkmate_2.3.1             reshape2_1.4.4             
 [73] snow_0.4-4                  generics_0.1.3             
 [75] gtable_0.3.5                R.methodsS3_1.8.2          
 [77] class_7.3-22                tidyr_1.3.1                
 [79] data.table_1.15.4           ScaledMatrix_1.10.0        
 [81] BiocSingular_1.18.0         sp_2.1-4                   
 [83] utf8_1.2.4                  XVector_0.42.0             
 [85] BiocGenerics_0.48.1         ggrepel_0.9.5              
 [87] pillar_1.9.0                stringr_1.5.1              
 [89] later_1.3.2                 dplyr_1.1.4                
 [91] lattice_0.22-6              deldir_2.0-4               
 [93] tidyselect_1.2.1            SingleCellExperiment_1.24.0
 [95] knitr_1.48                  git2r_0.33.0               
 [97] IRanges_2.36.0              SummarizedExperiment_1.32.0
 [99] scattermore_1.2             stats4_4.3.3               
[101] xfun_0.45                   Biobase_2.62.0             
[103] matrixStats_1.3.0           stringi_1.8.4              
[105] workflowr_1.7.1             lazyeval_0.2.2             
[107] yaml_2.3.8                  evaluate_0.24.0            
[109] codetools_0.2-20            tibble_3.2.1               
[111] colorRamp2_0.1.0            cli_3.6.2                  
[113] reticulate_1.38.0           systemfonts_1.1.0          
[115] munsell_0.5.1               jquerylib_0.1.4            
[117] Rcpp_1.0.12                 GenomeInfoDb_1.38.8        
[119] doSNOW_1.0.20               globals_0.16.3             
[121] png_0.1-8                   ggplot2_3.5.1              
[123] bitops_1.0-7                listenv_0.9.1              
[125] SpatialExperiment_1.12.0    viridisLite_0.4.2          
[127] scales_1.3.0                purrr_1.0.2                
[129] crayon_1.5.3                rlang_1.1.4                
[131] cowplot_1.1.3