Visium CytAssist Multi-omics Human Glioblastoma
Source:vignettes/visium_cytassist_human_glioblastoma.Rmd
visium_cytassist_human_glioblastoma.Rmd1 Dataset explanation
The Human glioblastoma (FFPE) dataset was obtained from 10x Genomics. The tissue was sectioned as described in Visium CytAssist Spatial Gene Expression for FFPE – Tissue Preparation Guide Demonstrated Protocol (CG000518). 5 µm tissue sections were placed on Superfrost glass slides, then IF stained following deparaffinization, then hard coverslipped. Sections were imaged, decoverslipped, followed by Demonstrated Protocol (CG000494).
More information about this dataset can be found here.
2 Download dataset
dir.create("data")
download.file(url = "https://cf.10xgenomics.com/samples/spatial-exp/2.1.0/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma_raw_feature_bc_matrix.tar.gz",
destfile = "data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma_raw_feature_bc_matrix.tar.gz")
download.file(url = "https://cf.10xgenomics.com/samples/spatial-exp/2.1.0/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma_spatial.tar.gz",
destfile = "data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma_spatial.tar.gz")
untar("data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma_raw_feature_bc_matrix.tar.gz",
exdir = "data")
untar("data/CytAssist_FFPE_Protein_Expression_Human_Glioblastoma_spatial.tar.gz",
exdir = "data")3 Start Giotto
# Ensure Giotto Suite is installed
if(!"Giotto" %in% installed.packages()) {
pak::pkg_install("drieslab/Giotto")
}
# Ensure the Python environment for Giotto has been installed
genv_exists <- Giotto::checkGiottoEnvironment()
if(!genv_exists){
# The following command need only be run once to install the Giotto environment
Giotto::installGiottoEnvironment()
}
library(Giotto)
# 1. set results directory
results_folder <- "/path/to/results/"
# 2. set giotto python path
# set python path to your preferred python version path
# set python path to NULL if you want to automatically install (only the 1st time) and use the giotto miniconda environment
python_path <- NULL
# 3. create giotto instructions
instructions <- createGiottoInstructions(save_dir = results_folder,
save_plot = TRUE,
show_plot = FALSE,
return_plot = FALSE,
python_path = python_path)4 Create Giotto object
The minimum requirements are
- matrix with expression information (or path to)
- x,y(,z) coordinates for cells or spots (or path to)
createGiottoVisiumObject() will automatically detect both rna and protein modalities within the expression matrix creating a multi-omics Giotto object.
# Provide path to visium folder
data_path <- "/path/to/data/"
# Create Giotto object
visium <- createGiottoVisiumObject(visium_dir = data_path,
expr_data = "raw",
png_name = "tissue_lowres_image.png",
gene_column_index = 2,
instructions = instructions)How to work with Giotto instructions that are part of your Giotto object:
- Show the instructions associated with your Giotto object with showGiottoInstructions()
- Change one or more instructions with changeGiottoInstructions()
- Replace all instructions at once with replaceGiottoInstructions()
- Read or get a specific Giotto instruction with readGiottoInstructions()
# show instructions associated with the giotto object
showGiottoInstructions(visium)5 Processing
- Filter features and cells based on detection frequencies
- Normalize expression matrix (log transformation, scaling factor and/or z-scores)
- Add cell and feature statistics (optional)
- Adjust expression matrix for technical covariates or batches (optional).
# Subset on spots that were covered by tissue
metadata <- pDataDT(visium)
in_tissue_barcodes <- metadata[in_tissue == 1]$cell_ID
visium <- subsetGiotto(visium,
cell_ids = in_tissue_barcodes)
## Visualize aligned tissue
spatPlot2D(gobject = visium,
point_alpha = 0.7)
# Filtering, normalization, and statistics
## RNA feature
visium <- filterGiotto(gobject = visium,
expression_threshold = 1,
feat_det_in_min_cells = 50,
min_det_feats_per_cell = 1000,
expression_values = "raw",
verbose = TRUE)
visium <- normalizeGiotto(gobject = visium,
scalefactor = 6000,
verbose = TRUE)
visium <- addStatistics(gobject = visium)
### Visualize number of features after processing
spatPlot2D(gobject = visium,
point_alpha = 0.7,
cell_color = "nr_feats",
color_as_factor = FALSE)
## Protein feature
visium <- filterGiotto(gobject = visium,
spat_unit = "cell",
feat_type = "protein",
expression_threshold = 1,
feat_det_in_min_cells = 50,
min_det_feats_per_cell = 1,
expression_values = "raw",
verbose = TRUE)
visium <- normalizeGiotto(gobject = visium,
spat_unit = "cell",
feat_type = "protein",
scalefactor = 6000,
verbose = TRUE)
visium <- addStatistics(gobject = visium,
spat_unit = "cell",
feat_type = "protein")
### Visualize number of features after processing
spatPlot2D(gobject = visium,
spat_unit = "cell",
feat_type = "protein",
point_alpha = 0.7,
cell_color = "nr_feats",
color_as_factor = FALSE)
6 Dimension Reduction
# Identify highly variable features (HVF)
visium <- calculateHVF(gobject = visium)
### Visualize RNA PCA
plotPCA(gobject = visium)
## Protein
visium <- runPCA(gobject = visium,
spat_unit = "cell",
feat_type = "protein")
screePlot(visium,
spat_unit = "cell",
feat_type = "protein",
ncp = 30)
### Visualize Protein PCA
plotPCA(gobject = visium,
spat_unit = "cell",
feat_type = "protein")
7 Clustering
# cluster and run UMAP
# sNN network (default)
## RNA feature
visium <- createNearestNetwork(gobject = visium,
dimensions_to_use = 1:10,
k = 30)
## Protein feature
visium <- createNearestNetwork(gobject = visium,
spat_unit = "cell",
feat_type = "protein",
dimensions_to_use = 1:10,
k = 30)
# Leiden clustering
## RNA feature
visium <- doLeidenCluster(gobject = visium,
resolution = 1,
n_iterations = 1000)
## Protein feature
visium <- doLeidenCluster(gobject = visium,
spat_unit = "cell",
feat_type = "protein",
resolution = 1,
n_iterations = 1000)
# UMAP
## RNA feature
visium <- runUMAP(visium,
dimensions_to_use = 1:10)
plotUMAP(gobject = visium,
cell_color = "leiden_clus",
show_NN_network = TRUE,
point_size = 2)
## Protein feature
visium <- runUMAP(visium,
spat_unit = "cell",
feat_type = "protein",
dimensions_to_use = 1:10)
plotUMAP(gobject = visium,
spat_unit = "cell",
feat_type = "protein",
cell_color = "leiden_clus",
show_NN_network = TRUE,
point_size = 2)
# Visualize spatial plot
## RNA feature
spatPlot2D(gobject = visium,
show_image = TRUE,
cell_color = "leiden_clus",
point_size = 2)
## Protein feature
spatPlot2D(gobject = visium,
spat_unit = "cell",
feat_type = "protein",
show_image = TRUE,
cell_color = "leiden_clus",
point_size = 2)
8 Multi-omics integration
The Weighted Nearest Neighbors allows to integrate two or more modalities acquired from the same sample. WNN will re-calculate the clustering to provide an integrated umap and leiden clustering. For running WNN, the Giotto object must contain the results of running PCA calculation for each modality.
# Calculate kNN
## RNA modality
visium <- createNearestNetwork(gobject = visium,
type = "kNN",
dimensions_to_use = 1:10,
k = 20)
## Protein modality
visium <- createNearestNetwork(gobject = visium,
spat_unit = "cell",
feat_type = "protein",
type = "kNN",
dimensions_to_use = 1:10,
k = 20)
# Run WNN
visium <- runWNN(visium,
spat_unit = "cell",
feat_types = c("rna", "protein"),
reduction_methods = c("pca", "pca"),
reduction_names = c("pca", "protein.pca"),
k = 20,
verbose = TRUE)
# Run Integrated umap
visium <- runIntegratedUMAP(visium,
feat_types = c("rna", "protein"),
force = FALSE)
# Calculate integrated clusters
visium <- doLeidenCluster(gobject = visium,
spat_unit = "cell",
feat_type = "rna",
nn_network_to_use = "kNN",
network_name = "integrated_kNN",
name = "integrated_leiden_clus",
resolution = 0.5)
# Visualize integrated umap
plotUMAP(gobject = visium,
spat_unit = "cell",
feat_type = "rna",
cell_color = "integrated_leiden_clus",
dim_reduction_name = "integrated.umap",
point_size = 1.5,
title = "Integrated UMAP using Integrated Leiden clusters",
axis_title = 12,
axis_text = 10 )
# Visualize spatial plot with integrated clusters
spatPlot2D(visium,
spat_unit = "cell",
feat_type = "rna",
cell_color = "integrated_leiden_clus",
point_size = 2,
show_image = FALSE,
title = "Integrated Leiden clustering")
9 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 deldir_2.0-4 rlang_1.1.4
[4] magrittr_2.0.3 RcppAnnoy_0.0.22 GiottoUtils_0.1.12
[7] matrixStats_1.4.1 compiler_4.4.1 png_0.1-8
[10] systemfonts_1.1.0 vctrs_0.6.5 pkgconfig_2.0.3
[13] SpatialExperiment_1.14.0 crayon_1.5.3 fastmap_1.2.0
[16] backports_1.5.0 magick_2.8.5 XVector_0.44.0
[19] labeling_0.4.3 utf8_1.2.4 rmarkdown_2.28
[22] UCSC.utils_1.0.0 ragg_1.3.3 purrr_1.0.2
[25] xfun_0.47 beachmat_2.20.0 zlibbioc_1.50.0
[28] GenomeInfoDb_1.40.1 jsonlite_1.8.9 DelayedArray_0.30.1
[31] BiocParallel_1.38.0 terra_1.7-78 irlba_2.3.5.1
[34] parallel_4.4.1 R6_2.5.1 RColorBrewer_1.1-3
[37] reticulate_1.39.0 parallelly_1.38.0 GenomicRanges_1.56.1
[40] scattermore_1.2 Rcpp_1.0.13 SummarizedExperiment_1.34.0
[43] knitr_1.48 future.apply_1.11.2 R.utils_2.12.3
[46] IRanges_2.38.1 Matrix_1.7-0 igraph_2.0.3
[49] tidyselect_1.2.1 rstudioapi_0.16.0 abind_1.4-8
[52] yaml_2.3.10 codetools_0.2-20 listenv_0.9.1
[55] lattice_0.22-6 tibble_3.2.1 Biobase_2.64.0
[58] withr_3.0.1 evaluate_1.0.0 future_1.34.0
[61] pillar_1.9.0 MatrixGenerics_1.16.0 checkmate_2.3.2
[64] stats4_4.4.1 plotly_4.10.4 generics_0.1.3
[67] dbscan_1.2-0 sp_2.1-4 S4Vectors_0.42.1
[70] ggplot2_3.5.1 munsell_0.5.1 scales_1.3.0
[73] gtools_3.9.5 globals_0.16.3 glue_1.7.0
[76] lazyeval_0.2.2 tools_4.4.1 GiottoVisuals_0.2.5
[79] data.table_1.16.0 ScaledMatrix_1.12.0 cowplot_1.1.3
[82] grid_4.4.1 tidyr_1.3.1 colorspace_2.1-1
[85] SingleCellExperiment_1.26.0 GenomeInfoDbData_1.2.12 BiocSingular_1.20.0
[88] rsvd_1.0.5 cli_3.6.3 textshaping_0.4.0
[91] fansi_1.0.6 S4Arrays_1.4.1 viridisLite_0.4.2
[94] dplyr_1.1.4 uwot_0.2.2 gtable_0.3.5
[97] R.methodsS3_1.8.2 digest_0.6.37 BiocGenerics_0.50.0
[100] SparseArray_1.4.8 ggrepel_0.9.6 rjson_0.2.23
[103] htmlwidgets_1.6.4 farver_2.1.2 htmltools_0.5.8.1
[106] R.oo_1.26.0 lifecycle_1.0.4 httr_1.4.7