14 Multi-omics integration
Joselyn Cristina Chávez Fuentes
August 7th 2024
14.1 The CytAssist technology
The Visium CytAssist Spatial Gene and Protein Expression assay is designed to introduce simultaneous Gene Expression and Protein Expression analysis to FFPE samples processed with Visium CytAssist. The assay uses NGS to measure the abundance of oligo-tagged antibodies with spatial resolution, in addition to the whole transcriptome and a morphological image.
Figure 14.1: CystAssits multi-omics diagram. Source: 10X genomics.
The 10X human immune cell profiling panel features 35 antibodies from Abcam and Biolegend, and includes cell surface and intracellular targets. The rna probes hybridize to ~18,000 genes, or RNA targets, within the tissue section to achieve whole transcriptome gene expression profiling. The remaining steps, starting with probe extension, follow the standard Visium workflow outside of the instrument.
Figure 14.2: CytAssist workflow. Source: 10X genomics.
14.2 Introduction to the spatial dataset
The Human Tonsil (FFPE) dataset was obtained from 10X Genomics. The tissue was sectioned as described in Visium CytAssist Spatial Gene and Protein Expression for FFPE – Tissue Preparation Guide (CG000660). 5 µm tissue sections were placed on Superfrost glass slides, deparaffinized, H&E stained (CG000658) and coverslipped. Sections were imaged, decoverslipped, followed by decrosslinking per the Staining Demonstrated Protocol (CG000658).
More information about this dataset can be found here.
14.3 Download dataset
You need to download the expression matrix and spatial information by running these commands:
dir.create("data/03_session3")
download.file(url = "https://cf.10xgenomics.com/samples/spatial-exp/2.1.0/CytAssist_FFPE_Protein_Expression_Human_Tonsil/CytAssist_FFPE_Protein_Expression_Human_Tonsil_raw_feature_bc_matrix.tar.gz",
destfile = "data/03_session3/CytAssist_FFPE_Protein_Expression_Human_Tonsil_raw_feature_bc_matrix.tar.gz")
download.file(url = "https://cf.10xgenomics.com/samples/spatial-exp/2.1.0/CytAssist_FFPE_Protein_Expression_Human_Tonsil/CytAssist_FFPE_Protein_Expression_Human_Tonsil_spatial.tar.gz",
destfile = "data/03_session3/CytAssist_FFPE_Protein_Expression_Human_Tonsil_spatial.tar.gz")After downloading, unzip the gz files. You should get the “raw_feature_bc_matrix” and “spatial” folders inside “data/03_session3/”.
14.4 Create the Giotto object
The minimum requirements are:
- matrix with expression information (or the path to)
- x,y(,z) coordinates for cells or spots (or the path to)
createGiottoVisiumObject() will automatically detect both RNA and Protein modalities in the expression matrix and will create a multi-omics Giotto object.
library(Giotto)
## Set instructions
results_folder <- "results/03_session3/"
python_path <- NULL
instructions <- createGiottoInstructions(
save_dir = results_folder,
save_plot = TRUE,
show_plot = FALSE,
return_plot = FALSE,
python_path = python_path
)
# Provide the path to the data folder
data_path <- "data/03_session3/"
# Create object directly from the data folder
visium_tonsil <- createGiottoVisiumObject(
visium_dir = data_path,
expr_data = "raw",
png_name = "tissue_lowres_image.png",
gene_column_index = 2,
instructions = instructions
)- Print the information of the object, note that both rna and protein are listed in the expression slot.
14.5 Subset on spots that were covered by tissue
spatPlot2D(
gobject = visium_tonsil,
cell_color = "in_tissue",
point_size = 2,
cell_color_code = c("0" = "lightgrey", "1" = "blue"),
show_image = TRUE,
image_name = "image"
)
Figure 14.3: Spatial plot of the CytAssist human tonsil sample, color indicates wheter the spot is in tissue (1) or not (0).
Use the metadata table to identify the spots corresponding to the tissue area, given by the “in_tissue” column. Then use the spot IDs to subset the giotto object.
14.6 RNA processing
- Run the Filtering, normalization, and statistics steps using only the RNA feature.
visium_tonsil <- filterGiotto(
gobject = visium_tonsil,
expression_threshold = 1,
feat_det_in_min_cells = 50,
min_det_feats_per_cell = 1000,
expression_values = "raw",
verbose = TRUE)
visium_tonsil <- normalizeGiotto(gobject = visium_tonsil,
scalefactor = 6000,
verbose = TRUE)
visium_tonsil <- addStatistics(gobject = visium_tonsil)- Dimension reduction
Identify the highly variable features using the RNA features, then calculate the principal components based on the HVFs.
visium_tonsil <- calculateHVF(gobject = visium_tonsil)
visium_tonsil <- runPCA(gobject = visium_tonsil)- Clustering
Calculate the UMAP, tSNE, and shared nearest neighbor network using the first 10 principal components for the RNA modality.
visium_tonsil <- runUMAP(visium_tonsil,
dimensions_to_use = 1:10)
visium_tonsil <- runtSNE(visium_tonsil,
dimensions_to_use = 1:10)
visium_tonsil <- createNearestNetwork(gobject = visium_tonsil,
dimensions_to_use = 1:10,
k = 30)Calculate the RNA-based Leiden clusters.
- Visualization
Plot the RNA-based UMAP with the corresponding RNA-based Leiden cluster per spot.
plotUMAP(gobject = visium_tonsil,
cell_color = "leiden_clus",
show_NN_network = TRUE,
point_size = 2)
Figure 14.4: RNA UMAP, color indicates the RNA-based Leiden clusters.
Plot the spatial distribution of the RNA-based Leiden cluster per spot.
Figure 14.5: Spatial distribution of RNA-based Leiden clusters.
14.7 Protein processing
- Run the Filtering, normalization, and statistics steps for the protein modality.
visium_tonsil <- filterGiotto(gobject = visium_tonsil,
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_tonsil <- normalizeGiotto(gobject = visium_tonsil,
spat_unit = "cell",
feat_type = "protein",
scalefactor = 6000,
verbose = TRUE)
visium_tonsil <- addStatistics(gobject = visium_tonsil,
spat_unit = "cell",
feat_type = "protein")- Dimension reduction
Calculate the principal components using all the proteins available in the dataset.
- Clustering
Calculate the UMAP, tSNE, and shared nearest neighbors network using the first 10 principal components for the Protein modality.
visium_tonsil <- runUMAP(visium_tonsil,
spat_unit = "cell",
feat_type = "protein",
dimensions_to_use = 1:10)
visium_tonsil <- runtSNE(visium_tonsil,
spat_unit = "cell",
feat_type = "protein",
dimensions_to_use = 1:10)
visium_tonsil <- createNearestNetwork(gobject = visium_tonsil,
spat_unit = "cell",
feat_type = "protein",
dimensions_to_use = 1:10,
k = 30)Calculate the Protein-based Leiden clusters.
visium_tonsil <- doLeidenCluster(gobject = visium_tonsil,
spat_unit = "cell",
feat_type = "protein",
resolution = 1,
n_iterations = 1000)- Visualization
Plot the Protein UMAP and color the spots using the Protein-based Leiden clusters.
plotUMAP(gobject = visium_tonsil,
spat_unit = "cell",
feat_type = "protein",
cell_color = "leiden_clus",
show_NN_network = TRUE,
point_size = 2)
Figure 14.6: Protein UMAP, color indicates the Protein-based Leiden clusters.
Plot the spatial distribution of the Protein-based Leiden clusters.
spatPlot2D(gobject = visium_tonsil,
spat_unit = "cell",
feat_type = "protein",
show_image = TRUE,
cell_color = "leiden_clus",
point_size = 3)
Figure 14.7: Spatial distribution of Protein-based Leiden clusters.
14.8 Multi-omics integration
- Calculate kNN
Calculate the k nearest neighbors network for each modality (RNA and Protein), using the first 10 principal components of each feature type.
## RNA modality
visium_tonsil <- createNearestNetwork(gobject = visium_tonsil,
type = "kNN",
dimensions_to_use = 1:10,
k = 20)
## Protein modality
visium_tonsil <- createNearestNetwork(gobject = visium_tonsil,
spat_unit = "cell",
feat_type = "protein",
type = "kNN",
dimensions_to_use = 1:10,
k = 20)- Run WNN
Run the Weighted Nearest Neighbor analysis to weight the contribution of each feature type per spot. The results will be saved in the multiomics slot of the giotto object.
visium_tonsil <- runWNN(visium_tonsil,
spat_unit = "cell",
modality_1 = "rna",
modality_2 = "protein",
pca_name_modality_1 = "pca",
pca_name_modality_2 = "protein.pca",
k = 20,
integrated_feat_type = NULL,
matrix_result_name = NULL,
w_name_modality_1 = NULL,
w_name_modality_2 = NULL,
verbose = TRUE)- Run Integrated umap
Calculate the UMAP using the weights of each feature per spot.
visium_tonsil <- runIntegratedUMAP(visium_tonsil,
modality1 = "rna",
modality2 = "protein",
spread = 7,
min_dist = 1,
force = FALSE)- Calculate integrated clusters
Calculate the multiomics-based Leiden clusters using the weights of each feature per spot.
visium_tonsil <- doLeidenCluster(gobject = visium_tonsil,
spat_unit = "cell",
feat_type = "rna",
nn_network_to_use = "kNN",
network_name = "integrated_kNN",
name = "integrated_leiden_clus",
resolution = 1)- Visualize the integrated UMAP
Plot the integrated UMAP and color the spots using the integrated Leiden clusters.
plotUMAP(gobject = visium_tonsil,
spat_unit = "cell",
feat_type = "rna",
cell_color = "integrated_leiden_clus",
dim_reduction_name = "integrated.umap",
point_size = 2,
title = "Integrated UMAP using Integrated Leiden clusters")
Figure 14.8: Integrated UMAP. Color represents the integrated Leiden clusters.
- Visualize spatial plot with integrated clusters
Plot the spatial distribution of the integrated Leiden clusters.
spatPlot2D(visium_tonsil,
spat_unit = "cell",
feat_type = "rna",
cell_color = "integrated_leiden_clus",
point_size = 3,
show_image = TRUE,
title = "Integrated Leiden clustering")
Figure 14.9: Spatial distribution of the integrated Leiden clusters.
14.9 Session info
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5
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-arm64/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_4.1.0 GiottoClass_0.3.3
loaded via a namespace (and not attached):
[1] colorRamp2_0.1.0 deldir_2.0-4
[3] rlang_1.1.4 magrittr_2.0.3
[5] RcppAnnoy_0.0.22 GiottoUtils_0.1.10
[7] matrixStats_1.3.0 compiler_4.4.1
[9] png_0.1-8 systemfonts_1.1.0
[11] vctrs_0.6.5 reshape2_1.4.4
[13] stringr_1.5.1 pkgconfig_2.0.3
[15] SpatialExperiment_1.14.0 crayon_1.5.3
[17] fastmap_1.2.0 backports_1.5.0
[19] magick_2.8.4 XVector_0.44.0
[21] labeling_0.4.3 utf8_1.2.4
[23] rmarkdown_2.27 UCSC.utils_1.0.0
[25] ragg_1.3.2 purrr_1.0.2
[27] xfun_0.46 beachmat_2.20.0
[29] zlibbioc_1.50.0 GenomeInfoDb_1.40.1
[31] jsonlite_1.8.8 DelayedArray_0.30.1
[33] BiocParallel_1.38.0 terra_1.7-78
[35] irlba_2.3.5.1 parallel_4.4.1
[37] R6_2.5.1 stringi_1.8.4
[39] RColorBrewer_1.1-3 reticulate_1.38.0
[41] parallelly_1.37.1 GenomicRanges_1.56.1
[43] scattermore_1.2 Rcpp_1.0.13
[45] bookdown_0.40 SummarizedExperiment_1.34.0
[47] knitr_1.48 future.apply_1.11.2
[49] R.utils_2.12.3 IRanges_2.38.1
[51] Matrix_1.7-0 igraph_2.0.3
[53] tidyselect_1.2.1 rstudioapi_0.16.0
[55] abind_1.4-5 yaml_2.3.9
[57] codetools_0.2-20 listenv_0.9.1
[59] lattice_0.22-6 tibble_3.2.1
[61] plyr_1.8.9 Biobase_2.64.0
[63] withr_3.0.0 Rtsne_0.17
[65] evaluate_0.24.0 future_1.33.2
[67] pillar_1.9.0 MatrixGenerics_1.16.0
[69] checkmate_2.3.1 stats4_4.4.1
[71] plotly_4.10.4 generics_0.1.3
[73] dbscan_1.2-0 sp_2.1-4
[75] S4Vectors_0.42.1 ggplot2_3.5.1
[77] munsell_0.5.1 scales_1.3.0
[79] globals_0.16.3 gtools_3.9.5
[81] glue_1.7.0 lazyeval_0.2.2
[83] tools_4.4.1 GiottoVisuals_0.2.4
[85] data.table_1.15.4 ScaledMatrix_1.12.0
[87] cowplot_1.1.3 grid_4.4.1
[89] tidyr_1.3.1 colorspace_2.1-0
[91] SingleCellExperiment_1.26.0 GenomeInfoDbData_1.2.12
[93] BiocSingular_1.20.0 rsvd_1.0.5
[95] cli_3.6.3 textshaping_0.4.0
[97] fansi_1.0.6 S4Arrays_1.4.1
[99] viridisLite_0.4.2 dplyr_1.1.4
[101] uwot_0.2.2 gtable_0.3.5
[103] R.methodsS3_1.8.2 digest_0.6.36
[105] BiocGenerics_0.50.0 SparseArray_1.4.8
[107] ggrepel_0.9.5 farver_2.1.2
[109] rjson_0.2.21 htmlwidgets_1.6.4
[111] htmltools_0.5.8.1 R.oo_1.26.0
[113] lifecycle_1.0.4 httr_1.4.7