Deconvolution
Last updated: 2024-03-21
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Knit directory: Celina/
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This is a step by step guide for performing cell type deconvolution of your non-single cell spatial transcriptomic data using CARD. If your spatial transcriptomic data is already at single cell level, you may skip this section.
Load in data
The processed example data can be downloaded from here.
# load in data
load(paste0("./data/scRNA_data_RCC_PD47171in_tumor_interface.RData"))
# single cell reference data
load(paste0("./data/data_RCC_PD47171in_tumor_interface.RData"))
# 10x visium spatial transcriptomics data
names(sc_section)
names(data_section)Prepare datasets
For spot level spatial transcriptomics data, we will do cell type deconvolution analysis first to infer the cell type proportions at each spot. The datasets needed are:
single cell reference dataset, we need gene expression count matrix (
sc_section$count), cell type annotation for each cell (sc_section$meta$broad_type);spatial transcriptomics dataset, we need gene expression count matrix (
data_section$count), spatial coordinate matrix (data_section$location).
Perform deconvolution
library(CARD)
sc_meta_in = data.frame("cellType" = sc_section$meta$broad_type)
rownames(sc_meta_in) = colnames(sc_section$count)
sc_meta_in$cellID = rownames(sc_meta_in)
sc_meta_in$sampleInfo=1
location=data_section$location
colnames(location) = c("x","y")
sc_meta_in_subset = sc_meta_in
scRNA_count_subset = sc_section$count
CARD_obj = createCARDObject(
sc_count = scRNA_count_subset,
sc_meta = sc_meta_in_subset,
spatial_count = data_section$count,
spatial_location = location,
ct.varname = "cellType",
ct.select = unique(sc_meta_in$cellType),
sample.varname = "sampleInfo",
minCountGene = 100,
minCountSpot = 20)
CARD_obj = CARD_deconvolution(CARD_object = CARD_obj)
CARD_obj@Proportion_CARD[1:2,]
# match results with spatial locations, save results to data_section
celltype_proportion = CARD_obj@Proportion_CARD
data_section$celltype_proportion = celltype_proportion
id = match(rownames(celltype_proportion), rownames(location))
data_section$location_coord = data_section$location[id,]
data_section$count_use = data_section$count[,id]Visualize deconvolution result
library(viridis)
library(ggplot2)
loc_x = data_section$location[,1]
loc_y = data_section$location[,2]
prop = data_section$celltype_proportion[,"RCC"]
datt = data.frame(loc_x,loc_y,prop)
title_in = "RCC cell type proportion"
p1 = ggplot() +
scale_fill_brewer()+
scale_color_viridis_c()+
scale_color_viridis(discrete = TRUE)+
geom_point(data=datt, aes(x = loc_x, y = loc_y, color = prop),alpha = 0.9, size = prop*5,shape=16) +
ggtitle(paste0(title_in)) +
scale_color_viridis(option = "H")+
theme_void() +
theme(plot.title = element_text(size = 30), text = element_text(size = 30), legend.position = "right")+ labs(color=NULL)
print(p1)
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3; LAPACK version 3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 cli_3.6.2 knitr_1.45 rlang_1.1.2
[5] xfun_0.41 stringi_1.8.3 promises_1.2.1 jsonlite_1.8.8
[9] workflowr_1.7.1 glue_1.7.0 rprojroot_2.0.4 git2r_0.33.0
[13] htmltools_0.5.7 httpuv_1.6.13 sass_0.4.8 fansi_1.0.5
[17] rmarkdown_2.25 jquerylib_0.1.4 evaluate_0.23 tibble_3.2.1
[21] fastmap_1.1.1 yaml_2.3.8 lifecycle_1.0.4 stringr_1.5.1
[25] compiler_4.3.3 fs_1.6.3 Rcpp_1.0.12 pkgconfig_2.0.3
[29] rstudioapi_0.15.0 later_1.3.2 digest_0.6.33 R6_2.5.1
[33] utf8_1.2.4 pillar_1.9.0 magrittr_2.0.3 bslib_0.6.1
[37] tools_4.3.3 cachem_1.0.8