Last updated: 2024-03-21
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Knit directory: Celina/
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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)# filter genes:
# 1. remove mt genes:
mito_genes = unique(c(grep("^MT-", rownames(data_section$count)), grep("^mt-", rownames(data_section$count))))
# 2. remove genes expressed in less than 20 locations
low_genes = which(rowSums(data_section$count>0)<20)
remove_genes = unique(c(mito_genes, low_genes))
if(length(remove_genes)>0){
data_expr_in = data_section$count[-remove_genes,]
}else{
data_expr_in = data_section$count
}
library(CELINA)
Obj = Create_Celina_Object(celltype_mat = t(data_section$celltype_proportion),
gene_expression_mat = as.matrix(data_expr_in),
location = as.matrix(data_section$location_coord),
covariates = NULL,
project = "Kidney Cancer")In this step, we preprocess the gene expression matrix by normalizing the counts and filtering genes.
# if you want to further filter cell types based on their total proportion across spots, or you only want to test a subset of cell types, you can select the cell types here:
filtered_cell_types = colnames(data_section$celltype_proportion)[which(colSums(data_section$celltype_proportion) > (dim(data_section$celltype_proportion)[1]*0.01))]
filtered_cell_types
Obj = preprocess_input(Obj,
# Celina object
cell_types_to_test = filtered_cell_types,
# a vector of cell types to be used for testing
scRNA_count = as.matrix(sc_section$count),
# a gene x cell expression matrix of reference scRNA-seq data
sc_cell_type_labels = sc_section$meta$broad_type,
# a vector of cell type labels for each cell in scRNA_count
threshold = 5e-5)
print(names(Obj@genes_list))Obj = Calculate_Kernel(Obj, approximation = FALSE)
# this RCC tumor interface data is small with only 2048 spots, so we didn't select the approximation option.Here we choose RCC from filtered_cell_types as an example, because
RCC represents renal cell carcinoma cell which is a type of cancer cell
and is of our interest. Or you can also input
celltype_to_test=filtered_cell_types, to test all genes and
cell type pairs.
Obj = Testing_interaction_all(Obj,celltype_to_test = "RCC", num_cores=20)
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