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/artista_data.RData"))For each cell, we assume we already know the cell type label, which
is saved in data_use$cluster in the below example.
make_celltype_mat = function(celltype_annotation){
celltype_mat = matrix(0,length(celltype_annotation),length(c(table(celltype_annotation))))
for(i in 1:length(table(celltype_annotation))){
celltypename = names(table(celltype_annotation))[i]
celltype_mat[,i] = 0
celltype_mat[which(celltype_annotation==celltypename),i] =1
}
colnames(celltype_mat) = names(table(celltype_annotation))
return(celltype_mat)
}
# Now we can create the cell type matrix, just like the cell type proportion matrix we had in the spot level data, but now for single cell level data, the values are binary indicators rather than 0-1 proportion values.
data_use$celltype = make_celltype_mat(data_use$cluster)
rownames(data_use$celltype) = colnames(data_use$count_use)library(CELINA)
Obj = Create_Celina_Object(celltype_mat = t(data_use$celltype),
gene_expression_mat = as.matrix(data_use$count_use),
location = as.matrix(data_use$location_coord),
covariates = NULL,
project = "Artista")In this step, we preprocess the gene expression matrix by normalizing the counts and filtering genes.
filtered_cell_types = colnames(data_use$celltype)[which(colSums(data_use$celltype) > (dim(data_use$celltype)[1]*0.01))]
filtered_cell_types
Obj = preprocess_input(Obj,
# Celina object
cell_types_to_test = filtered_cell_types,
# a vector of cell types to be used for testing
scRNA_count = as.matrix(data_use$count_use),
# a gene x cell expression matrix of reference scRNA-seq data
# in single cell level data, we just use the spatial count matrix
sc_cell_type_labels = data_use$cluster,
# a vector of cell type labels for each cell
threshold = 5e-5)
print(names(Obj@genes_list))Obj = Calculate_Kernel(Obj, approximation = FALSE)Obj = Testing_interaction_all(Obj,celltype_to_test = filtered_cell_types, num_cores=10)
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