Last updated: 2024-03-21
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Knit directory: Celina/
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While this is a single cell resolution Starmap-PLUS data, state-to-art cell segmentation methods are still limited in their ability to accurately define cell boundaries. Therefore, we first performed cell type deconvolution on the segmented cells by treating them as near-cellular spots, and then applied Celina to detect ct-SVGs
The processed example data can be downloaded from here.
# load in data
load(paste0("./data/hippocampus_scRNA_reference.RData"))
# single cell reference data
load(paste0("./data/starmap_plus_data_use.RData"))library(CELINA)
Obj = Create_Celina_Object(celltype_mat = t(data_use$celltype_proportion),
gene_expression_mat = as.matrix(data_use$count_use),
location = as.matrix(data_use$location_use),
covariates = NULL,
project = "starmap")In this step, we preprocess the gene expression matrix by normalizing the counts and filtering genes.
filtered_cell_types = colnames(data_use$celltype_proportion)[which(colSums(data_use$celltype_proportion) > 100)]
filtered_cell_types
Obj = preprocess_input(Obj,
# Celina object
cell_types_to_test = filtered_cell_types,
# a vector of cell types to be used for testing
scRNA_count = as.matrix(sc_expr),
# a gene x cell expression matrix of reference scRNA-seq data
sc_cell_type_labels = sc_meta_in$cellType,
# a vector of cell type labels for each cell in scRNA_count
threshold = 5e-5)
print(names(Obj@genes_list))# This dataset has 9244 locations, we use approximation kernels here:
Obj = Calculate_Kernel(Obj, approximation = TRUE,sparseKernel = TRUE,sparseKernel_tol = 1e-4)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