suppressPackageStartupMessages({
library(CellChat)
library(SingleCellExperiment)
library(future)
library(patchwork)
library(NMF)
library(ggalluvial)
library(ComplexHeatmap)
library(dplyr)
})
options(future.globals.maxSize = 10000 * 1024 ^ 2,
future.seed = TRUE,
future.rng.onMisuse = "ignore")
## Decide whether to force re-execution of CellChat even if files exist
force_cellchat <- TRUERun CellChat, endometrial atlas
Load packages
Read data
sce <- readRDS("invivo_cell_atlas.rds")
clustercolumn <- "celltype_annot"
table(sce$Stage, useNA = "ifany")
early-mid-secretory early-secretory late-secretory menstrual
37466 24385 16459 2233
mid-secretory proliferative
30137 62887 # Define stages to include
stages <- list(menstrual_proliferative = c("menstrual", "proliferative"))Run CellChat (for each stage)
minClusterSize <- 100
for (stgn in names(stages)) {
if (!file.exists(paste0("cellchat_", stgn, ".rds")) || force_cellchat) {
sub <- sce[, sce$Stage %in% stages[[stgn]]]
## Remove rare cell types
tbl <- table(sub[[clustercolumn]])
kp <- names(tbl)[tbl >= minClusterSize]
sub <- sub[, sub[[clustercolumn]] %in% kp]
## Create CellChat object
cellchat <- createCellChat(object = sub, group.by = clustercolumn)
## Define which CellChatDB to use
CellChatDB <- CellChatDB.human
## Potentially subset the CellChatDB
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling")
# CellChatDB.use <- CellChatDB
cellchat@DB <- CellChatDB.use
## Subset to only signalling genes
cellchat <- subsetData(cellchat)
future::plan("multisession", workers = 8)
## Process the expression data
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
## Infer cell-cell communication network
cellchat <- computeCommunProb(cellchat, raw.use = TRUE, type = "triMean",
population.size = FALSE)
## To apply less stringent filtering
# cellchat <- computeCommunProb(cellchat, raw.use = TRUE, type = "truncatedMean",
# trim = 0.1)
## Filter out the cell-cell communication if there are only few cells
cellchat <- filterCommunication(cellchat, min.cells = minClusterSize)
## Calculate communication probability on pathway level
cellchat <- computeCommunProbPathway(cellchat)
## Aggregated cell-cell communication network by counting the number of
## links or summarizing the communication probability
cellchat <- aggregateNet(cellchat)
## Compute network centrality scores
## Unless 'future.rng.onMisuse' is set to "ignore", this
## generates warnings about random numbers being generated without
## this being set
cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP")
## Save object
saveRDS(cellchat, file = paste0("cellchat_", stgn, ".rds"))
## Close background workers
future::plan("sequential")
}
}[1] "Create a CellChat object from a SingleCellExperiment object"
The `logcounts` assay is used
The `colData` assay in the SingleCellExperiment object is used as cell meta information
Set cell identities for the new CellChat object
The cell groups used for CellChat analysis are Arterial B_cell cDC2 ciliated cycling dStromal eM1 eM2 ePV eStromal Fibroblast_basalis hormone responsive early hormone responsive late HOXA13 ILC3 luminal early Monocyte mPV pDC Peripheral_lymphocyte pre-ciliated remodeling secretory mid sHormones T_cell_CD4 T_cell_CD8 T_cell_cycling T_Reg transcriptionally active uNK1 uNK2 uNK3 uSMCs Venous
triMean is used for calculating the average gene expression per cell group.
[1] ">>> Run CellChat on sc/snRNA-seq data <<< [2025-06-30 21:30:33.605831]"
[1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2025-06-30 22:48:16.290903]"Session info
sessionInfo()R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Rocky Linux 8.10 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: FlexiBLAS OPENBLAS; LAPACK version 3.12.0
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: Europe/Zurich
tzcode source: system (glibc)
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ComplexHeatmap_2.22.0 ggalluvial_0.12.5
[3] NMF_0.28 cluster_2.1.6
[5] rngtools_1.5.2 registry_0.5-1
[7] patchwork_1.3.0 future_1.40.0
[9] SingleCellExperiment_1.28.1 SummarizedExperiment_1.36.0
[11] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
[13] IRanges_2.40.1 S4Vectors_0.44.0
[15] MatrixGenerics_1.18.1 matrixStats_1.5.0
[17] CellChat_1.6.1 bigmemory_4.6.4
[19] Biobase_2.66.0 BiocGenerics_0.52.0
[21] ggplot2_3.5.2 igraph_2.1.4
[23] dplyr_1.1.4
loaded via a namespace (and not attached):
[1] pbapply_1.7-2 rlang_1.1.6 magrittr_2.0.3
[4] clue_0.3-66 GetoptLong_1.0.5 gridBase_0.4-7
[7] compiler_4.4.2 systemfonts_1.2.2 png_0.1-8
[10] vctrs_0.6.5 reshape2_1.4.4 stringr_1.5.1
[13] pkgconfig_2.0.3 shape_1.4.6.1 crayon_1.5.3
[16] fastmap_1.2.0 XVector_0.46.0 backports_1.5.0
[19] scuttle_1.16.0 rmarkdown_2.29 UCSC.utils_1.2.0
[22] network_1.19.0 purrr_1.0.4 bluster_1.16.0
[25] xfun_0.52 beachmat_2.22.0 zlibbioc_1.52.0
[28] jsonlite_2.0.0 DelayedArray_0.32.0 BiocParallel_1.40.2
[31] uuid_1.2-1 broom_1.0.8 irlba_2.3.5.1
[34] parallel_4.4.2 R6_2.6.1 stringi_1.8.7
[37] RColorBrewer_1.1-3 limma_3.62.2 reticulate_1.42.0
[40] parallelly_1.43.0 car_3.1-3 Rcpp_1.0.14
[43] iterators_1.0.14 knitr_1.50 future.apply_1.11.3
[46] FNN_1.1.4.1 Matrix_1.7-1 tidyselect_1.2.1
[49] dichromat_2.0-0.1 abind_1.4-8 yaml_2.3.10
[52] doParallel_1.0.17 codetools_0.2-20 listenv_0.9.1
[55] lattice_0.22-6 tibble_3.2.1 plyr_1.8.9
[58] withr_3.0.2 coda_0.19-4.1 evaluate_1.0.3
[61] circlize_0.4.16 pillar_1.10.2 BiocManager_1.30.25
[64] ggpubr_0.6.0 carData_3.0-5 foreach_1.5.2
[67] generics_0.1.3 scales_1.3.0.9000 ggnetwork_0.5.13
[70] globals_0.17.0 glue_1.8.0 metapod_1.14.0
[73] tools_4.4.2 BiocNeighbors_2.0.1 ScaledMatrix_1.14.0
[76] RSpectra_0.16-2 locfit_1.5-9.12 ggsignif_0.6.4
[79] scran_1.34.0 cowplot_1.1.3 tidyr_1.3.1
[82] edgeR_4.4.2 colorspace_2.1-1 GenomeInfoDbData_1.2.13
[85] BiocSingular_1.22.0 rsvd_1.0.5 Formula_1.2-5
[88] cli_3.6.4 bigmemory.sri_0.1.8 S4Arrays_1.6.0
[91] svglite_2.1.3 gtable_0.3.6 rstatix_0.7.2
[94] digest_0.6.37 dqrng_0.4.1 SparseArray_1.6.2
[97] ggrepel_0.9.6 rjson_0.2.23 htmlwidgets_1.6.4
[100] sna_2.8 farver_2.1.2 htmltools_0.5.8.1
[103] lifecycle_1.0.4 httr_1.4.7 statmod_1.5.0
[106] GlobalOptions_0.1.2 statnet.common_4.11.0