cellchatE11.5
Ehsan Razmara Monash Uni
14/03/2022
library(CellChat)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union## Loading required package: igraph##
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## as_data_frame, groups, union## The following objects are masked from 'package:stats':
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## union## Loading required package: ggplot2library(patchwork)
library(Seurat)## Registered S3 method overwritten by 'spatstat.geom':
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## print.boxx cli## Attaching SeuratObjectlibrary(SeuratObject)
library(SeuratWrappers)
library(dplyr)
library(future)##
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## %->%, %<-%library(Seurat)
library(devtools)## Loading required package: usethislibrary(NMF)## Loading required package: pkgmaker## Loading required package: registry## Loading required package: rngtools## Loading required package: cluster## NMF - BioConductor layer [OK] | Shared memory capabilities [NO: bigmemory] | Cores 15/16## To enable shared memory capabilities, try: install.extras('
## NMF
## ')##
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## algorithm, comparelibrary(ggalluvial)
pbmc <- readRDS("/mnt/nectar_volume/home/eraz0001/KELLY 2020/E11.5/Final_15_clusters.RDS")
data.input <- GetAssayData(pbmc, assay = "RNA", slot = "data")
labels <- Idents(pbmc)
meta <- data.frame(group = labels, row.names = names(labels))
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "group")## Create a CellChat object from a data matrix## Set cell identities for the new CellChat object## The cell groups used for CellChat analysis are DCh Osteocytes Osteoblast HtCh Osteoblasts Endothelial Cells Hoxd13+ Chondrocytes Terminal HTCh Fibroblasts Immune cellsgroupSize <- as.numeric(table(cellchat@idents))
groupSize## [1] 553 199 194 190 339 319 148 133 118 102 38CellChatDB <- CellChatDB.mouse
showDatabaseCategory(CellChatDB)
dplyr::glimpse(CellChatDB$interaction)## Rows: 2,019
## Columns: 11
## $ interaction_name <chr> "TGFB1_TGFBR1_TGFBR2", "TGFB2_TGFBR1_TGFBR2", "TGFB…
## $ pathway_name <chr> "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TG…
## $ ligand <chr> "Tgfb1", "Tgfb2", "Tgfb3", "Tgfb1", "Tgfb1", "Tgfb2…
## $ receptor <chr> "TGFbR1_R2", "TGFbR1_R2", "TGFbR1_R2", "ACVR1B_TGFb…
## $ agonist <chr> "TGFb agonist", "TGFb agonist", "TGFb agonist", "TG…
## $ antagonist <chr> "TGFb antagonist", "TGFb antagonist", "TGFb antagon…
## $ co_A_receptor <chr> "", "", "", "", "", "", "", "", "", "", "", "", "",…
## $ co_I_receptor <chr> "TGFb inhibition receptor", "TGFb inhibition recept…
## $ evidence <chr> "KEGG: mmu04350", "KEGG: mmu04350", "KEGG: mmu04350…
## $ annotation <chr> "Secreted Signaling", "Secreted Signaling", "Secret…
## $ interaction_name_2 <chr> "Tgfb1 - (Tgfbr1+Tgfbr2)", "Tgfb2 - (Tgfbr1+Tgfbr2…CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling")set the used database in the object
cellchat@DB <- CellChatDB.usePreprocessing the expression data for cell-cell communication analysis
# subset the expression data of signaling genes for saving computation cost
cellchat <- subsetData(cellchat)future::plan("multiprocess", workers = 2)
cellchat <- identifyOverExpressedGenes(cellchat, data.input)
cellchat <- identifyOverExpressedInteractions(cellchat)
cellchat <- projectData(cellchat, PPI.mouse)Part II: Inference of cell-cell communication network
Compute the communication probability and infer cellular communication network
cellchat <- computeCommunProb(cellchat)
cellchat <- filterCommunication(cellchat, min.cells = 10)cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)groupSize <- as.numeric(table(cellchat@idents))
par(mfrow = c(1,2), xpd=TRUE)
netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= T, title.name = "Number of Interactions")
netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= T, title.name = "Interaction Weights/Strength")
mat <- cellchat@net$weight
par(mfrow = c(3,4), xpd=TRUE)
for (i in 1:nrow(mat)) {
mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))
mat2[i, ] <- mat[i, ]
netVisual_circle(mat2, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i])
}
cellchat@netP[["pathways"]]## [1] "MK" "ANGPTL" "PTN" "ncWNT" "BMP" "MIF"
## [7] "PDGF" "WNT" "VISFATIN" "SEMA3" "FGF" "VEGF"
## [13] "GALECTIN" "IGF" "APELIN" "CXCL" "PROS" "GAS"
## [19] "GDF" "ANGPT" "KIT" "EGF" "CCL"pathways.show <- c("MK")
vertex.receiver = seq(1,4)
netVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver, layout = "hierarchy")
# Circle plot
par(mfrow=c(1,1))
netVisual_aggregate(cellchat, signaling = pathways.show, layout = "circle")
Heatmap
par(mfrow=c(1,1))
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds")## Do heatmap based on a single object
netAnalysis_contribution(cellchat, signaling = pathways.show <- c("MK"), title = "Contribution of each L-R/all")
netAnalysis_contribution(cellchat, signaling = pathways.show <- c(cellchat@netP[["pathways"]]), title = "Contribution of each L-R/all")
pairLR1 <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE)
LR.show <- pairLR1[2,] # show one ligand-receptor pair
# Hierarchy plot
vertex.receiver = seq(1,4) # a numeric vector
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, vertex.receiver = vertex.receiver, layout = "hierarchy")
# Circle plot
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "circle")
## [[1]]# Chord diagram
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "chord")
## [[1]]netVisual_bubble(cellchat, sources.use = 7, targets.use = c(1:11), remove.isolate = FALSE,font.size.title = 15)## Comparing communications on a single object
netVisual_bubble(cellchat, sources.use= 7, targets.use = c(1:5,7), signaling = cellchat@netP[["pathways"]], remove.isolate = T)## Comparing communications on a single object
pairLR.use <- extractEnrichedLR(cellchat, signaling = cellchat@netP[["pathways"]])
netVisual_bubble(cellchat, sources.use = c(1:8), targets.use = c(1:8), pairLR.use = pairLR.use, remove.isolate = TRUE)## Comparing communications on a single object
netVisual_chord_gene(cellchat, sources.use = 6, targets.use = c(1:7), legend.pos.x = 15)
netVisual_chord_gene(cellchat, sources.use=7, targets.use = c(1:7), signaling = c("MK"), legend.pos.x = 8)## You may try the function `netVisual_chord_cell` for visualizing individual signaling pathway
show all the significant signaling pathways from some cell groups (defined by ‘sources.use’) to other cell groups (defined by ‘targets.use’)
netVisual_chord_gene(cellchat, sources.use = 7, targets.use = c(1:11), slot.name = "netP", legend.pos.x = 10)
## Plot the signaling gene expression distribution using violin/dot plot
plotGeneExpression(cellchat, signaling = cellchat@netP[["pathways"]])## Scale for 'y' is already present. Adding another scale for 'y', which will
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plotGeneExpression(cellchat, signaling = "MK", enriched.only = FALSE)## Scale for 'y' is already present. Adding another scale for 'y', which will
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pathways.show <- cellchat@netP[["pathways"]]
cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP")
netAnalysis_signalingRole_network(cellchat, signaling = pathways.show, width = 8, height = 2.5, font.size = 15, font.size.title = 12)
gg2 = # Signaling role analysis on the cell-cell communication networks of interest
gg1 <- netAnalysis_signalingRole_scatter(cellchat)## Signaling role analysis on the aggregated cell-cell communication network from all signaling pathwaysgg2 <- netAnalysis_signalingRole_scatter(cellchat, signaling = c("SEMA3", "IGF"))## Signaling role analysis on the cell-cell communication network from user's inputgg1 + gg2
# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
ht1 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "outgoing")
ht2 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "incoming")
ht1 + ht2
library(NMF)
library(ggalluvial)selectK(cellchat, pattern = "outgoing")
nPatterns = 7cellchat <- identifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)
netAnalysis_river(cellchat, pattern = "outgoing",font.size = 3)## Please make sure you have load `library(ggalluvial)` when running this function
netAnalysis_dot(cellchat, pattern = "outgoing")
Identify and visualize incoming communication pattern of target cells
selectK(cellchat, pattern = "incoming")
#Cophenetic values begin to drop when the number of incoming patterns is 4.
nPatterns = 4
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = nPatterns)
netAnalysis_river(cellchat, pattern = "incoming")## Please make sure you have load `library(ggalluvial)` when running this function
session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.2 (2020-06-22)
## os Ubuntu 20.04.3 LTS
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## ui X11
## language (EN)
## collate en_AU.UTF-8
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## date 2022-03-16
## pandoc 2.11.4 @ /usr/lib/rstudio-server/bin/pandoc/ (via rmarkdown)
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## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [1] CRAN (R 4.0.2)
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## gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.2)
## highr 0.9 2021-04-16 [1] CRAN (R 4.0.2)
## htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.0.2)
## htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.0.2)
## httpuv 1.6.5 2022-01-05 [1] CRAN (R 4.0.2)
## httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2)
## ica 1.0-2 2018-05-24 [1] CRAN (R 4.0.2)
## igraph * 1.2.11 2022-01-04 [1] CRAN (R 4.0.2)
## IRanges 2.24.1 2020-12-12 [1] Bioconductor
## irlba 2.3.5 2021-12-06 [1] CRAN (R 4.0.2)
## iterators * 1.0.13 2020-10-15 [1] CRAN (R 4.0.2)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.0.2)
## jsonlite 1.7.3 2022-01-17 [1] CRAN (R 4.0.2)
## KernSmooth 2.23-17 2020-04-26 [2] CRAN (R 4.0.2)
## knitr 1.37 2021-12-16 [1] CRAN (R 4.0.2)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.0.2)
## later 1.3.0 2021-08-18 [1] CRAN (R 4.0.2)
## lattice 0.20-41 2020-04-02 [2] CRAN (R 4.0.2)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.0.2)
## leiden 0.3.9 2021-07-27 [1] CRAN (R 4.0.2)
## lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.0.2)
## listenv 0.8.0 2019-12-05 [1] CRAN (R 4.0.2)
## lmtest 0.9-39 2021-11-07 [1] CRAN (R 4.0.2)
## magick 2.7.3 2021-08-18 [1] CRAN (R 4.0.2)
## magrittr 2.0.2 2022-01-26 [1] CRAN (R 4.0.2)
## MASS 7.3-51.6 2020-04-26 [2] CRAN (R 4.0.2)
## Matrix 1.4-0 2021-12-08 [1] CRAN (R 4.0.2)
## matrixStats 0.61.0 2021-09-17 [1] CRAN (R 4.0.2)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.0.2)
## mgcv 1.8-31 2019-11-09 [2] CRAN (R 4.0.2)
## mime 0.12 2021-09-28 [1] CRAN (R 4.0.2)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.0.2)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.2)
## network 1.17.1 2021-06-14 [1] CRAN (R 4.0.2)
## nlme 3.1-148 2020-05-24 [2] CRAN (R 4.0.2)
## NMF * 0.23.0 2020-08-01 [1] CRAN (R 4.0.2)
## parallelly 1.30.0 2021-12-17 [1] CRAN (R 4.0.2)
## patchwork * 1.1.1 2020-12-17 [1] CRAN (R 4.0.2)
## pbapply 1.5-0 2021-09-16 [1] CRAN (R 4.0.2)
## pillar 1.7.0 2022-02-01 [1] CRAN (R 4.0.2)
## pkgbuild 1.3.1 2021-12-20 [1] CRAN (R 4.0.2)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.2)
## pkgload 1.2.4 2021-11-30 [1] CRAN (R 4.0.2)
## pkgmaker * 0.32.2 2020-10-20 [1] CRAN (R 4.0.2)
## plotly 4.10.0 2021-10-09 [1] CRAN (R 4.0.2)
## plyr 1.8.6 2020-03-03 [1] CRAN (R 4.0.2)
## png 0.1-7 2013-12-03 [1] CRAN (R 4.0.2)
## polyclip 1.10-0 2019-03-14 [1] CRAN (R 4.0.2)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.2)
## processx 3.5.2 2021-04-30 [1] CRAN (R 4.0.2)
## promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.0.2)
## ps 1.6.0 2021-02-28 [1] CRAN (R 4.0.2)
## purrr 0.3.4 2020-04-17 [1] CRAN (R 4.0.2)
## R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.0.2)
## R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.0.2)
## R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.0.2)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.0.2)
## RANN 2.6.1 2019-01-08 [1] CRAN (R 4.0.2)
## RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 4.0.2)
## Rcpp 1.0.8 2022-01-13 [1] CRAN (R 4.0.2)
## RcppAnnoy 0.0.19 2021-07-30 [1] CRAN (R 4.0.2)
## registry * 0.5-1 2019-03-05 [1] CRAN (R 4.0.2)
## remotes 2.4.2 2021-11-30 [1] CRAN (R 4.0.2)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.0.2)
## reticulate 1.24 2022-01-26 [1] CRAN (R 4.0.2)
## rjson 0.2.21 2022-01-09 [1] CRAN (R 4.0.2)
## rlang 1.0.0 2022-01-26 [1] CRAN (R 4.0.2)
## rmarkdown 2.11 2021-09-14 [1] CRAN (R 4.0.2)
## rngtools * 1.5.2 2021-09-20 [1] CRAN (R 4.0.2)
## ROCR 1.0-11 2020-05-02 [1] CRAN (R 4.0.2)
## rpart 4.1-15 2019-04-12 [2] CRAN (R 4.0.2)
## rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.0.2)
## RSpectra 0.16-0 2019-12-01 [1] CRAN (R 4.0.2)
## rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.0.2)
## rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.0.2)
## Rtsne 0.15 2018-11-10 [1] CRAN (R 4.0.2)
## S4Vectors 0.28.1 2020-12-09 [1] Bioconductor
## sass 0.4.0 2021-05-12 [1] CRAN (R 4.0.2)
## scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.2)
## scattermore 0.7 2020-11-24 [1] CRAN (R 4.0.2)
## sctransform 0.3.3 2022-01-13 [1] CRAN (R 4.0.2)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.0.2)
## Seurat * 4.1.0 2022-01-14 [1] CRAN (R 4.0.2)
## SeuratObject * 4.0.4 2021-11-23 [1] CRAN (R 4.0.2)
## SeuratWrappers * 0.3.0 2022-02-02 [1] Github (satijalab/seurat-wrappers@8510069)
## shape 1.4.6 2021-05-19 [1] CRAN (R 4.0.2)
## shiny 1.7.1 2021-10-02 [1] CRAN (R 4.0.2)
## sna 2.6 2020-10-06 [1] CRAN (R 4.0.2)
## spatstat.core 2.3-2 2021-11-26 [1] CRAN (R 4.0.2)
## spatstat.data 2.1-2 2021-12-17 [1] CRAN (R 4.0.2)
## spatstat.geom 2.3-1 2021-12-10 [1] CRAN (R 4.0.2)
## spatstat.sparse 2.1-0 2021-12-17 [1] CRAN (R 4.0.2)
## spatstat.utils 2.3-0 2021-12-12 [1] CRAN (R 4.0.2)
## statnet.common 4.5.0 2021-06-05 [1] CRAN (R 4.0.2)
## stringi 1.7.6 2021-11-29 [1] CRAN (R 4.0.2)
## stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
## survival 3.1-12 2020-04-10 [2] CRAN (R 4.0.2)
## svglite 2.0.0 2021-02-20 [1] CRAN (R 4.0.2)
## systemfonts 1.0.3 2021-10-13 [1] CRAN (R 4.0.2)
## tensor 1.5 2012-05-05 [1] CRAN (R 4.0.2)
## testthat 3.1.2 2022-01-20 [1] CRAN (R 4.0.2)
## tibble 3.1.6 2021-11-07 [1] CRAN (R 4.0.2)
## tidyr 1.2.0 2022-02-01 [1] CRAN (R 4.0.2)
## tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.0.2)
## usethis * 2.1.5 2021-12-09 [1] CRAN (R 4.0.2)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.0.2)
## uwot 0.1.11 2021-12-02 [1] CRAN (R 4.0.2)
## vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.0.2)
## viridisLite 0.4.0 2021-04-13 [1] CRAN (R 4.0.2)
## withr 2.4.3 2021-11-30 [1] CRAN (R 4.0.2)
## xfun 0.29 2021-12-14 [1] CRAN (R 4.0.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.0.2)
## yaml 2.2.2 2022-01-25 [1] CRAN (R 4.0.2)
## zoo 1.8-9 2021-03-09 [1] CRAN (R 4.0.2)
##
## [1] /mnt/nectar_volume/home/eraz0001/R/x86_64-pc-linux-gnu-library/4.0
## [2] /mnt/nectar_volume/software/apps/R/4.0.2/lib/R/library
##
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