Recluster endothelial cells
Belinda Phipson
10/31/2019
Last updated: 2021-02-09
Checks: 5 2
Knit directory: Human_Development_snRNAseq/
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Load libraries and functions
library(edgeR)
library(RColorBrewer)
library(org.Hs.eg.db)
library(limma)
library(Seurat)
library(monocle)
library(cowplot)
library(DelayedArray)
library(scran)
library(NMF)
library(workflowr)
library(ggplot2)
library(clustree)
library(dplyr)targets <- read.delim("/Users/phipsonbelinda/Documents/MCRI_collab/Porrello/Human_Development_snRNAseq/data/targets.txt",header=TRUE, stringsAsFactors = FALSE)
targets$FileName2 <- paste(targets$FileName,"/",sep="")
targets$Group_ID2 <- gsub("LV_","",targets$Group_ID)
group <- c("Fetal_1","Fetal_2","Fetal_3",
"Young_1","Young_2","Young_3",
"Adult_1","Adult_2","Adult_3",
"Diseased_1","Diseased_2",
"Diseased_3","Diseased_4")
m <- match(group, targets$Group_ID2)
targets <- targets[m,]fetal.integrated <- readRDS(file="./output/fetal-int.Rds")
load(file="./output/fetalObjs.Rdata")
young.integrated <- readRDS(file="./output/young-int.Rds")
load(file="./output/youngObjs.Rdata")
adult.integrated <- readRDS(file="./output/adult-int.Rds")
load(file="./output/adultObjs.Rdata")Set default clustering resolution
# Default 0.3
Idents(fetal.integrated) <- fetal.integrated$integrated_snn_res.0.3
DimPlot(fetal.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()
# Default 0.3
DimPlot(young.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()
# Default 0.6
DimPlot(adult.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()Merge all data together
heart <- merge(fetal.integrated, y = c(young.integrated, adult.integrated), project = "heart")
table(heart$orig.ident)
DefaultAssay(object = heart) <- "RNA"Get endothelial cells only
endo <- subset(heart,subset = Broad_celltype == "Endothelial cells")
dim(endo)Check for poor quality cells
Check for cells with very low number of uniquely detected genes.
par(mfrow=c(1,2))
plot(density(endo$nFeature_RNA),main="Number of genes detected")
abline(v=500,col=2)
plot(density(endo$nCount_RNA),main="Library size")
abline(v=2500,col=2)
#endo <- subset(endo, subset = nFeature_RNA > 500 & nCount_RNA > 2500)
dim(endo)[1] 17926 5501table(endo$biorep)
a1 a2 a3 f1 f2 f3 y1 y2 y3
599 466 165 735 715 1298 511 462 550 Run new integration with SCtransform normalisation
endo.list <- SplitObject(endo, split.by = "biorep")for (i in 1:length(endo.list)) {
endo.list[[i]] <- SCTransform(endo.list[[i]], verbose = FALSE)
}kf <- min(sapply(endo.list, ncol))
endo.anchors <- FindIntegrationAnchors(object.list = endo.list, dims=1:30,anchor.features = 3000,k.filter=kf)
endo.integrated <- IntegrateData(anchorset = endo.anchors,dims=1:30)Perform clustering
DefaultAssay(object = endo.integrated) <- "integrated"Perform scaling and PCA
endo.integrated <- ScaleData(endo.integrated, verbose = FALSE)
endo.integrated <- RunPCA(endo.integrated, npcs = 50, verbose = FALSE)ElbowPlot(endo.integrated,ndims=50)
VizDimLoadings(endo.integrated, dims = 1:4, reduction = "pca")
DimPlot(endo.integrated, reduction = "pca",group.by="orig.ident")
DimPlot(endo.integrated, reduction = "pca",group.by="biorep")
DimPlot(endo.integrated, reduction = "pca",group.by="sex")
DimPlot(endo.integrated, reduction = "pca",group.by="batch")
DimHeatmap(endo.integrated, dims = 1:15, cells = 500, balanced = TRUE)
DimHeatmap(endo.integrated, dims = 16:30, cells = 500, balanced = TRUE)
#DimHeatmap(endo.integrated, dims = 31:45, cells = 500, balanced = TRUE)Perform nearest neighbours clustering
endo.integrated <- FindNeighbors(endo.integrated, dims = 1:20)
endo.integrated <- FindClusters(endo.integrated, resolution = 0.1)table(Idents(endo.integrated))
0 1 2 3 4 5 6
2059 1623 760 428 367 142 122 par(mfrow=c(1,1))
par(mar=c(5,4,2,2))
barplot(table(Idents(endo.integrated)),ylab="Number of cells",xlab="Clusters")
title("Number of cells in each cluster")
Visualisation with TSNE
set.seed(10)
endo.integrated <- RunTSNE(endo.integrated, reduction = "pca", dims = 1:20)DimPlot(endo.integrated, reduction = "tsne",label=TRUE,label.size = 6,pt.size = 0.5)+NoLegend()
pdf(file="./output/Figures/tsne-endoALL-res01.pdf",width=10,height=8,onefile = FALSE)
DimPlot(endo.integrated, reduction = "tsne",label=TRUE,label.size = 6,pt.size = 0.5)+NoLegend()
dev.off()DimPlot(endo.integrated, reduction = "tsne", group.by = "orig.ident")
DimPlot(endo.integrated, reduction = "tsne", split.by = "orig.ident")
DimPlot(endo.integrated, reduction = "tsne", group.by = "biorep")
DimPlot(endo.integrated, reduction = "tsne", group.by = "sex")
DimPlot(endo.integrated, reduction = "tsne", split.by = "sex")
DimPlot(endo.integrated, reduction = "tsne", group.by = "batch")
par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(endo.integrated),endo.integrated$biorep)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(9),legend=TRUE)
par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(endo.integrated),endo.integrated$orig.ident)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(3))
legend("topleft",legend=colnames(tab),fill=ggplotColors(3))
Visualisation with clustree
clusres <- c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2)for(i in 1:length(clusres)){
endo.integrated <- FindClusters(endo.integrated,
resolution = clusres[i])
}pct.male <- function(x) {mean(x=="m")}
pct.female <- function(x) {mean(x=="f")}
pct.fetal <- function(x) {mean(x=="fetal")}
pct.young <- function(x) {mean(x=="young")}
pct.adult <- function(x) {mean(x=="adult")}clustree(endo.integrated, prefix = "integrated_snn_res.")
clustree(endo.integrated, prefix = "integrated_snn_res.",
node_colour = "sex", node_colour_aggr = "pct.female",assay="RNA")
clustree(endo.integrated, prefix = "integrated_snn_res.",
node_colour = "sex", node_colour_aggr = "pct.male",assay="RNA")
clustree(endo.integrated, prefix = "integrated_snn_res.",
node_colour = "orig.ident", node_colour_aggr = "pct.fetal",assay="RNA")
clustree(endo.integrated, prefix = "integrated_snn_res.",
node_colour = "orig.ident", node_colour_aggr = "pct.young",assay="RNA")
clustree(endo.integrated, prefix = "integrated_snn_res.",
node_colour = "orig.ident", node_colour_aggr = "pct.adult",assay="RNA")
Save Seurat object
DefaultAssay(endo.integrated) <- "RNA"
Idents(endo.integrated) <- endo.integrated$integrated_snn_res.0.1# saveRDS(endo.integrated,file="./output/RDataObjects/endo-int-FYA-filtered.Rds")
#endo.integrated <- readRDS(file="./output/RDataObjects/endo-int-FYA.Rds")# Load unfiltered counts matrix for every sample (object all)
#load("./output/RDataObjects/all-counts.Rdata")
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] splines parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] dplyr_1.0.2 clustree_0.4.3
[3] ggraph_2.0.4 NMF_0.23.0
[5] cluster_2.1.0 rngtools_1.5
[7] pkgmaker_0.32.2 registry_0.5-1
[9] scran_1.18.1 SingleCellExperiment_1.12.0
[11] SummarizedExperiment_1.20.0 GenomicRanges_1.42.0
[13] GenomeInfoDb_1.26.1 DelayedArray_0.16.0
[15] MatrixGenerics_1.2.0 matrixStats_0.57.0
[17] cowplot_1.1.0 monocle_2.18.0
[19] DDRTree_0.1.5 irlba_2.3.3
[21] VGAM_1.1-4 ggplot2_3.3.2
[23] Matrix_1.2-18 Seurat_3.2.2
[25] org.Hs.eg.db_3.12.0 AnnotationDbi_1.52.0
[27] IRanges_2.24.0 S4Vectors_0.28.0
[29] Biobase_2.50.0 BiocGenerics_0.36.0
[31] RColorBrewer_1.1-2 edgeR_3.32.0
[33] limma_3.46.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] reticulate_1.18 tidyselect_1.1.0
[3] RSQLite_2.2.1 htmlwidgets_1.5.2
[5] grid_4.0.2 combinat_0.0-8
[7] docopt_0.7.1 BiocParallel_1.24.1
[9] Rtsne_0.15 munsell_0.5.0
[11] codetools_0.2-18 ica_1.0-2
[13] statmod_1.4.35 future_1.20.1
[15] miniUI_0.1.1.1 withr_2.3.0
[17] colorspace_2.0-0 fastICA_1.2-2
[19] highr_0.8 knitr_1.30
[21] rstudioapi_0.13 ROCR_1.0-11
[23] tensor_1.5 listenv_0.8.0
[25] labeling_0.4.2 git2r_0.27.1
[27] slam_0.1-47 GenomeInfoDbData_1.2.4
[29] polyclip_1.10-0 farver_2.0.3
[31] bit64_4.0.5 pheatmap_1.0.12
[33] rprojroot_2.0.2 parallelly_1.21.0
[35] vctrs_0.3.5 generics_0.1.0
[37] xfun_0.19 R6_2.5.0
[39] doParallel_1.0.16 graphlayouts_0.7.1
[41] rsvd_1.0.3 locfit_1.5-9.4
[43] bitops_1.0-6 spatstat.utils_1.17-0
[45] assertthat_0.2.1 promises_1.1.1
[47] scales_1.1.1 gtable_0.3.0
[49] beachmat_2.6.2 globals_0.14.0
[51] goftest_1.2-2 tidygraph_1.2.0
[53] rlang_0.4.9 lazyeval_0.2.2
[55] checkmate_2.0.0 yaml_2.2.1
[57] reshape2_1.4.4 abind_1.4-5
[59] backports_1.2.0 httpuv_1.5.4
[61] tools_4.0.2 gridBase_0.4-7
[63] ellipsis_0.3.1 ggridges_0.5.2
[65] Rcpp_1.0.5 plyr_1.8.6
[67] sparseMatrixStats_1.2.0 zlibbioc_1.36.0
[69] purrr_0.3.4 RCurl_1.98-1.2
[71] densityClust_0.3 rpart_4.1-15
[73] deldir_0.2-3 pbapply_1.4-3
[75] viridis_0.5.1 zoo_1.8-8
[77] ggrepel_0.8.2 fs_1.5.0
[79] magrittr_2.0.1 data.table_1.13.2
[81] lmtest_0.9-38 RANN_2.6.1
[83] whisker_0.4 fitdistrplus_1.1-1
[85] patchwork_1.1.0 mime_0.9
[87] evaluate_0.14 xtable_1.8-4
[89] sparsesvd_0.2 gridExtra_2.3
[91] HSMMSingleCell_1.10.0 compiler_4.0.2
[93] tibble_3.0.4 KernSmooth_2.23-18
[95] crayon_1.3.4 htmltools_0.5.0
[97] mgcv_1.8-33 later_1.1.0.1
[99] tidyr_1.1.2 DBI_1.1.0
[101] tweenr_1.0.1 MASS_7.3-53
[103] igraph_1.2.6 pkgconfig_2.0.3
[105] plotly_4.9.2.1 scuttle_1.0.3
[107] foreach_1.5.1 dqrng_0.2.1
[109] XVector_0.30.0 stringr_1.4.0
[111] digest_0.6.27 sctransform_0.3.1
[113] RcppAnnoy_0.0.17 spatstat.data_1.5-2
[115] rmarkdown_2.5 leiden_0.3.5
[117] uwot_0.1.9 DelayedMatrixStats_1.12.1
[119] shiny_1.5.0 lifecycle_0.2.0
[121] nlme_3.1-150 jsonlite_1.7.1
[123] BiocNeighbors_1.8.1 viridisLite_0.3.0
[125] pillar_1.4.7 lattice_0.20-41
[127] fastmap_1.0.1 httr_1.4.2
[129] survival_3.2-7 glue_1.4.2
[131] qlcMatrix_0.9.7 FNN_1.1.3
[133] spatstat_1.64-1 png_0.1-7
[135] iterators_1.0.13 bluster_1.0.0
[137] bit_4.0.4 ggforce_0.3.2
[139] stringi_1.5.3 blob_1.2.1
[141] BiocSingular_1.6.0 memoise_1.1.0
[143] future.apply_1.6.0