Recluster smooth muscle cells
Belinda Phipson
10/30/2019
Last updated: 2021-02-09
Checks: 6 1
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("./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$Group_ID2)
targets <- targets[m,]fetal.integrated <- readRDS(file="./output/RDataObjects/fetal-int.Rds")
load(file="./output/RDataObjects/fetalObjs.Rdata")
young.integrated <- readRDS(file="./output/RDataObjects/young-int.Rds")
load(file="./output/RDataObjects/youngObjs.Rdata")
adult.integrated <- readRDS(file="./output/RDataObjects/adult-int.Rds")
load(file="./output/RDataObjects/adultObjs.Rdata")Set default clustering resolution
# Default 0.3
Idents(fetal.integrated) <- fetal.integrated$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")
DefaultAssay(object = heart) <- "RNA"Get smooth muscle cells only
smc <- subset(heart,subset = Broad_celltype == "Smooth muscle cells")
dim(smc)Check for poor quality cells
Check for cells with very low number of uniquely detected genes.
par(mfrow=c(1,2))
plot(density(smc$nFeature_RNA),main="Number of genes detected")
abline(v=500,col=2)
plot(density(smc$nCount_RNA),main="Library size")
abline(v=2500,col=2)
#smc <- subset(smc, subset = nFeature_RNA > 500 & nCount_RNA > 2500)
dim(smc)[1] 17926 430table(smc$biorep)
a1 a2 a3 f1 f2 f3 y1 y2 y3
22 49 13 54 20 136 59 28 49 Run new integration with SCtransform normalisation
There are very few cells for each biological replicate, so I will normalise and integrate the data by group rather than biological replicate.
smc.list <- SplitObject(smc, split.by = "orig.ident")for (i in 1:length(smc.list)) {
smc.list[[i]] <- SCTransform(smc.list[[i]], verbose = FALSE)
}kf <- min(sapply(smc.list, ncol))
smc.anchors <- FindIntegrationAnchors(object.list = smc.list, dims=1:30,anchor.features = 3000,k.filter=kf)
smc.integrated <- IntegrateData(anchorset = smc.anchors,dims=1:30)Perform clustering
DefaultAssay(object = smc.integrated) <- "integrated"Perform scaling and PCA
smc.integrated <- ScaleData(smc.integrated, verbose = FALSE)
smc.integrated <- RunPCA(smc.integrated, npcs = 50, verbose = FALSE)
ElbowPlot(smc.integrated,ndims=50)VizDimLoadings(smc.integrated, dims = 1:4, reduction = "pca")
DimPlot(smc.integrated, reduction = "pca",group.by="orig.ident")
DimPlot(smc.integrated, reduction = "pca",group.by="biorep")
DimPlot(smc.integrated, reduction = "pca",group.by="sex")
DimPlot(smc.integrated, reduction = "pca",group.by="batch")
DimHeatmap(smc.integrated, dims = 1:15, cells = 500, balanced = TRUE)
#DimHeatmap(smc.integrated, dims = 16:30, cells = 500, balanced = TRUE)
#DimHeatmap(smc.integrated, dims = 31:45, cells = 500, balanced = TRUE)Perform nearest neighbours clustering
smc.integrated <- FindNeighbors(smc.integrated, dims = 1:10)
smc.integrated <- FindClusters(smc.integrated, resolution = 0.1)table(Idents(smc.integrated))
0 1 2
232 174 24 par(mfrow=c(1,1))
par(mar=c(5,4,2,2))
barplot(table(Idents(smc.integrated)),ylab="Number of cells",xlab="Clusters")
title("Number of cells in each cluster")
Visualisation with TSNE
set.seed(10)
smc.integrated <- RunTSNE(smc.integrated, reduction = "pca", dims = 1:10)DimPlot(smc.integrated, reduction = "tsne",label=TRUE,label.size = 6,pt.size = 0.5)+NoLegend()
pdf(file="./output/Figures/tsne-smcALL-res01.pdf",width=10,height=8,onefile = FALSE)
DimPlot(smc.integrated, reduction = "tsne",label=TRUE,label.size = 6,pt.size = 0.5)+NoLegend()
dev.off()DimPlot(smc.integrated, reduction = "tsne", group.by = "orig.ident")
DimPlot(smc.integrated, reduction = "tsne", split.by = "orig.ident")
DimPlot(smc.integrated, reduction = "tsne", group.by = "biorep")
DimPlot(smc.integrated, reduction = "tsne", group.by = "sex")
DimPlot(smc.integrated, reduction = "tsne", split.by = "sex")
DimPlot(smc.integrated, reduction = "tsne", group.by = "batch")
par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(smc.integrated),smc.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(smc.integrated),smc.integrated$orig.ident)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(3))
legend("topleft",legend=colnames(tab),fill=ggplotColors(3))
Visualisation with clustree
clusres <- c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2)for(i in 1:length(clusres)){
smc.integrated <- FindClusters(smc.integrated,
resolution = clusres[i])
}pct.male <- function(x) {mean(x=="m")}
pct.female <- function(x) {mean(x=="f")}
pct.fetal <- function(x) {mean(x=="fetal")}
pct.young <- function(x) {mean(x=="young")}
pct.adult <- function(x) {mean(x=="adult")}clustree(smc.integrated, prefix = "integrated_snn_res.")
clustree(smc.integrated, prefix = "integrated_snn_res.",
node_colour = "sex", node_colour_aggr = "pct.female",assay="RNA")
clustree(smc.integrated, prefix = "integrated_snn_res.",
node_colour = "sex", node_colour_aggr = "pct.male",assay="RNA")
clustree(smc.integrated, prefix = "integrated_snn_res.",
node_colour = "orig.ident", node_colour_aggr = "pct.fetal",assay="RNA")
clustree(smc.integrated, prefix = "integrated_snn_res.",
node_colour = "orig.ident", node_colour_aggr = "pct.young",assay="RNA")
clustree(smc.integrated, prefix = "integrated_snn_res.",
node_colour = "orig.ident", node_colour_aggr = "pct.adult",assay="RNA")
Save Seurat object
DefaultAssay(smc.integrated) <- "RNA"
Idents(smc.integrated) <- smc.integrated$integrated_snn_res.0.1saveRDS(smc.integrated,file="./output/RDataObjects/smc-int-FYA-filtered.Rds")
#smc.integrated <- readRDS(file="./output/RDataObjects/smc-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] knitr_1.30 rstudioapi_0.13
[21] ROCR_1.0-11 tensor_1.5
[23] listenv_0.8.0 labeling_0.4.2
[25] git2r_0.27.1 slam_0.1-47
[27] GenomeInfoDbData_1.2.4 polyclip_1.10-0
[29] farver_2.0.3 bit64_4.0.5
[31] pheatmap_1.0.12 rprojroot_2.0.2
[33] parallelly_1.21.0 vctrs_0.3.5
[35] generics_0.1.0 xfun_0.19
[37] R6_2.5.0 doParallel_1.0.16
[39] graphlayouts_0.7.1 rsvd_1.0.3
[41] locfit_1.5-9.4 bitops_1.0-6
[43] spatstat.utils_1.17-0 assertthat_0.2.1
[45] promises_1.1.1 scales_1.1.1
[47] gtable_0.3.0 beachmat_2.6.2
[49] globals_0.14.0 goftest_1.2-2
[51] tidygraph_1.2.0 rlang_0.4.9
[53] lazyeval_0.2.2 checkmate_2.0.0
[55] yaml_2.2.1 reshape2_1.4.4
[57] abind_1.4-5 backports_1.2.0
[59] httpuv_1.5.4 tools_4.0.2
[61] gridBase_0.4-7 ellipsis_0.3.1
[63] ggridges_0.5.2 Rcpp_1.0.5
[65] plyr_1.8.6 sparseMatrixStats_1.2.0
[67] zlibbioc_1.36.0 purrr_0.3.4
[69] RCurl_1.98-1.2 densityClust_0.3
[71] rpart_4.1-15 deldir_0.2-3
[73] pbapply_1.4-3 viridis_0.5.1
[75] zoo_1.8-8 ggrepel_0.8.2
[77] fs_1.5.0 magrittr_2.0.1
[79] data.table_1.13.2 lmtest_0.9-38
[81] RANN_2.6.1 whisker_0.4
[83] fitdistrplus_1.1-1 patchwork_1.1.0
[85] mime_0.9 evaluate_0.14
[87] xtable_1.8-4 sparsesvd_0.2
[89] gridExtra_2.3 HSMMSingleCell_1.10.0
[91] compiler_4.0.2 tibble_3.0.4
[93] KernSmooth_2.23-18 crayon_1.3.4
[95] htmltools_0.5.0 mgcv_1.8-33
[97] later_1.1.0.1 tidyr_1.1.2
[99] DBI_1.1.0 tweenr_1.0.1
[101] MASS_7.3-53 igraph_1.2.6
[103] pkgconfig_2.0.3 plotly_4.9.2.1
[105] scuttle_1.0.3 foreach_1.5.1
[107] dqrng_0.2.1 XVector_0.30.0
[109] stringr_1.4.0 digest_0.6.27
[111] sctransform_0.3.1 RcppAnnoy_0.0.17
[113] spatstat.data_1.5-2 rmarkdown_2.5
[115] leiden_0.3.5 uwot_0.1.9
[117] DelayedMatrixStats_1.12.1 shiny_1.5.0
[119] lifecycle_0.2.0 nlme_3.1-150
[121] jsonlite_1.7.1 BiocNeighbors_1.8.1
[123] viridisLite_0.3.0 pillar_1.4.7
[125] lattice_0.20-41 fastmap_1.0.1
[127] httr_1.4.2 survival_3.2-7
[129] glue_1.4.2 qlcMatrix_0.9.7
[131] FNN_1.1.3 spatstat_1.64-1
[133] png_0.1-7 iterators_1.0.13
[135] bluster_1.0.0 bit_4.0.4
[137] ggforce_0.3.2 stringi_1.5.3
[139] blob_1.2.1 BiocSingular_1.6.0
[141] memoise_1.1.0 future.apply_1.6.0