library(Seurat)
## Attaching SeuratObject
library(SeuratWrappers)
library(slingshot)
## Loading required package: princurve
library(cowplot)
library(ggplot2)
library(devtools)
## Loading required package: usethis
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
pbmc <- readRDS("/mnt/nectar_volume/home/eraz0001/pbmc_human/pbmc_tutorial_final.rds")
current.cluster.ids <- c(0:8)
new.cluster.ids <- c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T", "B", "CD8 T", "FCGR3A+ Mono", "NK", "DC", "Platelet")
pbmc@meta.data$celltype <- plyr::mapvalues(x = pbmc@meta.data[,"seurat_clusters"], from = current.cluster.ids, to = new.cluster.ids)
head(pbmc@meta.data)
## orig.ident nCount_RNA nFeature_RNA percent.mt percent_ribo
## AAACATACAACCAC-1 pbmc3k 2419 779 3.0177759 43.69574
## AAACATTGAGCTAC-1 pbmc3k 4903 1352 3.7935958 42.40261
## AAACATTGATCAGC-1 pbmc3k 3147 1129 0.8897363 31.68097
## AAACCGTGCTTCCG-1 pbmc3k 2639 960 1.7430845 24.25161
## AAACCGTGTATGCG-1 pbmc3k 980 521 1.2244898 14.89796
## AAACGCACTGGTAC-1 pbmc3k 2163 781 1.6643551 36.19972
## RNA_snn_res.0.5 seurat_clusters celltype
## AAACATACAACCAC-1 2 2 Memory CD4 T
## AAACATTGAGCTAC-1 3 3 B
## AAACATTGATCAGC-1 2 2 Memory CD4 T
## AAACCGTGCTTCCG-1 1 1 CD14+ Mono
## AAACCGTGTATGCG-1 6 6 NK
## AAACGCACTGGTAC-1 2 2 Memory CD4 T
DimPlot(pbmc)
sub1 <- subset(pbmc, cells=rownames(pbmc@meta.data[pbmc@meta.data$celltype %in% c("Naive CD4 T", "B", "CD14+ Mono", "Memory CD4 T"),]))
sub_sce1 <- as.SingleCellExperiment(sub1)
kiri <- sub_sce1@int_colData$reducedDims@listData$PCA[,1:20]
sub_sce1@int_colData$reducedDims@listData$PCA <- kiri
temp <- grep( "^MT-", rownames(sub_sce1), ignore.case = F, value = T)
sub_sce1 <- sub_sce1[!rownames(sub_sce1) %in% temp,]
temp2 <- grep( "^RP", rownames(sub_sce1), ignore.case = F, value = T)
sub_sce1 <- sub_sce1[!rownames(sub_sce1) %in% temp2,]
sub_slingshot1 <- slingshot(sub_sce1, clusterLabels = "celltype", reducedDim = 'UMAP', start.clus="Naive CD4 T", end.clus="B")
## Using full covariance matrix
summary(sub_slingshot1$slingPseudotime_1)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.962 17.616 15.486 20.917 36.797 536
print(SlingshotDataSet(sub_slingshot1))
## class: SlingshotDataSet
##
## Samples Dimensions
## 2007 2
##
## lineages: 2
## Lineage1: Naive CD4 T CD14+ Mono B
## Lineage2: Naive CD4 T Memory CD4 T
##
## curves: 2
## Curve1: Length: 36.797 Samples: 1404.51
## Curve2: Length: 11.712 Samples: 1151.79
sub1@meta.data$celltype <- factor(sub1@meta.data$celltype, levels = c("Memory CD4 T", "Naive CD4 T", "CD14+ Mono", "B"))
print(SlingshotDataSet(sub_slingshot1))
## class: SlingshotDataSet
##
## Samples Dimensions
## 2007 2
##
## lineages: 2
## Lineage1: Naive CD4 T CD14+ Mono B
## Lineage2: Naive CD4 T Memory CD4 T
##
## curves: 2
## Curve1: Length: 36.797 Samples: 1404.51
## Curve2: Length: 11.712 Samples: 1151.79
t1 <- sub_slingshot1$slingPseudotime_1
gene.list1 <- c("CD14","LYZ","IL7R","CCR7")
loess_data1 <- BiocGenerics::as.data.frame(sub1@assays$RNA@data[gene.list1,])
loess_data1 = loess_data1[,order(t1)]
temp1 <- loess_data1
temp1 <- t(temp1)
temp1 = as.data.frame(temp1)
temp1$index = 1:nrow(temp1)
temp1$ct = sub1@meta.data$celltype[order(t1)]
p1 <- ggplot(temp1, aes(y = CD14, x = index)) + geom_smooth(method = loess,level=1-1e-10) + coord_cartesian(ylim = c(0, 4.2))
p2 <- ggplot(temp1, aes(y = LYZ, x = index)) + geom_smooth(method = loess,level=1-1e-10) + coord_cartesian(ylim = c(0, 4.2))
p3 <- ggplot(temp1, aes(y = IL7R, x = index)) + geom_smooth(method = loess,level=1-1e-10) + coord_cartesian(ylim = c(0, 4.2))
p4 <- ggplot(temp1, aes(y = CCR7, x = index)) + geom_smooth(method = loess,level=1-1e-10) + coord_cartesian(ylim = c(0, 4.2))
plot_grid(p1, p2, p3, p4)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.2 (2020-06-22)
## os Ubuntu 20.04.4 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_AU.UTF-8
## ctype en_AU.UTF-8
## tz Australia/Melbourne
## date 2022-06-27
## pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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## Biobase 2.50.0 2020-10-27 [1] Bioconductor
## BiocGenerics 0.36.1 2021-04-16 [1] Bioconductor
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##
## [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
##
## ──────────────────────────────────────────────────────────────────────────────
dev.off()
## null device
## 1