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)
pbmc.markers <- FindAllMarkers(pbmc, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
## Calculating cluster Naive CD4 T
## Calculating cluster CD14+ Mono
## Calculating cluster Memory CD4 T
## Calculating cluster B
## Calculating cluster CD8 T
## Calculating cluster FCGR3A+ Mono
## Calculating cluster NK
## Calculating cluster DC
## Calculating cluster Platelet
pbmc.markers %>%
group_by(cluster) %>%
slice_max(n = 10, order_by = avg_log2FC)
## # A tibble: 90 × 7
## # Groups: cluster [9]
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 1.17e- 83 1.33 0.435 0.108 1.60e- 79 Naive CD4 T CCR7
## 2 1.74e-109 1.07 0.897 0.593 2.39e-105 Naive CD4 T LDHB
## 3 3.28e- 49 1.05 0.333 0.103 4.50e- 45 Naive CD4 T LEF1
## 4 9.31e- 44 1.03 0.328 0.11 1.28e- 39 Naive CD4 T PRKCQ-AS1
## 5 6.66e- 49 0.983 0.623 0.358 9.13e- 45 Naive CD4 T NOSIP
## 6 8.94e- 79 0.954 0.838 0.403 1.23e- 74 Naive CD4 T CD3D
## 7 4.69e- 43 0.946 0.435 0.184 6.43e- 39 Naive CD4 T PIK3IP1
## 8 2.44e- 33 0.933 0.262 0.087 3.34e- 29 Naive CD4 T MAL
## 9 3.05e- 53 0.849 0.722 0.399 4.19e- 49 Naive CD4 T CD3E
## 10 3.86e-110 0.779 0.99 0.977 5.29e-106 Naive CD4 T RPS3A
## # … with 80 more rows
pbmc.markers %>%
group_by(cluster) %>%
top_n(n = 3, wt = avg_log2FC) -> top3
markers <- top3$gene
markers
## [1] "LDHB" "CCR7" "LEF1" "S100A9" "S100A8" "LYZ"
## [7] "LTB" "CD2" "AQP3" "CD79A" "CD79B" "TCL1A"
## [13] "CCL5" "GZMK" "GZMH" "FCGR3A" "LST1" "FCER1G"
## [19] "GZMB" "FGFBP2" "GNLY" "FCER1A" "HLA-DQA1" "HLA-DPB1"
## [25] "PF4" "GNG11" "PPBP"
DotPlot(pbmc, features = markers, cols=c("#5F4B8BFF", "#ED2B33FF"), assay = "RNA", col.min = 0.3, col.max = 0.8, dot.min=0.12, dot.scale = 1,
cluster.idents=F)+
scale_size(range = c(0, 5))+
scale_size_area(max_size = 5)+
#scale_color_viridis_c(name = 'log2 (count + 1)') +
cowplot::theme_cowplot() +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 8, family="TT Times New Roman"),
axis.text.y = element_text(angle = 0, vjust = 0.5, hjust=1, size = 8.5, family="TT Times New Roman"),
legend.text = element_text(size=8),
legend.title = element_text(size = 9)) +
scale_color_gradientn(colours = viridis::magma(20), limits = c(0,1), oob = scales::squish, name = 'log2 (count + 1)')
## Scale for 'size' is already present. Adding another scale for 'size', which
## will replace the existing scale.
## Scale for 'size' is already present. Adding another scale for 'size', which
## will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Warning: Removed 120 rows containing missing values (geom_point).
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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##
## [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
##
## ──────────────────────────────────────────────────────────────────────────────