Ecoli Pig Single Nucleus Kidney Annotations
Kimberly Olney
06/08/2023
Set working directory
knitr::opts_knit$set(root.dir = ".")Libraris, paths, colors
source("/research/labs/neurology/fryer/m239830/Ecoli_pigs/snRNA/scripts/R/file_paths_and_colours.R")
tissue <- "Kidney"Read in object
# read object
pigs.unannotated <- readRDS("../../rObjects/kidney_unannotated.rds")Define resolution and groups
# set levels and idents
Idents(pigs.unannotated) <- "seurat_clusters"
DefaultAssay(pigs.unannotated) <- "RNA"Unannotated UMAP
dittoDimPlot(object = pigs.unannotated,
var = "seurat_clusters",
reduction.use = "umap",
do.label = TRUE,
labels.highlight = TRUE)
Mitochondrial contamination
# umap percent.mt split by sample
FeaturePlot(pigs.unannotated,
reduction = "umap",
features = "percent.mt",
split.by = "sample",
min.cutoff = 'q10',
label = TRUE)
Cells per cluster
n_cells <- FetchData(pigs.unannotated,
vars = c("ident", "treatment")) %>%
dplyr::count(ident,treatment) %>%
tidyr::spread(ident, n)
n_cells## treatment 0 1 2 3 4 5 6 7 8 9 10 11 12
## 1 Ecoli 1766 1590 1487 1261 796 773 683 615 421 266 247 205 161Cluster tree
pigs.unannotated <- BuildClusterTree(object = pigs.unannotated,
dims = 1:11,
reorder = FALSE,
reorder.numeric = FALSE)
tree <- pigs.unannotated@tools$BuildClusterTree
tree$tip.label <- paste0("Cluster ", tree$tip.label)
tree_1 <- ggtree::ggtree(tree, aes(x, y)) +
scale_y_reverse() +
ggtree::geom_tree() +
ggtree::theme_tree() +
ggtree::geom_tiplab(offset = 1) +
ggtree::geom_tippoint(color = colors[1:length(tree$tip.label)],
shape = 16, size = 5) +
coord_cartesian(clip = 'off') +
theme(plot.margin = unit(c(0,2.5,0,0), 'cm'))
tree_1
Marker exploration
FindAllMarkers will find markers differentially expressed in each identity group by comparing it to all of the others - you don’t have to manually define anything. Note that markers may bleed over between closely-related groups - they are not forced to be specific to only one group. This is what most people use (and likely what you want).
Find all markers
# read object
all.markers <- readRDS(paste0("../../rObjects/", tolower(tissue), "_unannotated_all_markers.rds"))
# more stringent filtering
all.markers <- all.markers[all.markers$p_val_adj < 0.05,]
markers.strict <- all.markers[
all.markers$delta_pct > summary(all.markers$delta_pct)[5],]
# compare
table(all.markers$cluster)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12
## 300 295 285 237 460 266 293 293 206 258 116 150 184table(markers.strict$cluster)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12
## 61 19 57 21 91 49 49 76 86 101 59 79 82# subset
cluster0 <- markers.strict[markers.strict$cluster == 0,]
cluster1 <- markers.strict[markers.strict$cluster == 1,]
cluster2 <- markers.strict[markers.strict$cluster == 2,]
cluster3 <- markers.strict[markers.strict$cluster == 3,]
cluster4 <- markers.strict[markers.strict$cluster == 4,]
cluster5 <- markers.strict[markers.strict$cluster == 5,]
cluster6 <- markers.strict[markers.strict$cluster == 6,]
cluster7 <- markers.strict[markers.strict$cluster == 7,]
cluster8 <- markers.strict[markers.strict$cluster == 8,]
cluster9 <- markers.strict[markers.strict$cluster == 9,]
cluster10 <- markers.strict[markers.strict$cluster == 10,]
cluster11 <- markers.strict[markers.strict$cluster == 11,]
cluster12 <- markers.strict[markers.strict$cluster == 12,]PCs
# Printing out the most variable genes driving PCs
print(x = pigs.unannotated[["pca"]],
dims = 1:5,
nfeatures = 10)## PC_ 1
## Positive: CYP4A24, FGB, DAB2, HAO2, SLC13A3, GPX3, SLC4A4, NAMPT, CYP24A1, EXT1
## Negative: ERBB4, SLC8A1, ENSSSCG00000063471, ENSSSCG00000056698, MECOM, SLC12A3, ENOX1, TXNIP, ENSSSCG00000061089, EFEMP1
## PC_ 2
## Positive: ERBB4, SLC12A3, ENOX1, SLC8A1, PLCB1, EFEMP1, EGF, SLC12A1, ENSSSCG00000010893, MECOM
## Negative: PTPRQ, ENSSSCG00000063471, MAGI2, ENSSSCG00000016548, PDE3A, RPGRIP1L, THSD7A, ENSSSCG00000063404, DCLK1, COL4A3
## PC_ 3
## Positive: PTPRQ, ERBB4, ENSSSCG00000063471, MAGI2, SLC12A3, ENOX1, ENSSSCG00000016548, SLC8A1, PDE3A, THSD7A
## Negative: ESRRG, ADGRF5, BCAT1, FOXI1, ENSSSCG00000054874, FOXP1, SLC26A4, TBC1D1, ADGRF1, RHOBTB2
## PC_ 4
## Positive: SLC8A1, TRPV5, ENPP1, MECOM, TRPV6, ENSSSCG00000063471, NR3C2, GHSR, CRYBG1, ENSSSCG00000015565
## Negative: ERBB4, ENOX1, SLC12A1, EGF, PLCB1, THSD4, ENSSSCG00000010893, DOK6, ZNF385D, DDIT4L
## PC_ 5
## Positive: SERPINE1, ENSSSCG00000029160, CD74, TCF4, DCLK1, RPGRIP1L, LDB2, STC1, FLT1, CLDN5
## Negative: PTPRQ, MAGI2, COL4A3, ENSSSCG00000063404, ENSSSCG00000016548, PDE3A, THSD7A, PTPRO, SAMD12, PDE10ATop variable genes
# print top variable genes
top100 <- pigs.unannotated@assays$SCT@var.features[1:100]
top100## [1] "SLC8A1" "PTPRQ" "CEMIP"
## [4] "ENSSSCG00000054874" "SLC26A4" "ENSSSCG00000063471"
## [7] "ERBB4" "SLC12A3" "MAGI2"
## [10] "ENSSSCG00000037358" "ENOX1" "ESRRG"
## [13] "FOXI1" "CHODL" "BCAT1"
## [16] "ADGRF5" "EGF" "SERPINE1"
## [19] "ENSSSCG00000056698" "ENSSSCG00000029160" "STC1"
## [22] "A2M" "TIMP3" "THSD7A"
## [25] "TRPV5" "SLPI" "TRPV6"
## [28] "PCK1" "SLC40A1" "CALD1"
## [31] "MECOM" "DCLRE1C" "PDE3A"
## [34] "ENSSSCG00000044696" "SLC12A1" "DEFB1.1"
## [37] "ENSSSCG00000008998" "PLAT" "ZNF521"
## [40] "REN" "NRG1" "RPGRIP1L"
## [43] "SLC13A1" "DCLK1" "CLIC5"
## [46] "SAMD12" "RHOBTB1" "GPX3"
## [49] "GTPBP10" "PTPRO" "NYAP2"
## [52] "FOXP1" "SLIT2" "TGM3"
## [55] "ENSSSCG00000050067" "EFEMP1" "CXCL10"
## [58] "FAM171B" "PDE10A" "PTGER3"
## [61] "CLU" "ENSSSCG00000016548" "CYP4A24"
## [64] "ENSSSCG00000056609" "FLT1" "COL4A3"
## [67] "SLC26A7" "ARSB" "FGB"
## [70] "ADGRG6" "DACH1" "TIMP1"
## [73] "ADGRF1" "G6PC1" "HPGD"
## [76] "CLDN5" "ENSSSCG00000014062" "ESRRB"
## [79] "ENSSSCG00000063404" "CIITA" "ENSSSCG00000010893"
## [82] "KCTD9" "ENSSSCG00000051520" "TBC1D1"
## [85] "SLC16A9" "SAMD5" "ENSSSCG00000055373"
## [88] "ENSSSCG00000062072" "GATA3" "RHOBTB2"
## [91] "NUPR1" "HS6ST2" "FGFR1"
## [94] "MAN1A1" "SAT1" "ENSSSCG00000061089"
## [97] "ENSSSCG00000042325" "HERC1" "SORBS2"
## [100] "CYP24A1"Kidney cell types
The human protein atlas https://www.proteinatlas.org/ENSG00000085276-MECOM/single+cell+type/kidney\
kidney_cells <- c("CD19", "CR2", "MS4A1", "GZMB", "IL3RA", "CEBPE", "HDC", "MS4A2", "CD163", "CD68", "MARCO", "MRC1", "MSR1", "FCGR3A", "KIR2DL4", "PIM2", "CD3E", "CD4", "CD8A", "FOXP3", "IL17A", "CD34", "PECAM1", "SELE", "SLC2A1", "VWF", "MBP", "MPZ", "S100B", "SOX10", "COL3A1", "FBN1", "LUM", "ACTA2", "ACTG2", "CNN1", "MYH11", "AQP2", "CLDN8", "PVALB", "TMEM213", "SLC12A1", "TMEM72", "UMOD", "MIOX", "NAT8", "SLC22A8", "TMEM174")
VlnPlot(pigs.unannotated,
features = kidney_cells,
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster Annotation
Cluster 0 - Hepatocytes / Proximal tubular
FGB - Hepatocytes - Metabolism (mainly)
SLC4A4 -Astrocytes - Unknown function (mainly) /Proximal tubular
FGG - Hepatocytes /Proximal tubular
SLC2A2 - Hepatocytes / Proximal tubular
Hepatocytes in the kidney: https://pubmed.ncbi.nlm.nih.gov/9770301/
# Number of cells per condition
n_cells[,c(1,2)]## treatment 0
## 1 Ecoli 1766# UMAP with only cluster 0
DimPlot(object = subset(pigs.unannotated, seurat_clusters == "0"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[1])
VlnPlot(pigs.unannotated,
features = cluster0$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster0$gene[1:10]## [1] "FGB" "SLC4A4" "FGG"
## [4] "HAO2" "FTL" "SLC13A3"
## [7] "SLC2A2" "ENSSSCG00000024911" "LGMN"
## [10] "MT1A"Cluster 1 - Hepatocytes / Proximal tubular
VMP1 - Monocytes - Innate immune response (mainly) / marcophage ACO1
- Proximal tubular cells
SLC39A14 - Hepatocytes
LGMN - Macrophages - Innate immune response (mainly)
NKAIN3 - Astrocytes? LBP - Hepatocytes - Metabolism (mainly)
# Number of cells per condition
n_cells[,c(1,3)]## treatment 1
## 1 Ecoli 1590DimPlot(object = subset(pigs.unannotated, seurat_clusters == "1"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[2])
VlnPlot(pigs.unannotated,
features = cluster1$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster1$gene[1:10]## [1] "VMP1" "ACO1" "SLC39A14" "LGMN" "NKAIN3" "NAMPT"
## [7] "NDRG1" "PITPNC1" "DAB2" "LBP"Cluster 2 - Proximal tubular
RHOBTB1 - Syncytiotrophoblasts - Pregnancy (mainly) / Proximal
tubular SLC5A12 - Proximal enterocytes - Digestion (mainly) / Proximal
tubular ARSB - Oligodendrocyte precursor
# Number of cells per condition
n_cells[,c(1,4)]## treatment 2
## 1 Ecoli 1487DimPlot(object = subset(pigs.unannotated, seurat_clusters == "2"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[3])
VlnPlot(pigs.unannotated,
features = cluster2$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster2$gene[1:10]## [1] "CYP4A24" "ENSSSCG00000007858" "RHOBTB1"
## [4] "ARSB" "HAO2" "SLC5A12"
## [7] "CUBN" "ANKS1A" "ENSSSCG00000003891"
## [10] "SLC13A3"Cluster 3 - Proximal tubular
CYP24A1 - Respiratory epithelial cells / Proximal tubular SLC13A1 - Proximal tubular cellsÂ
# Number of cells per condition
n_cells[,c(1,4)]## treatment 2
## 1 Ecoli 1487DimPlot(object = subset(pigs.unannotated, seurat_clusters == "3"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[4])
VlnPlot(pigs.unannotated,
features = cluster3$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster3$gene[1:10]## [1] "CYP24A1" "SLC13A1" "DAB2"
## [4] "NAMPT" "CYP4A24" "ENSSSCG00000058016"
## [7] "HAO2" "NDRG1" "CGNL1"
## [10] "GRAMD1B"Cluster 4 - B-cells / Monocytes / Proximal tubular
BIRC3 - B-cells
NFKBIA - Monocytes - Innate immune response (mainly) / marcophagesÂ
HSPG2 - Adipocytes & Endothelial cells - Mixed function (mainly) /
B-cells
# Number of cells per condition
n_cells[,c(1,5)]## treatment 3
## 1 Ecoli 1261DimPlot(object = subset(pigs.unannotated, seurat_clusters == "4"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[5])
VlnPlot(pigs.unannotated,
features = cluster4$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster4$gene[1:10]## [1] "NFKBIA" "ENSSSCG00000063471" "LDB2"
## [4] "DCLK1" "BIRC3" "PALM2AKAP2"
## [7] "ENSSSCG00000001341" "ENSSSCG00000052432" "ENSSSCG00000044696"
## [10] "HSPG2"Cluster 5 - Neurons / Distal tubular
SLC12A1 - distal tubular cells
THSD4 - Squamous epithelial cells / collecting duct ERBB4 - neurons /
collecting duct / distal tubular cells
ENOX1 - neurons / distal tubular cells
PLCB1 - neurons
# Number of cells per condition
n_cells[,c(1,6)]## treatment 4
## 1 Ecoli 796DimPlot(object = subset(pigs.unannotated, seurat_clusters == "5"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[6])
VlnPlot(pigs.unannotated,
features = cluster5$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster5$gene[1:10]## [1] "SLC12A1" "ERBB4" "ENOX1"
## [4] "PLCB1" "ENSSSCG00000010893" "THSD4"
## [7] "TXNIP" "SAMD4A" "GPR39"
## [10] "EFEMP1"Cluster 6 - Immune response / collecting duct
MECOM - microglia TRPV5 - Oligodendrocytes / collecting duct NR3C2
- neurons / B-cells / collecting duct
SLC8A1 - neurons / macrophages / collecting duct ENPP1 - connective
tissue
# Number of cells per condition
n_cells[,c(1,7)]## treatment 5
## 1 Ecoli 773DimPlot(object = subset(pigs.unannotated, seurat_clusters == "6"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[7])
VlnPlot(pigs.unannotated,
features = cluster6$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster6$gene[1:10]## [1] "MECOM" "TRPV5" "NR3C2"
## [4] "ENSSSCG00000063471" "GHSR" "SLC8A1"
## [7] "ENPP1" "ENSSSCG00000015565" "CRYBG1"
## [10] "EFEMP1"Cluster 7 - Innate immune response / B-cells
ADGRF5 - Adipocytes & Endothelial cells / B-cells
BCAT1 - Macrophages
ESRRG - neurons ADGRF1 - Respiratory epithelial cells / collecting
duct UVRAG - B-cells / Macrophages
# Number of cells per condition
n_cells[,c(1,8)]## treatment 6
## 1 Ecoli 683DimPlot(object = subset(pigs.unannotated, seurat_clusters == "7"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[8])
VlnPlot(pigs.unannotated,
features = cluster7$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster7$gene[1:10]## [1] "ADGRF5" "BCAT1" "ESRRG"
## [4] "ADGRF1" "UVRAG" "FOXI1"
## [7] "KIT" "TBC1D1" "ENSSSCG00000052253"
## [10] "WDSUB1"Cluster 8 - Distal tubular
ERBB4 - neurons / collecting duct /Distal tubular
ENOX1 - neurons / oligodendrocytes / Distal tubular
EGF - Skeletal myocytes / Distal tubular / Proximal tubular KNG1 -
Hepatocytes / Distal tubular
# Number of cells per condition
n_cells[,c(1,9)]## treatment 7
## 1 Ecoli 615DimPlot(object = subset(pigs.unannotated, seurat_clusters == "8"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[9])
VlnPlot(pigs.unannotated,
features = cluster8$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster8$gene[1:10]## [1] "SLC12A3" "ERBB4" "ENOX1"
## [4] "DACH1" "PLCB1" "IER3"
## [7] "ENSSSCG00000027903" "EGF" "TXNIP"
## [10] "KNG1"Cluster 9 - Macrophages
SERPINE1 - mural / macrophage
CD74 - Dendritic cells / B-cell / macrophage CXCL10 - Monocytes /
macrophage
# Number of cells per condition
n_cells[,c(1,10)]## treatment 8
## 1 Ecoli 421DimPlot(object = subset(pigs.unannotated, seurat_clusters == "9"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[10])
VlnPlot(pigs.unannotated,
features = cluster9$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster9$gene[1:10]## [1] "ENSSSCG00000060989" "SERPINE1" "CD74"
## [4] "ENSSSCG00000052432" "RPGRIP1L" "DCLK1"
## [7] "CXCL10" "SLC3A1" "TCF4"
## [10] "ENSSSCG00000001229"Cluster 10 - Immune response
SLC8A1 - Neuron TRPV5 - Oligodendrocytes MECOM - Endothelial -
Glandular & Luminal cells - Unknown function (mainly)
ENPP1 - Fibroblast like TRPV6 - Serous glandular cells - Salivary
secretion (mainly)
# Number of cells per condition
n_cells[,c(1,11)]## treatment 9
## 1 Ecoli 266DimPlot(object = subset(pigs.unannotated, seurat_clusters == "10"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[11])
VlnPlot(pigs.unannotated,
features = cluster10$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster10$gene[1:10]## [1] "SLC8A1" "TRPV5" "MECOM" "ENPP1" "GHSR" "NR3C2" "EFEMP1" "CRYBG1"
## [9] "TRPV6" "PAK5"Cluster 11 - Neuron
SLC26A4 - neuron NYAP2 - interneuron FOXP1 - neuronÂ
# Number of cells per condition
n_cells[,c(1,12)]## treatment 10
## 1 Ecoli 247DimPlot(object = subset(pigs.unannotated, seurat_clusters == "11"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[12])
VlnPlot(pigs.unannotated,
features = cluster11$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster11$gene[1:10]## [1] "ENSSSCG00000054874" "SLC26A4" "NYAP2"
## [4] "FOXP1" "MAN1A1" "ATP6V1C2"
## [7] "STAP1" "TLDC2" "LIMCH1"
## [10] "TBC1D1"Cluster 12 - Neuron
PTPRQ - neuron
MAGI2 - neuron / oligodendrocytes
ENSSSCG00000063404 = SLC30A3 - neuronÂ
# Number of cells per condition
n_cells[,c(1,12)]## treatment 10
## 1 Ecoli 247DimPlot(object = subset(pigs.unannotated, seurat_clusters == "11"),
reduction = "umap",
label = TRUE,
label.box = TRUE,
label.size = 3,
repel = TRUE,
cols = colors[13])
VlnPlot(pigs.unannotated,
features = cluster11$gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
cluster12$gene[1:10]## [1] "PTPRQ" "MAGI2" "ENSSSCG00000063404"
## [4] "ENSSSCG00000063471" "ENSSSCG00000016548" "COL4A3"
## [7] "PDE3A" "WT1" "THSD7A"
## [10] "PLEKHG1"Assign identities
# Rename all identities
pigs.annotated <- RenameIdents(object = pigs.unannotated,
"0" = "Hepatocytes",
"1" = "Hepatocytes",
"2" = "Proximal tubular",
"3" = "Proximal tubular",
"4" = "B-cells",
"5" = "Neuron",
"6" = "Immune response",
"7" = "Innate immune response",
"8" = "Distal tubular",
"9" = "Macrophage",
"10" = "Immune response",
"11" = "Neuron",
"12" = "Neuron")
pigs.annotated$annotated_clusters <- factor(Idents(pigs.annotated),
levels = c("Distal tubular",
"Proximal tubular",
"Hepatocytes",
"B-cells",
"Immune response",
"Innate immune response",
"Macrophage",
"Neuron"))
Idents(pigs.annotated) <- "annotated_clusters"UMAP - annotated
u1 <- DimPlot(pigs.annotated,
group.by = "annotated_clusters",
cols = colors,
raster = FALSE,
label = FALSE) +
ggtitle("Ecoli pig Kidney")
u1
u2 <- DimPlot(pigs.annotated,
group.by = "annotated_clusters",
cols = colors,
dims = c(2,3),
raster = FALSE,
label = FALSE) +
ggtitle("Ecoli pig Kidney")
u2
Cell count
n_cells <- FetchData(pigs.annotated,
vars = c("ident", "sample")) %>%
dplyr::count(ident,sample) %>%
tidyr::spread(ident, n)
n_cells## sample Distal tubular Proximal tubular Hepatocytes B-cells Immune response
## 1 E1 421 2748 3356 796 930
## Innate immune response Macrophage Neuron
## 1 615 266 1139Tree annoated
pigs.annotated <- BuildClusterTree(object = pigs.annotated,
dims = 1:11,
reorder = FALSE,
reorder.numeric = FALSE)
tree <- pigs.annotated@tools$BuildClusterTree
tree$tip.label <- paste0("", tree$tip.label)
tree_an <- ggtree::ggtree(tree, aes(x, y)) +
scale_y_reverse() +
ggtree::geom_tree() +
ggtree::theme_tree() +
ggtree::geom_tiplab(offset = 1) +
ggtree::geom_tippoint(color = colors[1:length(tree$tip.label)],
shape = 16, size = 5) +
coord_cartesian(clip = 'off') +
theme(plot.margin = unit(c(0,5,0,0), 'cm'))
tree_an
Bulk DEGs
DEGs <- read.delim("/research/labs/neurology/fryer/m239830/Ecoli_pigs/bulkRNA/results/star_high_dose_exclude_S4_S5_E3_E5_E7/DEGs/Ecoli_Kidney_gene_DEGs_FDRq0.05.txt")
up_DEGs <- subset(DEGs, logFC > 2) # log2FC greater than 2
# order from greatest to least fold change
up_DEGs <- up_DEGs[order(-up_DEGs$logFC),]
up_DEGs_gene <- up_DEGs$gene_nameTop up-regualted bulk DEGs
Feature plot
up_DEGs_gene_list <- list(c(up_DEGs_gene))
pigs.annotated <- AddModuleScore(object = pigs.annotated,
features = up_DEGs_gene_list,
name = "DEGs")
FeaturePlot(object = pigs.annotated, features = "DEGs1", label = FALSE)+
scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdYlBu")))
Violins
# 1-10
VlnPlot(pigs.annotated,
features = up_DEGs_gene[1:10],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
# 11-20
VlnPlot(pigs.annotated,
features = up_DEGs_gene[11:20],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
# 21-30
VlnPlot(pigs.annotated,
features = up_DEGs_gene[21:30],
cols = colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cytokines
A list of reported cytokines was downloaded from https://www.immport.org/shared/genelists\
cytokines <- read.delim("/research/labs/neurology/fryer/m239830/Ecoli_pigs/cytokines.txt")
cytokine_gene <- cytokines$Symbol
cytokine_gene_list <- list(cytokine_gene)
pigs.annotated <- AddModuleScore(object = pigs.annotated,
features = cytokine_gene_list,
name = "cytokines")
FeaturePlot(object = pigs.annotated, features = "cytokines1") +
scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdYlBu")))