vis_lymphnode_leukocytes

Visualize expression across different conditions (Leukocytes in lymphnodes)

The experiment consist of 3 main factors of interest:

• sample origin: tumor, skin, lymph node

• main cell type class: leucocytes, LECs

• tumor type: YUMM, YUMMER

As a first check we are interested in the expression of specific marker genes/known marker and how they vary across combinations of these factors.

Preamble

library(dplyr)
library(tidyr)
library(ggplot2)
library(pheatmap)
library(patchwork)
library(Seurat)
library(stringr)
library(ggthemes)
#library(SingleCellExperiment)
library(gridExtra)

library(ggrepel)

Data objects

From the FGCZ we have different data objects:

1. Tumor LECs integrated (only tumor LECs, but have been integrated with skin samples) (data/scData_LEC_tumor.rds)

2. Tumor + skin LECs integrated (data/scData_LEC_tumor_skin.rds)

3. All skin + tumor cells

4. All lymphnode cells

Here we work with object 4 - All leukocytes from LN in tumor samples.

#### ----- change to local path to scData.rds object here ----- #####
# e.g. seurat <- readRDS("/home/project/data_folder/scData.rds)")
seurat<- readRDS(file.path("/Users/thomarin/Documents/PhD/Tumor project/Sequencing experiment/August 2023 first analysis shallow sequencing/rds file/scData_LN_CD45.rds"))
#### --------------------------------------------------------- #####


# correct condition assignment!!
seurat$cond <- seurat[[]] |> 
  mutate(
    cond = case_when(
       str_detect(Sample, "YUMM[0-9]") ~ "YUMM",
       str_detect(Sample, "YUMMER") ~ "YUMMER",
       str_detect(Sample, "Skin") ~ "skin"
    )
  ) |> select(cond)

# check assignment
table(seurat$Sample, seurat$cond)

              
               YUMM YUMMER
  LnYUMM1_1A   1064      0
  LnYUMM1_1B    962      0
  LnYUMM2_1A    821      0
  LnYUMM2_1B    693      0
  LnYUMM5_2A   2366      0
  LnYUMM5_2B   2236      0
  LnYUMM6_2A    766      0
  LnYUMM6_2B    814      0
  LnYUMMER4_1A    0   4809
  LnYUMMER4_1B    0   4142
  LnYUMMER7_2A    0   3324
  LnYUMMER7_2B    0   3258
  LnYUMMER8_2A    0   3366
  LnYUMMER8_2B    0   3241

table(seurat$cond)

  YUMM YUMMER 
  9722  22140 

DefaultAssay(seurat) <- "SCT"

cluster_marker <- c("Cd3e", "Cd8a", "Cd4" ,"Tcf7", "Lef1", "Pdcd1", "Sell", "Tigit", "Cxcr4", "Ccr7", "Foxp3", "Ctla4", "Trdc", "Il12b", "Itgae", "Xcr1", "Cd207", "Notch2", "Itgax", "Itgam", "Il1a", "Cd80", "Cd19", "Prox1", "Pecam1", "Csf2rb", "Csf3r", "Il1r2", "Acta2", "Pdgfrb", "Ctsk", "Cd74", "H2-Ab1", "H2-Aa", "Krt15", "Krt5", "Cd79a", "Ccl3", "Ccl4", "Cd63", "Cxcr3", "Fcgr1", "Fcgr3", "Ncam1", "Cd24a", "Klrb1c", "Cd62l")

cond_marker <- c("Cd3e", "Cd8a", "Cd4", "Tcf7", "Lef1", "Pdcd1", "Foxp3", "Cxcr4", "Ifng")

Overview

Mean molecule/feature counts

# mean molecules/group
mean_mol <- seurat[[]] |> group_by(cond) |>  summarise(mean_mol = mean(nCount_RNA))
mean_mol

# A tibble: 2 × 2
  cond   mean_mol
  <chr>     <dbl>
1 YUMM      2468.
2 YUMMER    2635.

# mean unique genes/group
mean_feature <- seurat[[]] |> group_by(cond) |>  summarise(mean_gene = mean(nFeature_RNA))
mean_feature

# A tibble: 2 × 2
  cond   mean_gene
  <chr>      <dbl>
1 YUMM       1241.
2 YUMMER     1320.

Distribution of conditions all together

DimPlot(seurat, group.by = "ident", 
        #split.by = "cond", 
        reduction = "umap", 
        label = F) + 
  ggtitle("") 

  #theme(legend.position = "none")

Distribution of conditions per cluster

cond_dat <- seurat[[]] |> group_by(cond, ident) |>  summarise(n_cells = n())

`summarise()` has grouped output by 'cond'. You can override using the
`.groups` argument.

ggplot(cond_dat, aes(fill=cond, y=n_cells, x=ident)) + 
  geom_bar(position="stack", stat="identity") + 
  scale_fill_tableau() + 
  theme_bw()

ggplot(cond_dat, aes(fill=cond, y=n_cells, x=ident)) + 
  geom_bar(position="fill", stat="identity") + 
  scale_fill_tableau() + 
  theme_bw()

ggplot(cond_dat, aes(fill=ident, y=n_cells, x=cond)) + 
  geom_bar(position="fill", stat="identity") + 
  scale_fill_tableau(palette = "Tableau 20") + 
  theme_bw()

Distribution of cells within umap

DimPlot(seurat, 
        group.by = "ident", 
        split.by = "cond", 
        reduction = "umap",
        label = T) + 
  ggtitle("") + 
  theme(legend.position = "none")

#Almut please put number of clusters inside the plot

Cluster marker gene expression

Cluster	Genes	Cluster	Genes
0 - naive CD8 T cells	Cd3e, Cd8a, Tcf7, Lef1, Sell	9 - B cells	Cd79a, H2-Aa, H2-Ab1, Cd19, Sell
1 - naive CD8 T cells	Cd3e, Cd8a, Tcf7, Lef1, Sell	10 - naive CD4 T cells	Cd3e, Cd4, Tsf7
2 - Monocytes	Ccr7, Csf2rb, Cd63	11 - B cells	Cd79a, H2-Aa, H2-Ab1, Cd19, Sell
3 - NK cells	Tsf7, Sell, Itgax, Klra8	12 - T regs	Cd3e, Cd4, Sell, Tigit, FoxP3, Ctla4
4 - exhausted CD8 T cells	Cd3e, Cd8a, Pdcd1, Ctla4	13 - TGD	Cd3e, Trdc, Tsf7
5 - TGD	Cd3e, Tsf7, Sell, Trdc	14 - CD8 T cells	Cd3e, Cd8a
6 - Granulocytes	Tcf7, Ccr7, Csf2rb, Cd74, H2-Ab1, H2-Aa, Cd63	15 - DCs	Itgax, Itgam, Csf2rb, Il1r2, Cd74, H2-Aa, H2-Ab1
7 - Tregs	Cd3e, Cd4, Sell, Tigit, FoxP3, Ctla4	16 - LECs	Prox1, Pecam1
8 - Granulocytes	Ccr7, Cd80, Csf2rb, Cd63	17 - T cells	CD4, Sell

Dimplots

FeaturePlot(object = seurat, features = cluster_marker, ncol = 3)

Warning: Could not find Krt5 in the default search locations, found in RNA
assay instead

Warning in FetchData.Seurat(object = object, vars = c(dims, "ident", features),
: The following requested variables were not found: Krt15, Cd62l

Violinplots

VlnPlot(object = seurat, features = cluster_marker, ncol = 2, assay = "SCT", add.noise = F)

Warning: Could not find Krt5 in the default search locations, found in RNA
assay instead

Warning in FetchData.Seurat(object = object, vars = features, slot = slot): The
following requested variables were not found: Krt15, Cd62l

Dotplots

DotPlot(object = seurat, features = cluster_marker) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

Warning: Could not find Krt5 in the default search locations, found in RNA
assay instead

Warning in FetchData.Seurat(object = object, vars = features, cells = cells):
The following requested variables were not found: Krt15, Cd62l

Conditional marker expression

Expression by condition as Dimplot

FeaturePlot(object = seurat, features = cond_marker, ncol = 2, split.by = "cond")

Expression as violin plot grouped by cluster

VlnPlot(object = seurat, 
        features = cond_marker, 
        assay = "SCT", 
        add.noise = F, 
        split.by = "cond",
        ncol = 1) + 
  plot_layout(guides = 'collect') &
  scale_fill_tableau() &
  theme_bw()

Expression as violin plot clusters combined

VlnPlot(object = seurat, 
        features = cond_marker,
        group.by = "cond",
        assay = "SCT", 
        add.noise = F,
        ncol = 2) + 
  plot_layout(guides = 'collect') &
  scale_fill_tableau() &
  theme_bw()

Expression as violin plot cluster 0

# Subset your Seurat object to only include cells from cluster 7
cluster0_seurat <- subset(seurat, idents = 0)

# Create the violin plot for cluster 7
VlnPlot(object = cluster0_seurat, 
        features = cond_marker,
        group.by = "cond",
        assay = "SCT", 
        add.noise = F,
        ncol = 2) + 
  plot_layout(guides = 'collect') &
  scale_fill_tableau() &
  theme_bw()

Expression as violin plot cluster 5

# Subset your Seurat object to only include cells from cluster 16
cluster5_seurat <- subset(seurat, idents = 5)

# Create the violin plot for cluster 7
VlnPlot(object = cluster5_seurat, 
        features = cond_marker,
        group.by = "cond",
        assay = "SCT", 
        add.noise = FALSE,
        ncol = 2) + 
  plot_layout(guides = 'collect') &
  scale_fill_tableau() &
  theme_bw()