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README.Rmd
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---
title: Oral Atlas
output: rmarkdown::github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction {.tabset .tabset-pills}
The following document includes the code used to analyze datasets and generate figures related to single-cell RNA sequencing from Williams et al., 2021 (doi: [10.1016/j.cell.2021.05.013](https://doi.org/10.1016/j.cell.2021.05.013))
## Setup {.tabset .tabset-pills}
>This section covers dataset setup in R and **Figure S1A-C**
### Data aquisition
The following datasets were used in the manuscript:
* Oral (Williams et al., 2021): [GSE164241](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164241)
* Skin (He et al., 2020): [GSE147424](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147424)
* Lung (Reyfman et al., 2019): [GSE122960](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122960)
* Ileum (Martin et al., 2019): [GSE134809](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134809)
* Salivary Gland (Huang et al., 2021): [covid19cellatlas](https://www.covid19cellatlas.org/index.healthy.html)
### Libraries
```{r, eval=FALSE}
library(Seurat)
library(dplyr)
library(ggplot2)
library(patchwork)
library(tidyr)
library(tidyverse)
library(reshape2)
library(scales)
library(gdata)
library(ggraph)
library(scater)
library(cowplot)
library(SingleR)
library(data.table)
library(paletteer)
library(R.utils)
library(ComplexHeatmap)
library(clustree)
library(viridis)
library(pheatmap)
library(devtools)
library(Scillus)
library(scFunctions)
library(ggcharts)
library(schex)
library(Cairo)
library(cartography)
library(kableExtra)
library(knitr)
library(patchwork)
library(gsfisher)
library(BiocParallel)
library(nichenetr)
library(easyalluvial)
library(purrr)
```
### Data import
Deposited data was downloaded and converted to Seurat objects (one example per dataset shown for brevity):
(@) Oral/Ileum:
```{r, eval=FALSE}
sample1.data <- Read10X(data.dir = "/path/to/data/folder/")
sample1 <- CreateSeuratObject(counts = sample1.data, project = "sample1")
```
(@) Skin:
```{r, eval=FALSE}
sample1count <- read.table(file="/path/to/data/folder/sample1.txt",sep=",",header = TRUE, row.names = NULL)
names <- make.unique(as.character(sample1count$X))
rownames(sample1count) <- names
sample1count <- sample1count[,-1] # get rid of old names
sample1 <- CreateSeuratObject(counts = sample1count, min.cells = 3, min.features = 200)
```
(@) Lung:
```{r, eval=FALSE}
sample1.data <- Read10X_h5("/path/to/data/folder/sample1.h5")
sample1 <- CreateSeuratObject(counts = sample1.data, project = "sample1")
```
(@) Salivary gland:
```{r, eval=FALSE}
library(SeuratDisk)
library("reticulate")
anndata<-import ("anndata")
adata = anndata$read("warner20_salivary_gland.processed.h5ad")
adata$write("warner20_salivary_gland.processed.gzip.h5ad", compression="gzip")
Convert("warner20_salivary_gland.processed.gzip.h5ad", dest = "h5seurat", overwrite = TRUE)
SG <- LoadH5Seurat("warner20_salivary_gland.processed.gzip.h5seurat")
```
Individual Seurat objects were merged according to tissue type/disease status (one example is shown for all tissue types). *The data deposited at covid19atlas.org was pre-processed and pre-merged, and therefore did not require this step.*
```{r, eval=FALSE}
GM <- merge(GM136,
y = c(GM143, GM144, GM147, GM148, GM183, GM184a, GM238, GM241, GM242, GM169, GM283, GM289),
add.cell.ids = c("GM136", "GM143", "GM144", "GM147", "GM148", "GM183", "GM184a",
"GM238", "GM241", "GM242", "GM169", "GM283", "GM289"),
project = "GM")
```
In subsequent sections, the following variable names were used for each merged dataset:
+ `GM` - Healthy gingival mucosa
+ `BM` - Healthy buccal mucosa
+ `PD` - Disease (periodontitis) gingival mucosa
+ `HO` - Healthy buccal and gingival mucosa
+ `HL` - Healthy lung
+ `HI` - Healthy ileum
+ `HS` - Healthy skin
+ `SG` - Healthy salivary gland
### Data quality control
Datasets underwent quality control as follows (only oral datasets shown for brevity):
```{r, eval=FALSE}
# save pre-qc data
preQC_GM <- as.data.frame(table(GM$orig.ident))
preQC_BM <- as.data.frame(table(BM$orig.ident))
preQC_PD <- as.data.frame(table(PD$orig.ident))
# generate mitochrondial gene metadata
GM[["percent.mt"]] <- PercentageFeatureSet(GM,
pattern = "^MT-")
BM[["percent.mt"]] <- PercentageFeatureSet(BM,
pattern = "^MT-")
PD[["percent.mt"]] <- PercentageFeatureSet(PD,
pattern = "^MT-")
# generate pre-qc violin plots
preQC.Vln_GM <- VlnPlot(GM,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3,
group.by = "orig.ident",
pt.size = 0)
preQC.Vln_BM <- VlnPlot(BM,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3,
group.by = "orig.ident",
pt.size = 0)
preQC.Vln_PD <- VlnPlot(PD,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3,
group.by = "orig.ident",
pt.size = 0)
# filter low quality and potential doublet cells
GM <- subset(GM,
subset = nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 15)
BM <- subset(BM,
subset = nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 15)
PD <- subset(PD,
subset = nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 15)
# save post-qc data
postQC_GM <- as.data.frame(table(GM$orig.ident))
postQC_BM <- as.data.frame(table(BM$orig.ident))
postQC_PD <- as.data.frame(table(PD$orig.ident))
# generate post-qc violin plots
postQC.Vln_GM <- VlnPlot(GM,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3,
group.by = "orig.ident",
pt.size = 0)
postQC.Vln_BM <- VlnPlot(BM,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3,
group.by = "orig.ident",
pt.size = 0)
postQC.Vln_PD <- VlnPlot(PD,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3,
group.by = "orig.ident",
pt.size = 0)
# format pre/post-qc data for graphing
qcSum_GM <- merge(preQC_GM,
postQC_GM,
by = "Var1")
names(qcSum_GM)[1] <- "Patient"
names(qcSum_GM)[2] <- "preQC"
names(qcSum_GM)[3] <- "postQC"
qcSum_GM <- reshape2::melt(qcSum_GM[,c('Patient','preQC','postQC')],
id.vars = 1)
names(qcSum_GM)[3] <- "Cells"
qcSum_BM <- merge(preQC_BM,
postQC_BM,
by = "Var1")
names(qcSum_BM)[1] <- "Patient"
names(qcSum_BM)[2] <- "preQC"
names(qcSum_BM)[3] <- "postQC"
qcSum_BM <- reshape2::melt(qcSum_BM[,c('Patient','preQC','postQC')],
id.vars = 1)
names(qcSum_BM)[3] <- "Cells"
qcSum_PD <- merge(preQC_PD,
postQC_PD,
by = "Var1")
names(qcSum_PD)[1] <- "Patient"
names(qcSum_PD)[2] <- "preQC"
names(qcSum_PD)[3] <- "postQC"
qcSum_PD <- reshape2::melt(qcSum_PD[,c('Patient','preQC','postQC')],
id.vars = 1)
names(qcSum_PD)[3] <- "Cells"
# plot cell numbers pre/post-qc
cellNumSummary_GM <- ggplot(qcSum_GM,
aes(x = Patient,
y = Cells)) +
geom_bar(aes(fill = variable),
stat = "identity",
position = "dodge") +
theme_cowplot() +
theme(legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5)) +
scale_fill_manual(values = c("grey", "black"))
cellNumSummary_BM <- ggplot(qcSum_BM,
aes(x = Patient,
y = Cells)) +
geom_bar(aes(fill = variable),
stat = "identity",
position = "dodge") +
theme_cowplot() +
theme(legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5)) +
scale_fill_manual(values = c("grey", "black"))
cellNumSummary_PD <- ggplot(qcSum_PD,
aes(x = Patient,
y = Cells)) +
geom_bar(aes(fill = variable),
stat = "identity",
position = "dodge") +
theme_cowplot() +
theme(legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5)) +
scale_fill_manual(values = c("grey", "black"))
```
#### Figure S1A-C
```{r, eval=FALSE}
library(patchwork)
FigS1A <- (preQC.Vln_GM / preQC.Vln_BM / preQC.Vln_PD)
FigS1B <- (postQC.Vln_GM / postQC.Vln_BM / postQC.Vln_PD)
FigS1C <- cellNumSummary_GM / (cellNumSummary_BM | cellNumSummary_PD) +
plot_layout(guides = "collect")
```
## Gingival vs. Buccal {.tabset .tabset-pills}
>This section covers **Figure 1B,D, Figure S1D, Figure 2, Figure S2, Figure 3A-D, Figure 7B Table S5**
### Data preparation
#### Transformation
```{r, eval=FALSE}
BM <- NormalizeData(BM)
BM <- FindVariableFeatures(BM, nfeatures = 4000)
GM <- NormalizeData(GM)
GM <- FindVariableFeatures(GM, nfeatures = 4000)
```
#### Integration and batch correction
```{r, eval=FALSE}
sampleList <- list(GM, BM)
oral.anchors <- FindIntegrationAnchors(object.list = sampleList, dims = 1:50)
oralIntegrated <- IntegrateData(anchorset = oral.anchors, dims = 1:50)
```
#### Dimensionality reduction and cell clustering
```{r, eval=FALSE}
# cell-cycle scoring and regression
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
oralIntegrated <- CellCycleScoring(oralIntegrated,
s.features = s.genes,
g2m.features = g2m.genes,
set.ident = TRUE)
# standard workflow for clustering
oralIntegrated <- ScaleData(oralIntegrated,
vars.to.regress = c("S.Score", "G2M.Score"),
verbose = FALSE)
oralIntegrated <- RunPCA(oralIntegrated,
npcs = 50,
verbose = FALSE)
oralIntegrated <- FindNeighbors(oralIntegrated,
reduction = "pca",
dims = 1:50)
oralIntegrated <- FindClusters(oralIntegrated,
resolution = seq(from = 0.1,
to = 1.0,
by = 0.1))
oralIntegrated <- RunUMAP(oralIntegrated,
reduction = "pca",
dims = 1:50)
# assess cluster tree
clustree(oralIntegrated)
```
### High-level summary
#### Cell classification
```{r, eval=FALSE}
# set correct assay and idents for GEX
DefaultAssay(oralIntegrated) <- "RNA"
Idents(oralIntegrated) <- "integrated_snn_res.1"
all.markers.1 <- FindAllMarkers(oralIntegrated,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.75)
significant.markers.1 <- all.markers.1[all.markers.1$p_val_adj < 0.2, ]
# remove RBC contaminated clusters and clusters with multiple genotypes (likely doublets)
oralIntegrated <- subset(oralIntegrated,
idents = c("21", "22", "26", "31", "33"),
invert = TRUE)
oralIntegrated <- RenameIdents(oralIntegrated,
"0"="Endothelial",
"1"="Immune",
"2"="Endothelial",
"3"="Immune",
"4"="Immune",
"5"="Endothelial",
"6"="Endothelial",
"7"="Fibroblast",
"8"="Fibroblast",
"9"="Endothelial",
"10"="Fibroblast",
"11"="Fibroblast",
"12"="Endothelial",
"13"="Epithelial",
"14"="Fibroblast",
"15"="Epithelial",
"16"="Endothelial",
"17"="Endothelial",
"18"="Immune",
"19"="Immune",
"20"="Immune",
"23"="Immune",
"24"="Epithelial",
"25"="Epithelial",
"27"="Endothelial",
"28"="Endothelial",
"29"="Other",
"30"="Immune",
"32" = "Immune")
# save general cell type names to metadata and relevel based on order in figures
levels(oralIntegrated) <- c("Endothelial",
"Fibroblast",
"Immune",
"Epithelial",
"Other")
oralIntegrated$generalCellTypes <- [email protected]
```
##### Figure 1B
```{r, eval=FALSE}
pal <- paletteer_d("ggsci::nrc_npg")[c(1,3,4,9,5)]
Idents(oralIntegrated) <- 'generalCellTypes'
Fig1B_dimPlot <- DimPlot(oralIntegrated,
reduction = "umap",
cols = pal) +
theme_void()
Fig1B_prop <- plot_stat(oralIntegrated,
plot_type = "prop_fill",
group_by = "project") +
scale_fill_manual(values = pal) +
theme_void() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text = element_text(size = 4)) +
guides(fill = FALSE)
# proportion of major cell types by sample for stats
Fig1B_propBySample <- plot_stat(oralIntegrated,
plot_type = "prop_fill",
group_by = "orig.ident") +
scale_fill_manual(values = pal) +
theme_void() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text = element_text(size = 4),
axis.text.x = element_text(angle = 90)) +
guides(fill = FALSE)
Fig1BStats <- as.data.frame(Fig1B_propBySample[["data"]])
Fig1BStats <- Fig1BStats[order(Fig1BStats$cluster),]
write.csv(Fig1BStats, "Fig1B_forStatistics.csv")
```
##### Figure S1D
```{r, eval=FALSE}
# find cell-type defining markers
generalCellTypeMarkers <- FindAllMarkers(oralIntegrated,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.5)
generalSignificant <- generalCellTypeMarkers[generalCellTypeMarkers$p_val_adj < 0.2, ]
generalTop20 <- generalSignificant %>%
group_by(cluster) %>%
top_n(n=20, wt=avg_logFC)
FigS1D <- plot_heatmap(dataset = oralIntegrated,
markers = generalTop20,
sort_var = c("generalCellTypes", "orig.ident"),
anno_var = c("generalCellTypes"),
anno_colors = c("Reds"),
n = 20)
```
#### Gene expression
```{r, eval=FALSE}
# isolate different cell types
Idents(oralIntegrated) <- "generalCellTypes"
endo <- subset(oralIntegrated,
idents = c("Endothelial"))
epi <- subset(oralIntegrated,
idents = c("Epithelial"))
fib <- subset(oralIntegrated,
idents = c("Fibroblast"))
im <- subset(oralIntegrated,
idents = c("Immune"))
# selected genes to display
curatedGenesEndo <- c("ACKR1",
"RAMP2",
"SELE",
"VWF",
"PECAM1")
curatedGenesFib <- c("LUM",
"COL3A1",
"DCN",
"COL1A1",
"CFD")
curatedGenesIm <- c("CD69",
"CD52",
"CXCR4",
"PTPRC",
"HCST")
curatedGenesEpi <- c("KRT14",
"KRT5",
"S100A2",
"CSTA",
"SPRR1B")
```
##### Figure 1D
```{r, eval=FALSE}
# set ident (BM, GM)
Idents(endo) <- "project"
Idents(im) <- "project"
Idents(fib) <- "project"
Idents(epi) <- "project"
# violin plots
bigPlot1 <- VlnPlot(endo,
features = curatedGenesEndo,
ncol = 5,
pt.size = 0,
assay = "RNA",
combine = FALSE,
cols = c("black", "grey"))
bigPlot2 <- VlnPlot(fib,
features = curatedGenesFib,
ncol = 5,
pt.size = 0,
assay = "RNA",
combine = FALSE,
cols = c("black", "grey"))
bigPlot3 <- VlnPlot(im,
features = curatedGenesIm,
ncol = 5,
pt.size = 0,
assay = "RNA",
combine = FALSE,
cols = c("black", "grey"))
bigPlot4 <- VlnPlot(epi,
features = curatedGenesEpi,
ncol = 5,
pt.size = 0,
assay = "RNA",
combine = FALSE,
cols = c("black", "grey"))
bigPlot <- c(bigPlot1, bigPlot2, bigPlot3, bigPlot4)
# format graphs
for(i in 1:length(bigPlot)) {
bigPlot[[i]] <- bigPlot[[i]] +
theme(axis.text.x = element_text(size = 7, angle = 0),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(size=7, angle = 0)) +
guides(fill = FALSE) +
coord_flip()
}
Fig1D <- cowplot::plot_grid(plotlist = bigPlot, ncol = 5)
```
### Endothelial subset
#### Cell classification
```{r, eval=FALSE}
DefaultAssay(endo) <- "RNA"
Idents(endo) <- "integrated_snn_res.1"
# endo GEX
endoMarkers <- FindAllMarkers(endo,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.75)
endoSig <- endoMarkers[endoMarkers$p_val_adj < 0.2, ]
# rename
endo <- RenameIdents(endo,
"0" = "H.VEC 1.1",
"2" = "H.VEC 1.2",
"5" = "H.VEC 1.3",
"6" = "H.VEC 1.4",
"9" = "H.SMC",
"12" = "H.VEC 1.1",
"16" = "H.LEC",
"17" = "H.SMC",
"27" = "H.SMC",
"28" = "H.VEC 1.1")
# relevel
levels(endo) <- c("H.VEC 1.1",
"H.VEC 1.2",
"H.VEC 1.3",
"H.VEC 1.4",
"H.SMC",
"H.LEC")
# endo metadata
endo$endoTypes <- Idents(endo)
```
##### Figure 2A
```{r, eval=FALSE}
endo <- RunUMAP(endo, dims = 1:50)
endoColors <- c(carto.pal(pal1 = "red.pal",
n1 = 20)[c(20,8,2,14)],
carto.pal(pal1 = "wine.pal",
n1 = 10)[c(3,7)])
Idents(endo) <- "endoTypes"
Fig2A_dimPlot <- DimPlot(object = endo,
label = FALSE,
reduction = "umap",
cols = endoColors) +
theme_void() +
theme(legend.text = element_text(size = 11),
legend.justification = c(1,0))
Fig2A_prop <- plot_stat(endo,
plot_type = "prop_fill",
group_by = "project") +
scale_fill_manual(values = endoColors) +
theme_void() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text = element_text(size = 4)) +
guides(fill = FALSE)
# stats for 2A
Fig2A_propBySample <- plot_stat(endo,
plot_type = "prop_fill",
group_by = "orig.ident")
Fig2AStats <- as.data.frame(Fig2A_propBySample[["data"]])
Fig2AStats <- Fig2AStats[order(Fig2AStats$cluster),]
write.csv(Fig2AStats, "Fig2A_forStats.csv")
# prop plots for cartoon
endoPropBySubset <- Fig2A_prop[["data"]]
Fig2B_cartoonProp <- ggplot(endoPropBySubset,
aes(fill=group,
y = freq,
x = cluster)) +
geom_bar(position = "fill",
stat = "identity") +
theme_cowplot()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
```
#### Gene expression
```{r, eval=FALSE}
# endo GEX based on new cluster names
Idents(endo) <- "endoTypes"
endoSig <- endoMarkers[endoMarkers$p_val_adj < 0.2, ]
endoTop <- endoSig %>%
group_by(cluster) %>%
top_n(n=3, wt=avg_logFC)
endoTop <- endoTop[!duplicated(endoTop$gene),] %>%
map_df(rev)
write.csv(endoSig, "endo_clusterMarkers_significant.csv")
```
##### Figure 2B
```{r, eval=FALSE}
Fig2B <- DotPlot(endo,
features = endoTop$gene,
col.min = 0,
scale.by = "size",
dot.min = 0.01,
dot.scale = 3) +
coord_flip() +
scale_color_gradient2(low = "grey90",
high = "black") +
theme(axis.title = element_blank(),
text = element_text(size = 9.5),
axis.text.x = element_text(angle = 90,
vjust = 0.5)) +
guides(colour = guide_colorbar(title = "avg.exp",
order = 1),
size = guide_legend(title = "%cell.exp",
order = 2))
```
#### Pathway analysis
```{r, eval=FALSE}
# helper function
endo.expressed <- getExpressedGenesFromSeuratObject(endo,
unique([email protected]),
min.pct = 0.25)
annotation <- fetchAnnotation(species = "hs")
endoMarkers.gsf <- FindAllMarkers(endo,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25)
endoMarkers.gsf$entrezID <- as.character(annotation$entrez_id[match(endoMarkers.gsf$gene,
annotation$gene_name)])
endoMarkers.gsf <- endoMarkers.gsf[!is.na(endoMarkers.gsf$entrezID),]
background_entrez <- as.character(annotation$entrez_id[match(endo.expressed,
annotation$gene_name)])
background_entrez <- background_entrez[!is.na(background_entrez)]
endoMarkers.gsf.filtered <- endoMarkers.gsf[endoMarkers.gsf$p_val_adj < 0.05,]
endoGO <- runGO.all(results=endoMarkers.gsf.filtered,
species = "hs",
background_ids = background_entrez,
gene_id_col="entrezID",
gene_id_type="entrez",
sample_col="cluster",
p_col="p_val_adj",
p_threshold=0.05)
endoGO.filtered <- endoGO[endoGO$ontology=="BP",]
endoGO.filtered <- filterGenesets(endoGO.filtered,
min_foreground_genes = 2,
max_genes_geneset = 500,
min_odds_ratio = 2,
p_col = "p.val",
padjust_method = "BH",
use_adjusted_pvalues = FALSE,
pvalue_threshold = 0.05)
# pathways to highlight
endopathways <- c("antigen processing and presentation",
"endothelium development",
"response to lipopolysaccharide",
"cellular response to type I interferon",
"muscle contraction",
"lymphatic endothelial cell fate commitment")
# select genes associated w/ pathways of interest
endoGO.genes <- endoGO.filtered[endoGO.filtered$description %in% endopathways,11]
endoGO.genes2 <- unlist(strsplit(endoGO.genes, ","))
endoGO.genes2 <- endoGO.genes2[!duplicated(endoGO.genes2)]
# new object w/ average expression for all the genes
endoAvgExpression <- AverageExpression(object = endo,
return.seurat = TRUE)
```
##### Figure S2A
```{r, eval=FALSE}
FigureS2A <- DoHeatmap(endoAvgExpression,
features = endoGO.genes2,
disp.min = -2,
group.bar = FALSE,
label = FALSE,
raster = FALSE) +
scale_fill_gradient2(low = "steelblue4",
mid = "black",
high = "cornsilk",
breaks=c(-2.5,0,2.5),
labels=c(-2.5,0,2.5),
limits = c(-2.5,2.5)) +
theme(axis.text.y = element_text(size = 6))
```
### Epithelial subset
#### Cell classification
```{r, eval=FALSE}
Idents(epi) <- "integrated_snn_res.1"
# find known clusters (cornified) are combined into one based on the global clustering @ resolution 1
# recluster to see if they can be resolved
epi2 <- epi
DefaultAssay(epi2) <- "integrated"
epi2 <- FindNeighbors(epi2,
dims = 1:50)
epi2 <- FindClusters(epi2,
resolution = seq(from = 0.1,
to = 1.0,
by = 0.1))
Idents(epi2) <- "integrated_snn_res.0.1"
epi2Markers <- FindAllMarkers(epi2,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.75)
epi2Sig <- epi2Markers[epi2Markers$p_val_adj < 0.2, ]
epi2Top <- epi2Sig %>%
group_by(cluster) %>%
top_n(n=5, wt=avg_logFC)
print(epi2Top, n=40)
epi <- epi2
epi2 <- NULL
# rename
epi <- RenameIdents(epi, "0"="H.Epi 1.1", "1"="H.Epi 1.1", "2"="H.Epi 1.2", "3"="H.Epi 1.3", "4"="H.Mel")
# relevel
levels(epi) <- c("H.Epi 1.1", "H.Epi 1.2", "H.Epi 1.3", "H.Epi 1.4", "H.Mel")
# save metadata
epi$epiTypes <- Idents(epi)
```
##### Figure 2C
```{r, eval=FALSE}
epi <- RunUMAP(epi, dims = 1:50)
epiColors <- carto.pal(pal1 = "brown.pal",
n1 = 20)[c(20, 15, 10, 5)]
Idents(epi) <- "epiTypes"
Fig2C <- DimPlot(object = epi,
label = FALSE,
reduction = "umap",
cols = epiColors) +
theme_void() +
theme(legend.text = element_text(size = 11),
legend.justification = c(1,0))
Fig2C_prop <- plot_stat(epi,
plot_type = "prop_fill",
group_by = "project") +
scale_fill_manual(values = epiColors) +
theme_void() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text = element_text(size = 4)) +
guides(fill = FALSE)
# stats for 2C
Fig2C_propBySample <- plot_stat(epi,
plot_type = "prop_fill",
group_by = "orig.ident")
Fig2CStats <- as.data.frame(Fig2C_propBySample[["data"]])
Fig2CStats <- Fig2CStats[order(Fig2CStats$cluster),]
write.csv(Fig2CStats, "Fig2C_forStats.csv")
# prop plots for cartoon
endoPropBySubset <- Fig2C_prop[["data"]]
Fig2D_cartoonProp <- ggplot(epiPropBySubset,
aes(fill=group,
y = freq,
x = cluster)) +
geom_bar(position = "fill",
stat = "identity") +
theme_cowplot()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
```
#### Gene expression
```{r, eval=FALSE}
# epi GEX based on new cluster names
epiMarkers <- FindAllMarkers(epi,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.75)
epiSig <- epiMarkers[epiMarkers$p_val_adj < 0.2, ]
write.csv(epiSig, "epi_clusterMarkers_significant_R_1.csv")
epiTop <- epiSig %>%
group_by(cluster) %>%
top_n(n=5, wt=avg_logFC)
epiTop <- epiTop[!duplicated(epiTop$gene),]
customEpi <- rev(c("KRT14", "KRT5", "SFN", "KRT15", "DSP",
"FDCSP", "IL36G", "MMP12", "ODAM", "SLPI",
"CRNN", "CNFN", "TGM3", "SPRR3", "SBSN",
"MITF", "DCT", "PMEL", "TYR", "MLANA"))
```
##### Figure 2D
```{r, eval=FALSE}
Fig2D <- DotPlot(epi,
features = customEpi,
col.min = 0,
scale.by = "size",
dot.min = 0.01,
dot.scale = 3) +
coord_flip() +
scale_color_gradient2(low = "grey90", high = "black") +
theme(axis.title = element_blank(),
text = element_text(size = 9.5),
axis.text.x = element_text(angle = 90,
vjust = 0.5))+
guides(colour = guide_colorbar(title = "avg.exp",
order = 1),
size = guide_legend(title = "%cell.exp",
order = 2))
```
#### Pathway analysis
```{r, eval=FALSE}
# helper function
Idents(epi) <- "epiTypes"
epi.expressed <- getExpressedGenesFromSeuratObject(epi,
unique([email protected]),
min.pct = 0.25)
annotation <- fetchAnnotation(species = "hs")
epiMarkers.gsf <- FindAllMarkers(epi,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25)
epiMarkers.gsf$entrezID <- as.character(annotation$entrez_id[match(epiMarkers.gsf$gene,
annotation$gene_name)])
epiMarkers.gsf <- epiMarkers.gsf[!is.na(epiMarkers.gsf$entrezID),]
background_entrez <- as.character(annotation$entrez_id[match(epi.expressed,
annotation$gene_name)])
background_entrez <- background_entrez[!is.na(background_entrez)]
epiMarkers.gsf.filtered <- epiMarkers.gsf[epiMarkers.gsf$p_val_adj < 0.05,]
epiGO <- runGO.all(results=epiMarkers.gsf.filtered,
species = "hs",
background_ids = background_entrez,
gene_id_col="entrezID",
gene_id_type="entrez",
sample_col="cluster",
p_col="p_val_adj",
p_threshold=0.05)
epiGO.filtered <- epiGO[epiGO$ontology=="BP",]
epiGO.filtered <- filterGenesets(epiGO.filtered,
min_foreground_genes = 3,
max_genes_geneset = 500,
min_odds_ratio = 2,
p_col = "p.val",
padjust_method = "BH",
use_adjusted_pvalues = TRUE,
pvalue_threshold = 0.05)
# pathways to highlight
epipathways <- c("keratinocyte differentiation",
"leukocyte chemotaxis",
"cornification",
"pigmentation")
# select genes associated w/ pathways of interest
epiGO.genes <- epiGO.filtered[epiGO.filtered$description %in% epipathways,11]
epiGO.genes2 <- unlist(strsplit(epiGO.genes, ","))
epiGO.genes2 <- epiGO.genes2[!duplicated(epiGO.genes2)]
# new object w/ average expression for all the genes
epiAvgExpression <- AverageExpression(object = epi, return.seurat = TRUE)
```
##### Figure S2B
```{r, eval=FALSE}
FigureS2B <- DoHeatmap(epiAvgExpression,
features = epiGO.genes2,
disp.min = -2,
group.bar = FALSE,
label = FALSE,
raster = FALSE) +
scale_fill_gradient2(low = "steelblue4",
mid = "black",
high = "cornsilk",
breaks=c(-2.5,0,2.5),
labels=c(-2.5,0,2.5),
limits = c(-2.5,2.5)) +
theme(axis.text.y = element_text(size = 6))
```
### Fibroblast subset
#### Cell classification
```{r, eval=FALSE}
DefaultAssay(fib) <- "RNA"
Idents(fib) <- "integrated_snn_res.1"
# fib GEX
fibMarkers <- FindAllMarkers(fib,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.75)
fibSig <- fibMarkers[fibMarkers$p_val_adj < 0.2, ]
# rename
fib <- RenameIdents(fib,
"7" = "H.Fib 1.1",
"8" = "H.Fib 1.2",
"10" = "H.Fib 1.3",
"11" = "H.Fib 1.4",
"14" = "H.Fib 1.5")
# relevel
levels(fib) <- c("H.Fib 1.1",
"H.Fib 1.2",
"H.Fib 1.3",
"H.Fib 1.4",
"H.Fib 1.5")
# fib metadata
fib$fibTypes <- Idents(fib)
```
##### Figure 2E
```{r, eval=FALSE}
fib <- RunUMAP(fib, dims = 1:50)
fibColors <- c(paletteer_d("ggsci::green_material")[c(10, 7, 4, 2)],
paletteer_d("ggsci::lime_material")[c(6)])
Idents(fib) <- "fibTypes"
Fig2E_dimPlot <- DimPlot(object = fib,
label = FALSE,
reduction = "umap",
cols = fibColors) +
theme_void() +
theme(legend.text = element_text(size = 11),
legend.justification = c(1,0))
Fig2E_prop <- plot_stat(fib,
plot_type = "prop_fill",
group_by = "project") +
scale_fill_manual(values = fibColors) +
theme_void() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text = element_text(size = 4)) +
guides(fill = FALSE)
# stats for 2E
Fig2E_propBySample <- plot_stat(fib,
plot_type = "prop_fill",
group_by = "orig.ident")
Fig2EStats <- as.data.frame(Fig2E_propBySample[["data"]])
Fig2EStats <- Fig2EStats[order(Fig2EStats$cluster),]
write.csv(Fig2EStats, "Fig2E_forStats.csv")