EAE_OL.qmd

---
title: "Myelin insulation as a risk factor for axonal degeneration in autoimmune demyelinating disease. Reanalysis of scRNAseq of myelinating oligodendrocytes in EAE"
format: gfm

---

# OLG reanalysis

This script contains reanalysis of external OLG scRNA-seq data (EAE vs WT)

-   mouse: Falcao et al 2018, Meijer et al 2022

## Initialization

### Environment preparation

```{r, include=FALSE}
#| echo: false
#attach renv
#library(renv)

#initiate renv
#renv::init()
```

### Document setup

```{r}
knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_chunk$set(error = FALSE)
```

### Install and attach required packages

```{r}
#| output: false

#scRNA-seq analysis
library(Seurat)
library(Signac)
library(SeuratDisk)
library(SingleCellExperiment)
library(dittoSeq)
library(sctransform)
library(scran)
library(scater)
library(scuttle)
library(gprofiler2)
library('org.Mm.eg.db')
library('org.Hs.eg.db')
library(scCustomize)

#DEG analysis
library(DESeq2)

#data wrangling
library(tidyverse)
library(textshape)
library(curl)
library(GEOquery)
library(purrr)
library(PCAtools)


#visualization
library(ggrepel)
library(cowplot)
library(ggVennDiagram)
library(pheatmap)
library(ComplexHeatmap)
library(simplifyEnrichment)
library(viridis)
library(RColorBrewer)
library(circlize)
library(scales)
library(gridtext)
library(ggtext)
library(facefuns)

```

### Define input and output directories

```{r}
#| output: false
wd = getwd()

indir = paste0(wd,'/scRNAdata/OL/')
dir.create(indir,recursive = TRUE)

outdir = paste0(wd,'/Output/OL/')
dir.create(outdir,recursive = TRUE)

figdir = paste0(wd,'/figures/OL/')
dir.create(figdir,recursive = TRUE)
```

## Data download

### Download data for Meijer et al. 2022

```{r}
gse_meijer = 'GSE193238'
if(!file.exists(paste0(indir,gse_meijer,'/GSE193238_EAE_CtrlPeak_multiomics_GEO.rds'))){
  supp_meijer <- getGEOSuppFiles(gse_meijer,
                               baseDir = indir,
                               makeDirectory = TRUE,
                               filter_regex = '.rds')
}
meijer = readRDS(paste0(indir,gse_meijer,'/GSE193238_EAE_CtrlPeak_multiomics_GEO.rds'))
meijer$condition = meijer$sample %>% as.factor %>% recode(Ctr = 'no EAE',EAE_peak = 'EAE')
#the file compression seems to be corruputed. it's possible you need to download the file by hand and exctract it manually. The correct file size should be 3.2 GB
```

### Download data for Falcao et al. 2018

```{r}
gse_falcao = 'GSE113973'
#the geo record contains individual count matrices for each plate 
supp_falcao = paste0(indir,gse_falcao,'/',dir(paste0(indir,gse_falcao)))
if(length(supp_falcao) == 1) {
supp_falcao <- getGEOSuppFiles(gse_falcao,
                               baseDir = indir,
                               makeDirectory = TRUE,
                               filter_regex = 'counts.tab.gz',
                               fetch_files = TRUE) %>% 
                rownames}
falcao_exp = lapply(supp_falcao,function(x) {
  exp = read_tsv(x)
  genes = make.unique(exp$gene)
  mat = as.matrix(exp[-1])
  rownames(mat) = genes
  return = mat  })

#the annotation data is not contained in the GEO record but can only be retrieved from the UCSC cell browser entry of the dataset
# Download annotation
falcao_meta = paste0(outdir,'falcao_annotation.rds')
if(!file.exists(paste0(outdir,'falcao_annotation.rds'))) {
      falcao_meta = curl_download(url = 'https://cells.ucsc.edu/oligo-lineage-ms/Annotation.rds',
                                  destfile = paste0(outdir,'falcao_annotation.rds'))}
falcao_meta = readRDS(paste0(outdir,'falcao_annotation.rds'))

# split annotation 
falcao_meta = split(falcao_meta, falcao_meta$Plate)

#clean up rownames
falcao_meta = lapply(falcao_meta,function(x) {
                     tmp = x
                     rownames(tmp) = gsub('counts_...\\.tab\\.','',rownames(tmp))
                     return = tmp})
#name expression matrices 
names(falcao_exp) = names(falcao_meta)

#subset expression matrices to only include annotated cells
falcao_exp_filt = purrr::imap(falcao_exp,function(x,y){
  x[,rownames(falcao_meta[[y]])]
})

#create seurat objects from annotation and expression matrices 
falcao_seurat = purrr::imap(falcao_exp_filt,function(x,y){
  CreateSeuratObject(counts = x,
                     meta.data = falcao_meta[[y]])
})

#merge all plates into one 
falcao = Merge_Seurat_List(falcao_seurat)
falcao$condition = falcao$Group %>% as.factor %>% recode(Ctrl = 'no EAE',EAE = 'EAE', )
```

## Data analysis

### Falcao et al 2018

#### Subset to only oligodendrocytes

```{r}
falcao$MOL <- grepl('^MOL',falcao@meta.data$Renamed_clusternames)
falcao.mol <- falcao %>% subset(MOL)
DefaultAssay(falcao.mol)<-'RNA'
```

#### aggregate counts for pseudo-bulk

```{r}
#aggregate counts
falcao.summed<-AggregateExpression(falcao.mol, 
                                   group.by = c('Plate'), 
                                   assays = 'RNA', 
                                   slot = 'counts',
                              
                                   return.seurat = FALSE)

#extract matrix and transpose row and columns
falcao.cts<-falcao.summed$RNA

#generate sample metadata
falcao.coldata<-data_frame(samples = colnames(falcao.cts))
  #from plate 290 to 295, groups info: Ctrl, Ctrl, EAE, EAE,EAE, Ctrl
  falcao.coldata$condition<-c('Ctrl', 'Ctrl', rep('EAE', 3), 'Ctrl')
  #make samples to rownames
  rownames(falcao.coldata) = falcao.coldata$samples
  
#observe assembled count matrix and metadata
  head(falcao.cts)
  head(falcao.coldata)
```

#### QC using PCA

```{r}
falcao.pca.raw<-prcomp(t(falcao.cts)) #run PCA, scale=T

#extract calculated variances to assist visualization
  #get percentage of variant explained by each pc
falcao.percentVAR<- round(100*falcao.pca.raw$sdev^2/sum(falcao.pca.raw$sdev^2), 1)

  #get sd ratios of PC2/PC1 to adjust plot center
falcao.sd.ratio<-sqrt(falcao.percentVAR[2]/falcao.percentVAR[2])

#organize PC1, PC2 coordinate, and group information into one data frame

falcao.dataGG<-data.frame(PC1 = falcao.pca.raw$x[,1], PC2 = falcao.pca.raw$x[,2],
                          condition=falcao.coldata$condition)

#plot sample distances
(falcao.pca.plt<-ggplot(falcao.dataGG, aes(PC1, PC2, label= rownames(falcao.dataGG)))+
    geom_point(aes(colour=condition))+
    ggtitle('Falcao et al 2018, MOL pseudobulk profiles PCA')+
    xlab(paste0('PC1, VarExp: ', falcao.percentVAR[1], '%'))+ #xaxis title
    ylab(paste0('PC2, VarExp: ', falcao.percentVAR[2], '%'))+ #yaxis title
    theme(plot.title = element_text(hjust = 0.5))+ #put title in the middle
    coord_fixed(ratio = falcao.sd.ratio)+ #coordinate ratio based on sd ratio
    scale_color_manual(values = c('darkorange','dodgerblue3'))+
    geom_text_repel(aes(color=factor(condition)), fontface=2)
  )

```

-   observed a clear outlier: SS2-16-292

#### QC using sample embedding

```{r}
#process & embed falcao data
falcao.mol = 
  falcao.mol %>% 
    NormalizeData(object = .,normalization.method = "LogNormalize",scale.factor = 1e4) %>%
    FindVariableFeatures() %>% 
    ScaleData(features = rownames(.)) %>% 
    RunPCA() %>% 
    RunUMAP(dims = 1:10)
```

```{r}
DimPlot(falcao.mol,group.by = 'Plate')
```

-   in UMAP representation of single MOL cells all cells of plate SS2-16-292 seem to cluster separately from the rest as well

```{r}
#remove sample SS2-16-292 for Seurat data 
falcao.mol = 
  subset(falcao.mol, Plate != 'SS2-16-292')
  falcao.mol %>% 
    NormalizeData(object = .,normalization.method = "LogNormalize",scale.factor = 1e4) %>%
    FindVariableFeatures() %>% 
    ScaleData(features = rownames(.)) %>% 
    RunPCA() %>% 
    RunUMAP(dims = 1:10)
```

#### Pseudo-bulk DEG using DESeq2

```{r}
#remove sample SS2-16-292 for pseudobulk
falcao.mol <- falcao.mol %>% subset(Plate != 'SS2-16-292')

#clean outlier from pseudobulk
falcao.cts<-falcao.cts[,colnames(falcao.cts) != 'SS2-16-292']
falcao.coldata<-falcao.coldata[row.names(falcao.coldata) != 'SS2-16-292', ,drop=FALSE]

#Create DESeq2 object
  falcao.dds<-DESeqDataSetFromMatrix(countData = falcao.cts, colData = falcao.coldata,
                                       design = ~ condition)

#filter genes that has lower than 10 reads
falcao.dds<-falcao.dds[rowSums(counts(falcao.dds)) >=10, ]

############to do, PCA or MDS control

#run DESeq
falcao.dds<-DESeq(falcao.dds)
resultsNames(falcao.dds)

#extract results
falcao.res<-results(falcao.dds, name = "condition_EAE_vs_Ctrl")
falcao.res
```

#### extract significant result, observe DEG balance using volcano plot

```{r}
colnames(falcao.res)<-paste0('Falcao_EAEvsCtrl_',colnames(falcao.res))
falcao.res<-as.data.frame(na.omit(falcao.res))

volcano<-function(x, plt_title){
  fc_position<-grep('log2FoldChange', colnames(x))
  adjp_position<-grep('padj', colnames(x))
  
  x$diffexpressed<-'NO'
  x$diffexpressed[x[,fc_position] > 0 & x[,adjp_position] < 0.05] <- 'UP'
  x$diffexpressed[x[,fc_position] < 0 & x[,adjp_position] < 0.05] <- 'DOWN'
  
  x$deg_label<-NA
  x$deg_label[x$diffexpressed != 'NO'] <- rownames(x)[x$diffexpressed != 'NO']
  
  ggplot(data = x, aes(x = x[,fc_position], y = -log10(x[,adjp_position]), col = diffexpressed, 
                       #label = deg_label
                       ))+
    geom_point()+
    theme_minimal()+
    #geom_text_repel(max.overlaps = 20)+
    scale_color_manual(values = c('lightblue3','grey','lightcoral'))+
    geom_vline(xintercept = 0, col = 'grey48', linetype = 'longdash')+
    geom_hline(yintercept = -log10(0.05), col='grey48', linetype = 'longdash')+
    ggtitle(plt_title)
}

#plot a volcano for falcao pseudobulk result
volcano(x = falcao.res, plt_title = 'Falcao EAEvsCtrl pseudobulk volcano plot')
```

### Meijer et al 2020

#### Subset to Oligodendrocytes

```{r}
#only keep MOLs
unique(meijer@meta.data$Final_celltypes)

meijer <- subset(meijer, 
                 subset = Final_celltypes %in% unique(grep('MOL',
                                                           meijer$Final_celltypes,
                                                           value = TRUE)))
#visualize subset data
DimPlot(meijer, group.by = 'Final_celltypes', label = TRUE)
```

#### repeat basic data processing

```{r}
#generate pure RNA profile obj
meijer.re<-CreateSeuratObject(counts = meijer@assays$RNA@counts, meta.data = meijer@meta.data, project = 'meijer.re')

#renormalize, rescale, pca, umap 
meijer.re = 
  meijer.re %>% 
    NormalizeData(object = .,normalization.method = "LogNormalize",scale.factor = 1e4) %>%
    FindVariableFeatures() %>% 
    ScaleData(features = rownames(.)) %>% 
    RunPCA() %>% 
    RunUMAP(dims = 1:15)

DimPlot(meijer.re, group.by = 'Final_celltypes')
```

#### DEG using wilcox test

Meijer data DGE analysis and result visualization because of the lack of replicates, DGE analysis is run using wilcox test

```{r}
Idents(meijer.re)<- 'sample'

meijer.res<-FindMarkers(meijer.re, ident.1 = 'EAE_peak', ident.2 = 'Ctr', min.pct = 0.1, logfc.threshold = 0.1)

head(meijer.res)

colnames(meijer.res)[c(2,5)]<-c('log2FoldChange','padj')
colnames(meijer.res)<-paste0('meijer_EAEvsCtrl_', colnames(meijer.res))

#plot a volcano for wheeler pseudobulk result
volcano(x = meijer.res, plt_title = 'Meijer EAEvsCtrl DGE volcano plot')

###still some batch effects after bulk normlization, but looks ok
```

##Enrichment analysis - run ORA analysis for GO:BP - for up and down regulated genes, respectively

```{r}
#| warning: false
#| error: false

#ORA GOBP enrichment for mouse data

mouse.marker<-list(falcao.res, meijer.res)
#merge significant result into one matrix, use adjP 0.05 as cutoff
mouse.sig.up<-lapply(mouse.marker, function(x){
  pvalue<-grep('padj',colnames(x))
  fc<-grep('log2FoldChange', colnames(x))
  return(subset(x, subset = x[,pvalue] < 0.05 & x[, fc] > 0))
})

mouse.sig.down<-lapply(mouse.marker, function(x){
  pvalue<-grep('padj',colnames(x))
  fc<-grep('log2FoldChange', colnames(x))
  return(subset(x, subset = x[,pvalue] < 0.05 & x[, fc] < 0))
})

mouse.sig<-c(mouse.sig.up, mouse.sig.down)

names(mouse.sig)<-c('falcao_up','meijer_up','falcao_down','meijer_down')

#loop analysis for all four comparisons and store gprofiler2 result
mouse.ora<-vector(mode = 'list', length = 4)
for (i in 1:4){
  #gprofiler2 enrichment
  gostres_loop<-gost(query = rownames(mouse.sig[[i]]),
                     organism = 'mmusculus', ordered_query = FALSE, multi_query = FALSE,
                     significant = TRUE, exclude_iea = TRUE, evcodes = TRUE,
                     measure_underrepresentation = FALSE,
                     user_threshold = 0.05, correction_method = 'g_SCS',
                     domain_scope = 'annotated', custom_bg = NULL,
                     numeric_ns = '', sources = c('GO:BP'), as_short_link = FALSE)
  
  #export original result table
  mouse.ora[[i]]<-gostres_loop$result
  write.csv(gostres_loop$result[, c(1:13, 16)],
            file = paste0(outdir, 'gprofiler_GOBP_', names(mouse.sig)[i],'.csv'))
  
  #prepare GEM table for cytoscape visualization
  gem<-gostres_loop$result[,c('term_id','term_name','p_value','intersection')]
  colnames(gem)<-c('GO.ID','Description','p.val','Genes')
  gem$FDR<-gem$p.val
  gem$Phenotype = paste0('+', i)
  gem<-gem[,c('GO.ID','Description','p.val','FDR','Phenotype','Genes')]
  
  write.table(gem,
            file = paste0(outdir, 'gprofiler_GOBP_GEM_', names(mouse.sig)[i],'.txt'),
            sep = '\t', quote = FALSE, row.names = FALSE)
}

```

## Figures

### volcano plot

```{r, include=FALSE}
### Functions for plotting 

#falcao.res<-read.csv('../falcao_mol_pseudobulk_DESeq2_result.csv', stringsAsFactors = #FALSE, row.names = 1)
#meijer.res<-read.csv('../Meijer_MOL_DEG.csv', stringsAsFactors = FALSE, row.names = 1)

#rerun the plotting function
reverselog_trans <- function(base = exp(1)) {
        trans <- function(x) -log(x, base)
        inv <- function(x) base^(-x)
        trans_new(paste0("reverselog-", format(base)), trans, inv, 
                  log_breaks(base = base), 
                  domain = c(1e-100, Inf))}
 

volcano<-function(x, plt_title){
  fc_position<-grep('log2FoldChange', colnames(x))
  adjp_position<-grep('padj', colnames(x))
  
  x$diffexpressed<-'NO'
  x$diffexpressed[x[,fc_position] > 0 & x[,adjp_position] < 0.05] <- 'UP'
  x$diffexpressed[x[,fc_position] < 0 & x[,adjp_position] < 0.05] <- 'DOWN'
  
  x$deg_label<-NA
  x$deg_label[x$diffexpressed != 'NO'] <- rownames(x)[x$diffexpressed != 'NO']
  
  ggplot(data = x, 
         aes(x = x[,fc_position], 
             y = x[,adjp_position], 
             col = diffexpressed))+
    geom_point()+
    theme_minimal()+
    #geom_text_repel(max.overlaps = 20)+
    scale_color_manual(values = c('lightblue3','grey','lightcoral'))+
    scale_y_continuous(trans = reverselog_trans(10)) +
    geom_vline(xintercept = 0, col = 'grey48', linetype = 'longdash')+
    geom_hline(yintercept = -log10(0.05), col='grey48', linetype = 'longdash')+
    ggtitle(plt_title) + 
    ylab('adjusted p value') + 
    xlab('log2 fold change') + 
    theme(legend.position = 'none')}
```

```{r,fig.width=5,fig.height=5}
#| warning: false
#| error: false


volcano_falcao = volcano(x = falcao.res, plt_title = 'Falcao et al.')
ggsave(paste0(figdir,'volcano_falcao.svg'),volcano_falcao,dpi = 600,width = 5,height = 5,units = 'cm')
volcano_falcao

volcano_meijer = volcano(x = meijer.res, plt_title = 'Meijer et al.')
ggsave(paste0(figdir,'volcano_meijer.svg'),volcano_meijer,dpi = 600,width = 5,height = 5,units = 'cm')

volcano_meijer


```

### GO aggreated heatmap using simplifyEnrichment

```{r}
#| warning: false
#| error: false

#note: the package only serves for visualizing GO database enriched results
#re-read in all go results
dir<-list.files(path = outdir, pattern = '.csv')

en.results = lapply(paste0(outdir,dir), function(x){
  tmp = read.csv(x, stringsAsFactors = FALSE) 
  tmp2 = tmp[, c('p_value','term_id','term_name')] %>%
    dplyr::mutate(.,sig_level = -log10(p_value)) %>%
    dplyr::rename(., p.adjust = p_value) 
  return(tmp2)
})
```

```{r}
#upregulated gene enriched GO BP terms
  #organize in a list with required column names
mouse.up.go<-list(en.results[[2]], en.results[[4]])
names(mouse.up.go)<-c('Falcao','Meijer')

mouse.down.go<-list(en.results[[1]], en.results[[3]])
names(mouse.down.go)<-c('Falcao','Meijer')

#visualization script met error, potentially due to bug
#tried install Macports and ghostscript to handle pdf file exportation
#transfer this part to ubuntu to operate the following code 
plot_go_up = simplifyGOFromMultipleLists(mouse.up.go, 
                            padj_cutoff = 0.05, 
                            ont = 'BP',
                            exclude_words = c('process','biological','responses','regulation'),
                            db = 'org.Mm.eg.db',
                           #change gradient color
                           heatmap_param = list(col = c('steelblue4', 'white', 'orange3'),
                                                breaks = c(1, 0.05, 0.0005)))

plot_go_down = simplifyGOFromMultipleLists(mouse.down.go, 
                            padj_cutoff = 0.05, 
                            ont = 'BP',
                            exclude_words = c('process','biological','responses','regulation'),
                            db = 'org.Mm.eg.db',
                            #change gradient color
                            heatmap_param = list(col = c('steelblue4', 'white', 'orange3'),
                            breaks = c(1, 0.05, 0.0005)))

```

### VlnPlots

#### Setup

##### Gene lists

```{r}
schirmer_genes = list(
  mhc_i = c('B2m','H2-K1','H2-D1'),
  oligodendrocyte_differentiation = c('Bcas1','Opalin','Sgms1','Sema6a'),
  cell_stress = c('Atf4','Faim2','Junb'),
  iron_accumulation = c('Ftl1','Fth1')
)
names(schirmer_genes) = c('MHC I','Oligodendrocyte differentiation and Cell-cell interaction','Cell stress','Iron accumulation')  
names(schirmer_genes) = purrr::imap(schirmer_genes, function(x,y){paste0(length(x),'_',y)})


```

##### Plot configuration

```{r}
VlnPlotTheme = theme(panel.background = element_blank(),
                     axis.line.x = element_line(),
                     text=element_text(size=8),
                     axis.text = element_text(size=8,color = 'black'))

#set graphical plot paramters 
cols = c('#D4D4D4','plum2')
#colsCas = c('noEAE' = "#D4D4D4", 'EAE' = 'plum2')
colsDots = c('black','#008000')
jWidth = 0.3
dWidth = 0.6
pSize = 0.2
pAlpha = 0.3 
colsCas = c('noEAE' = "#D4D4D4", 'EAE' = 'plum2')

```

```{r}
falcao = falcao.mol
meijer = meijer.re
```

```{r}
#| include: false
#### Function for plotting function to get expression levels 


get_exp = function(
            obj1 = falcao,
            obj1_name = 'Falcao',
            obj2 = meijer,
            obj2_name = 'Meijer',
            genes = schirmer_genes$`3_Cell stress`,
            group_by = 'condition'){
  #get levels of the grouping variable 
  grp_lev = levels(as.factor(obj1@meta.data[[group_by]]))
  
  #get gene expression values for first dataset, first group-level
    obj1_grp1 = FetchData(obj1,vars = genes, slot = 'data',cells = colnames(obj1)[obj1@meta.data[group_by] == grp_lev[1]]) %>% 
                gather() %>% 
                mutate(object = obj1_name,
                       group = grp_lev[1])
    
  #get gene expression values for first dataset, second group-level
  obj1_grp2 = FetchData(obj1,vars = genes, slot = 'data',cells = colnames(obj1)[obj1@meta.data[group_by] == grp_lev[2]]) %>% 
              gather() %>% 
              mutate(object = obj1_name,
                     group = grp_lev[2])
   
    #get gene expression values for second dataset, first group-level
  obj2_grp1 = FetchData(obj2,vars = genes, slot = 'data',cells = colnames(obj2)[obj2@meta.data[group_by] == grp_lev[1]]) %>% 
              gather() %>% 
              mutate(object = obj2_name,
                     group = grp_lev[1])
  
      #get gene expression values for second dataset, second group-level
  obj2_grp2 = FetchData(obj2,vars = genes, slot = 'data',cells = colnames(obj2)[obj2@meta.data[group_by] == grp_lev[2]]) %>% 
              gather() %>% 
              mutate(object = obj2_name,
                     group = grp_lev[2])
  
  df = rbind(obj1_grp1,obj1_grp2,obj2_grp1,obj2_grp2)

  df$group = factor(df$group,levels = levels(obj1@meta.data[[group_by]]))
  df}
```

```{r}
#| include: false
#### Function for plotting 
plot_exp = function(df,name){
  return = ggplot(df, aes(x = factor(key), 
                          y = value,
                          fill = factor(group))) +
                geom_split_violin(scale = 'width') + 
                geom_jitter(position = position_jitterdodge(
                                          jitter.width = jWidth, 
                                          dodge.width = dWidth),
                            size = pSize,
                            alpha = pAlpha,
                            stroke = 0,
#                            aes(color = group)
                            ) + 
                scale_fill_discrete(type = cols) + 
#                scale_color_discrete(type = colsDots) + 
                facet_wrap(~object)+
                theme(axis.text.x = element_text(angle = 45, hjust = 1),
                      legend.position = 'none',
                      axis.line.y = element_line(),
                      axis.title.y = element_text()) + 
                xlab(name) + 
                ylab('Log1p-normalized expression') + 
                VlnPlotTheme
}
```

#### plot genes dysregulated in human MOL (Schirmer et al)

```{r, fig.width=5, fig.height = 5}
genes_exp = lapply(schirmer_genes,function(x) get_exp(genes = x))

plots = purrr::imap(genes_exp, function(x,y) {
  name = strsplit(y,'_')[[1]][2]
  plot_exp(x,name)}) 

#add legend to last plot
plots[[4]]  = plots[[4]] + theme(legend.position = 'right',
                                 legend.title = element_blank())
#increase widths parameter to accomodate  legend
names(plots)[4] = paste0('3',gsub('\\d','',names(plots)[4]))
plots
```

```{r, include = FALSE}
purrr::imap(plots,function(x,y) {
  len = as.integer(strsplit(y,'_')[[1]][1])
  name = strsplit(y,'_')[[1]][2]
  ggsave(paste0(figdir,name,'.svg'), plot = x, dpi = 1200, width = (2*len+2), height = 6, units = 'cm')})
```

#### Plot Slc16a1 expression

```{r,fig.width = 5,fig.height=5}
genes_exp = get_exp(genes = 'Slc16a1')

plots = plot_exp(genes_exp, 'Slc16a1') + theme(axis.text.x = element_blank(),
                                               axis.ticks.x = element_blank(),
                                               legend.position = 'right',
                                               legend.title = element_blank())
plots
```

```{r, include = FALSE}
ggsave(paste0(figdir,'Slc16a1.svg'), plot = plots, dpi = 1200, width = 6, height = 6, units = 'cm')
```

## sessionInfo

```{r}
sessionInfo()
```

```{r include=FALSE}
#renv::snapshot()
```