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FDD3451.MUSIC.Rmd
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---
title: "FDD3451"
author: "Hong"
date: "2024-03-01"
output:
word_document: default
html_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, class.source = "watch-out",
fig.width=3, fig.height=3,fig.align='left',
fig.pos = "H")
```
#### Loading packages
```{r, include=FALSE}
pkgs = c("tidyverse","hablar","sjmisc","xlsx","gplots","smplot2", "Seurat","openxlsx",
"readxl","MuSiC","SingleCellExperiment","reshape2","cowplot","bseqsc")
for (i in pkgs){
if(! i %in% installed.packages()){
BiocManager::install(i, ask = F, update = F)
}
}
invisible(lapply(pkgs, function(x) library(x, character.only=TRUE)))
rm(pkgs, i)
```
- Reproduce the code provided in paper
- Compare the influence of different measures on the output
- Compare to other method
#### Material and method
1 - Reproducibility
Trough a step-by-step replication of the MUSIC tutorial,I got most of code run successfully but encountered challenges with function bulk_construct(). This function is used for the last step of 'Estimation of cell type proportions with pre-grouping of cell types', which handles the colinearity in bulk sequencing data due to closely related cell types in solid tissues.
```{r, include=FALSE}
### Bulk RNA-seq
GSE50244.bulk.eset = readRDS('data/GSE50244bulkeset.rds')
bulk.mtx = exprs(GSE50244.bulk.eset)
bulk.meta = phenoData(GSE50244.bulk.eset)@data
#### Single-cell
EMTAB.sce = readRDS('data/EMTABesethealthy.rds')
```
2 - Robustness of MUSIC
2.1 - Tuning parameters - Threshold of convergence (eps)
```{r, include=FALSE}
# Estimate cell type proportions
# Create dataframe for beta cell proportions and HbA1c levels
res = ''
Est.prop.list = list()
eps = c(0.1, 0.01, 0.001, 0.0001)
for(i in eps){
Est.prop.res = music_prop(bulk.mtx = bulk.mtx,
sc.sce = EMTAB.sce,
eps = i,
clusters = 'cellType',
samples = 'sampleID',
select.ct = c('alpha', 'beta', 'delta', 'gamma',
'acinar', 'ductal'), verbose = F)
m.prop.ana = data.frame(
pData(GSE50244.bulk.eset)[rep(1:89, 1),
c('age', 'bmi', 'hba1c', 'gender')],
ct.prop = Est.prop.res$Est.prop.weighted[, 2],
eps = paste0('eps: ', i),
Method = 'MuSiC'
) %>% rownames_to_column(., var = 'Sample')
colnames(m.prop.ana)[1:5] = c('Sample','Age', 'BMI', 'HbA1c', 'Gender')
m.prop.ana = subset(m.prop.ana,!is.na(HbA1c))
m.prop.ana$Disease = factor(c('Normal', 'T2D')[(m.prop.ana$HbA1c > 6.5) + 1], c('Normal', 'T2D'))
m.prop.ana$D = (m.prop.ana$Disease == 'T2D') / 5
res = rbind(res, m.prop.ana)
}
res = res[-1,]
p1 = res %>%
convert(num(HbA1c, ct.prop, D)) %>%
convert(chr(eps)) %>%
ggplot(., aes(HbA1c, ct.prop)) +
#geom_smooth(method = 'lm', se = FALSE, col = 'black', lwd = 0.25) +
sm_statCorr() +
geom_point(aes(fill = eps, color = Disease, stroke = D,
shape = Disease), size = 2, alpha = 0.7) +
facet_wrap(~ eps) +
ggtitle('HbA1c vs Beta Cell Type Proportion') + theme_minimal() +
scale_colour_manual( values = c('white', "gray20")) +
scale_shape_manual(values = c(21, 24))
```
2.2 - Tuning parameters - nu
```{r, include=FALSE}
# Estimate cell type proportions
# Create dataframe for beta cell proportions and HbA1c levels
res = ''
Est.prop.list = list()
nu = c(1e-02, 1e-04, 1e-06, 1e-08)
for(i in nu){
Est.prop.res = music_prop(bulk.mtx = bulk.mtx,
sc.sce = EMTAB.sce,
nu = i,
clusters = 'cellType',
samples = 'sampleID',
select.ct = c('alpha', 'beta', 'delta', 'gamma',
'acinar', 'ductal'), verbose = F)
m.prop.ana = data.frame(
pData(GSE50244.bulk.eset)[rep(1:89, 1),
c('age', 'bmi', 'hba1c', 'gender')],
ct.prop = Est.prop.res$Est.prop.weighted[, 2],
nu = paste0('nu: ', i),
Method = 'MuSiC'
) %>% rownames_to_column(., var = 'Sample')
colnames(m.prop.ana)[1:5] = c('Sample','Age', 'BMI', 'HbA1c', 'Gender')
m.prop.ana = subset(m.prop.ana,!is.na(HbA1c))
m.prop.ana$Disease = factor(c('Normal', 'T2D')[(m.prop.ana$HbA1c > 6.5) + 1], c('Normal', 'T2D'))
m.prop.ana$D = (m.prop.ana$Disease == 'T2D') / 5
res = rbind(res, m.prop.ana)
}
res = res[-1,]
p1 = res %>%
convert(num(HbA1c, ct.prop, D)) %>%
convert(chr(nu)) %>%
ggplot(., aes(HbA1c, ct.prop)) +
#geom_smooth(method = 'lm', se = FALSE, col = 'black', lwd = 0.25) +
sm_statCorr() +
geom_point(aes(fill = nu, color = Disease, stroke = D,
shape = Disease), size = 2, alpha = 0.7) +
facet_wrap(~ nu) +
ggtitle('HbA1c vs Beta Cell Type Proportion') + theme_minimal() +
scale_colour_manual( values = c('white', "gray20")) +
scale_shape_manual(values = c(21, 24))
compar = list(c("nu: 0.01", "nu: 1e-04"), c("nu: 1e-06", "nu: 1e-04"), c("nu: 1e-04", "nu: 1e-08"))
p2 = res %>%
convert(num(HbA1c, ct.prop, D)) %>%
convert(chr(nu)) %>%
ggplot(., aes(nu, ct.prop)) + geom_boxplot(outlier.color = NA) +
geom_jitter(aes(color = Disease),position = position_dodge2(width = .5)) +
theme_minimal() + theme(legend.position = 'right',
axis.text = element_text(size = 9, color = 'black'),
axis.title.x = element_blank()) +
stat_compare_means(comparisons = compar)
ggsave(p1, file = 'cell.pro.nu.tunning.pdf', width = 6, height = 4)
ggsave(p2, file = 'cell.pro.nu.tunning.box.pdf.pdf', width = 4, height = 3.2)
```
2.3 - Tuning parameters - iter.max
```{r, include=FALSE}
# Estimate cell type proportions
# Create dataframe for beta cell proportions and HbA1c levels
res = ''
Est.prop.list = list()
iter = c(500, 800, 1000, 1200)
for(i in iter){
Est.prop.res = music_prop(bulk.mtx = bulk.mtx,
sc.sce = EMTAB.sce,
iter.max = i,
clusters = 'cellType',
samples = 'sampleID',
select.ct = c('alpha', 'beta', 'delta', 'gamma',
'acinar', 'ductal'), verbose = F)
m.prop.ana = data.frame(
pData(GSE50244.bulk.eset)[rep(1:89, 1),
c('age', 'bmi', 'hba1c', 'gender')],
ct.prop = Est.prop.res$Est.prop.weighted[, 2],
iter = paste0('iter: ', i),
Method = 'MuSiC'
) %>% rownames_to_column(., var = 'Sample')
colnames(m.prop.ana)[1:5] = c('Sample','Age', 'BMI', 'HbA1c', 'Gender')
m.prop.ana = subset(m.prop.ana,!is.na(HbA1c))
m.prop.ana$Disease = factor(c('Normal', 'T2D')[(m.prop.ana$HbA1c > 6.5) + 1], c('Normal', 'T2D'))
m.prop.ana$D = (m.prop.ana$Disease == 'T2D') / 5
res = rbind(res, m.prop.ana)
}
res = res[-1,]
p1 = res %>%
convert(num(HbA1c, ct.prop, D)) %>%
convert(chr(iter)) %>%
ggplot(., aes(HbA1c, ct.prop)) +
#geom_smooth(method = 'lm', se = FALSE, col = 'black', lwd = 0.25) +
sm_statCorr() +
geom_point(aes(fill = iter, color = Disease, stroke = D,
shape = Disease), size = 2, alpha = 0.7) +
facet_wrap(~ iter) +
ggtitle('HbA1c vs Beta Cell Type Proportion') + theme_minimal() +
scale_colour_manual( values = c('white', "gray20")) +
scale_shape_manual(values = c(21, 24))
compar = list(c("iter: 500", "iter: 1000"), c("iter: 800", "iter: 1000"), c("iter: 1200", "iter: 1000"))
p2 = res %>%
convert(num(HbA1c, ct.prop, D)) %>%
convert(chr(iter)) %>% filter(iter == 'iter: 500' | iter == 'iter: 1000') %>%
select(ct.prop, iter, Sample) %>% spread(iter, ct.prop) %>%
ggplot(., aes(`iter: 1000`, `iter: 500`)) +
geom_point(color = 'lightblue') +
theme_bw() + theme(legend.position = 'right',
axis.text = element_text(size = 9, color = 'black'))
p3 = res %>%
convert(num(HbA1c, ct.prop, D)) %>%
convert(chr(iter)) %>% filter(iter == 'iter: 1200' | iter == 'iter: 1000') %>%
select(ct.prop, iter, Sample) %>% spread(iter, ct.prop) %>%
ggplot(., aes(`iter: 1000`, `iter: 1200`)) +
geom_point(color = 'lightblue') +
theme_bw() + theme(legend.position = 'right',
axis.text = element_text(size = 9, color = 'black'))
ggsave(p1, file = 'cell.pro.iter.tunning.pdf', width = 6, height = 4)
ggsave(p2+p3, file = 'cell.pro.iter.tunning.corr.pdf.pdf', width = 6, height = 2.5)
```
2.4 - Influence of gene numbers on the MUSIC prediction
```{r, include=FALSE}
bulk.meta$Disease = factor( c('Normal', 'T2D')[(bulk.meta$hba1c > 6.5) + 1], c('Normal', 'T2D'))
### table random samples by group: T2D or normal
gene_size = c(.1, .3, .5, .8, 1)
# Create dataframe for beta cell proportions and HbA1c levels
res = ''
j = 1
for(i in 1:length(gene_size)) {
set.seed(567)
Est.prop.res = music_prop(
bulk.mtx = bulk.mtx %>% as.data.frame() %>% sample_frac(gene_size[i]) %>% as.matrix(),
sc.sce = EMTAB.sce,
eps = i,
clusters = 'cellType',
samples = 'sampleID',
select.ct = c('alpha', 'beta', 'delta', 'gamma',
'acinar', 'ductal'),
verbose = F
)
m.prop.ana = data.frame(
pData(GSE50244.bulk.eset)[,
c('age', 'bmi', 'hba1c', 'gender')],
ct.prop = Est.prop.res$Est.prop.weighted[, 2],
Size = paste0(gene_size[i]*100, '% ','genes'),
Method = 'MuSiC'
) %>% rownames_to_column(., var = 'Sample')
colnames(m.prop.ana)[1:5] = c('Sample','Age', 'BMI', 'HbA1c', 'Gender')
m.prop.ana = subset(m.prop.ana, !is.na(HbA1c))
m.prop.ana$Disease = factor( c('Normal', 'T2D')[(m.prop.ana$HbA1c > 6.5) + 1], c('Normal', 'T2D') )
m.prop.ana$D = (m.prop.ana$Disease == 'T2D')/5
res = rbind(res, m.prop.ana)
j = j + 1
}
res = res[-1,]
res %>%
convert(num(HbA1c, ct.prop, D)) %>%
convert(chr(Size)) %>%
mutate(Size = factor(Size, levels = c("10% genes","30% genes",
"50% genes", "80% genes", "100% genes"))) %>%
ggplot(., aes(HbA1c, ct.prop)) +
#geom_smooth(method = 'lm', se = FALSE, col = 'black', lwd = 0.25) +
sm_statCorr() +
geom_point(aes(fill = Size, color = Disease, stroke = D,
shape = Disease), size = 2, alpha = 0.7) +
facet_wrap(~ Size, nrow = 1, ncol = 5) +
ggtitle('HbA1c vs Beta Cell Type Proportion') + theme_minimal() +
scale_colour_manual( values = c('white', "gray20")) +
scale_shape_manual(values = c(21, 24))
```
2.4 (test) Influence of samples size on the MUSIC prediction
```{r, include=FALSE}
bulk.meta$Disease = factor( c('Normal', 'T2D')[(bulk.meta$hba1c > 6.5) + 1], c('Normal', 'T2D'))
### table random samples by group: T2D or normal
sample_size = c(.8, .9)
# Create dataframe for beta cell proportions and HbA1c levels
res = ''
j = 1
for(i in 1:length(sample_size)) {
set.seed(567)
bulk_selected = bulk.meta %>% rownames_to_column(., 'ID') %>% group_by(Disease) %>% sample_frac(sample_size[i])
Est.prop.res = music_prop(
bulk.mtx = bulk.mtx[,bulk_selected$ID],
sc.sce = EMTAB.sce,
clusters = 'cellType',
samples = 'sampleID',
select.ct = c('alpha', 'beta', 'delta', 'gamma',
'acinar', 'ductal'),
verbose = F
)
m.prop.ana = data.frame(
pData(GSE50244.bulk.eset)[bulk_selected$ID,
c('age', 'bmi', 'hba1c', 'gender')],
ct.prop = Est.prop.res$Est.prop.weighted[, 2],
Samplesize = paste0('Sample Size', sample_size[i]*100, '%'),
Method = 'MuSiC'
) %>% rownames_to_column(., var = 'Sample')
colnames(m.prop.ana)[1:5] = c('Sample','Age', 'BMI', 'HbA1c', 'Gender')
m.prop.ana = subset(m.prop.ana, !is.na(HbA1c))
m.prop.ana$Disease = factor( c('Normal', 'T2D')[(m.prop.ana$HbA1c > 6.5) + 1], c('Normal', 'T2D') )
m.prop.ana$D = (m.prop.ana$Disease == 'T2D')/5
res = rbind(res, m.prop.ana)
j = j + 1
}
res = res[-1,]
res %>%
convert(num(HbA1c, ct.prop, D)) %>%
convert(chr(Size)) %>%
mutate(Size = factor(Size, levels = c("10% genes","30% genes",
"50% genes", "80% genes", "100% genes"))) %>%
ggplot(., aes(HbA1c, ct.prop)) +
#geom_smooth(method = 'lm', se = FALSE, col = 'black', lwd = 0.25) +
sm_statCorr() +
geom_point(aes(fill = Size, color = Disease, stroke = D,
shape = Disease), size = 2, alpha = 0.7) +
facet_wrap(~ Size, nrow = 1, ncol = 5) +
ggtitle('HbA1c vs Beta Cell Type Proportion') + theme_minimal() +
scale_colour_manual( values = c('white', "gray20")) +
scale_shape_manual(values = c(21, 24))
```
2.5 Performance of MUSIC on in-house data
```{r, include=FALSE}
meta_gather = function(){
meta = read.csv(file = paste0(workdir,'/01.Data/_OLINK_and_RNAseq_samples_by_Diagnosis.all.csv')) %>%
select(Patient.Code,BMI, FIB.4.Index,
Age..in.years., Albumin..g.dL., ALT..IU.L., AST..IU.L.,
Platelets..x10.9., NASH.score, VIRUS, Aetiology,
Fibrosis.Stage, BCLC.Score, AFP, VIRUS)
return(meta)
}
workdir = '../../../00-ResearchProjects/00-SysMedicine/NAFLD/LiverDisease'
load(paste0(workdir, '/VIRALNASHARLD_HCC/06.Deconlution/tissue.Rdata'))
load(paste0(workdir, '/01.Data/LiverDisease.ran.seq.txi.Rdata'))
count_bulk = txi_kallisto$counts %>% as.data.frame()
tissue1 = CreateSeuratObject(counts = tissue@data, meta.data = [email protected])
load(paste0(workdir, '/VIRALNASHARLD_HCC_3/02.Results/Olink_proteomics.STAT.limma.Rdata'))
meta_RNA = read.csv(paste0(workdir, '/01.Data/_OLINK_and_RNAseq_samples_by_Diagnosis.RNA.csv')) %>%
select(U, ID)
mathched_RNA = olink$metadat %>%
left_join(meta_gather(), by = c("U" = "Patient.Code")) %>%
filter(!is.na(Fibrosis.Group.in.paper)) %>%
left_join(meta_RNA) %>%
filter(Fibrosis.Group.in.paper != 'Healthy') %>%
mutate(Fibrosis.Group.in.paper = case_when(
Fibrosis.Group.in.paper == 'F0-2' ~ 'F02',
TRUE ~ Fibrosis.Group.in.paper
))
count_bulk = count_bulk %>% dplyr::select(mathched_RNA$ID)
#Idents(object = tissue1) = "liver"
#tissue1 = subset(x = tissue1, idents = c('Cirrhotic1', 'Cirrhotic2', 'Cirrhotic3',
# 'Cirrhotic4','Cirrhotic5'),invert = FALSE)
Idents(tissue1) = 'annotation_indepth'
markers_genes <- FindAllMarkers(tissue1,
log2FC.threshold = 0.2,
test.use = "wilcox",
min.pct = 0.1,
min.diff.pct = 0.2,
only.pos = TRUE,
max.cells.per.ident = 50,
assay = "RNA")
linage2markers = markers_genes %>%
filter(p_val_adj < 0.05)
sec = as.SingleCellExperiment(tissue1)
########## Find out common genes
c.genes = sort(intersect(rownames(count_bulk), rownames(sec)))
sec = sec[c.genes,]
count_bulk = as.matrix(count_bulk[c.genes,])
########## using common genes
clusters.type = as.character(unique(sec$annotation_indepth))
names(clusters.type) = clusters.type
Est.prop.res = music_prop(bulk.mtx = count_bulk,
sc.sce = sec,
clusters = 'annotation_indepth',
samples = 'dataset')
pdf('cell.pro.pdf', width = 6, height = 2.5)
Heatmap(Est.prop.res$Est.prop.weighted, show_row_names = F, column_names_gp = gpar(fontsize = 7),
show_column_dend = F, col = colorRamp2(c(0,0.001, 1), c(alpha("#C7522B",0.1), alpha("#BBC297", 0.1),alpha("#3C5941", 0.1))),
name = 'Pro.')
dev.off()
```
3.1 and 3.2 DWLS, reproducibility and gene number influence
```{r}
setwd("DWLS-master/ISC")
#load packages and functions
source("DWLS-master/Deconvolution_functions.R")
load('prediction.DWLS.Rdata')
all_res = ''
genesize = c(0.1, 0.3, 0.5, 0.8, 1)
for(i in genesize) {
bulkData.sub1 = sample_frac(as.data.frame(bulkData[, 1:30]), i)
colnames(bulkData.sub1) = colnames(bulkData)[1:30]
#allCounts_DWLS <- NULL
#allCounts_OLS <- NULL
allCounts_SVR <- NULL
for (j in 1:(dim(bulkData.sub1)[2])) {
print(colnames(bulkData.sub1)[j])
S <-
sig.matrix.mean %>% column_to_rownames(., var = 'gene') %>% as.matrix()
Bulk <- bulkData.sub1[, j] # extract every sample
names(Bulk) <- rownames(bulkData.sub1)
Genes <- intersect(rownames(S), names(Bulk))
S = S[Genes, ]
Bulk <- Bulk[Genes]
#solOLS <- solveOLS(S, Bulk)
#solDWLS <- solveDampenedWLS(S, Bulk)
solSVR <- solveSVR(S, Bulk)
#allCounts_DWLS <- cbind(allCounts_DWLS, solDWLS)
#allCounts_OLS <- cbind(allCounts_OLS, solOLS)
allCounts_SVR <- cbind(allCounts_SVR, solSVR)
}
colnames(allCounts_SVR) <- colnames((bulkData.sub1))
allCounts_SVR = allCounts_SVR %>% as.data.frame() %>%
rownames_to_column(., var = 'cell_type') %>%
mutate(gene_size = paste0(i*100, '%'))
all_res = rbind(all_res, allCounts_SVR)
}
all_res = all_res[-1,]
meta = read.csv('../../../00-ResearchProjects/00-SysMedicine/NAFLD/LiverDisease/01.Data/_OLINK_and_RNAseq_samples_by_Diagnosis.RNA.csv') %>% select(ID, FIB.4.Index)
mappingDF = read.xlsx('../../../00-ResearchProjects/00-SysMedicine/NAFLD/LiverDisease/VIRALNASHARLD_HCC/06.Deconlution/results/EMS84316-supplement-Suppl_Table_8 nno.xlsx')
p3 = all_res %>% gather(ID,percentage,-c(cell_type, gene_size)) %>% left_join(meta) %>%
filter(cell_type == 'Hepatocytes') %>%
convert(num(percentage, FIB.4.Index)) %>%
filter(percentage != 0) %>%
mutate(gene_size = factor(gene_size, levels = c("10%", "20%",
"30%","50%","80%", "100%"))) %>%
ggplot(., aes(log(FIB.4.Index), percentage)) +
#geom_smooth(method = 'lm', se = FALSE, col = 'black', lwd = 0.25) +
sm_statCorr(label_y = 0.7, text_size = 3) +
geom_point(aes(color = gene_size), size = 2, alpha = 0.7) +
facet_wrap(~ gene_size, nrow = 1, ncol = 5) +
ggtitle('FIB-4 vs Hepatocytes Proportion') + theme_minimal() +
scale_shape_manual(values = c(21, 24))
ggsave(p3, file = 'cell.pro.geneSize.DWLS.pdf', width = 8, height = 2.5)
```
3.3 and 3.4 DWLS, (https://github.com/dtsoucas/DWLS)
```{r}
allCounts_DWLS<-NULL
allCounts_OLS<-NULL
allCounts_SVR<-NULL
for(j in 1:(dim(bulkData)[2])){
print(colnames(bulkData)[j])
S<- sig.matrix.mean %>% column_to_rownames(., var = 'gene') %>% as.matrix()
Bulk<-bulkData[,j] # extract every sample
names(Bulk)<-rownames(bulkData)
Genes<-intersect(rownames(S),names(Bulk))
S = S[Genes,]
Bulk<-Bulk[Genes]
solOLS<-solveOLS(S,Bulk)
solDWLS<-solveDampenedWLS(S,Bulk)
solSVR<-solveSVR(S,Bulk)
allCounts_DWLS<-cbind(allCounts_DWLS,solDWLS)
allCounts_OLS<-cbind(allCounts_OLS,solOLS)
allCounts_SVR<-cbind(allCounts_SVR,solSVR)
}
mappingDF = read.xlsx(paste0(workdir, '/VIRALNASHARLD_HCC/06.Deconlution/results/EMS84316-supplement-Suppl_Table_8 nno.xlsx'))
resuktpred = Est.prop.res$Est.prop.weighted %>% as.data.frame() %>%
select_if(colSums(.) != 0) %>%
rownames_to_column(., var = 'ID') %>%
gather(cell.type, pro, -ID) %>%
convert(num(pro)) %>%
left_join(mathched_RNA) %>%
left_join(mappingDF, by = c("cell.type" = 'annotation_indepth')) %>%
mutate(cluster = factor(cluster, levels = unique(cluster)))
metadata = olink$metadat %>%
left_join(meta_gather(), by = c("U" = "Patient.Code")) %>%
filter(!is.na(Fibrosis.Group.in.paper)) %>%
left_join(meta_RNA) %>%
filter(Fibrosis.Group.in.paper != 'Healthy') %>%
select(ID, Fibrosis.Group.in.paper, BMI, `Albumin..g.dL.`,
`ALT..IU.L.`, `AST..IU.L.`, `Platelets..x10.9.`, `FIB.4.Index`) %>%
set_colnames(c("ID", "Fibrosis.Group.in.paper","BMI",
"Albumin", "ALT","AST","Platelets","FIB4"))
resuktpred = metadata %>% select(ID, Fibrosis.Group.in.paper) %>%
left_join(
allCounts_SVR %>% as.data.frame() %>%
rownames_to_column(., var = 'cell.type') %>%
gather(ID, pro,-cell.type) %>% convert(num(pro))
) %>%
mutate(cell.type = str_replace_all(cell.type, '_', ' ')) %>%
mutate(cell.type = str_replace_all(cell.type, '([0-9])', '(\\1)')) %>%
left_join(mappingDF, by = c("cell.type" = 'annotation_indepth')) %>%
mutate(cluster = factor(cluster, levels = unique(cluster)))
prep_stacked_bar = resuktpred %>%
mutate(Fibrosis.Group.in.paper = factor(Fibrosis.Group.in.paper,
levels = c("F4","F3","F0-2"))) %>%
ggplot(., aes(Fibrosis.Group.in.paper, pro)) +
geom_col(aes(fill = cluster),
position = 'fill') +
scale_y_continuous(expand = range(0,0)) +
theme_classic() +
scale_fill_manual(values = alpha(hcl.colors(44, palette = "Fall"),0.5)) +
labs(y = 'Cell Proportion', x = '', fill = 'Cell Cluster') +
plot_theme('nogrid') +
theme(axis.text = element_text(size = 7, color = 'black'),
axis.title = element_text(size = 8, color = 'black')) +
coord_flip()
ggsave(prep_stacked_bar, file = 'decon.prediction.DWLS.pdf',
width = 7.3, height = 2.8)
```