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marker_heatmap.R
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source('Codes/Functions.R')
source('~/XSpecies/Codes/convert_human_to_ortholog_functions.R')
library(Hmisc)
library(RColorBrewer)
library(viridis)
library(reshape2)
########################################################
############ Importing the nuc-seq data #########
## importing the gene expression data
merged_samples_sub = readRDS('~/rat_sham_sn_data/standardQC_results/sham_sn_merged_annot_standardQC.rds')
merged_samples_all = readRDS('~/rat_sham_sn_data/standardQC_results/sham_sn_merged_annot_standardQC_allfeatures.rds')
'Cd68'%in% rownames(GetAssayData(merged_samples_all, 'scale.data'))
'Cd68'%in% rownames(GetAssayData(merged_samples_sub, 'scale.data'))
markers_test <- c('Alb', 'Apoa1', 'Apoc1', 'Apoc3', 'Apoe', 'Fabp1', 'Itih4', 'Orm1', 'Pigr', 'Serpina1', 'Tf', 'Ttr')
markers_test %in% rownames(GetAssayData(merged_samples_all, 'scale.data'))
markers_test[!markers_test %in% rownames(GetAssayData(merged_samples_all, 'scale.data'))]
dim(merged_samples_all)
dim(merged_samples_sub)
#### checking if the order of cells have been preserved in the data containing all the genes
sum(colnames(merged_samples_all) != colnames(merged_samples_sub))
head(merged_samples_all)
head(merged_samples_sub)
Resolution = 2.5
resolutions = Resolution
merged_samples_sub <- FindClusters(merged_samples_sub, resolution = Resolution, verbose = FALSE)
table(merged_samples_sub$SCT_snn_res.2.5)
merged_samples_all$SCT_snn_res.2.5 = merged_samples_sub$SCT_snn_res.2.5
merged_samples = merged_samples_all
####################################################
CV_Hep = c(23, 8, 3, 13, 32, 26, 12, 17, 1, 15)
CV_Hep_Markers = c('Akr1c1', 'Ahr', 'Glul',
'Notum', 'Rcan1', 'Cyp2f4', 'Cyp27a1', 'Cyp7a1', 'Cyp2e1', 'Cyp8b1')
other_Hep = c(21, 6, 20, 31, 9)
other_Hep_Markers = c('Mt2A', 'Hamp')
Periportal_Hep = c(27, 0, 4, 10, 16, 7, 2, 5) #removed 15
Periportal_Hep_Markers=c('Alb', 'Apoa1', 'Apoc1', 'Apoc3', 'Apoe', 'Fabp1',
'Itih4', 'Orm1', 'Pigr', 'Serpina1', 'Tf', 'Ttr')
Cholagiocytes = 29
Cholagiocytes_Markers = c('Epcam', 'Sox9', 'Anxa4')
General_Macrophage_Markers = c('Cd68', 'Clec4f')
Non_inf_mac = 19
Non_inf_mac_Markers = c('Cd5l', 'Marco', 'Cd163', 'C1qa', 'C1qc', 'Aif1',
'Slc11a', 'Hmox1', 'Vsig4', 'Ptas1', 'Ccl6')
Inf_mac = 33
Inf_mac_Markers = c('Cd74', 'RT1-Ba', 'RT1-Bb', 'RT1-Da', 'RT1-Db1', 'Lyz2')
Mesenchymal = c(24)
Mesenchymal_Markers = c('Col3a1', 'Colec10', 'Colec11', 'Ecm1') #'Stab2', 'Vwf',
Endothelial = c(11, 30)
Endothelial_Markers = c('Aqp1', 'Fcgr2b', 'Gpr182', 'Lyve1', 'Stab2')
clusters_to_include = c(CV_Hep, other_Hep, Periportal_Hep, Cholagiocytes,
Non_inf_mac, Inf_mac, Mesenchymal, Endothelial)
markers_list = c(CV_Hep_Markers, other_Hep_Markers, Periportal_Hep_Markers, Cholagiocytes_Markers,
General_Macrophage_Markers, Non_inf_mac_Markers, Inf_mac_Markers,
Mesenchymal_Markers, Endothelial_Markers)
[email protected] <- merged_samples$SCT_snn_res.2.5
merged_samples_subset = merged_samples[, merged_samples$SCT_snn_res.2.5 %in% clusters_to_include]
dim(merged_samples_subset)
dim(merged_samples)
annot = c(rep(NA, ncol(merged_samples_subset)))
sapply(1:ncol(merged_samples_subset) , function(i){
if (merged_samples_subset$SCT_snn_res.2.5[i] %in% CV_Hep) annot[i] <<- 'Hep 1'
if (merged_samples_subset$SCT_snn_res.2.5[i] %in% other_Hep) annot[i] <<- 'Hep'
if (merged_samples_subset$SCT_snn_res.2.5[i] %in% Periportal_Hep) annot[i] <<- 'Hep 3'
if (merged_samples_subset$SCT_snn_res.2.5[i] %in% Cholagiocytes) annot[i] <<- 'cholagiocyte'
if (merged_samples_subset$SCT_snn_res.2.5[i] %in% Non_inf_mac) annot[i] <<- 'nonInfMac'
if (merged_samples_subset$SCT_snn_res.2.5[i] %in% Inf_mac) annot[i] <<- 'InfMac'
if (merged_samples_subset$SCT_snn_res.2.5[i] %in% Mesenchymal) annot[i] <<- 'mesenchymal'
if (merged_samples_subset$SCT_snn_res.2.5[i] %in% Endothelial) annot[i] <<- 'endothelial'
if(i %% 100 == 0 ) print(i)
})
merged_samples_subset$manual_annotation = annot
[email protected] <- factor(x = [email protected], levels = unique(clusters_to_include))
[email protected] <- factor(x = merged_samples_subset$manual_annotation, levels = unique(merged_samples_subset$manual_annotation))
#saveRDS(merged_samples_subset, '~/rat_sham_sn_data/standardQC_results/sham_sn_merged_annot_standardQC_allfeatures_heatmap_v.rds')
#merged_samples_subset <- readRDS('~/rat_sham_sn_data/standardQC_results/sham_sn_merged_annot_standardQC_allfeatures_heatmap_v.rds')
DoHeatmap(
merged_samples_subset,
features = markers_list,
cells = NULL,
group.by = "ident",
group.bar = TRUE,
group.colors = NULL,
disp.min = -2.5,
disp.max = NULL,
slot = "scale.data", #"scale.data", data, counts
assay = 'RNA',
label = TRUE,
size = 5.5,
hjust = 0,
angle = 45,
raster = TRUE,
draw.lines = TRUE,
lines.width = NULL,
group.bar.height = 0.02,
combine = TRUE
)
c('Ptas1', 'Slc11a', 'Cd68', 'Anxa4', 'Sox9', 'Cyp7a1') %in% rownames(merged_samples_subset)
counts(merged_samples_sub)
dim(merged_samples_sub)
################################
##### Generating average expression based heatmap
get_scaled_by_gene <- function(x){
y <- scale(t(x), center = TRUE, scale = TRUE)
y[is.nan(y)] <- 0
y2 <- t(y)
y2 <- as.data.frame(y2)
return(y2)
}
#merged_samples_subset2 <- CreateAssayObject(GetAssayData(merged_samples_subset)[rownames(merged_samples_subset) %in% markers_list,])
merged_samples_subset <- merged_samples_subset[rownames(merged_samples_subset) %in% markers_list,]
dim(merged_samples_subset)
#merged_samples_subset2$manual_annotation = merged_samples_subset$manual_annotation
cluster_names = as.character(merged_samples_subset$SCT_snn_res.2.5 )
cluster_names_types = names(table(cluster_names))
### dividing the expression matrix based on the clusters
cluster_expression <- sapply(1:length(cluster_names_types), function(i){
a_cluster_name = cluster_names_types[i]
GetAssayData(merged_samples_subset, 'data')[,cluster_names == a_cluster_name]
}, simplify = F)
names(cluster_expression) = cluster_names_types
lapply(cluster_expression, head)
## calculate the average expression of each gene in each cluster
cluster_average_exp <- lapply(cluster_expression, function(x){
df = data.frame(average=rowSums(x)/ncol(x))
return(df)
})
lapply(cluster_average_exp, dim)
## Concatenate all the clusters together to make a matrix
cluster_average_exp_df = do.call(cbind,cluster_average_exp)
colnames(cluster_average_exp_df) = paste0(names(cluster_average_exp))
head(cluster_average_exp_df)
## scale and center all the genes in the matrix
cluster_average_exp_df_scaled <- get_scaled_by_gene(cluster_average_exp_df) ## scaling over the clusters of interest
colnames(cluster_average_exp_df_scaled)
rownames(cluster_average_exp_df_scaled)
anno_df = c(rep(NA, ncol(cluster_average_exp_df_scaled)))
sapply(1:ncol(merged_samples_subset) , function(i){
if (colnames(cluster_average_exp_df_scaled)[i] %in% CV_Hep) anno_df[i] <<- 'Hep 1'
if (colnames(cluster_average_exp_df_scaled)[i] %in% other_Hep) anno_df[i] <<- 'Hep 2'
if (colnames(cluster_average_exp_df_scaled)[i] %in% Periportal_Hep) anno_df[i] <<- 'Hep 3'
if (colnames(cluster_average_exp_df_scaled)[i] %in% Cholagiocytes) anno_df[i] <<- 'Cholagiocyte'
if (colnames(cluster_average_exp_df_scaled)[i] %in% Non_inf_mac) anno_df[i] <<- 'Non-inf Mac'
if (colnames(cluster_average_exp_df_scaled)[i] %in% Inf_mac) anno_df[i] <<- 'Inf Mac'
if (colnames(cluster_average_exp_df_scaled)[i] %in% Mesenchymal) anno_df[i] <<- 'Mesenchymal'
if (colnames(cluster_average_exp_df_scaled)[i] %in% Endothelial) anno_df[i] <<- 'Endothelial'
})
anno_df_genes = c(rep(NA, nrow(cluster_average_exp_df_scaled)))
sapply(1:ncol(merged_samples_subset) , function(i){
if (rownames(cluster_average_exp_df_scaled)[i] %in% CV_Hep_Markers) anno_df_genes[i] <<- 'Hep 1'
if (rownames(cluster_average_exp_df_scaled)[i] %in% other_Hep_Markers) anno_df_genes[i] <<- 'Hep 2'
if (rownames(cluster_average_exp_df_scaled)[i] %in% Periportal_Hep_Markers) anno_df_genes[i] <<- 'Hep 3'
if (rownames(cluster_average_exp_df_scaled)[i] %in% Cholagiocytes_Markers) anno_df_genes[i] <<- 'Cholagiocyte'
if (rownames(cluster_average_exp_df_scaled)[i] %in% c(General_Macrophage_Markers, Non_inf_mac_Markers)) anno_df_genes[i] <<- 'Non-inf Mac'
if (rownames(cluster_average_exp_df_scaled)[i] %in% Inf_mac_Markers) anno_df_genes[i] <<- 'Inf Mac'
if (rownames(cluster_average_exp_df_scaled)[i] %in% Mesenchymal_Markers) anno_df_genes[i] <<- 'Mesenchymal'
if (rownames(cluster_average_exp_df_scaled)[i] %in% Endothelial_Markers) anno_df_genes[i] <<- 'Endothelial'
})
length(anno_df_genes)
##### ordering clusters
cluster_orders = as.character(c(CV_Hep, other_Hep, Periportal_Hep, Cholagiocytes, Non_inf_mac,
Inf_mac, Mesenchymal, Endothelial))
anno_df = data.frame(cluster=colnames(cluster_average_exp_df_scaled),annot=anno_df)
cluster_average_exp_df_scaled = cluster_average_exp_df_scaled[,cluster_orders]
anno_df = anno_df[match(colnames(cluster_average_exp_df_scaled), anno_df$cluster),]
head(anno_df)
dim(cluster_average_exp_df_scaled)
#### ordering genes
gene_orders = c(CV_Hep_Markers, other_Hep_Markers, Periportal_Hep_Markers, Cholagiocytes_Markers,
General_Macrophage_Markers, Non_inf_mac_Markers, Inf_mac_Markers,
Mesenchymal_Markers, Endothelial_Markers)
gene_orders = unique(gene_orders[gene_orders %in% rownames(cluster_average_exp_df_scaled)])
anno_df_genes = data.frame(genes=row.names(cluster_average_exp_df_scaled),annot=anno_df_genes)
cluster_average_exp_df_scaled = cluster_average_exp_df_scaled[gene_orders,]
dim(cluster_average_exp_df_scaled)
anno_df_genes = anno_df_genes[match(row.names(cluster_average_exp_df_scaled), anno_df_genes$genes),]
dim(anno_df)
dim(anno_df_genes)
row.names(anno_df) = anno_df$cluster
row.names(anno_df_genes) = anno_df_genes$genes
head(anno_df)
head(anno_df_genes)
anno_df = anno_df[,-1,drop=F]
anno_df_genes = anno_df_genes[,-1,drop=F ]
dim(cluster_average_exp_df_scaled)
#mat_breaks <- seq(min(cluster_average_exp_df_scaled), max(cluster_average_exp_df_scaled), length.out = 10)
annot_info <- read.csv('figure_panel/Annotations_SingleNuc_Rat_June_8_2023.csv')
annot_info <- annot_info[1:34, 1:4]
colnames(annot_info)[1] = 'clusters'
show_col(color_df$colors)
c(CV_Hep_Markers, other_Hep_Markers, Periportal_Hep_Markers, Cholagiocytes_Markers,
General_Macrophage_Markers, Non_inf_mac_Markers, Inf_mac_Markers,
Mesenchymal_Markers, Endothelial_Markers)
fc <- colorRampPalette(c("green", "darkgreen"))
annot_info.df = data.frame(table(annot_info$label))
fc <- colorRampPalette(c('palevioletred1'))
cvHep_c = fc(annot_info.df$Freq[annot_info.df$Var1=='Hep 1']) #cvHep
fc <- colorRampPalette(c('orchid3'))
Hep_c = fc(annot_info.df$Freq[annot_info.df$Var1=='Hep 2'])
fc <- colorRampPalette(c('maroon1'))
ppHep_c = fc(annot_info.df$Freq[annot_info.df$Var1=='Hep 3']) #ppHep
fc <- colorRampPalette(c('mistyrose'))
unknown_c = fc(annot_info.df$Freq[annot_info.df$Var1=='Unknown/High Mito'])
fc <- colorRampPalette(c('cyan')) #coral
chol_c = fc(annot_info.df$Freq[annot_info.df$Var1=='Cholangiocytes'])
infMac_c = '#117733'
nonInfMac_c = '#999933' #'#47A265'
LSEC_c = c('#FFE729', '#FFE729')
Stellate_c = '#6699CC'
anno_df_col = anno_df
anno_df_col$colors=c(cvHep_c, Hep_c, ppHep_c, chol_c, nonInfMac_c, infMac_c, Stellate_c, LSEC_c)
##### makeing the input list for the pheatmap annotation colors
show_col(unique(anno_df_col$colors))
anno_df_col_list = unique(anno_df_col$colors)
names(anno_df_col_list) = unique(anno_df_col$CellType)
anno_df_col_list
colnames(anno_df) = 'CellType'
colnames(anno_df_genes) = 'CellType'
pheatmap(t(cluster_average_exp_df_scaled), cluster_rows = FALSE,
cluster_cols = FALSE,
annotation_row = data.frame(anno_df),
annotation_col = data.frame(anno_df_genes),
color= inferno(20, direction = +1),
border_color= FALSE,
annotation_names_row=FALSE,
annotation_names_col=FALSE,
annotation_colors = list(CellType=anno_df_col_list),
annotation_legend = TRUE
)
all_hep_markers = c(CV_Hep_Markers, other_Hep_Markers, Periportal_Hep_Markers)
all_hep_clusters = c(CV_Hep, other_Hep, Periportal_Hep)
cluster_average_exp_df_scaled_sub = cluster_average_exp_df_scaled[,!colnames(cluster_average_exp_df_scaled) %in% all_hep_clusters]
cluster_average_exp_df_scaled_sub = cluster_average_exp_df_scaled_sub[!rownames(cluster_average_exp_df_scaled_sub) %in% all_hep_markers,]
anno_df_sub = data.frame(CellType=anno_df[!anno_df$CellType %in% c('Hep 1', 'Hep 2', 'Hep 3'),])
rownames(anno_df_sub) = rownames(anno_df)[!anno_df$CellType %in% c('Hep 1', 'Hep 2', 'Hep 3')]
anno_df_genes_sub = data.frame(CellType=anno_df_genes[!anno_df_genes$CellType %in% c('Hep 1', 'Hep 2', 'Hep 3'),])
rownames(anno_df_genes_sub) = rownames(anno_df_genes)[!anno_df_genes$CellType %in% c('Hep 1', 'Hep 2', 'Hep 3')]
pheatmap(t(cluster_average_exp_df_scaled_sub), cluster_rows = FALSE,
cluster_cols = FALSE,
annotation_row = data.frame(anno_df_sub),
annotation_col = data.frame(anno_df_genes_sub),
color= inferno(20, direction = +1),
border_color= FALSE,
annotation_names_row=FALSE,
annotation_names_col=FALSE,
annotation_colors = list(CellType=anno_df_col_list[4:length(anno_df_col_list)]),
annotation_legend = TRUE
)
?pheatmap