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Unrooted dendrogram.R
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Unrooted dendrogram.R
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library(tidyverse)
library(ggtree)
library(ape)
load("./SC_neuron_seurat.Rda")
seurat_object@active.assay <- 'RNA'
# Calculate the mean expression per gene per cluster
mean_expression <- as.data.frame(AverageExpression(object = seurat_object, group.by = "SCT_snn_res.0.8",assays = 'RNA'))
colnames( mean_expression) <- gsub('RNA.','',colnames( mean_expression))
colnames( mean_expression) <- gsub('Inhibitory.Neuron','I',colnames( mean_expression))
colnames( mean_expression) <- gsub('Excitatory.Neuron','E',colnames( mean_expression))
colnames( mean_expression) <- gsub('Inhibitory.Excitatory.neuron','I/E',colnames( mean_expression))
colnames( mean_expression) <- gsub('.I',' I',colnames( mean_expression))
colnames( mean_expression) <- gsub('.E',' E',colnames( mean_expression))
colnames( mean_expression) <- gsub('I E','I/E',colnames( mean_expression))
mean_expression_filtered <- mean_expression[which(rowMeans(mean_expression) > 0), ]
distance_metrics <- dist(t(mean_expression_filtered), method = "euclidean")
hc <- hclust(distance_metrics, method = "complete")
tree =as.phylo(hc)
cols<- read.csv('cols.csv',header = T,row.names = 1)
rownames(cols) <- gsub('Inhibitory.Neuron','I',rownames(cols))
rownames(cols) <- gsub('Excitatory.Neuron','E',rownames(cols))
rownames(cols) <- gsub('Inhibitory Excitatory.neuron','I/E',rownames(cols))
tip_col=c('#E41A1C',
'#8C4C6A',
'#F58499',
'#4384BE',
'#1F5575',
'#1C8D76',
'#2AA478',
'#76C582',
'#B7DB7A',
'#C3D257',
'#606592',
'#3C8A9B',
'#47A363',
'#4EAD4B',
'#619462',
'#747C78',
'#9A509E',
'#C8734D',
'#DE8524',
'#F38E3A',
'#E589C2',
'#D690C6',
'#C797C9',
'#BA98C2',
'#B58698',
'#AA6245',
'#A05C35',
'#8B7364',
'#768994',
'#4FA5DB',
'#2A4386')
cols=c('black',
'black',
'black',
'#8C4C6A',
'#B58698',
'black',
'black',
'#B7DB7A',
'black',
'#619462',
'#F38E3A',
'black',
'#DE8524',
'black',
'#C797C9',
'black',
'#4EAD4B',
'#C8734D',
'black',
'black',
'#2AA478',
'black',
'#8B7364',
'#768994',
'black',
'black',
'#4384BE',
'#D690C6',
'black',
'black',
'#C3D257',
'#4FA5DB',
'black',
'black',
'#E589C2',
'black',
'#9A509E',
'#AA6245',
'black',
'black',
'#747C78',
'#A05C35',
'black',
'#76C582',
'black',
'black',
'#8C4C6A',
'black',
'#E41A1C',
'#1F5575',
'black',
'#3C8A9B',
'black',
'#606592',
'#47A363',
'black',
'#BA98C2',
'black',
'#1C8D76',
'#2A4386'
)
plot(tree, type="unrooted",label.offset=0.01,
rotate.tree=180, no.margin=FALSE, cex=1, edge.width=4,edge.color=cols)
pdf('tree.pdf',width = 8,height = 8)
plot(tree, type="unrooted",label.offset=20,
rotate.tree=180, no.margin=FALSE, cex=1, edge.width=4,edge.color=cols,tip.color=tip_col)
dev.off()