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Deconvolution Analysis

Mehran Piran, Laura Perlaza-Jimenez 05/08/2023

Deconvolution Analysis

Data preprocessing

A reference count gene expression matrix was created by selecting the top 5,000 most variable genes and randomly choosing 50 cells for each cell type from the original single-cell RNA-seq data. This reference matrix was then provided to an online tool called CIBERSORTx. Additionally, the complete bulk RNA-seq count matrix was also provided to the software in order to generate the signature matrix and produce deconvolution results. These results display the percentage of each cell type in all bulk samples in a tabular format.

Plotting deconvolution results

rm(list=ls());

Load libraries

library(reshape2)
library(ggplot2)
library(tidyverse)
library(Seurat)

Get working path and set it

path_wd<-getwd()
setwd(path_wd)

read file

mat <- read.csv("../Data/complete_dataset.csv")

Melting the matrix

deconv<-melt(mat)

Correcting names for samples

deconv$Samples[ deconv$Mixture=="d20_Hyp1_S4_R1_001"]="h1_d20"
deconv$Samples[ deconv$Mixture=="d20_Hyp2_S5_R1_001"]="h2_d20"
deconv$Samples[ deconv$Mixture=="d20_Hyp3_S6_R1_001"]="h3_d20"
deconv$Samples[ deconv$Mixture=="d20_Norm1_S1_R1_001"]="n1_d20"
deconv$Samples[ deconv$Mixture=="d20_Norm2_S2_R1_001"]="n2_d20"
deconv$Samples[ deconv$Mixture=="d20_Norm3_S3_R1_001"]="n3_d20"
deconv$Samples[ deconv$Mixture=="d25_Hyp1_S10_R1_001"]="h1_d25"
deconv$Samples[ deconv$Mixture=="d25_Hyp2_S11_R1_001"]="h2_d25"
deconv$Samples[ deconv$Mixture=="d25_Hyp3_S12_R1_001"]="h3_d25"
deconv$Samples[ deconv$Mixture=="d25_Norm1_S7_R1_001"]="n1_d25"
deconv$Samples[ deconv$Mixture=="d25_Norm2_S8_R1_001"]="n2_d25"
deconv$Samples[ deconv$Mixture=="d25_Norm3_S9_R1_001"]="n3_d25"


# order Sampless
deconv$Samples=factor(deconv$Samples,
                     levels= c("n1_d20","n2_d20","n3_d20","h1_d20","h2_d20","h3_d20",
                          "n1_d25","n2_d25","n3_d25","h1_d25","h2_d25","h3_d25"))

# fix cell labels 
deconv$cell_label<-factor(gsub("X","",deconv$variable))

# order cell labels
deconv$cell_label <- factor(deconv$cell_label,levels = deconv$cell_label %>% unique())

Plot proportions

ggplot(deconv,aes(Samples,value,fill=cell_label))+
  geom_bar(stat = "identity")+
  theme_classic(base_size = 15)+theme(axis.text.x = element_text(angle = 90))+
  labs(fill = "Clusters")

ggplot(deconv,aes(Samples,value,fill=cell_label))+
  geom_bar(stat = "identity")+
  theme_classic(base_size = 15)+geom_col(color = "white")+theme(axis.text.x = element_text(angle = 90))+
  labs(fill = "Clusters")

Plot proportions day 20

deconv_subset<-deconv[ deconv$Samples %in% c("n1_d20","n2_d20","n3_d20","h1_d20","h2_d20","h3_d20"),]

ggplot(deconv_subset,aes(Samples,value,fill=cell_label))+
  geom_bar(stat = "identity")+
  theme_classic(base_size = 15)+theme(axis.text.x = element_text(angle = 90))+
  labs(fill = "Clusters")

ggplot(deconv_subset,aes(Samples,value,fill=cell_label))+
  geom_bar(stat = "identity")+
  theme_classic(base_size = 15)+geom_col(color = "white")+theme(axis.text.x = element_text(angle = 90))+
  labs(fill = "Clusters")

Plot proportions day 25

deconv_subset<-deconv[ deconv$Samples %in% c("n1_d25","n2_d25","n3_d25","h1_d25","h2_d25","h3_d25"),]

ggplot(deconv_subset,aes(Samples,value,fill=cell_label))+
  geom_bar(stat = "identity")+
  theme_classic(base_size = 15)+theme(axis.text.x = element_text(angle = 90))+
  labs(fill = "Clusters")

ggplot(deconv_subset,aes(Samples,value,fill=cell_label))+
  geom_bar(stat = "identity")+
  theme_classic(base_size = 15)+geom_col(color = "white")+theme(axis.text.x = element_text(angle = 90))+
  labs(fill = "Clusters")