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DEGSeq2_MV_Only.R
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DEGSeq2_MV_Only.R
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###############################################################################
################# CIBERSORTx cell type quantification ##########################
# load libraries in R
## @knitr Load_libraries
setwd("/media/owen/Backup Plus/UCSF_Project/02_PROCESSED_DATA/PNOC/ANALYSIS/Bulk_RNA/01_Input_Data/Mouse/")
library(MASS)
library(reshape2)
library("FactoMineR")
library("factoextra")
library(heatmaply)
library(ggpubr)
library(DESeq2)
library(glmGamPoi)
library(EnhancedVolcano)
library(BiocParallel)
register(MulticoreParam(12))
library(ggplot2)
library(scales) # needed for oob parameter
library(viridis)
library("tibble")
library(reshape2)
library(ggdendro)
library(gridExtra)
library(gtable)
library(grid)
library(clusterProfiler)
library(enrichplot)
require(DOSE)
library(europepmc)
library(pathview)
library(survival)
library(survminer)
require("survival")
library("forestmodel")
library(devtools)
#install_github("KatrionaGoldmann/volcano3D", force = TRUE)
library(volcano3D)
#install_github("KatrionaGoldmann/volcano3Ddata")
library(volcano3Ddata)
library(volcano3D)
library(kableExtra)
library("ggrepel")
library("plotly")
library("dplyr")
library(knitr)
library(htmltools)
library(htmlTable)
library(htmlwidgets)
library( digest)
library( DT)
library( fs)
library(future)
library(ggrepel)
library(ggtree)
library(parallel)
library(tibble)
library(data.table)
library(speckle)
library(rstatix)
plan("multicore", workers = 12)
options(future.globals.maxSize = (1014*1024^2)*60)
## @knitr loadData
CIBERSORTx_SingleR_matrix <- read.csv("CIBERSORTx_Corrected_CellTypes_Martrix.csv", header = T, row.names = 1)
CIBERSORTx_SingleR_matrix <- t(CIBERSORTx_SingleR_matrix)
CIBERSORTx_SingleR_matrix <- as.data.frame(CIBERSORTx_SingleR_matrix)
CIBERSORTx_SingleR_matrix <- rownames_to_column(CIBERSORTx_SingleR_matrix, var = "CellType")
## @knitr CIBERSORTx_PCA
iris.pca <- PCA(CIBERSORTx_SingleR_matrix[,-1], graph = FALSE)
fviz_pca_ind(iris.pca, geom.ind = "point", pointshape = 21,
pointsize = 2,
fill.ind = CIBERSORTx_SingleR_matrix$CellType,
col.ind = "black",
palette = c("#0062B4FF", "#FFBF47", "#FF2700FF", "mediumseagreen", "sienna4", "violet", "violetred", "violetred4", "#CC313D", "blue", "limegreen", "#3B9AB2","deeppink", "#BF812D"),
addEllipses = TRUE,
label = "var",
col.var = "black",
repel = TRUE,
legend.title = "Cell Types") +
ggtitle("2D PCA-plot from CIBERSORTx Signature Matrix") +
theme(plot.title = element_text(hjust = 0.5))
CIBERSORTx_SingleR_matrix <- read.csv("CIBERSORTx_Corrected_CellTypes_Martrix.csv", header = T, row.names = 1)
## @knitr CIBERSORTx_Heatmap
data <- CIBERSORTx_SingleR_matrix
data <- as.matrix(data)
library("RColorBrewer")
cellType = c(Malignant = "#CC313D", Neurons = "#3B9AB2", Endothelial = "mediumseagreen", Monocytes = "limegreen", Microglia = "blue", M2_Macrophages = "violetred4", M1_Macrophages = "violetred", M0_Macrophages = "violet", CD4 = "#FF2700FF", B = "#FFBF47", Astrocytes = "#0062B4FF", Oligodendrocytes = "#BF812D", NK_cells = "deeppink", Epithelial = "sienna4")
col<- colorRampPalette(c("cyan", "black", "red"))(256)
heatmap(data, ColSideColors = cellType, col = col)
## @knitr CIBERSORTx_Correlation_Heatmap
heatmaply_cor(
cor(data),
xlab = "Features",
ylab = "Features",
k_col = 2,
k_row = 2
)
## @knitr CIBERSORTx_Correlation_Heatmap_pvalues
r <- cor(data)
cor.test.p <- function(x){
FUN <- function(x, y) cor.test(x, y)[["p.value"]]
z <- outer(
colnames(x),
colnames(x),
Vectorize(function(i,j) FUN(x[,i], x[,j]))
)
dimnames(z) <- list(colnames(x), colnames(x))
z
}
p <- cor.test.p(data)
heatmaply_cor(
r,
node_type = "scatter",
point_size_mat = -log10(p),
point_size_name = "-log10(p-value)",
label_names = c("x", "y", "Correlation"))
## @knitr CIBERSORTx_cell_fraction_results
CIBERSORTx_Results_SingleR <- read.csv("CIBERSORTx_Corrected_CellTypes_Results_website.csv", header = T, row.names = 1)
CIBERSORTx_Results_SingleR <- round(CIBERSORTx_Results_SingleR, digits = 2)
heatmaply(CIBERSORTx_Results_SingleR,
cellnote = CIBERSORTx_Results_SingleR
)
theme_set(
theme_minimal() +
theme(legend.position = "right")
)
## @knitr CIBERSORTx_cell_fraction_barplot
CIBERSORTx_Results_SingleR <- read.csv("CIBERSORTx_Corrected_CellTypes_Results_t_test_MV.csv", header = T)
melt_data_propeller <- melt(CIBERSORTx_Results_SingleR, id=c("Mixture","Viral_Status"))
ggplot(melt_data_propeller, mapping = aes(x = Mixture, y = value, fill = variable)) +
geom_bar(position= "stack", stat = "identity") +
scale_fill_manual(labels = c("Malignant", "Neurons", "Endothelial", "Monocytes", "Microglia", "M2_Macrophages", "M1_Macrophages", "M0_Macrophages", "CD4", "B", "Astrocytes", "Oligodendrocytes", "NK_cells", "Epithelial"), values =c( "#CC313D", "#3B9AB2", "mediumseagreen", "limegreen", "blue", "violetred4", "violetred", "violet", "#FF2700FF", "#FFBF47", "#0062B4FF", "#BF812D", "deeppink", "sienna4")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
## @knitr CIBERSORTx_cell_fraction_boxplot
df <- read.csv("CIBERSORTx_Corrected_CellTypes_Results_t_test_MV.csv", header = T)
df$Viral_Status <- factor(df$Viral_Status, levels = c("Control_MV", "HI_MV_NIS", "Mid_treatment_MV", "Post_treatment_MV"))
# Visualize: Specify the comparisons you want
my_comparisons = list(c("Control_MV", "HI_MV_NIS"), c("Control_MV", "Mid_treatment_MV"), c("Control_MV", "Post_treatment_MV"), c("Mid_treatment_MV", "Post_treatment_MV"))
p1 = ggboxplot(df, x = "Viral_Status", y = c("M0_Macrophages", "M1_Macrophages", "M2_Macrophages"), fill = "Viral_Status", legend = "none", combine = TRUE,
bxp.errorbar = TRUE,
bxp.errorbar.width = 0.4, palette = c("#00AFBB", "#E7B800",
"green", "red", "navy", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7",
"#52854C", "#4E84C4", "purple", "black", "blue"),ggtheme = theme_bw())+
rotate_x_text(angle = 45)+
stat_compare_means(comparisons = my_comparisons, paired = FALSE, method = "t.test")+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 0.05) +
ggtitle("CIBERSORTx Cell Type Comparison") + theme(
plot.title = element_text(color="black", size=14),
axis.title.x = element_text(color="black", size=14),
axis.title.y = element_text(color="black", size=14)
)
p1
df$Viral_Status <- factor(df$Viral_Status, levels = c("Control_MV", "HI_MV_NIS", "Mid_treatment_MV", "Post_treatment_MV"))
# Visualize: Specify the comparisons you want
my_comparisons = list(c("Control_MV", "HI_MV_NIS"), c("Control_MV", "Mid_treatment_MV"), c("Control_MV", "Post_treatment_MV"), c("Mid_treatment_MV", "Post_treatment_MV"))
p2 = ggboxplot(df, x = "Viral_Status", y = c("NK_cells", "CD4"), fill = "Viral_Status", legend = "none", combine = TRUE,
bxp.errorbar = TRUE,
bxp.errorbar.width = 0.4, palette = c("#00AFBB", "#E7B800",
"green", "red", "navy", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7",
"#52854C", "#4E84C4", "purple", "black", "blue"),ggtheme = theme_bw())+
rotate_x_text(angle = 45)+
stat_compare_means(comparisons = my_comparisons, paired = FALSE, method = "t.test")+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 0.05) +
ggtitle("CIBERSORTx Cell Type Comparison") + theme(
plot.title = element_text(color="black", size=14),
axis.title.x = element_text(color="black", size=14),
axis.title.y = element_text(color="black", size=14)
)
p2
df$Viral_Status <- factor(df$Viral_Status, levels = c("Control_MV", "HI_MV_NIS", "Mid_treatment_MV", "Post_treatment_MV"))
# Visualize: Specify the comparisons you want
my_comparisons = list(c("Control_MV", "HI_MV_NIS"), c("Control_MV", "Mid_treatment_MV"), c("Control_MV", "Post_treatment_MV"), c("Mid_treatment_MV", "Post_treatment_MV"))
p3 = ggboxplot(df, x = "Viral_Status", y = c("Monocytes", "Microglia", "B"), fill = "Viral_Status", legend = "none", combine = TRUE,
bxp.errorbar = TRUE,
bxp.errorbar.width = 0.4, palette = c("#00AFBB", "#E7B800",
"green", "red", "navy", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7",
"#52854C", "#4E84C4", "purple", "black", "blue"),ggtheme = theme_bw())+
rotate_x_text(angle = 45)+
stat_compare_means(comparisons = my_comparisons, paired = FALSE, method = "t.test")+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 0.1) +
ggtitle("CIBERSORTx Cell Type Comparison") + theme(
plot.title = element_text(color="black", size=14),
axis.title.x = element_text(color="black", size=14),
axis.title.y = element_text(color="black", size=14)
)
p3
df$Viral_Status <- factor(df$Viral_Status, levels = c("Control_MV", "HI_MV_NIS", "Mid_treatment_MV", "Post_treatment_MV"))
# Visualize: Specify the comparisons you want
my_comparisons = list(c("Control_MV", "HI_MV_NIS"), c("Control_MV", "Mid_treatment_MV"), c("Control_MV", "Post_treatment_MV"), c("Mid_treatment_MV", "Post_treatment_MV"))
p4 = ggboxplot(df, x = "Viral_Status", y = c("Malignant", "Neurons"),fill = "Viral_Status", legend = "none", combine = TRUE,
bxp.errorbar = TRUE,
bxp.errorbar.width = 0.4, palette = c("#00AFBB", "#E7B800",
"green", "red", "navy", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7",
"#52854C", "#4E84C4", "purple", "black", "blue"),ggtheme = theme_bw())+
rotate_x_text(angle = 45)+
stat_compare_means(comparisons = my_comparisons, paired = FALSE, method = "t.test")+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 1.2) +
ggtitle("CIBERSORTx Cell Type Comparison") + theme(
plot.title = element_text(color="black", size=14),
axis.title.x = element_text(color="black", size=14),
axis.title.y = element_text(color="black", size=14)
)
p4
df$Viral_Status <- factor(df$Viral_Status, levels = c("Control_MV", "HI_MV_NIS", "Mid_treatment_MV", "Post_treatment_MV"))
# Visualize: Specify the comparisons you want
my_comparisons = list(c("Control_MV", "HI_MV_NIS"), c("Control_MV", "Mid_treatment_MV"), c("Control_MV", "Post_treatment_MV"), c("Mid_treatment_MV", "Post_treatment_MV"))
p5 = ggboxplot(df, x = "Viral_Status", y = c("Endothelial", "Astrocytes", "Oligodendrocytes"), fill = "Viral_Status", legend = "none", combine = TRUE,
bxp.errorbar = TRUE,
bxp.errorbar.width = 0.4, palette = c("#00AFBB", "#E7B800",
"green", "red", "navy", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7",
"#52854C", "#4E84C4", "purple", "black", "blue"),ggtheme = theme_bw())+
rotate_x_text(angle = 45)+
stat_compare_means(comparisons = my_comparisons, paired = FALSE, method = "t.test")+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 0.10) +
ggtitle("CIBERSORTx Cell Type Comparison") + theme(
plot.title = element_text(color="black", size=14),
axis.title.x = element_text(color="black", size=14),
axis.title.y = element_text(color="black", size=14)
)
p5
ggarrange(p1, p2, p3, p4, p5, nrow = 5, common.legend = FALSE)
# stat.test <- melt_data_propeller %>%
# group_by(Viral_Status) %>%
# tukey_hsd(value ~ variable)
# propeller_test_SingleR <-propeller(clusters = melt_data_propeller$variable,
# sample = melt_data_propeller$Mixture,
# group = melt_data_propeller$Viral_Status)
# propeller_test_SingleR <- as.data.frame(propeller_test_SingleR)
# propeller_test_SingleR <- rownames_to_column(propeller_test_SingleR, var = "cellType")
#write.csv(stat.test, file="tukey_hsd_SingleR.csv")
########################################################################################################################################################
############################ Now let's take a look at the viral composition ############################################################################
#setwd("/media/owen/Backup Plus/UCSF_Project/02_PROCESSED_DATA/PNOC/ANALYSIS/Bulk_RNA/05_Output/Mouse/Virus/")
CIBERSORTx_Virus_matrix <- read.table("CIBERSORTx_Virus_Matrix_Normalized_Results.txt", header = T, sep = "\t", row.names = 1)
CIBERSORTx_Virus_matrix <- t(CIBERSORTx_Virus_matrix)
CIBERSORTx_Virus_matrix <- as.data.frame(CIBERSORTx_Virus_matrix)
CIBERSORTx_Virus_matrix <- rownames_to_column(CIBERSORTx_Virus_matrix, var = "CellType")
## @knitr CIBERSORTx_PCA2
res.pca <- prcomp(CIBERSORTx_Virus_matrix[,-1], scale = TRUE)
Viral_Status <- as.factor(CIBERSORTx_Virus_matrix$CellType)
fviz_pca_ind(res.pca, geom.ind = "point",
pointsize = 5,
col.ind = Viral_Status, # color by groups
palette = c("#00AFBB", "#FC4E07"),
addEllipses = TRUE, # Concentration ellipses
legend.title = "Viral_Status",
repel = TRUE
)
CIBERSORTx_Virus_matrix <- read.table("CIBERSORTx_Virus_Matrix_Normalized_Results.txt", header = T, sep = "\t", row.names = 1)
## @knitr CIBERSORTx_Heatmap2
data <- CIBERSORTx_Virus_matrix
data <- as.matrix(data)
heatmap(data, cexRow = 1, cexCol = 1,
main = "CIBERSORTx Virus Matrix")
## @knitr CIBERSORTx_Correlation_Heatmap2
heatmaply_cor(
cor(data),
xlab = "Features",
ylab = "Features",
k_col = 2,
k_row = 2
)
## @knitr CIBERSORTx_Correlation_Heatmap_pvalues2
r <- cor(data)
cor.test.p <- function(x){
FUN <- function(x, y) cor.test(x, y)[["p.value"]]
z <- outer(
colnames(x),
colnames(x),
Vectorize(function(i,j) FUN(x[,i], x[,j]))
)
dimnames(z) <- list(colnames(x), colnames(x))
z
}
p <- cor.test.p(data)
heatmaply_cor(
r,
node_type = "scatter",
point_size_mat = -log10(p),
point_size_name = "-log10(p-value)",
label_names = c("x", "y", "Correlation")
)
## @knitr CIBERSORTx_cell_fraction_results2
CIBERSORTx_Results_Virus <- read.csv("CIBERSORTx_Virus_SMode_Normalized_Results_website.csv", header = T, row.names = 1)
heatmaply(
CIBERSORTx_Results_Virus,
cellnote = CIBERSORTx_Results_Virus
)
theme_set(
theme_minimal() +
theme(legend.position = "right")
)
## @knitr CIBERSORTx_cell_fraction_barplot2
CIBERSORTx_Results_Virus <- read.csv("CIBERSORTx_Virus_SMode_Normalized_Results_t_test.csv", header = T)
melt_data <- melt(CIBERSORTx_Results_Virus, id=c("Mixture","Viral_Status"))
ggplot(melt_data, mapping = aes(x = Mixture, y = value, fill = variable)) +
geom_bar(position= "stack", stat = "identity") +
scale_fill_manual(labels = c("Measles_virus_infected", "Non_Infected"), values = c( "#E7B800", "#00AFBB")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
## @knitr CIBERSORTx_cell_fraction_boxplot2
df <- read.csv("CIBERSORTx_Virus_SMode_Normalized_Results_t_test.csv", header = T)
df$Viral_Status <- factor(df$Viral_Status, levels = c("Control_MV", "HI_MV_NIS", "Mid_treatment_MV", "Post_treatment_MV", "Control_Toca", "HI_Toca", "Mid_treatment_Toca", "Post_treatment_Toca"))
# Visualize: Specify the comparisons you want
my_comparisons = list(c("Control_MV", "HI_MV_NIS"), c("Control_MV", "Mid_treatment_MV"), c("Control_MV", "Post_treatment_MV"), c("Mid_treatment_MV", "Post_treatment_MV"), c("Control_Toca", "HI_Toca"), c("Control_Toca", "Mid_treatment_Toca"), c("Control_Toca", "Post_treatment_Toca"), c("Mid_treatment_Toca", "Post_treatment_Toca"))
p6 = ggboxplot(df, x = "Viral_Status", y = c("Measles_virus_infected", "Non_Infected"), add = "jitter", legend = "none", combine = TRUE,
color = "Viral_Status", palette = c("#00AFBB", "#E7B800",
"green", "red", "navy", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7",
"#52854C", "#4E84C4", "purple", "black", "blue"),ggtheme = theme_bw())+
rotate_x_text(angle = 45)+
stat_compare_means(comparisons = my_comparisons, paired = FALSE, method = "t.test")+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 0.5) +
ggtitle("CIBERSORTx Proportion of Infected Cells Comparison") + theme(
plot.title = element_text(color="black", size=14),
axis.title.x = element_text(color="black", size=14),
axis.title.y = element_text(color="black", size=14)
)
p6
###############################################################################
################# DEGSeq2 analysis of viral infections ##########################
## @knitr PrepareData
#setwd("/media/owen/Backup Plus/UCSF_Project/02_PROCESSED_DATA/PNOC/ANALYSIS/Bulk_RNA/01_Input_Data/Mouse")
# Read in the raw read counts
rawCounts <- read.csv("Mouse_Bulk_RNA_seq_Raw_Reads.csv")
#head(rawCounts)
# Read in the sample mappings
sampleData <- read.csv("Meta_Data_SingleR.csv")
#head(sampleData)
# Also save a copy for later
sampleData_v2 <- sampleData
# Convert count data to a matrix of appropriate form that DEseq2 can read
geneID <- rawCounts$gene_name
sampleIndex <- grepl("X\\d+", colnames(rawCounts))
rawCounts <- as.matrix(rawCounts[,sampleIndex])
rownames(rawCounts) <- geneID
#head(rawCounts)
# Convert sample variable mappings to an appropriate form that DESeq2 can read
#head(sampleData)
rownames(sampleData) <- sampleData$Sample_ID
keep <- c("Sample_Number", "Viral_Status")
sampleData <- sampleData[,keep]
colnames(sampleData) <- c("Sample", "Virus")
sampleData$Virus <- factor(sampleData$Virus)
#head(sampleData)
# Put the columns of the count data in the same order as rows names of the sample mapping, then make sure it worked
rawCounts <- rawCounts[,unique(rownames(sampleData))]
all(colnames(rawCounts) == rownames(sampleData))
# rename the tissue types
Virus_Infection <- function(x){
x <- switch(as.character(x), "Control_MV"="Control MV", "Control_Toca"="Control Toca", "HI_MV_NIS"="Heat Inactivated MV", "HI_Toca"="Heat Inactivated Toca", "Mid_treatment_MV"="Mid treatment MV",
"Mid_treatment_Toca"="Mid treatment Toca", "Post_treatment_MV"="Post treatment MV", "Post_treatment_Toca"="Post treatment Toca")
return(x)
}
sampleData$Virus <- unlist(lapply(sampleData$Virus, Virus_Infection))
# Order the tissue types so that it is sensible and make sure the control sample is first: normal sample -> primary tumor -> metastatic tumor
sampleData$Virus <- factor(sampleData$Virus, levels=c("Control MV", "Control Toca", "Heat Inactivated MV", "Heat Inactivated Toca", "Mid treatment MV",
"Mid treatment Toca", "Post treatment MV", "Post treatment Toca"))
# Create the DEseq2DataSet object
deseq2Data <- DESeqDataSetFromMatrix(countData=rawCounts, colData=sampleData, design= ~ Sample + Virus)
dim(deseq2Data)
dim(deseq2Data[rowSums(counts(deseq2Data)) > 5, ])
# Perform pre-filtering of the data
deseq2Data <- deseq2Data[rowSums(counts(deseq2Data)) > 5, ]
# Register the number of cores to use
## @knitr Run_DESeq2
# 1. Run pipeline for differential expression steps (if you set up parallel processing, set parallel = TRUE here)
deseq2Data <- DESeq(deseq2Data, test="LRT", reduced=~1, parallel = TRUE)
# Pull out counts from DESeq object and normalize using the same method to perform differential gene expression
# dds <- estimateSizeFactors(deseq2Data)
# dds <- counts(dds, normalized=TRUE)
# Extract differential expression results
CvMT <- results(deseq2Data, contrast=c("Virus", "Post treatment MV", "Control MV"))
# Coerce to a data frame
CvMT <- as.data.frame(CvMT)
CvMT <- rownames_to_column(CvMT, var = "gene")
CvMT$significant <- ifelse(CvMT$padj < .05, "Significant", NA)
CvMT<-na.omit(CvMT)
write.csv(CvMT, file= "deseq2_control_vs_MV_PT.csv")
cat('\n', '<br>', '\n\n')
## @knitr Print_Tables
# Print the datatable with all markers
print( htmltools::tagList(DT::renderDataTable(CvMT, server = TRUE,
class = "compact",
filter="top",
rownames = FALSE,
colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
extensions = c('Buttons'),
options = list(
pageLength = 15,
dom = 'Bfrtip',
buttons = c('excel','csv','pdf','copy')
))))
## @knitr Contrast1
# Extract differential expression results
CvMHI <- results(deseq2Data, contrast=c("Virus", "Heat Inactivated MV", "Control MV",))
# Coerce to a data frame
CvMHI <- as.data.frame(CvMHI)
CvMHI <- rownames_to_column(CvMHI, var = "gene")
CvMHI$significant <- ifelse(CvMHI$padj < .05, "Significant", NA)
CvMHI<-na.omit(CvMHI)
## @knitr Print_Tables1
# Print the datatable with all markers
print( htmltools::tagList(DT::renderDataTable(CvMHI, server = TRUE,
class = "compact",
filter="top",
rownames = FALSE,
colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
extensions = c('Buttons'),
options = list(
pageLength = 15,
dom = 'Bfrtip',
buttons = c('excel','csv','pdf','copy')
))))
## @knitr Contrast2
# Extract differential expression results
CvM_MT <- results(deseq2Data, contrast=c("Virus", "Mid treatment MV", "Control MV"))
# Coerce to a data frame
CvM_MT <- as.data.frame(CvM_MT)
CvM_MT <- rownames_to_column(CvM_MT, var = "gene")
CvM_MT$significant <- ifelse(CvM_MT$padj < .05, "Significant", NA)
CvM_MT<-na.omit(CvM_MT)
## @knitr Print_Tables2
# Print the datatable with all markers
print( htmltools::tagList(DT::renderDataTable(CvM_MT, server = TRUE,
class = "compact",
filter="top",
rownames = FALSE,
colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
extensions = c('Buttons'),
options = list(
pageLength = 15,
dom = 'Bfrtip',
buttons = c('excel','csv','pdf','copy')
))))
## @knitr Contrast3
# Extract differential expression results
CvPTT <- results(deseq2Data, contrast=c("Virus", "Post treatment Toca", "Control Toca"))
# Coerce to a data frame
CvPTT <- as.data.frame(CvPTT)
CvPTT <- rownames_to_column(CvPTT, var = "gene")
CvPTT$significant <- ifelse(CvPTT$padj < .05, "Significant", NA)
CvPTT<-na.omit(CvPTT)
## @knitr Print_Tables3
# Print the datatable with all markers
print( htmltools::tagList(DT::renderDataTable(CvPTT, server = TRUE,
class = "compact",
filter="top",
rownames = FALSE,
colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
extensions = c('Buttons'),
options = list(
pageLength = 15,
dom = 'Bfrtip',
buttons = c('excel','csv','pdf','copy')
))))
## @knitr Contrast4
# Extract differential expression results
CvHIT <- results(deseq2Data, contrast=c("Virus", "Heat Inactivated Toca", "Control Toca"))
# Coerce to a data frame
CvHIT <- as.data.frame(CvHIT)
CvHIT <- rownames_to_column(CvHIT, var = "gene")
CvHIT$significant <- ifelse(CvHIT$padj < .05, "Significant", NA)
CvHIT<-na.omit(CvHIT)
## @knitr Print_Tables4
# Print the datatable with all markers
print( htmltools::tagList(DT::renderDataTable(CvHIT, server = TRUE,
class = "compact",
filter="top",
rownames = FALSE,
colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
extensions = c('Buttons'),
options = list(
pageLength = 15,
dom = 'Bfrtip',
buttons = c('excel','csv','pdf','copy')
))))
## @knitr Contrast5
# Extract differential expression results
CvMTT <- results(deseq2Data, contrast=c("Virus", "Mid treatment Toca", "Control Toca"))
# Coerce to a data frame
CvMTT <- as.data.frame(CvMTT)
CvMTT <- rownames_to_column(CvMTT, var = "gene")
CvMTT$significant <- ifelse(CvMTT$padj < .05, "Significant", NA)
CvMTT<-na.omit(CvMTT)
## @knitr Print_Tables5
# Print the datatable with all markers
print( htmltools::tagList(DT::renderDataTable(CvMTT, server = TRUE,
class = "compact",
filter="top",
rownames = FALSE,
colnames = c("gene", "baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "significant"),
extensions = c('Buttons'),
options = list(
pageLength = 15,
dom = 'Bfrtip',
buttons = c('excel','csv','pdf','copy')
))))
# Extract differential expression results
## @knitr DEGSeq2_Analysis
deseq2Results <- results(deseq2Data, contrast=c("Virus", "Post treatment MV", "Control MV"))
## @knitr DispersionPlot
plotDispEsts(deseq2Data, ylim = c(1e-6, 1e1) )
# View summary of results
#summary(deseq2Results)
# Using DEseq2 built in method
# plotMA(deseq2Results)
# Load libraries
# install.packages(c("ggplot2", "scales", "viridis"))
# Coerce to a data frame
deseq2ResDF <- as.data.frame(deseq2Results)
# Examine this data frame
#head(deseq2ResDF)
# Set a boolean column for significance
deseq2ResDF$significant <- ifelse(deseq2ResDF$padj < .05, "Significant", NA)
# write these differentially expressed genes as a table and save to directory
write.csv(deseq2ResDF, file= "deseq2_control_vs_MV_PT.csv")
# Plot the results similar to DEseq2
MAPlot1 <- ggplot(deseq2ResDF, aes(baseMean, log2FoldChange, colour=significant)) + geom_point(size=1) + scale_y_continuous(limits=c(-3, 3), oob=squish) + scale_x_log10() + geom_hline(yintercept = 0, colour="tomato1", size=2) + labs(x="mean of normalized counts", y="log fold change") + scale_colour_manual(name="q-value", values=("Significant"="red"), na.value="grey50") + theme_bw() + ggtitle("Control vs MV Treatment")
# Let's add some more detail
MAPlot2 <- ggplot(deseq2ResDF, aes(baseMean, log2FoldChange, colour=padj)) + geom_point(size=1) + scale_y_continuous(limits=c(-3, 3), oob=squish) + scale_x_log10() + geom_hline(yintercept = 0, colour="darkorchid4", size=1, linetype="longdash") + labs(x="mean of normalized counts", y="log fold change") + scale_colour_viridis(direction=-1, trans='sqrt') + theme_bw() + geom_density_2d(colour="black", size=2) + ggtitle("Control vs MV Treatment")
## @knitr MAPlots
MAPlot1 + MAPlot2
# Add rectangle around labels
deseq2ResDF2 <- rownames_to_column(deseq2ResDF, var = "gene")
# Add rectangle around labels
## @knitr MAPlots_Significant_Genes
ggmaplot(deseq2ResDF2, main = expression("Control MV" %->% "Post treatment MV"),
fdr = 0.05, fc = 1, size = 1.2,
palette = c("#B31B21", "#1465AC", "darkgray"),
genenames = as.vector(deseq2ResDF2$gene),
legend = "top", top = 20,
font.label = c("bold", 11), label.rectangle = TRUE,
font.legend = "bold",
font.main = "bold",
ggtheme = ggplot2::theme_minimal())
## @knitr VolcanoPlot
EnhancedVolcano(deseq2ResDF2,
lab = as.character(deseq2ResDF2$gene),
x = 'log2FoldChange',
y = 'padj',
xlim = c(-4, 4),
title = "Volcano Plot DEG (Control and Post treatment MV)",
pCutoff = 0.05,
FCcutoff = 0.5,
cutoffLineType = 'twodash',
cutoffLineWidth = 0.8,
pointSize = 2,
labSize = 4,
colAlpha = 1,
labCol = 'black',
labFace = 'bold',
boxedLabels = TRUE,
legendLabels=c('Not sig.','Log (base 2) FC','p-value',
'p-value & Log (base 2) FC'),
legendPosition = 'top',
legendLabSize = 12,
legendIconSize = 2,
drawConnectors = TRUE,
widthConnectors = 0.75)
# Extract counts for the gene otop2
otop2Counts <- plotCounts(deseq2Data, gene="Arg1", intgroup=c("Virus", "Sample"), returnData=TRUE)
# Plot the data using ggplot2
colourPallette <- c("#7145cd","#bbcfc4","#90de4a","#cd46c1","#77dd8e","#592b79","#d7c847","#6378c9","#619a3c","#d44473","#63cfb6","#dd5d36","#5db2ce","#8d3b28","#b1a4cb","#af8439","#c679c0","#4e703f","#753148","#cac88e","#352b48","#cd8d88","#463d25","#556f73")
## @knitr PlotCounts
ggplot(otop2Counts, aes(x=Sample, y=count, colour=Virus, group=Virus)) + geom_point() + geom_line() + theme_bw() + theme(axis.text.x=element_text(angle=15, hjust=1)) + scale_colour_manual(values=colourPallette) + guides(colour=guide_legend(ncol=3)) + ggtitle("Arginase 1 (M2 Macrophage Marker)")
deseq2ResDF["Arg1",]
rawCounts["Arg1",]
normals=row.names(sampleData[sampleData[,"Virus"]=="Control MV",])
primaries=row.names(sampleData[sampleData[,"Virus"]=="Post treatment MV",])
rawCounts["Arg1",normals]
rawCounts["Arg1",primaries]
## @knitr HeatmapTopGenes
# Transform count data using the variance stablilizing transform
deseq2VST <- vst(deseq2Data)
# Convert the DESeq transformed object to a data frame
deseq2VST <- assay(deseq2VST)
deseq2VST <- as.data.frame(deseq2VST)
deseq2VST$Gene <- rownames(deseq2VST)
#head(deseq2VST)
# Keep only the significantly differentiated genes where the fold-change was at least 3
sigGenes <- rownames(deseq2ResDF[deseq2ResDF$padj <= .05 & abs(deseq2ResDF$log2FoldChange) > 2,])
deseq2VST <- deseq2VST[deseq2VST$Gene %in% sigGenes,]
# Convert the VST counts to long format for ggplot2
# First compare wide vs long version
deseq2VST_wide <- deseq2VST
deseq2VST_long <- melt(deseq2VST, id.vars=c("Gene"))
#head(deseq2VST_wide)
#head(deseq2VST_long)
# Now overwrite our original data frame with the long format
deseq2VST <- melt(deseq2VST, id.vars=c("Gene"))
# Make a heatmap
heatmap <- ggplot(deseq2VST, aes(x=variable, y=Gene, fill=value)) + geom_raster() + scale_fill_viridis(trans="sqrt") + theme(axis.text.x=element_text(angle=65, hjust=1), axis.text.y=element_blank(), axis.ticks.y=element_blank())
# heatmap
# Convert the significant genes back to a matrix for clustering
deseq2VSTMatrix <- dcast(deseq2VST, Gene ~ variable)
rownames(deseq2VSTMatrix) <- deseq2VSTMatrix$Gene
deseq2VSTMatrix$Gene <- NULL
# Compute a distance calculation on both dimensions of the matrix
distanceGene <- dist(deseq2VSTMatrix)
distanceSample <- dist(t(deseq2VSTMatrix))
# Cluster based on the distance calculations
clusterGene <- hclust(distanceGene, method="ward.D2")
clusterSample <- hclust(distanceSample, method="ward.D2")
# Construct a dendogram for samples
# install.packages("ggdendro")
sampleModel <- as.dendrogram(clusterSample)
sampleDendrogramData <- segment(dendro_data(sampleModel, type = "rectangle"))
sampleDendrogram <- ggplot(sampleDendrogramData) + geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) + theme_dendro()
# Re-factor samples for ggplot2
deseq2VST$variable <- factor(deseq2VST$variable, levels=clusterSample$labels[clusterSample$order])
# Construct the heatmap. note that at this point we have only clustered the samples NOT the genes
heatmap <- ggplot(deseq2VST, aes(x=variable, y=Gene, fill=value)) + geom_raster() + scale_fill_viridis(trans="sqrt") + theme(axis.text.x=element_text(angle=65, hjust=1), axis.text.y=element_blank(), axis.ticks.y=element_blank())
#heatmap
# Combine the dendrogram and the heatmap
# install.packages("gridExtra")
#grid.arrange(sampleDendrogram, heatmap, ncol=1, heights=c(1,5))
# Load in libraries necessary for modifying plots
#install.packages("gtable")
# Modify the ggplot objects
sampleDendrogram_1 <- sampleDendrogram + scale_x_continuous(expand=c(.0085, .0085)) + scale_y_continuous(expand=c(0, 0))
heatmap_1 <- heatmap + scale_x_discrete(expand=c(0, 0)) + scale_y_discrete(expand=c(0, 0))
# Convert both grid based objects to grobs
sampleDendrogramGrob <- ggplotGrob(sampleDendrogram_1)
heatmapGrob <- ggplotGrob(heatmap_1)
# Check the widths of each grob
# sampleDendrogramGrob$widths
# heatmapGrob$widths
# Add in the missing columns
sampleDendrogramGrob <- gtable_add_cols(sampleDendrogramGrob, heatmapGrob$widths[7], 6)
sampleDendrogramGrob <- gtable_add_cols(sampleDendrogramGrob, heatmapGrob$widths[8], 7)
# Make sure every width between the two grobs is the same
maxWidth <- unit.pmax(sampleDendrogramGrob$widths, heatmapGrob$widths)
sampleDendrogramGrob$widths <- as.list(maxWidth)
heatmapGrob$widths <- as.list(maxWidth)
# Arrange the grobs into a plot
finalGrob <- arrangeGrob(sampleDendrogramGrob, heatmapGrob, ncol=1, heights=c(2,5))
# Draw the plot
#grid.draw(finalGrob)
# Re-order the sample data to match the clustering we did
sampleData_v2$Sample_ID <- factor(sampleData_v2$Sample_ID, levels=clusterSample$labels[clusterSample$order])
# Construct a plot to show the clinical data
colours <- c("#743B8B", "#8B743B", "#8B3B52", "#00AFBB", "#E7B800", "#FC4E07", "green", "red", "navy")
sampleClinical <- ggplot(sampleData_v2, aes(x=Sample_ID, y=1, fill=Viral_Status)) + geom_tile() + scale_x_discrete(expand=c(0, 0)) + scale_y_discrete(expand=c(0, 0)) + scale_fill_manual(name="Tissue", values=colours) + theme_void()
# Convert the clinical plot to a grob
sampleClinicalGrob <- ggplotGrob(sampleClinical)
# Make sure every width between all grobs is the same
maxWidth <- unit.pmax(sampleDendrogramGrob$widths, heatmapGrob$widths, sampleClinicalGrob$widths)
sampleDendrogramGrob$widths <- as.list(maxWidth)
heatmapGrob$widths <- as.list(maxWidth)
sampleClinicalGrob$widths <- as.list(maxWidth)
# Arrange and output the final plot
finalGrob <- arrangeGrob(sampleDendrogramGrob, sampleClinicalGrob, heatmapGrob, ncol=1, heights=c(2,1,5))
#grid.draw(finalGrob)
###############################################################################
################# Step 1: create dendrogram for genes ##########################
# we already did the clustering for genes in the tutorial, get the data to make a dendrogram with ggplot
geneModel <- as.dendrogram(clusterGene)
geneDendrogramData <- segment(dendro_data(geneModel, type = "rectangle"))
# construct the dendrogram in ggplot
geneDendrogram <- ggplot(geneDendrogramData) + geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) + coord_flip() + scale_y_reverse(expand=c(0, 0)) + scale_x_continuous(expand=c(0, 0)) + theme_dendro()
################################################################################
################# Step 2: Re-arrange the heatmap cells #########################
# re-factor genes for ggplot2
deseq2VST$Gene <- factor(deseq2VST$Gene, levels=clusterGene$labels[clusterGene$order])
# recreate the heatmap with this new factoring
heatmap <- ggplot(deseq2VST, aes(x=variable, y=Gene, fill=value)) + geom_raster() + scale_fill_viridis(trans="sqrt") + theme(axis.text.x=element_text(angle=65, hjust=1), axis.text.y=element_blank(), axis.ticks.y=element_blank())
################################################################################
################# Step 3: convert to everything to grobs #######################
# note! before this step as mentioned you might need to alter the expand parameters in the plot scales for all the plots we do that here
# convert the heatmap to a grob
heatmapGrob <- ggplotGrob(heatmap + scale_x_discrete(expand=c(0, 0)) + scale_y_discrete(expand=c(0, 0)))
# convert the dendrogram to a grob
# note! we flipped the axis above so the x-axis is now displayed as what we would think of as the y-axis
geneDendrogramGrob <- ggplotGrob(geneDendrogram + scale_x_discrete(expand=c(0, 0)))
# we already have a sample Dendrogram, but here it is again
sampleDendrogramGrob <- ggplotGrob(sampleDendrogram + scale_x_continuous(expand=c(.0085, .0085)) + scale_y_continuous(expand=c(0, 0)))
# we already have our sample clinical plot but here it is again
sampleClinicalGrob <- sampleClinicalGrob
################################################################################
######### Step 4: align the gene dendrograms to match the heatmap ##############
# check that the both the heatmap and gene dendrogram have the same number of vertical elements
#length(heatmapGrob$heights) == length(geneDendrogramGrob$heights)
# make sure every height between the two grobs is the same
maxHeight <- unit.pmax(geneDendrogramGrob$heights, heatmapGrob$heights)
geneDendrogramGrob$heights <- as.list(maxHeight)
heatmapGrob$heights <- as.list(maxHeight)
################################################################################
# Step 4b: we have a new heatmap so we need to re-align the horizontal elements #
# repeat the steps in the tutorial
# check the widths of each grob
# sampleDendrogramGrob$widths
# heatmapGrob$widths
# sampleClinicalGrob$widths
# add in the missing columns
sampleDendrogramGrob <- gtable_add_cols(sampleDendrogramGrob, heatmapGrob$widths[7], 6)
sampleDendrogramGrob <- gtable_add_cols(sampleDendrogramGrob, heatmapGrob$widths[8], 7)
# make sure every width between all grobs is the same
maxWidth <- unit.pmax(sampleDendrogramGrob$widths, heatmapGrob$widths, sampleClinicalGrob$widths)
sampleDendrogramGrob$widths <- as.list(maxWidth)
heatmapGrob$widths <- as.list(maxWidth)
sampleClinicalGrob$widths <- as.list(maxWidth)
################################################################################
############### Step 5: create a blank panel ###################################
# we can use grid graphics for this
blankPanel <- grid.rect(gp=gpar(col="white"))
################################################################################
############### Step 6: Arrange the final result ###############################
# arrange all the plots together
finalGrob_v2 <- arrangeGrob(blankPanel, sampleDendrogramGrob, blankPanel, sampleClinicalGrob, geneDendrogramGrob, heatmapGrob, ncol=2, nrow=3, widths=c(1,5), heights=c(2,.8,6))
# draw the final result
grid.draw(finalGrob_v2)
################################################################################
############### Pathway and Enrichment Analysis using DEG ###############################
## @knitr GO_Pathway_Analysis_MV
# we use ggplot2 to add x axis labels (ex: ridgeplot)
# SET THE DESIRED ORGANISM HERE
organism = "org.Mm.eg.db"
#BiocManager::install(organism, character.only = TRUE)
library(organism, character.only = TRUE)
# reading in data from deseq2
df = read.csv("deseq2_control_vs_MV_PT_Pathways.csv", header=TRUE)
# we want the log2 fold change
original_gene_list <- df$log2FoldChange
# name the vector
names(original_gene_list) <- df$gene
# omit any NA values
gene_list<-na.omit(original_gene_list)
# sort the list in decreasing order (required for clusterProfiler)
gene_list = sort(gene_list, decreasing = TRUE)
gse <- gseGO(
geneList=gene_list,
ont = "All",
OrgDb = organism,
keyType = "SYMBOL",
exponent = 1,
minGSSize = 10,
maxGSSize = 500,
eps = 1e-10,
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
verbose = TRUE,
seed = FALSE,
by = "fgsea")
dotplot(gse, showCategory=10, split=".sign") + facet_grid(.~.sign)
## @knitr EMapPlot_MV
x1 <- pairwise_termsim(gse)
emapplot(x1)
## @knitr cMapPlot_MV
# categorySize can be either 'pvalue' or 'geneNum'
cnetplot(x1, categorySize="pvalue", foldChange=gene_list, showCategory = 3)
## @knitr RidglePlot_MV
ridgeplot(gse) + labs(x = "enrichment distribution")
## @knitr GSEA_TopPathway_MV
# Use the `Gene Set` param for the index in the title, and as the value for geneSetId
#trace("gseaplot2", edit = TRUE)
gseaplot2(gse, geneSetID = 27, pvalue_table = TRUE, ES_geom = "line", title = gse$Description[27])
## @knitr Published_Articles_MV
terms <- gse$Description[1:3]
pmcplot(terms, 2010:2023, proportion=FALSE)
## @knitr KEGG_Pathways
# Convert gene IDs for gseKEGG function
# We will lose some genes here because not all IDs will be converted
ids<-bitr(names(original_gene_list), fromType = "SYMBOL", toType = "ENTREZID", OrgDb=organism)
# remove duplicate IDS (here I use "SYMBOL", but it should be whatever was selected as keyType)
dedup_ids = ids[!duplicated(ids[c("SYMBOL")]),]
# Create a new dataframe df2 which has only the genes which were successfully mapped using the bitr function above
df2 = df[df$gene %in% dedup_ids$SYMBOL,]
# Create a new column in df2 with the corresponding ENTREZ IDs
df2$Y = dedup_ids$ENTREZID
# Create a vector of the gene unuiverse
kegg_gene_list <- df2$log2FoldChange
# Name vector with ENTREZ ids
names(kegg_gene_list) <- df2$Y
# omit any NA values
kegg_gene_list<-na.omit(kegg_gene_list)
# sort the list in decreasing order (required for clusterProfiler)
kegg_gene_list = sort(kegg_gene_list, decreasing = TRUE)
kegg_organism = "mouse"
kk2 <- gseKEGG(geneList = kegg_gene_list,
organism = kegg_organism,
nPerm = 10000,
minGSSize = 3,
maxGSSize = 800,
pvalueCutoff = 0.05,
pAdjustMethod = "none",
keyType = "ncbi-geneid")
## @knitr KEGG_TopPathway_MV
dotplot(kk2, showCategory = 10, title = "Enriched Pathways" , split=".sign") + facet_grid(.~.sign)
## @knitr KEGG_Pathway_Enrichment
x2 <- pairwise_termsim(kk2)
emapplot(x2)