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DEGSeq2_comparison_across_strata_human_bulk_RNA.R
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DEGSeq2_comparison_across_strata_human_bulk_RNA.R
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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)
# Parameters for plots
theme_set(
theme_bw() +
theme(
plot.title = element_text(lineheight=.8, face="bold", hjust = 0.5, size = 14)
)
)
## @knitr PrepareData
setwd("/media/owen/Backup Plus/UCSF_Project/02_PROCESSED_DATA/PNOC/ANALYSIS/09_Bulk_RNA/Sabien/Comparison_across_strata")
# Read in the raw read counts
rawCounts <- read.csv("Combined.all_strata.exprs_matrix.csv", row.names = 1)
# NormDF <- QuantNorm(rawCounts)
# NormDF <- as.data.frame(NormDF)
# NormDF <- rownames_to_column(NormDF, var = "Gene")
#head(rawCounts)
# Read in the sample mappings
sampleData <- read.csv("Combined.all_strata.meta_data.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$Gene
sampleIndex <- grepl("PNOC005.\\d+", colnames(rawCounts))
rawCounts <- as.matrix(rawCounts[,sampleIndex])
rownames(rawCounts) <- geneID
#head(rawCounts)
# Convert sample variable mappings to an appropriate form that DESeq2 can read
#head(sampleData)
rownames(sampleData) <- sampleData$Mixture
keep <- c("Strata", "Days", "Strata_timepoint")
sampleData <- sampleData[,keep]
colnames(sampleData) <- c("Strata", "Time", "Strata_Timepoint")
sampleData$Strata_Timepoint <- factor(sampleData$Strata_Timepoint)
#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
Strata_Timepoint <- function(x){
x <- switch(as.character(x), "A_D0" = "A_D0", "A_D14" = "A_D14", "A_D28" = "A_D28", "B_D1" = "B_D1", "B_D4" = "B_D4", "B_D7" = "B_D7",
"B_D14" ="B_D14", "B_D28" = "B_D28", "C_D28" = "C_D28", "C_D7" = "C_D7", "C_D0" = "C_D0", "C_D56" = "C_D56", "B_D0" = "B_D0")
return(x)
}
sampleData$Strata_Timepoint <- unlist(lapply(sampleData$Strata_Timepoint, Strata_Timepoint))
# Order the tissue types so that it is sensible and make sure the control sample is first: normal sample -> primary tumor -> metastatic tumor
sampleData$Strata <- factor(sampleData$Strata_Timepoint, levels=c("A_D0", "A_D14", "A_D28", "B_D1", "B_D4", "B_D7", "B_D14", "B_D28", "C_D28", "C_D7", "C_D0", "C_D56", "B_D0"))
# Create the DEseq2DataSet object
deseq2Data <- DESeqDataSetFromMatrix(countData=rawCounts, colData=sampleData, design= ~ Strata_Timepoint)
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)
# Extract differential expression results
B_C_D28 <- results(deseq2Data, contrast=c("Strata_Timepoint", "B_D28", "C_D28"))
# Coerce to a data frame
B_C_D28 <- as.data.frame(B_C_D28)
B_C_D28 <- rownames_to_column(B_C_D28, var = "gene")
B_C_D28$significant <- ifelse(B_C_D28$padj < .05, "Significant", NA)
B_C_D28<-na.omit(B_C_D28)
# write these differentially expressed genes as a table and save to directory
write.csv(B_C_D28, file= "DEG_strata_B_vs_C_D28.csv")
## @knitr Print_Tables
# Print the datatable with all markers
print( htmltools::tagList(DT::renderDataTable(B_C_D28, 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
D0_v_D14_SA <- results(deseq2Data, contrast=c("Strata", "D0", "D14"))
# Coerce to a data frame
B_C <- as.data.frame(B_C)
B_C <- rownames_to_column(B_C, var = "gene")
B_C$significant <- ifelse(B_C$padj < .05, "Significant", NA)
B_C<-na.omit(B_C)
## @knitr Print_Tables1
# Print the datatable with all markers
print( htmltools::tagList(DT::renderDataTable(B_C, 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
A_C <- results(deseq2Data, contrast=c("Strata", "Strata A", "Strata C"))
# Coerce to a data frame
A_C <- as.data.frame(A_C)
A_C <- rownames_to_column(A_C, var = "gene")
A_C$significant <- ifelse(A_C$padj < .05, "Significant", NA)
A_C<-na.omit(A_C)
write.csv(A_C, file="DEG_strata_A_vs_C.csv")
## @knitr Print_Tables2
# Print the datatable with all markers
print( htmltools::tagList(DT::renderDataTable(A_C, 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("Time", "D0", "D7"))
## @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)
# 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("Day 0" %->% "Day 7"),
fdr = 0.05, fc = 1, size = 1.2,
palette = c("#B31B21", "#1465AC", "darkgray"),
genenames = as.vector(deseq2ResDF2$gene),
legend = "top", top = 50,
font.label = c("bold", 11), label.rectangle = TRUE,
font.legend = "bold",
font.main = "bold",
ggtheme = ggplot2::theme_minimal())
## @knitr VolcanoPlot
EnhancedVolcano(deseq2ResDF2,
lab = as.character(deseq2ResDF2$gene),
x = 'log2FoldChange',
y = 'padj',
xlim = c(-4, 4),
title = "Volcano Plot DEG (Day 0 vs Day 7)",
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="IFI27", intgroup=c("Time", "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=Time, group=Time)) + 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("IFI27 (Interferon Alpha Inducible Protein 27)")
deseq2ResDF["IFI27",]
rawCounts["IFI27",]
normals=row.names(sampleData[sampleData[,"Time"]=="D0",])
primaries=row.names(sampleData[sampleData[,"Time"]=="D7",])
rawCounts["IFI27",normals]
rawCounts["IFI27",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$Mixture <- factor(sampleData_v2$Mixture, 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=Mixture, y=1, fill=Days)) + geom_tile() + scale_x_discrete(expand=c(0, 0)) + scale_y_discrete(expand=c(0, 0)) + scale_fill_manual(name="Time", 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$widths <- as.list(maxWidth)
################################################################################
############### Step 5: create a blank panel ###################################
# we can use grid graphics for this
blankPanel <- grid.rect(gp=gpar(col="white"))
################################################################################
############### Step 6: Arrange the final result ###############################
# arrange all the plots together
finalGrob_v2 <- arrangeGrob(blankPanel, sampleDendrogramGrob, blankPanel, sampleClinicalGrob, geneDendrogramGrob, heatmapGrob, ncol=2, nrow=3, widths=c(1,5), heights=c(2,.8,6))
# draw the final result
grid.draw(finalGrob_v2)
################################################################################
############### Pathway and Enrichment Analysis using DEG ###############################
## @knitr GO_Pathway_Analysis_MV
# we use ggplot2 to add x axis labels (ex: ridgeplot)
# SET THE DESIRED ORGANISM HERE
organism = "org.Hs.eg.db"
#BiocManager::install(organism, character.only = TRUE)
library(organism, character.only = TRUE)
# reading in data from deseq2
df = read.csv("deseq2_day0_vs_day7_pathways.csv", header=TRUE)
# we want the log2 fold change
original_gene_list <- df$log2FoldChange
# name the vector
names(original_gene_list) <- df$Gene
# omit any NA values
gene_list<-na.omit(original_gene_list)
# sort the list in decreasing order (required for clusterProfiler)
gene_list = sort(gene_list, decreasing = TRUE)
gse <- gseGO(
geneList=gene_list,
ont = "All",
OrgDb = organism,
keyType = "SYMBOL",
exponent = 1,
minGSSize = 10,
maxGSSize = 500,
eps = 1e-10,
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
verbose = TRUE,
seed = FALSE,
by = "fgsea")
dotplot(gse, showCategory = 10, title = "Enriched Pathways" , split=".sign") + facet_grid(.~.sign)
## @knitr EMapPlot_MV
x1 <- pairwise_termsim(gse)
emapplot(x1)
## @knitr cMapPlot_MV
# categorySize can be either 'pvalue' or 'geneNum'
cnetplot(x1, categorySize="pvalue", foldChange=gene_list, showCategory = 5)
## @knitr RidglePlot_MV
ridgeplot(gse) + labs(x = "enrichment distribution")
## @knitr GSEA_TopPathway_MV
# Use the `Gene Set` param for the index in the title, and as the value for geneSetId
gseaplot(gse, by = "all", title = gse$Description[1], geneSetID = 1)
## @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$Y
# omit any NA values
kegg_gene_list<-na.omit(kegg_gene_list)
# sort the list in decreasing order (required for clusterProfiler)
kegg_gene_list = sort(kegg_gene_list, decreasing = TRUE)
kegg_organism = "human"
kk2 <- gseKEGG(geneList = kegg_gene_list,
organism = kegg_organism,
nPerm = 10000,
minGSSize = 3,
maxGSSize = 800,
pvalueCutoff = 0.05,
pAdjustMethod = "none",
keyType = "ncbi-geneid")
## @knitr KEGG_TopPathway_MV
dotplot(kk2, showCategory = 10, title = "Enriched Pathways" , split=".sign") + facet_grid(.~.sign)
## @knitr KEGG_Pathway_Enrichment
x2 <- pairwise_termsim(kk2)
emapplot(x2)
## @knitr cnet_KEGG_Pathway_Enrichment
# categorySize can be either 'pvalue' or 'geneNum'
cnetplot(x2, categorySize="pvalue", foldChange=gene_list)
## @knitr RidgePlot_KEGG_Pathway_Enrichment
ridgeplot(kk2) + labs(x = "enrichment distribution")
## @knitr Top_KEGG_Pathway_Enrichment
# Use the `Gene Set` param for the index in the title, and as the value for geneSetId
gseaplot(kk2, by = "all", title = kk2$Description[1], geneSetID = 1)
## @knitr Measles_KEGG_Pathway
# Produce the native KEGG plot (PNG)
Measles <- pathview(gene.data=kegg_gene_list, pathway.id="hsa05162", species = kegg_organism)
# Produce a different plot (PDF) (not displayed here)
Measles <- pathview(gene.data=kegg_gene_list, pathway.id="hsa05162", species = kegg_organism, kegg.native = F)
#########################################################################################################################################################
########################### Now let's repeat the analysis using Toca Time ##############################################################################
# Extract differential expression results
# For "Time" perform control vs treated comparison
## @knitr DEGSeq2_Analysis_Toca2
deseq2Results <- results(deseq2Data, contrast=c("Time", "D56", "D0"))
# 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)
# 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 MAPlots2
MAPlot1 + MAPlot2
# Add rectangle around labels
deseq2ResDF2 <- rownames_to_column(deseq2ResDF, var = "gene")
# Add rectangle around labels
## @knitr MAPlots_Significant_Genes2
ggmaplot(deseq2ResDF2, main = expression("Day 0" %->% "Day 56"),
fdr = 0.05, fc = 1, size = 1.2,
palette = c("#B31B21", "#1465AC", "darkgray"),
genenames = as.vector(deseq2ResDF2$gene),
legend = "top", top = 20,
font.label = c("bold", 11), label.rectangle = TRUE,
font.legend = "bold",
font.main = "bold",
ggtheme = ggplot2::theme_minimal())
## @knitr VolcanoPlot2
EnhancedVolcano(deseq2ResDF2,
lab = as.character(deseq2ResDF2$gene),
x = 'log2FoldChange',
y = 'padj',
xlim = c(-4, 4),
title = "Volcano Plot DEG (Day 0 vs Day 56)",
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="HOXA9", intgroup=c("Time", "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 PlotCounts2
ggplot(otop2Counts, aes(x=Sample, y=count, colour=Time, group=Time)) + 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("HOXA9")
deseq2ResDF["HOXA9",]
rawCounts["HOXA9",]
normals=row.names(sampleData[sampleData[,"Time"]=="D0",])
primaries=row.names(sampleData[sampleData[,"Time"]=="D56",])
rawCounts["HOXA9",normals]
rawCounts["HOXA9",primaries]
## @knitr HeatmapTopGenes2
# 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$Mixture <- factor(sampleData_v2$Mixture, 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=Mixture, y=1, fill=Days)) + geom_tile() + scale_x_discrete(expand=c(0, 0)) + scale_y_discrete(expand=c(0, 0)) + scale_fill_manual(name="Time", 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$widths <- as.list(maxWidth)
################################################################################
############### Step 5: create a blank panel ###################################
# we can use grid graphics for this
blankPanel <- grid.rect(gp=gpar(col="white"))
################################################################################
############### Step 6: Arrange the final result ###############################
# arrange all the plots together
finalGrob_v2 <- arrangeGrob(blankPanel, sampleDendrogramGrob, blankPanel, sampleClinicalGrob, geneDendrogramGrob, heatmapGrob, ncol=2, nrow=3, widths=c(1,5), heights=c(2,.8,6))
# draw the final result
grid.draw(finalGrob_v2)
################################################################################
############### Pathway and Enrichment Analysis using DEG ###############################
## @knitr GO_Pathway_Analysis_MV2
# we use ggplot2 to add x axis labels (ex: ridgeplot)
# SET THE DESIRED ORGANISM HERE
organism = "org.Hs.eg.db"
#BiocManager::install(organism, character.only = TRUE)
library(organism, character.only = TRUE)
# reading in data from deseq2
df = read.csv("deseq2_D0_vs_D56_pathways.csv", header=TRUE)
# we want the log2 fold change
original_gene_list <- df$log2FoldChange
# name the vector
names(original_gene_list) <- df$Gene
# omit any NA values
gene_list<-na.omit(original_gene_list)
# sort the list in decreasing order (required for clusterProfiler)
gene_list = sort(gene_list, decreasing = TRUE)
gse <- gseGO(
geneList=gene_list,
ont = "All",
OrgDb = organism,
keyType = "SYMBOL",
exponent = 1,
minGSSize = 10,
maxGSSize = 500,
eps = 1e-10,
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
verbose = TRUE,
seed = FALSE,
by = "fgsea")
dotplot(gse, showCategory=10, split=".sign") + facet_grid(.~.sign)
## @knitr EMapPlot_MV2
x1 <- pairwise_termsim(gse)
emapplot(x1)
## @knitr cMapPlot_MV2
# categorySize can be either 'pvalue' or 'geneNum'
cnetplot(x1, categorySize="pvalue", foldChange=gene_list, showCategory = 3)
## @knitr RidglePlot_MV2
ridgeplot(gse) + labs(x = "enrichment distribution")
## @knitr GSEA_TopPathway_MV2
# Use the `Gene Set` param for the index in the title, and as the value for geneSetId
gseaplot(gse, by = "all", title = gse$Description[1], geneSetID = 1)
## @knitr Published_Articles_MV2
terms <- gse$Description[1:3]
pmcplot(terms, 2010:2023, proportion=FALSE)
## @knitr KEGG_Pathways2
# Convert gene IDs for gseKEGG function
# We will lose some genes here because not all IDs will be converted
ids<-bitr(names(original_gene_list), fromType = "SYMBOL", toType = "ENTREZID", OrgDb=organism)
# remove duplicate IDS (here I use "SYMBOL", but it should be whatever was selected as keyType)
dedup_ids = ids[!duplicated(ids[c("SYMBOL")]),]
# Create a new dataframe df2 which has only the genes which were successfully mapped using the bitr function above
df2 = df[df$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