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lipidomics_analysis_pipeline.Rmd
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---
title: "Lipidomics Analysis"
author: "Andrés Gordo Ortiz (PSancho Lab)"
date: "`r format(Sys.Date(), '%B %d, %Y')`"
output:
html_document:
code_folding: hide
code_download: false
df_print: paged
theme: flatly
highlight: tango
toc: true
toc_float:
collapsed: false
smooth_scroll: true
number_sections: true
---
```{r logo, echo=FALSE}
htmltools::img(src = knitr::image_uri("logo.png"),
alt = 'logo',
style = 'position:absolute; top:0; right:0; width:150px; padding:10px;')
```
```{css, echo=FALSE}
/* Custom CSS to enhance styling */
body {
font-family: 'Gill Sans', sans-serif;
}
h1 {
color: #2c3e50;
font-family: 'Ubuntu', sans-serif; /* Set header font to Ubuntu */
}
h2 {
color: #3498db;
font-family: 'Ubuntu', sans-serif; /* Set header font to Ubuntu */
}
h3 {
color: #E888B4;
font-family: 'Ubuntu', sans-serif; /* Set header font to Ubuntu */
}
p {
text-align: justify;
}
```
```{r setup, include=FALSE}
library(ggplot2)
library(ggthemes)
library(plotly)
library(limma)
library(pheatmap)
library(tidyverse)
library(readxl)
library(lipidr)
library(DT)
library(limma)
library(cowplot)
library(svglite)
library(RColorBrewer)
library(gplots)
library(heatmaply)
library(ggvenn)
source('ggplot_theme_Publication.R')
dir.create("plots", showWarnings = FALSE, recursive = TRUE)
dir.create("data", showWarnings = FALSE, recursive = TRUE)
knitr::opts_chunk$set(echo=TRUE, warning=FALSE, message=FALSE)
```
# Data Tidying
```{r tidydata}
data_253 <- read_excel("Copia de in vitro_mzml-out_5_7_1000-1.xlsx",sheet = "253")
#Delete every column but 1,2 and 22:30
data_253 <- data_253[,c(1,22:30)]
#Delete every row with NAs
data_253 <- data_253[complete.cases(data_253),]
#Repeat everything but with sheet="215"
data_215 <- read_excel("Copia de in vitro_mzml-out_5_7_1000-1.xlsx",sheet = "215")
data_215 <- data_215[,c(1,22:30)]
data_215 <- data_215[complete.cases(data_215),]
#Reorder data_253 to match data_215[[1]]
data_253 <- data_253[match(data_215[[1]],data_253[[1]]),]
data_253[,1]<-data_215[,1]
#Create a new merged dataframe from the two
data_253_215 <- merge(data_253,data_215,by="NAME",all=TRUE)
#Prepare sample names data_253_215 column names
sample_names<-names(data_253_215[,-1])
#Simplify data samples to Sx
names(data_253_215)<-c("NAME",paste0("S",1:18))
#Make Clinical (phenotypic excel)
data_clin_invitro<-data.frame(sample=names(data_253_215[,-1]), cell_line=as.factor(rep(c("line_253","line_215"),each=9)),
treatment=rep(c("control", "eto", "mcm"), each=3, times=2),replica=as.factor(rep(c("rep_1","rep_2","rep_3"),times=6)),stringsAsFactors = FALSE)
#Eliminate parenthesis from data_253_215[,1]
data_matrix_invitro<-data_253_215
data_matrix_invitro[,1]<-gsub("\\(|\\)","",data_253_215[,1])
#Add a space between the last letter and first number of data_matrix[,1]
data_matrix_invitro[,1]<-gsub("(?<=[A-Za-z])(?=[0-9])"," ",data_matrix_invitro[,1],perl=TRUE)
#Delete all "-" in "-O" in data_matrix[,1]
data_matrix_invitro[,1]<-gsub("-O","O",data_matrix_invitro[,1])
#Change - symbol in some fatty acid names to make it standard
data_matrix_invitro[79,1]<-"DiHexCerd 18:1/14:13"
data_matrix_invitro[82,1]<-"DiHexCerd 18:1/16:15"
data_matrix_invitro[87,1]<-"DiHexCerd 18:1/22:21"
data_matrix_invitro[89,1]<-"DiHexCerd 18:1/24:23"
data_matrix_invitro[93,1]<-"DiHexCerd 20:1/12:11"
#Subset both cell lines
data_matrix_253<-data_matrix_invitro[, 1:10]
#Eliminate all rows with NAs in data_matrix_253
data_matrix_253<-data_matrix_253[complete.cases(data_matrix_253),]
data_matrix_215<-data_matrix_invitro[, c(1,11:19)]
#Eliminate all rows with NAs in data_matrix_253
data_matrix_215<-data_matrix_215[complete.cases(data_matrix_215),]
#Export data_clin and data_253_215 to .csv, without rownames
write.csv(data_clin_invitro,"data/data_clin_invitro.csv", row.names = FALSE)
write.csv(data_matrix_invitro,"data/data_matrix_invitro.csv", row.names = FALSE)
d_invitro <- as_lipidomics_experiment(read.csv("data_matrix_invitro.csv"))
d_invitro <- add_sample_annotation(d_invitro, "data_clin_invitro.csv")
```
# Quality Checking of Lipids
## Sample Quality
```{r qc_plot_samples}
#Quality check samples
qc_plot<-plot_samples(d_invitro, type = "boxplot", log = TRUE, color = "treatment")
qc_plot<-qc_plot +
theme_Publication() + # Change the theme to minimal
labs(title = "QC Boxplot: Distribution of Log2-Area per sample")
qc_plot
save_plot("plots/qc_plot_samples.png",qc_plot, base_width = 15, base_height = 7)
```
## Lipid Class Distribution
```{r qc_plot_lipid}
#Lipid class abundance
lipid_class_plot<-plot_lipidclass(d_invitro, "boxplot")
lipid_class_plot +
theme_Publication() +
labs(title = "Lipid Class Boxplot: Distribution of Log2-Area per Lipid subtype") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Angle the x-axis labels
save_plot("plots/qc_plot_lipids.png",lipid_class_plot +
theme_Publication() +
labs(title = "Lipid Class Boxplot: Distribution of Log2-Area per Lipid subtype") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)), base_width = 15, base_height = 7)
```
------------------------------------------------------------------------
# Principal Component Analysis
## All Samples (N253 & N215) {.tabset}
### By Cell Line
```{r pca_all_cellline}
exp_matrix<-assay(d_invitro)
exp_matrix_complete<-exp_matrix[complete.cases(exp_matrix),]
condition <- colData(d_invitro)$treatment
condition <- factor(condition)
sampleLabels<-rownames(colData(d_invitro))
cell_line<-colData(d_invitro)$cell_line
pca.res <- prcomp(t(exp_matrix_complete))
pc.var<-pca.res$sdev^2 # sdev^2 captures these eigenvalues from the PCA result
pc.per<-round(pc.var/sum(pc.var)*100, 1)
pca.res.df <- pca.res$x[,1:4] %>%
as_tibble() %>%
add_column(sample = sampleLabels,
group = condition,
cell_line= cell_line)
pca.plot_cell_line<- ggplot(pca.res.df) +
aes(x=PC1, y=PC2, label=sampleLabels, color = cell_line) +
geom_point(size=4) +
stat_ellipse() +
xlab(paste0("PC1 (",pc.per[1],"%",")")) +
ylab(paste0("PC2 (",pc.per[2],"%",")")) +
labs(title="PCA plot coloured by Cell Line",
caption=paste0("produced on ", Sys.time())) +
coord_fixed() +
theme_Publication()
pca.plot_cell_line
save_plot("plots/pca_all_samples_cell_line.png",pca.plot_cell_line, base_width = 15, base_height = 7)
```
### By Treatment
```{r pca_all_treatment}
pca.plot_treatment<- ggplot(pca.res.df) +
aes(x=PC1, y=PC2, label=sampleLabels, color = condition) +
geom_point(size=4) +
stat_ellipse() +
xlab(paste0("PC1 (",pc.per[1],"%",")")) +
ylab(paste0("PC2 (",pc.per[2],"%",")")) +
labs(title="PCA plot coloured by Treatment",
caption=paste0("produced on ", Sys.time())) +
coord_fixed() +
theme_Publication()
pca.plot_treatment
save_plot("plots/pca_all_samples_treatment.png",pca.plot_treatment, base_width = 15, base_height = 7)
```
### By Replicate
```{r pca_all_replicate}
replicate<-colData(d_invitro)$replica
pca.plot_replicate<- ggplot(pca.res.df) +
aes(x=PC1, y=PC2, label=sampleLabels, color = replicate) +
geom_point(size=4) +
stat_ellipse() +
xlab(paste0("PC1 (",pc.per[1],"%",")")) +
ylab(paste0("PC2 (",pc.per[2],"%",")")) +
labs(title="PCA plot coloured by Condition",
caption=paste0("produced on ", Sys.time())) +
coord_fixed() +
theme_Publication()
pca.plot_replicate
save_plot("plots/pca_all_samples_replicate.png",pca.plot_replicate, base_width = 15, base_height = 7)
```
### Small Multiples Plot
```{r smallmultiples_all}
pca.pivot <- pivot_longer(pca.res.df,
cols = PC1:PC4,
names_to = "PC",
values_to = "loadings")
small_multiples_plot <- ggplot(pca.pivot) +
aes(x = sample, y = loadings, fill = sample) +
geom_bar(stat = "identity") +
facet_wrap(~PC) +
labs(title = "PCA 'small multiples' plot",
caption = paste0("produced on ", Sys.time())) +
theme_bw() +
theme(legend.position = "none",
axis.title.y = element_blank()) +
coord_flip()
small_multiples_plot
save_plot("plots/small_multiples_plot_all_samples.png",small_multiples_plot, base_width = 15, base_height = 7)
```
------------------------------------------------------------------------
## N253 Cell Line only {.tabset}
### By Treatment
```{r pca_N253_treatment}
exp_matrix<-assay(d_invitro)
exp_matrix_complete<-exp_matrix[complete.cases(exp_matrix),]
exp_matrix_complete_N253<-exp_matrix_complete[,1:9]
condition <- colData(d_invitro)$treatment[1:9]
condition <- factor(condition)
sampleLabels<-rownames(colData(d_invitro))[1:9]
cell_line<-colData(d_invitro)$cell_line[1:9]
pca.res <- prcomp(t(exp_matrix_complete_N253))
pc.var<-pca.res$sdev^2 # sdev^2 captures these eigenvalues from the PCA result
pc.per<-round(pc.var/sum(pc.var)*100, 1)
pca.res.df <- pca.res$x[,1:4] %>%
as_tibble() %>%
add_column(sample = sampleLabels,
group = condition,
cell_line= cell_line)
pca.plot_treatment<- ggplot(pca.res.df) +
aes(x=PC1, y=PC2, label=sampleLabels, color = condition) +
geom_point(size=4) +
stat_ellipse() +
xlab(paste0("PC1 (",pc.per[1],"%",")")) +
ylab(paste0("PC2 (",pc.per[2],"%",")")) +
labs(title="PCA plot coloured by Condition in N253",
caption=paste0("produced on ", Sys.time())) +
coord_fixed() +
theme_Publication()
pca.plot_treatment
save_plot("plots/pca_N253_treatment.png",pca.plot_treatment, base_width = 15, base_height = 7)
```
### Small Multiples Plot
```{r smallmultiples_N253}
pca.pivot <- pivot_longer(pca.res.df,
cols = PC1:PC4,
names_to = "PC",
values_to = "loadings")
small_multiples_plot <- ggplot(pca.pivot) +
aes(x = sample, y = loadings, fill = sample) +
geom_bar(stat = "identity") +
facet_wrap(~PC) +
labs(title = "PCA 'small multiples' plot in N253",
caption = paste0("produced on ", Sys.time())) +
theme_bw() +
theme(legend.position = "none",
axis.title.y = element_blank()) +
coord_flip()
small_multiples_plot
save_plot("plots/small_multiples_plot_N253.png",small_multiples_plot, base_width = 15, base_height = 7)
```
## N215 Cell Line only {.tabset}
### By Treatment
```{r pca_N215_treatment}
exp_matrix<-assay(d_invitro)
exp_matrix_complete<-exp_matrix[complete.cases(exp_matrix),]
exp_matrix_complete_N215<-exp_matrix_complete[,10:18]
condition <- colData(d_invitro)$treatment[10:18]
condition <- factor(condition)
sampleLabels<-rownames(colData(d_invitro))[10:18]
cell_line<-colData(d_invitro)$cell_line[10:18]
pca.res <- prcomp(t(exp_matrix_complete_N215))
pc.var<-pca.res$sdev^2 # sdev^2 captures these eigenvalues from the PCA result
pc.per<-round(pc.var/sum(pc.var)*100, 1)
pca.res.df <- pca.res$x[,1:4] %>%
as_tibble() %>%
add_column(sample = sampleLabels,
group = condition,
cell_line= cell_line)
pca.plot_treatment<- ggplot(pca.res.df) +
aes(x=PC1, y=PC2, label=sampleLabels, color = condition) +
geom_point(size=4) +
stat_ellipse() +
xlab(paste0("PC1 (",pc.per[1],"%",")")) +
ylab(paste0("PC2 (",pc.per[2],"%",")")) +
labs(title="PCA plot coloured by Condition in N215",
caption=paste0("produced on ", Sys.time())) +
coord_fixed() +
theme_Publication()
pca.plot_treatment
save_plot("plots/pca_N215_treatment.png",pca.plot_treatment, base_width = 15, base_height = 7)
```
### Small Multiples Plot
```{r smallmultiples_N215}
pca.pivot <- pivot_longer(pca.res.df,
cols = PC1:PC4,
names_to = "PC",
values_to = "loadings")
small_multiples_plot <- ggplot(pca.pivot) +
aes(x = sample, y = loadings, fill = sample) +
geom_bar(stat = "identity") +
facet_wrap(~PC) +
labs(title = "PCA 'small multiples' plot in N215",
caption = paste0("produced on ", Sys.time())) +
theme_bw() +
theme(legend.position = "none",
axis.title.y = element_blank()) +
coord_flip()
small_multiples_plot
save_plot("plots/small_multiples_plot_N215.png",small_multiples_plot, base_width = 15, base_height = 7)
```
------------------------------------------------------------------------
## Top Lipids by Loading {.tabset}
### Eto vs Control
```{r top loadings eto vs control lipidr}
mvaresults = mva(d_invitro[,data_clin_invitro$treatment %in% c("control", "eto")], method = "OPLS-DA", group_col = "treatment")
top_loadings_plots<-plot_mva_loadings(mvaresults, color_by="Class", top.n=15)
top_loadings_plots +
theme_Publication() +
labs(title = "Loadings/Influence of each Lipid in OPLS-DA Analysis", subtitle = "Eto vs Control") +
guides(alpha = "none")
save_plot("plots/top_loadings_eto_vs_control.png",top_loadings_plots +
theme_Publication() +
labs(title = "Loadings/Influence of each Lipid in OPLS-DA Analysis", subtitle = "Eto vs Control") +
guides(alpha = "none"), base_width = 10, base_height = 7)
top<-top_lipids(mvaresults, top.n=100)
top_molecules<-top$Molecule
write.csv(top,"data/top_loadings_opls_eto_vs_control.csv", row.names = FALSE)
```
```{r top lipids eto vs control}
datatable(top[, c(2,7,8)],
extensions = c('KeyTable', "FixedHeader"),
caption = "Top 100 Lipids by Loading Relevance Eto vs Control",
options = list(keys = TRUE, searchHighlight = TRUE, pageLength = 10, lengthMenu = c("10", "25", "50", "100")))
```
### MCM vs Control
```{r top loadings mcm vs control lipidr}
mvaresults = mva(d_invitro[,data_clin_invitro$treatment %in% c("control", "mcm")], method = "OPLS-DA", group_col = "treatment")
top_loadings_plots<-plot_mva_loadings(mvaresults, color_by="Class", top.n=15)
top_loadings_plots +
theme_Publication() +
labs(title = "Loadings/Influence of each Lipid in OPLS-DA Analysis", subtitle = "MCM vs Control") +
guides(alpha = "none")
save_plot("plots/top_loadings_mcm_vs_control.png",top_loadings_plots +
theme_Publication() +
labs(title = "Loadings/Influence of each Lipid in OPLS-DA Analysis", subtitle = "MCM vs Control") +
guides(alpha = "none"), base_width = 10, base_height = 7)
top<-top_lipids(mvaresults, top.n=100)
top_molecules<-top$Molecule
write.csv(top,"data/top_loadings_opls_mcm_vs_control.csv", row.names = FALSE)
```
```{r top lipids mcm vs control}
datatable(top[, c(2,7,8)],
extensions = c('KeyTable', "FixedHeader"),
caption = "Top 100 Lipids by Loading Relevance MCM vs Control",
options = list(keys = TRUE, searchHighlight = TRUE, pageLength = 10, lengthMenu = c("10", "25", "50", "100")))
```
------------------------------------------------------------------------
# Differential Abundance of Lipids
## N215
### MCM vs Control
```{r limma and Vplot mcm vs control N215}
condition <- colData(d_invitro)$treatment[10:18]
condition <- factor(condition)
sampleLabels<-rownames(colData(d_invitro))[10:18]
design <- model.matrix(~0 + condition)
colnames(design) <- levels(condition)
lipid_matrix_norm <- voom(exp_matrix_complete_N215, design, plot = FALSE)
fit <- lmFit(lipid_matrix_norm, design)
contrast.matrix_control_vs_mcm <- makeContrasts(treatment = mcm - control,
levels=design)
fits <- contrasts.fit(fit, contrast.matrix_control_vs_mcm)
ebFit <- eBayes(fits)
myTopHits <- topTable(ebFit, adjust ="BH", coef=1, number=40000, sort.by="logFC")
myTopHits.df <- myTopHits %>%
as_tibble(rownames = "Lipid_Name")
write.csv(myTopHits.df, "data/stats_mcm_vs_control_N215.csv", row.names = FALSE)
vplot <- ggplot(myTopHits.df) +
aes(y=-log10(adj.P.Val), x=logFC, text = paste("Symbol:", Lipid_Name)) +
geom_point(size=2) +
geom_hline(yintercept = -log10(0.05), linetype="longdash", colour="grey", size=1) +
geom_vline(xintercept = 1, linetype="longdash", colour="#BE684D", size=1) +
geom_vline(xintercept = -1, linetype="longdash", colour="#2C467A", size=1) +
#annotate("rect", xmin = 1, xmax = 12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#BE684D") +
#annotate("rect", xmin = -1, xmax = -12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#2C467A") +
labs(title="Volcano plot [MCM vs Control] N215",
caption=paste0("produced on ", Sys.time())) +
scale_x_continuous(n.breaks = 8)+
theme_Publication()
save_plot("plots/volcano_mcm_vs_control_N215.svg",vplot)
save_plot("plots/volcano_mcm_vs_control_N215.png",vplot)
# Only in html ggplotly(vplot)
ggplotly(vplot)
```
#### Table of DE Lipids
To identify differentially expressed lipids, precision weights were first applied to each lipid based on its mean-variance relationship using [VOOM](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29). Then, linear modeling and bayesian stats were employed via [Limma](https://academic.oup.com/nar/article/43/7/e47/2414268) to find lipids that were over- or under-represented by **1-logfold or more, with a false-discovery rate (FDR) of 0.05**. Multiple testing was assessed through the Benjamini-Hochberg method.
```{r DEL mcm vs control N215}
results <- decideTests(ebFit, method="global", adjust.method="BH", p.value=0.05, lfc=1)
colnames(lipid_matrix_norm$E) <- sampleLabels
diffLipids_mcm_n215 <- lipid_matrix_norm$E[results[,1] !=0,]
diffLipids.df_mcm_n215 <- as_tibble(diffLipids_mcm_n215, rownames = "Lipid_Names")
resultsDEL<-myTopHits.df[myTopHits.df$Lipid_Name %in% diffLipids.df_mcm_n215$Lipid_Names,]
write.csv(resultsDEL, "data/diffLipids_mcm_vs_control_N215.csv", row.names = FALSE)
res_N215_mcm<-nrow(resultsDEL)
DEL_N215_mcm<-resultsDEL$Lipid_Name
```
```{r table DEL mcm vs control N215}
datatable(resultsDEL,
extensions = c('KeyTable', "FixedHeader"),
caption="Diferentially Expressed Lipids in MCM vs Control in N215",
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")))
```
### Eto vs Control
```{r limma and Vplot etoc vs control N215}
contrast.matrix_control_vs_eto <- makeContrasts(treatment = eto - control,
levels=design)
fits <- contrasts.fit(fit, contrast.matrix_control_vs_eto)
ebFit <- eBayes(fits)
myTopHits <- topTable(ebFit, adjust ="BH", coef=1, number=40000, sort.by="logFC")
myTopHits.df <- myTopHits %>%
as_tibble(rownames = "Lipid_Name")
write.csv(myTopHits.df, "data/stats_eto_vs_control_N215.csv", row.names = FALSE)
vplot <- ggplot(myTopHits.df) +
aes(y=-log10(adj.P.Val), x=logFC, text = paste("Symbol:", Lipid_Name)) +
geom_point(size=2) +
geom_hline(yintercept = -log10(0.05), linetype="longdash", colour="grey", size=1) +
geom_vline(xintercept = 1, linetype="longdash", colour="#BE684D", size=1) +
geom_vline(xintercept = -1, linetype="longdash", colour="#2C467A", size=1) +
#annotate("rect", xmin = 1, xmax = 12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#BE684D") +
#annotate("rect", xmin = -1, xmax = -12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#2C467A") +
labs(title="Volcano plot [Eto vs Control] N215",
caption=paste0("produced on ", Sys.time())) +
scale_x_continuous(n.breaks = 8)+
theme_Publication()
save_plot("plots/volcano_eto_vs_control_N215.svg",vplot)
save_plot("plots/volcano_eto_vs_control_N215.png",vplot)
# Only in html ggplotly(vplot)
ggplotly(vplot)
```
#### Table of DE Lipids
To identify differentially expressed lipids, precision weights were first applied to each lipid based on its mean-variance relationship using [VOOM](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29). Then, linear modeling and bayesian stats were employed via [Limma](https://academic.oup.com/nar/article/43/7/e47/2414268) to find lipids that were over- or under-represented by **1-logfold or more, with a false-discovery rate (FDR) of 0.05**. Multiple testing was assessed through the *Benjamini-Hochberg* method.
```{r DEL eto vs control N215}
results <- decideTests(ebFit, method="global", adjust.method="BH", p.value=0.05, lfc=1)
colnames(lipid_matrix_norm$E) <- sampleLabels
diffLipids_eto_n215 <- lipid_matrix_norm$E[results[,1] !=0,]
diffLipids.df_eto_n215 <- as_tibble(diffLipids_eto_n215, rownames = "Lipid_Names")
resultsDEL<-myTopHits.df[myTopHits.df$Lipid_Name %in% diffLipids.df_eto_n215$Lipid_Names,]
write.csv(resultsDEL, "data/diffLipids_eto_vs_control_N215.csv", row.names = FALSE)
res_N215_eto<-nrow(resultsDEL)
DEL_N215_eto<-resultsDEL$Lipid_Name
```
```{r table DEL eto vs control N215}
datatable(resultsDEL,
extensions = c('KeyTable', "FixedHeader"),
caption="Differentially Expressed Lipids in Eto vs Control N215",
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")))
```
## N253
### MCM vs Control
```{r limma and Vplot mcm vs control N253}
condition <- colData(d_invitro)$treatment[1:9]
condition <- factor(condition)
sampleLabels<-rownames(colData(d_invitro))[1:9]
design <- model.matrix(~0 + condition)
colnames(design) <- levels(condition)
lipid_matrix_norm <- voom(exp_matrix_complete_N253, design, plot = FALSE)
fit <- lmFit(lipid_matrix_norm, design)
contrast.matrix_control_vs_mcm <- makeContrasts(treatment = mcm - control,
levels=design)
fits <- contrasts.fit(fit, contrast.matrix_control_vs_mcm)
ebFit <- eBayes(fits)
myTopHits <- topTable(ebFit, adjust ="BH", coef=1, number=40000, sort.by="logFC")
myTopHits.df <- myTopHits %>%
as_tibble(rownames = "Lipid_Name")
write.csv(myTopHits.df, "data/stats_mcm_vs_control_N253.csv", row.names = FALSE)
vplot <- ggplot(myTopHits.df) +
aes(y=-log10(adj.P.Val), x=logFC, text = paste("Symbol:", Lipid_Name)) +
geom_point(size=2) +
geom_hline(yintercept = -log10(0.05), linetype="longdash", colour="grey", size=1) +
geom_vline(xintercept = 1, linetype="longdash", colour="#BE684D", size=1) +
geom_vline(xintercept = -1, linetype="longdash", colour="#2C467A", size=1) +
#annotate("rect", xmin = 1, xmax = 12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#BE684D") +
#annotate("rect", xmin = -1, xmax = -12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#2C467A") +
labs(title="Volcano plot [MCM vs Control] N253",
caption=paste0("produced on ", Sys.time())) +
scale_x_continuous(n.breaks = 8)+
theme_Publication()
save_plot("plots/volcano_mcm_vs_control_N253.svg",vplot)
save_plot("plots/volcano_mcm_vs_control_N253.png",vplot)
# Only in html ggplotly(vplot)
ggplotly(vplot)
```
#### Table of DE Lipids
To identify differentially expressed lipids, precision weights were first applied to each lipid based on its mean-variance relationship using [VOOM](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29). Then, linear modeling and bayesian stats were employed via [Limma](https://academic.oup.com/nar/article/43/7/e47/2414268) to find lipids that were over- or under-represented by **1-logfold or more, with a false-discovery rate (FDR) of 0.05**. Multiple testing was assessed through the Benjamini-Hochberg method.
```{r DEL mcm vs control N253}
results <- decideTests(ebFit, method="global", adjust.method="BH", p.value=0.05, lfc=1)
colnames(lipid_matrix_norm$E) <- sampleLabels
diffLipids_mcm_n253 <- lipid_matrix_norm$E[results[,1] !=0,]
diffLipids.df_mcm_n253 <- as_tibble(diffLipids_mcm_n253, rownames = "Lipid_Names")
resultsDEL<-myTopHits.df[myTopHits.df$Lipid_Name %in% diffLipids.df_mcm_n253$Lipid_Names,]
write.csv(resultsDEL, "data/diffLipids_mcm_vs_control_N253.csv", row.names = FALSE)
res_N253_mcm<-nrow(resultsDEL)
DEL_N253_mcm<-resultsDEL$Lipid_Name
```
```{r table DEL mcm vs control N253}
datatable(resultsDEL,
extensions = c('KeyTable', "FixedHeader"),
caption="Diferentially Expressed Lipids in MCM vs Control in N253",
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")))
```
### Eto vs Control
```{r limma and Vplot etoc vs control N253}
contrast.matrix_control_vs_eto <- makeContrasts(treatment = eto - control,
levels=design)
fits <- contrasts.fit(fit, contrast.matrix_control_vs_eto)
ebFit <- eBayes(fits)
myTopHits <- topTable(ebFit, adjust ="BH", coef=1, number=40000, sort.by="logFC")
myTopHits.df <- myTopHits %>%
as_tibble(rownames = "Lipid_Name")
write.csv(myTopHits.df, "data/stats_eto_vs_control_N253.csv", row.names = FALSE)
vplot <- ggplot(myTopHits.df) +
aes(y=-log10(adj.P.Val), x=logFC, text = paste("Symbol:", Lipid_Name)) +
geom_point(size=2) +
geom_hline(yintercept = -log10(0.05), linetype="longdash", colour="grey", size=1) +
geom_vline(xintercept = 1, linetype="longdash", colour="#BE684D", size=1) +
geom_vline(xintercept = -1, linetype="longdash", colour="#2C467A", size=1) +
#annotate("rect", xmin = 1, xmax = 12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#BE684D") +
#annotate("rect", xmin = -1, xmax = -12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#2C467A") +
labs(title="Volcano plot [Eto vs Control] N253",
caption=paste0("produced on ", Sys.time())) +
scale_x_continuous(n.breaks = 8)+
theme_Publication()
save_plot("plots/volcano_eto_vs_control_N253.svg",vplot)
save_plot("plots/volcano_eto_vs_control_N253.png",vplot)
# Only in html ggplotly(vplot)
ggplotly(vplot)
```
#### Table of DE Lipids
To identify differentially expressed lipids, precision weights were first applied to each lipid based on its mean-variance relationship using [VOOM](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29). Then, linear modeling and bayesian stats were employed via [Limma](https://academic.oup.com/nar/article/43/7/e47/2414268) to find lipids that were over- or under-represented by **1-logfold or more, with a false-discovery rate (FDR) of 0.05**. Multiple testing was assessed through the *Benjamini-Hochberg* method.
```{r DEL eto vs control N253}
results <- decideTests(ebFit, method="global", adjust.method="BH", p.value=0.05, lfc=1)
colnames(lipid_matrix_norm$E) <- sampleLabels
diffLipids_eto_n253 <- lipid_matrix_norm$E[results[,1] !=0,]
diffLipids.df_eto_n253 <- as_tibble(diffLipids_eto_n253, rownames = "Lipid_Names")
resultsDEL<-myTopHits.df[myTopHits.df$Lipid_Name %in% diffLipids.df_eto_n253$Lipid_Names,]
write.csv(resultsDEL, "data/diffLipids_eto_vs_control_N253.csv", row.names = FALSE)
res_N253_eto<-nrow(resultsDEL)
DEL_N253_eto<-resultsDEL$Lipid_Name
```
```{r table DEL eto vs control N253}
datatable(resultsDEL,
extensions = c('KeyTable', "FixedHeader"),
caption="Differentially Expressed Lipids in Eto vs Control N253",
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")))
```
## All Cell lines
### MCM vs Control
```{r limma and Vplot mcm vs control all}
condition <- colData(d_invitro)$treatment
condition <- factor(condition)
sampleLabels<-rownames(colData(d_invitro))
design <- model.matrix(~0 + condition)
colnames(design) <- levels(condition)
lipid_matrix_norm <- voom(exp_matrix_complete, design, plot = FALSE)
fit <- lmFit(lipid_matrix_norm, design)
contrast.matrix_control_vs_mcm <- makeContrasts(treatment = mcm - control,
levels=design)
fits <- contrasts.fit(fit, contrast.matrix_control_vs_mcm)
ebFit <- eBayes(fits)
myTopHits <- topTable(ebFit, adjust ="BH", coef=1, number=40000, sort.by="logFC")
myTopHits.df <- myTopHits %>%
as_tibble(rownames = "Lipid_Name")
write.csv(myTopHits.df, "data/stats_mcm_vs_control_all_lines.csv", row.names = FALSE)
vplot <- ggplot(myTopHits.df) +
aes(y=-log10(adj.P.Val), x=logFC, text = paste("Symbol:", Lipid_Name)) +
geom_point(size=2) +
geom_hline(yintercept = -log10(0.05), linetype="longdash", colour="grey", size=1) +
geom_vline(xintercept = 1, linetype="longdash", colour="#BE684D", size=1) +
geom_vline(xintercept = -1, linetype="longdash", colour="#2C467A", size=1) +
#annotate("rect", xmin = 1, xmax = 12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#BE684D") +
#annotate("rect", xmin = -1, xmax = -12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#2C467A") +
labs(title="Volcano plot [MCM vs Control] All Lines",
caption=paste0("produced on ", Sys.time())) +
scale_x_continuous(n.breaks = 8)+
theme_Publication()
save_plot("plots/volcano_mcm_vs_control_all.svg",vplot)
save_plot("plots/volcano_mcm_vs_control_all.png",vplot)
# Only in html ggplotly(vplot)
ggplotly(vplot)
```
#### Table of DE Lipids
To identify differentially expressed lipids, precision weights were first applied to each lipid based on its mean-variance relationship using [VOOM](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29). Then, linear modeling and bayesian stats were employed via [Limma](https://academic.oup.com/nar/article/43/7/e47/2414268) to find lipids that were over- or under-represented by **1-logfold or more, with a false-discovery rate (FDR) of 0.05**. Multiple testing was assessed through the Benjamini-Hochberg method.
```{r DEL mcm vs control all}
results <- decideTests(ebFit, method="global", adjust.method="BH", p.value=0.05, lfc=1)
colnames(lipid_matrix_norm$E) <- sampleLabels
diffLipids_mcm_all <- lipid_matrix_norm$E[results[,1] !=0,]
diffLipids.df_mcm_all <- as_tibble(diffLipids_mcm_all, rownames = "Lipid_Names")
resultsDEL<-myTopHits.df[myTopHits.df$Lipid_Name %in% diffLipids.df_mcm_all$Lipid_Names,]
write.csv(resultsDEL, "data/diffLipids_mcm_vs_control_all.csv", row.names = FALSE)
res_all_mcm<-nrow(resultsDEL)
DEL_all_mcm<-resultsDEL$Lipid_Name
```
```{r table DEL mcm vs control all}
datatable(resultsDEL,
extensions = c('KeyTable', "FixedHeader"),
caption="Diferentially Expressed Lipids in MCM vs Control in All Lines",
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")))
```
### Eto vs Control
```{r limma and Vplot etoc vs control all}
contrast.matrix_control_vs_eto <- makeContrasts(treatment = eto - control,
levels=design)
fits <- contrasts.fit(fit, contrast.matrix_control_vs_eto)
ebFit <- eBayes(fits)
myTopHits <- topTable(ebFit, adjust ="BH", coef=1, number=40000, sort.by="logFC")
myTopHits.df <- myTopHits %>%
as_tibble(rownames = "Lipid_Name")
write.csv(myTopHits.df, "data/stats_eto_vs_control_all.csv", row.names = FALSE)
vplot <- ggplot(myTopHits.df) +
aes(y=-log10(adj.P.Val), x=logFC, text = paste("Symbol:", Lipid_Name)) +
geom_point(size=2) +
geom_hline(yintercept = -log10(0.05), linetype="longdash", colour="grey", size=1) +
geom_vline(xintercept = 1, linetype="longdash", colour="#BE684D", size=1) +
geom_vline(xintercept = -1, linetype="longdash", colour="#2C467A", size=1) +
#annotate("rect", xmin = 1, xmax = 12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#BE684D") +
#annotate("rect", xmin = -1, xmax = -12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#2C467A") +
labs(title="Volcano plot [Eto vs Control] All Lines",
caption=paste0("produced on ", Sys.time())) +
scale_x_continuous(n.breaks = 8)+
theme_Publication()
save_plot("plots/volcano_eto_vs_control_all.svg",vplot)
save_plot("plots/volcano_eto_vs_control_all.png",vplot)
# Only in html ggplotly(vplot)
ggplotly(vplot)
```
#### Table of DE Lipids
To identify differentially expressed lipids, precision weights were first applied to each lipid based on its mean-variance relationship using [VOOM](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29). Then, linear modeling and bayesian stats were employed via [Limma](https://academic.oup.com/nar/article/43/7/e47/2414268) to find lipids that were over- or under-represented by **1-logfold or more, with a false-discovery rate (FDR) of 0.05**. Multiple testing was assessed through the *Benjamini-Hochberg* method.
```{r DEL eto vs control all}
results <- decideTests(ebFit, method="global", adjust.method="BH", p.value=0.05, lfc=1)
colnames(lipid_matrix_norm$E) <- sampleLabels
diffLipids_eto_all <- lipid_matrix_norm$E[results[,1] !=0,]
diffLipids.df_eto_all <- as_tibble(diffLipids_eto_all, rownames = "Lipid_Names")
resultsDEL<-myTopHits.df[myTopHits.df$Lipid_Name %in% diffLipids.df_eto_all$Lipid_Names,]
write.csv(resultsDEL, "data/diffLipids_eto_vs_control_all.csv", row.names = FALSE)
res_all_eto<-nrow(resultsDEL)
DEL_all_eto<-resultsDEL$Lipid_Name
```
```{r table DEL eto vs control all}
datatable(resultsDEL,
extensions = c('KeyTable', "FixedHeader"),
caption="Differentially Expressed Lipids in Eto vs Control All Lines",
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")))
```
## Statistical Power Effect
```{r stats plot }
stats_compare<-data.frame(del=c(res_all_eto, res_all_mcm, res_N253_eto, res_N253_mcm, res_N215_eto, res_N215_mcm), cell_line=rep(c("Both","N253", "N215"), each=2), comparison=rep(c("eto","mcm"),times=3))
# Plot a ggplot bar plot
ggplot(stats_compare, aes(x=cell_line, y=del, fill=comparison)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(title="Number of DE Lipids in Each Comparison",
y="Number of DE Lipids",
x="Subset Used",
caption=paste0("produced on ", Sys.time())) +
theme_Publication() +
scale_fill_manual(values=c("#2C467A", "#BE684D", "#F0E442")) +
theme(legend.position = "top")
```
## Venn Diagram {.tabset}
### Eto vs Control
```{r venn eto}
venn_eto <- ggvenn(data = list(N215 = DEL_N215_eto, N253 = DEL_N253_eto, Both=DEL_all_eto))
venn_eto
```
```{r venn eto tibble}
# Obtain all the DEL which are in common
common_eto<-Reduce(intersect, list(DEL_N215_eto, DEL_N253_eto, DEL_all_eto))
# Output a table with the list of comon_eto
common_eto.df<-as_tibble(common_eto)
write.csv(common_eto.df, "data/venn_intersection_DEL_eto.csv", row.names = FALSE)
datatable(common_eto.df,
extensions = c('KeyTable', "FixedHeader"),
caption="Common Differentially Expressed Lipids in Eto vs Control",
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")))
```
### MCM vs Control
```{r venn mcm}
venn_mcm <- ggvenn(data = list(N215 = DEL_N215_mcm, N253 = DEL_N253_mcm, Both=DEL_all_mcm))
venn_mcm
# Obtain all the DEL which are in common
common_mcm<-Reduce(intersect, list(DEL_N215_mcm, DEL_N253_mcm, DEL_all_mcm))
```
### Eto & MCM in N215
```{r venn eto mcm n215}
venn_eto_mcm_n215 <- ggvenn(data = list(N215_eto = DEL_N215_eto, N215_mcm = DEL_N215_mcm))
venn_eto_mcm_n215
```
```{r common eto mcm in N215}
common_eto_mcm_n215<-DEL_N215_eto[DEL_N215_eto %in% DEL_N215_mcm]
print(paste0("Common Lipids between MCM and Eto in N215 are ",paste(common_eto_mcm_n215, collapse=" ") ))
```
### Eto & MCM in N253
```{r venn eto mcm n253}
venn_eto_mcm_n253 <- ggvenn(data = list(N253_eto = DEL_N253_eto, N253_mcm = DEL_N253_mcm))
venn_eto_mcm_n253
```
```{r common eto mcm in N253}
common_eto_mcm_n253<-DEL_N253_eto[DEL_N253_eto %in% DEL_N253_mcm]
print(paste0("Common Lipids between MCM and Eto in N253 are ",paste(common_eto_mcm_n253, collapse=" ") ))
```
------------------------------------------------------------------------
# Heatmaps {.tabset}
Pearson correlation was used to cluster all differentially expressed lipids, which were then represented as heatmap with the data scaled by *Zscore* for each row. On the other hand, Spearman correlation was used to cluster all samples. Two *modules* can be seen, being either over- or under-represented depending on the condition of the samples.
## Eto vs Control All Cell lines
```{r heatmap DEL eto all, results='hide'}
myheatcolors <- rev(brewer.pal(name="RdBu", n=11))
clustRows <- hclust(as.dist(1-cor(t(diffLipids_eto_all), method="pearson")), method="complete")
clustColumns <- hclust(as.dist(1-cor(diffLipids_eto_all, method="spearman")), method="complete")
module.assign <- cutree(clustRows, k=2) #Important parametre
module.color <- rainbow(length(unique(module.assign)), start=0.1, end=0.9)
module.color <- module.color[as.vector(module.assign)]
module.assign_col<-cutree(clustColumns, k=2) #Important parametre
module.color_col <- rainbow(length(unique(module.assign_col)), start=0.1, end=0.9)
module.color_col <- module.color_col[as.vector(module.assign_col)]
svg(file = "plots/heatmap_DEL_eto_vs_control_alllines.svg")
heatmap.2(diffLipids_eto_all,
Rowv = as.dendrogram(clustRows),
Colv = as.dendrogram(clustColumns),
RowSideColors = module.color,
ColSideColors = module.color_col,
col = myheatcolors,
scale = "row",
labRow = NA,
density.info = "none",
trace = "none",
cexRow = 0.8, # Adjust row label size
cexCol = 0.8, # Adjust column label size
margins = c(10, 10), # Increase margin size
keysize = 1.2, # Adjust size of the color key
key.title = NA, # Remove color key title