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Analysis and visualization of LC-IM-MS data Nantes Oct 2019 for pitch.R
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Analysis and visualization of LC-IM-MS data Nantes Oct 2019 for pitch.R
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### Analysis and visualization of LC-IM-MS data, Nantes Oct 2019
# general information about the data --------------------------------------
# Intensities manually extracted from MassLynx (see power point presentation
# with results), binned by m/z, retention time and drift time
# peak at m/z 851.27, DT 6.5 and RT 25.4-25.9 has weird shape -> RT bin
# enlarged on 03. May 2021. In addition, DT estimate changed for no enzyme,
# 30 min: at RT 15.5 DT of 6.7 changed to 6.5, at RT 23.6 DT of 6.5 changed
# to 6.3
# packages ----------------------------------------------------------------
require(ggpubr)
require(dplyr)
require(ggplot2)
library(grid)
library(gridExtra)
library(cowplot)
library(tidyverse)
library(wesanderson)
library(reshape2)
fonts <- list(
sans = "Helvetica",
mono = "Consolas",
`Times New Roman` = "DejaVu Serif"
)
font_family<-"sans"
font_size<-8
font_size_titles<-10
'%!in%' <- function(x,y)!('%in%'(x,y))
# import data -------------------------------------------------------------
all<-read.table("C:/Users/admin/ownCloud/Deep-water kelp/Nantes2019/Results/LC-IM-MS_mod03May2021.txt",h=T,dec=",")
lam_stds<-all[all$Treatment=="Standard",]
lam_stds$RTandDT<-paste("RT=",lam_stds$RT_min," min DT=",lam_stds$DT_ms," ms",sep="")
wo_stds<-all[all$Treatment!="Standard",]
wo_stds$RTandDT<-paste("RT=",wo_stds$RT_min," min DT=",wo_stds$DT_ms," ms",sep="")
#filtering based on abundances in time series
wo_stds_filtered<-wo_stds%>%
filter(!RTandDT%in%c("RT=12.6 min DT=6 ms", "RT=24.2 min DT=6.6 ms", "RT=18.7 min DT=6.8 ms"))
wo_stds_filtered<-wo_stds_filtered[order(wo_stds_filtered$Intensity_IM,decreasing=T),]
wo_stds_filtered<-wo_stds_filtered[order(wo_stds_filtered$DP),]
palette_9extractions<-c("#74A089","#FDDDA0","#C93312","#0B775E", "#FAD510" ,"#F2300F", "#899DA4", "#FAEFD1", "#DC863B")
Fig_samples_filtered<-ggplot()+
geom_point(data=wo_stds_filtered,aes(x=Position,y=Intensity_IM,fill=RTandDT),pch=21, size=3)+
scale_y_log10()+
facet_wrap(facets=wo_stds_filtered$DP,nrow=1)+
scale_fill_manual(values=c(palette_9extractions[1:5],palette_9extractions[1:7],palette_9extractions[1:7]))+
guides(ncol=1)+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_text(colour ="#000000", size = font_size, family = font_family),
axis.title.y = element_text(colour="#000000", size = font_size, family = font_family),
axis.title.x = element_text(colour="#000000", size = font_size, family = font_family),
legend.title= element_text(colour="#000000",face="bold", size = font_size, family = font_family),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
# legend.position="none",
legend.box.background = element_rect(colour="black"),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank())
# check standards ---------------------------------------------------------
stds<-all[which(all$Treatment %in% c("Standard","none")),]
stds$RTandDT<-paste("RT=",stds$RT_min," min DT=",stds$DT_ms," ms",sep="")
ggplot(stds[stds$DP==5,])+
geom_bar(aes(x=RTandDT, y=Intensity_MZ, fill=Treatment),stat="identity",position=position_dodge2())
overSN<-stds[stds$Intensity_IM>=7000,]
ggplot(overSN[overSN$DP==3,])+
geom_bar(aes(x=RTandDT, y=Intensity_MZ, fill=Treatment),stat="identity",position=position_dodge2())
# compare std vs sample features ------------------------------------------
comparison<-all
comparison$RTandDT<-paste("RT=",comparison$RT_min," min DT=",comparison$DT_ms," ms",sep="")
comparison_end<-comparison[which(comparison)]
ggplot(comparison[comparison$DP==3,])+
geom_bar(aes(x=RTandDT, y=Intensity_MZ, fill=Treatment),stat="identity",position=position_dodge2())
# margi's approach --------------------------------------------------------
# highest features in digested samples
high_digests<-wo_stds %>%
group_by(Incubation_time_min, Treatment, DP) %>%
top_n(1, Intensity_MZ)
# highest features in standards
lam_stds %>%
group_by(DP) %>%
top_n(1, Intensity_MZ)
# the feature RT=14.2 min DT=4.1 ms m/z=527.16 represents the canonical product (DP3) [M+Na]
# the feature RT=17.8 min DT=5.3 ms m/z=689.22 represents the canonical product (DP4) [M+Na]
# the feature RT=25.7 min DT=6.5 ms m/z=851.28 represents the canonical product (DP5) [M+Na]
# remove non-increasing features ------------------------------------------
wo_stds<-wo_stds[wo_stds$RTandDT!="RT=12.6 min DT=6 ms",]
wo_stds<-wo_stds[wo_stds$RTandDT!="RT=15.5 min DT=6.5 ms",]
# check features statistically --------------------------------------------
wo_stds$Position<-NA
wo_stds$Position[wo_stds$Treatment=="none"]<- -3
wo_stds$Position[wo_stds$Treatment=="GH16" & wo_stds$Incubation_time_min==0]<-1
wo_stds$Position[wo_stds$Treatment=="GH16" & wo_stds$Incubation_time_min==10]<-10
wo_stds$Position[wo_stds$Treatment=="GH16" & wo_stds$Incubation_time_min==20]<-20
wo_stds$Position[wo_stds$Treatment=="GH16" & wo_stds$Incubation_time_min==30]<-30
# Linear model canonical product ------------------------------------------
canonical_prods<-wo_stds[which(wo_stds$RTandDT %in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms")),]
lm_canonical_DP3<-lm(log(Intensity_MZ)~Position, canonical_prods[canonical_prods$DP=="3",])
lm_canonical_DP4<-lm(log(Intensity_MZ)~Position, canonical_prods[canonical_prods$DP=="4",])
# natural log transform (for first order products)
wo_stds$DPlabel<-paste("DP", wo_stds$DP, sep="")
wo_stds$DPlabel<-factor(wo_stds$DPlabel, levels = c("DP5","DP4","DP3"))
#wo_stds$RTandDT<-factor(wo_stds$RTandDT, levels = c())
png(filename="C:/Users/admin/ownCloud/Deep-water kelp/Presentations/Thesis committee meeting 11May2021/Fig3a_sideprods_09May2021.png",
height = 10, width=18.9, units = "cm", res = 200)
ggplot()+
geom_point(data=wo_stds,aes(x=Position,y=log(Intensity_MZ),fill=RTandDT,), pch=21,size=3, alpha=0.5)+
scale_fill_discrete(name="Features", guide=guide_legend(reverse=T))+
# scale_y_continuous(trans = scales::log_trans())+
stat_cor(data=wo_stds[which(wo_stds$RTandDT %in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms") & wo_stds$Position!=-3),],
mapping=aes(x=Position,y=log(Intensity_MZ), label = paste(..rr.label..)),
r.accuracy = 0.01,
label.y = 16,
label.x = 15,
size = 3.5,
family = font_family) +
stat_cor(data=wo_stds[which(wo_stds$RTandDT %in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms") & wo_stds$Position!=-3),],
mapping=aes(x=Position,y=log(Intensity_MZ), label = paste(..p.label..)),
p.accuracy = 0.001,
label.y = 15.4,
label.x = 15,
size = 3.5,
family = font_family) + #add p value
geom_smooth(data=wo_stds[which(wo_stds$RTandDT %in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms") & wo_stds$Position!=-3),],
mapping=aes(x=Position,y=log(Intensity_MZ)),
method = "lm",
se = FALSE,
colour = "#616468",
alpha = 0.7,
size = 1) +
xlab("Incubation time (min)")+
ylab("ln(Intensity)")+
facet_grid(cols=vars(DPlabel))+
guides(ncol=1)+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_text(colour ="#000000", size = font_size, family = font_family),
axis.title.y = element_text(colour="#000000", size = font_size_titles, family = font_family),
axis.title.x = element_text(colour="#000000", size = font_size_titles, family = font_family),
legend.position = "none",
legend.title= element_text(colour="#000000", size = font_size_titles, family = font_family,
hjust = 0.5),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
legend.key = element_blank(),
legend.box.background = element_blank(),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
strip.background = element_blank(),
strip.text = element_text(colour="#000000", size=font_size, family = font_family))+
geom_rect(data=wo_stds, mapping = aes(xmin = -Inf, xmax=-1, ymin=-Inf, ymax=Inf), alpha=.002)
dev.off()
#### figure 3 again, wider but shorter -----
png(filename="C:/Users/admin/ownCloud/Deep-water kelp/Presentations/Thesis committee meeting 11May2021/Fig3a_sideprods_06May2021_shorter.png",
height = 8, width=18.9, units = "cm", res = 200)
ggplot()+
geom_point(data=wo_stds,aes(x=Position,y=log(Intensity_MZ),fill=RTandDT,), pch=21,size=2.5, alpha=0.5)+
scale_fill_discrete(name="Features", guide=guide_legend(reverse=T))+
scale_y_continuous(limits = c(7,17))+
stat_cor(data=wo_stds[which(wo_stds$RTandDT %in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms")),],
mapping=aes(x=Position,y=log(Intensity_MZ), label = paste(..rr.label..)),
r.accuracy = 0.01,
label.y = 16.7,
label.x = 11,
size = 3,
family = font_family) + # r squared
stat_cor(data=wo_stds[which(wo_stds$RTandDT %in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms")),],
mapping=aes(x=Position,y=log(Intensity_MZ), label = paste(..p.label..)),
p.accuracy = 0.001,
label.y = 16,
label.x = 11,
size = 3,
family = font_family) + #add p value
geom_smooth(data=wo_stds[which(wo_stds$RTandDT %in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms")),],
mapping=aes(x=Position,y=log(Intensity_MZ)),
method = "lm",
se = FALSE,
colour = "#616468",
alpha = 0.7,
size = 1) +
xlab("Incubation time (min)")+
ylab("ln(Intensity)")+
facet_grid(cols=vars(DPlabel))+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_text(colour ="#000000", size = font_size, family = font_family),
axis.title.y = element_text(colour="#000000", size = font_size_titles, family = font_family),
axis.title.x = element_text(colour="#000000", size = font_size_titles, family = font_family),
legend.title= element_text(colour="#000000", size = font_size_titles, family = font_family,
hjust = 0.5),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
legend.key = element_blank(),
legend.box.background = element_blank(),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
strip.background = element_blank(),
strip.text = element_text(colour="#000000", size=font_size_titles, family = font_family))+
guides(fill=guide_legend(ncol=2))
dev.off()
#double transform natural log (for second order products)
Fig_samples_points_second<-ggplot()+
geom_point(data=wo_stds,aes(x=Position,y=log(log(Intensity_MZ)),fill=RTandDT), pch=21,size=3, alpha=0.5)+
# scale_y_continuous(trans = scales::log_trans())+
stat_cor(data=wo_stds[which(wo_stds$RTandDT %!in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms")),],
mapping=aes(x=Position,y=log(log(Intensity_MZ)), label = paste(..rr.label..), group=RTandDT),
r.accuracy = 0.01,
label.y = c(3, 2.95, 2.9, 2.85, 3, 2.95, 2.9, 2.85, 2.8, 3, 2.95, 2.9, 2.85, 2.8, 2.75),
label.x = 0,
size = 3.5,
position = position_dodge2(),
family = "Avenir") +
stat_cor(data=wo_stds[which(wo_stds$RTandDT %!in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms")),],
mapping=aes(x=Position,y=log(log(Intensity_MZ)), label = paste(..p.label..), group=RTandDT),
p.accuracy = 0.001,
label.y = c(3, 2.95, 2.9, 2.85, 3, 2.95, 2.9, 2.85, 2.8, 3, 2.95, 2.9, 2.85, 2.8, 2.75),
label.x = 20,
size = 3.5,
family = "Avenir") + #add p value
geom_smooth(data=wo_stds[which(wo_stds$RTandDT %!in% c("RT=14.2 min DT=4.1 ms",
"RT=17.8 min DT=5.3 ms",
"RT=25.7 min DT=6.5 ms")),],
mapping=aes(x=Position,y=log(log(Intensity_MZ)), group=RTandDT),
method = "lm",
se = FALSE,
colour = "#616468",
alpha = 0.7,
size = 1) +
facet_grid(cols=vars(DP))+
guides(ncol=1)+
theme_light()
geom_point(data=wo_stds,aes(x=Position,y=log(log(Intensity_MZ)),fill=RTandDT,),pch=21,size=3, alpha=0.5)+
scale_y_continuous(trans = scales::log_trans())+
# scale_x_continuous(trans = "sqrt")+
facet_wrap(facets=wo_stds$DP,nrow=1)+
guides(ncol=1)+
theme_light()
Fig_samples_lines<-ggplot()+
geom_line(data=wo_stds,aes(x=Position,y=Intensity_MZ,color=RTandDT),alpha=0.8,size=1.2)+
scale_y_continuous(trans = scales::log_trans())+
# scale_x_continuous(trans = "sqrt")+
facet_wrap(facets=wo_stds$DP,nrow=1)+
guides(ncol=1)
# assessment multivariate -------------------------------------------------
wo_stds_cast<-dcast(wo_stds, Position ~ RTandDT, value.var = "Intensity_MZ")
# MANOVA test
res.man <- manova(cbind(wo_stds_cast[,4], wo_stds_cast[,3]) ~ wo_stds_cast[,1])
summary(res.man)
# I actually think this problem is not multivaryate but many individual test are
# necessary
lm_1<-lm(cbind(sqrt(wo_stds_cast[,5]),sqrt(wo_stds_cast[,6])) ~sqrt(wo_stds_cast[,1]))
summary(lm_1)
plot(lm_1)
# Trying with polynominal regression
#better: find proper function that describes enzyme reactions, fit nls (non linear least squares)
beta_1_3_DP3<-wo_stds[wo_stds$RT_min==14.2 & wo_stds$Position>=0,]
beta_1_3_DP3$Position2 = beta_1_3_DP3$Position^2
beta_1_3_DP3$Position3 = beta_1_3_DP3$Position^3
poly_regressor = lm(formula = Intensity_IM ~ Position+Position2,data = beta_1_3_DP3)
summary(poly_regressor)
X_grid = seq(min(beta_1_3_DP3$Position), max(beta_1_3_DP3$Position), 0.1)
ggplot() +
geom_point(aes(x = beta_1_3_DP3$Position, y = beta_1_3_DP3$Intensity_IM),colour = 'black') +
geom_line(aes(x = X_grid, y = predict(poly_regressor, newdata = data.frame(Position = X_grid, Position2=X_grid^2))),colour = 'red')+
ggtitle('Polynomial Regression')
beta_1_3_DP4<-wo_stds[wo_stds$RT_min==17.8 & wo_stds$Position>=0,]
beta_1_3_DP4$Position2 = beta_1_3_DP4$Position^2
beta_1_3_DP4$Position3 = beta_1_3_DP4$Position^3
poly_regressor = lm(formula = Intensity_IM ~ Position+Position2,data = beta_1_3_DP4)
summary(poly_regressor)
X_grid = seq(min(beta_1_3_DP4$Position), max(beta_1_3_DP4$Position), 0.1)
ggplot() +
geom_point(aes(x = beta_1_3_DP4$Position, y = beta_1_3_DP4$Intensity_IM),colour = 'black') +
geom_line(aes(x = X_grid, y = predict(poly_regressor, newdata = data.frame(Position = X_grid, Position2=X_grid^2))),colour = 'red')+
ggtitle('Polynomial Regression')
beta_1_3_DP5<-wo_stds[wo_stds$RT_min==25.9 & wo_stds$Position>=0,]
beta_1_3_DP5$Position2 = beta_1_3_DP5$Position^2
beta_1_3_DP5$Position3 = beta_1_3_DP5$Position^3
poly_regressor = lm(formula = Intensity_IM ~ Position+Position3,data = beta_1_3_DP5)
summary(poly_regressor)
X_grid = seq(min(beta_1_3_DP5$Position), max(beta_1_3_DP5$Position), 0.1)
ggplot() +
geom_point(aes(x = beta_1_3_DP5$Position, y = beta_1_3_DP5$Intensity_IM),colour = 'black') +
geom_line(aes(x = X_grid, y = predict(poly_regressor, newdata = data.frame(Position = X_grid, Position2=X_grid^2))),colour = 'red')+
ggtitle('Polynomial Regression')
### FIGURE FOR POMPU meeting in Berlin November 2019
wo_stds_filtered<-wo_stds_filtered[order(wo_stds_filtered$DT_ms),]
Fig_samples_filtered_DP3<-ggplot()+
geom_line(data=wo_stds_filtered[wo_stds_filtered$DP==3,],aes(x=Position,y=Intensity_IM,color=RTandDT),alpha=0.8, size=1.5)+
scale_y_log10(name="Intensity (A.U.)",limits=c(300,5000000))+
scale_color_manual(values=wes_palette("Darjeeling1", n=5) )+
scale_x_continuous(name="Incubation time",breaks=c(-10,0,10,20,30),labels=c("not\ndigested","0 min","10 min","20 min", "30 min"))+
guides(ncol=4)+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_text(colour ="#000000", size = font_size, family = font_family),
axis.title.y = element_text(colour="#000000", size = font_size, family = font_family),
axis.title.x = element_text(colour="#000000", size = font_size, family = font_family),
legend.title= element_text(colour="#000000",face="bold", size = font_size, family = font_family),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
legend.position="none",
legend.box.background = element_rect(colour="black"),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank())+
ggtitle(label="Trisaccharides")
Fig_samples_filtered_DP4<-ggplot()+
geom_line(data=wo_stds_filtered[wo_stds_filtered$DP==4,],aes(x=Position,y=Intensity_IM,color=RTandDT),alpha=0.8, size=1.5)+
scale_y_log10(name="Intensity (A.U.)",limits=c(300,5000000))+
scale_color_manual(values=c(wes_palette("Darjeeling1", n=1), wes_palette("Darjeeling2", n=5)))+
guides(ncol=4)+
scale_x_continuous(name="Incubation time",breaks=c(-10,0,10,20,30),labels=c("not\ndigested","0 min","10 min","20 min", "30 min"))+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_text(colour="#000000", size = font_size, family = font_family),
legend.title= element_text(colour="#000000",face="bold", size = font_size, family = font_family),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
legend.position="none",
legend.box.background = element_rect(colour="black"),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank())+
ggtitle(label="Tetrasaccharides")
Fig_samples_filtered_DP5<-ggplot()+
geom_point(data=wo_stds_filtered[wo_stds_filtered$DP==5,],aes(x=Position,y=Intensity_IM,fill=RTandDT),pch=21, size=3)+
scale_y_log10(name="Intensity (A.U.)",limits=c(300,5000000))+
scale_fill_manual(values=c("#74A089","#FDDDA0","#0B775E", "#FAEFD1","#F2300F", "#899DA4", "#FAD510","#C93312"))+
scale_x_continuous(name="Incubation time",breaks=c(-10,0,10,20,30),labels=c("not\ndigested","0 min","10 min","20 min", "30 min"))+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_text(colour ="#000000", size = font_size, family = font_family),
axis.title.y = element_blank(),
axis.title.x = element_text(colour="#000000", size = font_size, family = font_family),
legend.title= element_text(colour="#000000",face="bold", size = font_size, family = font_family),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
legend.position="none",
legend.box.background = element_rect(colour="black"),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank())+
ggtitle(label = "Pentasaccharides")
DP7_diversification<-plot_grid(Fig_samples_filtered_DP3,Fig_samples_filtered_DP4, align="h",nrow=1,rel_widths = c(1.2,1))
save_plot(filename = "C:/Users/admin/ownCloud/Deep-water kelp/Presentations/SAB2021_pitch/Fig_DP7_diversification.tiff",DP7_diversification,base_height = 5,base_width = 12)
# all lengths in one
# order features by mean int
wo_stds_filtered$RTandDT<-fct_reorder(.f=wo_stds_filtered$RTandDT,.x=wo_stds_filtered$Intensity_IM, .fun = max)
Fig_samples_filtered_allDPs<-ggplot()+
geom_line(data=wo_stds_filtered,aes(x=Position,y=Intensity_IM,color=RTandDT),alpha=0.6, size=1)+
scale_y_log10(name="Intensity (A.U.)",limits=c(300,5000000))+
scale_color_manual(values=c(rep("white", 16), rep("black",3)))+
scale_x_continuous(name="Incubation time",breaks=c(-10,0,10,20,30),labels=c("not\ndigested","0 min","10 min","20 min", "30 min"))+
guides(ncol=4)+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_text(colour ="#000000", size = font_size, family = font_family),
axis.title.y = element_text(colour="#000000", size = font_size, family = font_family),
axis.title.x = element_text(colour="#000000", size = font_size, family = font_family),
legend.title= element_text(colour="#000000",face="bold", size = font_size, family = font_family),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
legend.position="none",
legend.box.background = element_rect(colour="black"),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank())
### Figure without log 10
Fig_samples_filtered_DP3_nolog10<-ggplot()+
geom_point(data=wo_stds_filtered[wo_stds_filtered$DP==3,],aes(x=Position,y=Intensity_IM,fill=RTandDT),pch=21, size=3)+
scale_y_continuous(name="Intensity (A.U.)",limits=c(300,5000000))+
scale_fill_manual(values=c("#FDDDA0", "#F2300F","#C93312","#FAD510","#0B775E" ))+
scale_x_continuous(name="Incubation time",breaks=c(-10,0,10,20,30),labels=c("not\ndigested","0 min","10 min","20 min", "30 min"))+
guides(ncol=4)+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_text(colour ="#000000", size = font_size, family = font_family),
axis.title.y = element_text(colour="#000000", size = font_size, family = font_family),
axis.title.x = element_text(colour="#000000", size = font_size, family = font_family),
legend.title= element_text(colour="#000000",face="bold", size = font_size, family = font_family),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
legend.position="none",
legend.box.background = element_rect(colour="black"),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank())+
ggtitle(label="Trisaccharides")
Fig_samples_filtered_DP4_nolog10<-ggplot()+
geom_point(data=wo_stds_filtered[wo_stds_filtered$DP==4,],aes(x=Position,y=Intensity_IM,fill=RTandDT),pch=21, size=3)+
scale_y_continuous(name="Intensity (A.U.)",limits=c(300,5000000))+
scale_fill_manual(values=c("#74A089","#FDDDA0","#0B775E", "#FAD510" ,"#F2300F","#C93312"))+
guides(ncol=4)+
scale_x_continuous(name="Incubation time",breaks=c(-10,0,10,20,30),labels=c("not\ndigested","0 min","10 min","20 min", "30 min"))+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_text(colour ="#000000", size = font_size, family = font_family),
axis.title.y = element_blank(),
axis.title.x = element_text(colour="#000000", size = font_size, family = font_family),
legend.title= element_text(colour="#000000",face="bold", size = font_size, family = font_family),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
legend.position="none",
legend.box.background = element_rect(colour="black"),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank())+
ggtitle(label="Tetrasaccharides")
Fig_samples_filtered_DP5_nolog10<-ggplot()+
geom_point(data=wo_stds_filtered[wo_stds_filtered$DP==5,],aes(x=Position,y=Intensity_IM,fill=RTandDT),pch=21, size=3)+
scale_y_continuous(name="Intensity (A.U.)",limits=c(300,5000000))+
scale_fill_manual(values=c("#74A089","#FDDDA0","#0B775E", "#FAEFD1","#F2300F", "#899DA4", "#FAD510","#C93312"))+
scale_x_continuous(name="Incubation time",breaks=c(-10,0,10,20,30),labels=c("not\ndigested","0 min","10 min","20 min", "30 min"))+
theme(axis.text.x= element_text(colour="#000000", size = font_size, family = font_family),
axis.text.y = element_text(colour ="#000000", size = font_size, family = font_family),
axis.title.y = element_blank(),
axis.title.x = element_text(colour="#000000", size = font_size, family = font_family),
legend.title= element_text(colour="#000000",face="bold", size = font_size, family = font_family),
legend.text = element_text(colour="#000000", size = font_size, family = font_family),
legend.position="none",
legend.box.background = element_rect(colour="black"),
panel.background = element_rect(colour="#000000", fill=NA),
plot.background = element_rect(colour=NA, fill=NA),
axis.line = element_line(colour="#000000"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank())+
ggtitle(label = "Pentasaccharides")
DP7_diversification_nolog10<-plot_grid(Fig_samples_filtered_DP3_nolog10,Fig_samples_filtered_DP4_nolog10,Fig_samples_filtered_DP5_nolog10, align="h",nrow=1,rel_widths = c(1.05,1,1))
save_plot(filename = "C:/Users/hbuck/Dropbox/Deep-water kelp/Nantes2019/Fig_DP7_diversification_nolog10.tiff",DP7_diversification_nolog10,base_height = 5,base_width = 12)