list.files()

library(readxl)

excel_sheets("Fig 2 Source Data.xlsx")

library(dplyr)

# Figure 2a ------------------------------------------

# Figure 2a. metabolome #######

rm(list = ls())

dat.metab <- read_excel("Fig 2 Source Data.xlsx", sheet = "Fig 2a metabolome PCA")

library(ggplot2)

pca <- dat.metab

Fig2a.metab.pca <- ggplot(pca,aes(PC1,PC2))+

  geom_point(size=2,aes(col=Group,shape=Cohort))+

  scale_color_manual(values=c("#f0999f","#46bbc0"))+

  scale_shape_manual(values=c(16,15)) +

  theme_bw()+theme(axis.line = element_line(colour = "black"),

                   panel.grid.major = element_blank(),

                   panel.grid.minor = element_blank(),

                   panel.background = element_blank())

Fig2a.metab.pca

pca$GROUP = paste(pca$Group,"|", pca$Cohort,sep = "")

Fig2a.metab.pc1.density <-

  ggplot(pca) +

  geom_density(aes(x=PC1, group=GROUP, fill=Group, linetype=Cohort),

               color="black", alpha=0.6, position = 'identity') +

  scale_fill_manual(values=c("#f0999f","#46bbc0")) +

  theme_bw() +

  scale_linetype_manual(values = c("solid","dashed"))+

  labs(fill="")

Fig2a.metab.pc1.density

Fig2a.metab.pc2.density <-

  ggplot(pca) +

  geom_density(aes(x=PC2, group=GROUP, fill=Group, linetype=Cohort),

               color="black", alpha=0.6, position = 'identity') +

  scale_fill_manual(values=c("#f0999f","#46bbc0")) +

  theme_bw() +

  scale_linetype_manual(values = c("solid","dashed"))+

  labs(fill="") + 

  coord_flip()

Fig2a.metab.pc2.density

# Figure 2a. metabolome #######

excel_sheets("Fig 2 Source Data.xlsx")

rm(list = ls())

dat.trans <- read_excel("Fig 2 Source Data.xlsx", sheet = "Fig 2a transcriptome PCA")

#library(ggplot2)

pca <- dat.trans

Fig2a.trans.pca <- ggplot(pca,aes(PC1,PC2))+

  geom_point(size=2,aes(col=Group,shape=Cohort))+ 

  scale_color_manual(values=c("#f0999f","#46bbc0"))+

  scale_shape_manual(values=c(16,15)) +

  theme_bw()+theme(axis.line = element_line(colour = "black"),

                   panel.grid.major = element_blank(),

                   panel.grid.minor = element_blank(),

                   panel.background = element_blank())

Fig2a.trans.pca

pca$GROUP = paste(pca$Group,"|", pca$Cohort,sep = "")

Fig2a.trans.pc1.density <-

  ggplot(pca) +

  geom_density(aes(x=PC1, group=GROUP, fill=Group, linetype=Cohort),

               color="black", alpha=0.6, position = 'identity') +

  scale_fill_manual(values=c("#f0999f","#46bbc0")) +

  theme_bw() +

  scale_linetype_manual(values = c("solid","dashed"))+

  labs(fill="")

Fig2a.trans.pc1.density

Fig2a.trans.pc2.density <-

  ggplot(pca) +

  geom_density(aes(x=PC2, group=GROUP, fill=Group, linetype=Cohort),

               color="black", alpha=0.6, position = 'identity') +

  scale_fill_manual(values=c("#f0999f","#46bbc0")) +

  theme_bw() +

  scale_linetype_manual(values = c("solid","dashed"))+

  labs(fill="") + 

  coord_flip()

Fig2a.trans.pc2.density

# Figure 2a. sputum proteome #######

excel_sheets("Fig 2 Source Data.xlsx")

rm(list = ls())

dat.sputum <- read_excel("Fig 2 Source Data.xlsx", sheet = "Fig 2a sputum proteome PCA")

#library(ggplot2)

pca <- dat.sputum

Fig2a.sputum.pca <- ggplot(pca,aes(PC1,PC2))+

  geom_point(size=2,aes(col=Group))+

  scale_color_manual(values=c("#f0999f","#46bbc0"))+

  scale_shape_manual(values=c(16,15)) +

  theme_bw()+theme(axis.line = element_line(colour = "black"),

                   panel.grid.major = element_blank(),

                   panel.grid.minor = element_blank(),

                   panel.background = element_blank())

Fig2a.sputum.pca

Fig2a.sputum.pc1.density <-

  ggplot(pca) +

  geom_density(aes(x=PC1, group=Group, fill=Group),

               color="black", alpha=0.6, position = 'identity') +

  scale_fill_manual(values=c("#f0999f","#46bbc0")) +

  theme_bw() +

  scale_linetype_manual(values = c("solid","dashed"))+

  labs(fill="")

Fig2a.sputum.pc1.density

Fig2a.sputum.pc2.density <-

  ggplot(pca) +

  geom_density(aes(x=PC2, group=Group, fill=Group),

               color="black", alpha=0.6, position = 'identity') +

  scale_fill_manual(values=c("#f0999f","#46bbc0")) +

  theme_bw() +

  scale_linetype_manual(values = c("solid","dashed"))+

  labs(fill="") + 

  coord_flip()

Fig2a.sputum.pc2.density

# Figure 2a. serum proteome #######

excel_sheets("Fig 2 Source Data.xlsx")

rm(list = ls())

dat.serum <- read_excel("Fig 2 Source Data.xlsx", sheet = "Fig 2a serum proteome PCA")

#library(ggplot2)

pca <- dat.serum

Fig2a.serum.pca <- ggplot(pca,aes(PC1,PC2))+

  geom_point(size=2,aes(col=Group))+  

  scale_color_manual(values=c("#f0999f","#46bbc0"))+

  scale_shape_manual(values=c(16,15)) +

  theme_bw()+theme(axis.line = element_line(colour = "black"),

                   panel.grid.major = element_blank(),

                   panel.grid.minor = element_blank(),

                   panel.background = element_blank())

Fig2a.serum.pca

Fig2a.serum.pc1.density <-

  ggplot(pca) +

  geom_density(aes(x=PC1, group=Group, fill=Group),

               color="black", alpha=0.6, position = 'identity') +

  scale_fill_manual(values=c("#f0999f","#46bbc0")) +

  theme_bw() +

  scale_linetype_manual(values = c("solid","dashed"))+

  labs(fill="")

Fig2a.serum.pc1.density

Fig2a.serum.pc2.density <-

  ggplot(pca) +

  geom_density(aes(x=PC2, group=Group, fill=Group),

               color="black", alpha=0.6, position = 'identity') +

  scale_fill_manual(values=c("#f0999f","#46bbc0")) +

  theme_bw() +

  scale_linetype_manual(values = c("solid","dashed"))+

  labs(fill="") + 

  coord_flip()

Fig2a.serum.pc2.density

# Figure 2b ------------------------------------------

excel_sheets("Fig 2 Source Data.xlsx")

rm(list = ls())

metab.modules <- read_excel("Fig 2 Source Data.xlsx", sheet = "Fig 2b metab modules")

metab.features <- read_excel("Fig 2 Source Data.xlsx", sheet = "Fig 2b metab features")

# Figure 2b.density of modules ####

data <- metab.modules

library(reshape2)

data2<-melt(data,id.vals=c("NAME","CLASS","GROUP") )

md.levels <- c("MEbrown4","MEmediumpurple1","MEskyblue3","MElavenderblush3","MEslateblue",

               "MEsalmon2","MEbisque4","MEplum","MEmidnightblue","MEindianred4")

data2$variable=factor(data2$variable,levels=md.levels)

data2$GROUP4 <- paste(data2$GROUP,data2$CLASS, sep = "|")

Fig2b.density <- ggplot(data2,aes(data2$value))+

  geom_density(aes(group=data2$GROUP4,fill=data2$CLASS,alpha=0.4,linetype=data2$GROUP))+

  scale_color_manual(values=c("#f0999f","#46bbc0"))+

  facet_wrap(~data2$variable,scale="free",ncol=1)+

  scale_linetype_manual(values=c("solid","dashed"))+

  scale_x_continuous(limits=c(-0.2,0.2)) +

  theme_bw()+theme(axis.line = element_line(colour = "black"),

                   panel.grid.major = element_blank(),

                   panel.grid.minor = element_blank(),

                   panel.background = element_blank())

Fig2b.density

ggsave(Fig2b.density, filename = "Fig2b.density.pdf",device = "pdf", width = 4, height = 10)

# Figure 2b.heatmap ####

data <- metab.features 

data.Sz <- metab.features %>% select(starts_with("Z")) 

data.Gz <- metab.features %>% select(!starts_with("Z")) %>% select(-Metabolite, -Group)

colors<-c(seq(-4,0,length=51),seq(0.1,4,length=50))

my_palette<- colorRampPalette(c("#EE9494","#F6E572","#94EE9D","#94E1EE","#94B3EE","white","white","white","white","white","white","white"))(n=100)

library(gplots)

pdf( "Fig2b.metab.hm.Sz.pdf") #save figure2b.metab.heamap.Shenzhen in pdf

heatmap.2(as.matrix(data.Sz),

          breaks=colors, scale="row", trace="none", 

          Rowv=FALSE, dendrogram='column',hclustfun = function(x) hclust(x, method="ward.D2"),

          col=rev(my_palette))

dev.off()

pdf( "Fig2b.metab.hm.Gz.pdf") #save figure2b.metab.heamap.Guangzhou in pdf

heatmap.2(as.matrix(data.Gz),

          breaks=colors, scale="row", trace="none", 

          Rowv=FALSE, dendrogram='column',hclustfun = function(x) hclust(x, method="ward.D2"),

          col=rev(my_palette))  

dev.off()

## Figure 2c ------------------------------------------

excel_sheets("Fig 2 Source Data.xlsx")

rm(list = ls())

trans.modules <- read_excel("Fig 2 Source Data.xlsx", sheet = "Fig 2c trans modules")

trans.features <- read_excel("Fig 2 Source Data.xlsx", sheet = "Fig 2c trans features")

# Figure 2c.density of modules ####

data <- trans.modules

#library(reshape2)

data2<-melt(data,id.vals=c("NAME","CLASS","GROUP") )

md.levels <- c("MEcyan","MEdarkmagenta.1",

               "MEorangered4","MEskyblue2",

               "MEblueviolet","MEthistle4",

               "MElightyellow","MEchocolate4",

               "MEbisque4.1","MEsnow4")

data2$variable=factor(data2$variable,levels=md.levels)

data2$GROUP4 <- paste(data2$GROUP,data2$CLASS, sep = "|")

Fig2c.density <- ggplot(data2,aes(value))+

  geom_density(aes(group=GROUP4,fill=CLASS,alpha=0.4,linetype=GROUP))+

  scale_color_manual(values=c("#f0999f","#46bbc0"))+

  facet_wrap(~variable,scale="free",ncol=1)+

  scale_linetype_manual(values=c("solid","dashed"))+

  scale_x_continuous(limits=c(-0.2,0.2)) +

  theme_bw()+theme(axis.line = element_line(colour = "black"),

                   panel.grid.major = element_blank(),

                   panel.grid.minor = element_blank(),

                   panel.background = element_blank())

Fig2c.density

ggsave(Fig2c.density, filename = "Fig2c.density.pdf",device = "pdf", width = 4, height = 10)

# Figure 2c.heatmap ####

data <- trans.features 

data.Sz <- trans.features %>% select(starts_with("Z")) 

data.Gz <- trans.features %>% select(!starts_with("Z")) %>% select(-Gene, -Module)

colors <- c(seq(-4, 0, length=51), seq(0.1, 4, length=50))

my_palette<- colorRampPalette(c("#EE9494","#F6E572","#94EE9D","#94E1EE","#94B3EE","white","white","white","white","white","white","white"))(n=100)

#library(gplots)

#pdf( "Fig2c.trans.hm.Sz.pdf") #save figure2c.trans.heamap.Shenzhen in pdf

heatmap.2(as.matrix(data.Sz),

          breaks=colors, scale="row", trace="none", 

          Rowv=FALSE, dendrogram='column',hclustfun = function(x) hclust(x, method="ward.D2"),

          col=rev(my_palette))

#dev.off()

pdf( "Fig2c.trans.hm.Gz.pdf") #save figure2c.trans.heamap.Guangzhou in pdf

heatmap.2(as.matrix(data.Gz),

          breaks=colors, scale="row", trace="none", hclustfun = function(x) hclust(x, method="ward.D2"),

          Rowv=FALSE, dendrogram='column',

          col=rev(my_palette))

dev.off()

## Figure 2d ------------------------------------------

excel_sheets("Fig 2 data.xlsx")

rm(list = ls())

prot.features <- read_excel("Fig 2 Source Data.xlsx", sheet = "Fig 2d proteome features")

sputum.p <- prot.features %>% select(SampleID, Group, starts_with("Sputum"))

serum.p <- prot.features %>% select(SampleID, Group, starts_with("serum"))

# Figure2d. sputum proteome ####

data <- sputum.p

data.l <- reshape2::melt(data, id.vars=c("SampleID", "Group"))

sapply(data.l,class)

data.l_positive <- data.l %>% filter(value > 0)

H_median_df <- data.l_positive %>% filter(Group == "H") %>% 

  group_by(variable) %>% summarise(H_median =median(value)) %>%

  as.data.frame(stringsAsFactors=F)

plotDat <- 

  merge(data.l_positive,H_median_df,by="variable") %>%

  mutate(normalized_value = value/H_median) %>% 

  mutate(normalized.log = log10(normalized_value))

Fig2d.sputum <- ggplot(plotDat,aes(normalized.log))+

  geom_density(aes(group=Group,fill=Group,alpha=0.4))+

  scale_color_manual(values=c("#f0999f","#46bbc0"))+

  facet_wrap(~variable,scale="free",ncol = 1) +

  # scale_linetype_manual(values=c("solid","dashed"))+

  #scale_x_continuous(limits=c(-0.2,0.2))+

  theme_bw()+theme(axis.line = element_line(colour = "black"),

                   panel.grid.major = element_blank(),

                   panel.grid.minor = element_blank(),

                   panel.background = element_blank())

Fig2d.sputum 

ggsave(Fig2d.sputum, filename = 'Fig2d.sputum.pdf', device = "pdf",width = 4,height = 10)

# Figure2d. serum proteome ####

data <- serum.p

data.l <- reshape2::melt(data, id.vars=c("SampleID", "Group"))

sapply(data.l,class)

data.l_positive <- data.l %>% filter(value > 0)

H_median_df <- data.l_positive %>% filter(Group == "H") %>% 

  group_by(variable) %>% summarise(H_median =median(value)) %>%

  as.data.frame(stringsAsFactors=F)

plotDat <- 

  merge(data.l_positive,H_median_df,by="variable") %>%

  mutate(normalized_value = value/H_median) %>% 

  mutate(normalized.log = log10(normalized_value))

Fig2d.serum <- ggplot(plotDat,aes(normalized.log))+

  geom_density(aes(group=Group,fill=Group,alpha=0.4))+

  scale_color_manual(values=c("#f0999f","#46bbc0"))+

  facet_wrap(~variable,scale="free",ncol = 1) +

  # scale_linetype_manual(values=c("solid","dashed"))+

  scale_x_continuous(limits=c(-0.2,0.2))+

  theme_bw()+theme(axis.line = element_line(colour = "black"),

                   panel.grid.major = element_blank(),

                   panel.grid.minor = element_blank(),

                   panel.background = element_blank())

Fig2d.serum 

ggsave(Fig2d.serum, filename = 'Fig2d.serum.pdf', device = "pdf",width = 4,height = 4)