library(data.table)
library(dplyr)
library(reshape2)
library(ggplot2)
library(ggdendro)
library(readxl)
library(ggpubr)
excel_sheets("Fig S4 Source Data.xlsx")
# Figure S4 ---------------------------------
CorrDf_metab.metag_neu <- read_excel("Fig S4 Source Data.xlsx", sheet = "MetaG-MetaB Full")
CorrDf_metab.trans_neu <- read_excel("Fig S4 Source Data.xlsx", sheet = "Trans-MetaB")
CorrDf_spupro.trans_neu <- read_excel("Fig S4 Source Data.xlsx", sheet = "Trans-Sputpro")
CorrDf_serpro.trans_neu <- read_excel("Fig S4 Source Data.xlsx", sheet = "Trans-Serpro")
keyStr_df <- cbind.data.frame(dfnameStr = c("metag","metab","trans","serpro","spupro"),
colStr = c("MetaG","MetaB","Trans","Cyto","Cyto"),
stringsAsFactors=F)
# nodes and types
nodeXs <- c("metab","serpro","spupro") #do not change the sequences of elements in nodeXs
nodeYs <- c("metag","trans") #do not change the sequences of elements in nodeYs
ENtypes <- c("neu")
for(enType in ENtypes){
# enType = ENtypes[2]
for(nodex in nodeXs){
# nodex = nodeXs[1]
for(nodey in nodeYs){
# nodey = nodeYs[2]
corrDfName <- paste("CorrDf_",nodex,".",nodey,"_",enType,sep = "")
if(!exists(corrDfName)) next
CorrDf <- eval(parse(text = corrDfName))
NodeXCol <- which(sapply(CorrDf, function(x) all(grepl(keyStr_df$colStr[which(keyStr_df$dfnameStr == nodex)], x) )) )
colnames(CorrDf)[NodeXCol] <- "NodeX"
NodeYCol <- which(sapply(CorrDf, function(x) all(grepl(keyStr_df$colStr[which(keyStr_df$dfnameStr == nodey)], x) )) )
colnames(CorrDf)[NodeYCol] <- "NodeY"
# organize the orders of nodex and nodey
dat_r.w <- CorrDf %>% reshape2::dcast(NodeY ~ NodeX, value.var = "Correlation")
rownames(dat_r.w) <- dat_r.w$NodeY; dat_r.w <- dat_r.w[-1]
if(T){
df <- t(dat_r.w)
x <- as.matrix(scale(df))
dd.col <- as.dendrogram(hclust(dist(x)))
col.ord <- order.dendrogram(dd.col)
dd.row <- as.dendrogram(hclust(dist(t(x))))
row.ord <- order.dendrogram(dd.row)
xx <- scale(df)[col.ord, row.ord]
xx_names <- attr(xx, "dimnames")
#df <- as.data.frame(xx)
ddata_x <- dendro_data(dd.row)
ddata_y <- dendro_data(dd.col)
}
if(!exists(paste("order_",nodey,sep = ""))) assign(paste("order_",nodey,sep = ""), xx_names[[2]],envir = .GlobalEnv)
if(!exists(paste("order_",nodex,sep = ""))) assign(paste("order_",nodex,sep = ""), xx_names[[1]],envir = .GlobalEnv)
order_nodex <- eval(parse(text = paste("order_",nodex,sep = "")))
order_nodey <- eval(parse(text = paste("order_",nodey,sep = "")))
if(!all(unique(CorrDf$NodeX) %in% order_nodex) ) {print(paste("not all nodes of ", nodex," were in predefined order so stop",sep = ""));break}
if(!all(unique(CorrDf$NodeY) %in% order_nodey) ) {print(paste("not all nodes of ", nodey," were in predefined order so stop",sep = ""));break}
CorrDf$NodeX <- factor(CorrDf$NodeX, levels = order_nodex)
CorrDf$NodeY <- factor(CorrDf$NodeY, levels = order_nodey)
CorrDf$ColorType <- sapply(c(1:nrow(CorrDf)),
function(i) {
if(CorrDf$Linked[i] == "Y") return("Y") else if(CorrDf$`P-value`[i] >= 0.05) return("N_ns") else if(CorrDf$Correlation[i]>0) return("N_sig_posCorr") else return("N_sig_negCorr")
})
CorrDf$absCorr <- abs(CorrDf$Correlation)
CorrDf <- CorrDf %>% filter( NodeX %in% order_nodex) %>% filter(NodeY %in% order_nodey)
CorrDf$NodeX <- factor(CorrDf$NodeX, levels = order_nodex)
CorrDf$NodeY <- factor(CorrDf$NodeY, levels = order_nodey)
p <- ggplot(data = CorrDf, aes(x=NodeX,y=NodeY))+
geom_tile(aes(fill=ColorType, alpha=absCorr),color="white") +
theme(axis.text.x = element_text(angle = 90))+
#scale_fill_manual(values=c("white","#e2e2e2","#cc0202"))+
scale_fill_manual(values = c("white","#c1c1ff","#ffb6b6","#cc0202")) +
#scale_alpha(limits = c(0.0,1.0), range = c(0,0.6))+
theme( panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
# axis.title.x = element_text(colour=NA),
# axis.title.y = element_blank())
assign(paste("HeatP_",nodex,".",nodey,"_",enType,sep = ""), p, envir = .GlobalEnv)
}
}
assign(paste("order_metab_",enType,sep = ""), order_metab, envir = .GlobalEnv)
assign(paste("order_metag_",enType,sep = ""), order_metag, envir = .GlobalEnv)
assign(paste("order_trans_",enType,sep = ""), order_trans, envir = .GlobalEnv)
assign(paste("order_serpro_",enType,sep = ""), order_serpro, envir = .GlobalEnv)
assign(paste("order_spupro_",enType,sep = ""), order_spupro, envir = .GlobalEnv)
remove(order_metab,order_metag,order_trans, order_serpro, order_spupro)
}
# Fig S4. NEU integrated plot
ggarrange(ggarrange(HeatP_metab.trans_neu +
theme(legend.position = "none", axis.text.x = element_blank(),axis.text.y = element_blank()) +
xlab("MetaB") + ylab("Trans"),
HeatP_spupro.trans_neu +
theme(legend.position = "none", axis.text.x = element_blank(),axis.text.y = element_blank()) +
xlab("Sputum") + ylab("Trans"),
HeatP_serpro.trans_neu +
theme(legend.position = "none", axis.text.x = element_blank(),axis.text.y = element_blank()) +
xlab("Serum") + ylab("Trans"),
nrow = 1, widths = c(0.55,0.3,0.15)),
ggarrange(HeatP_metab.metag_neu +
theme(legend.position = "none", axis.text.x = element_blank(),axis.text.y = element_blank()) +
xlab("MetaB") + ylab("MetaG"),
ggplot() + geom_text(aes(x=0,y=0),label="NEU") + theme_dendro(),
nrow = 1, widths = c(0.55,0.45)),
nrow = 2,heights = c(0.6,0.4))
Datasets
Models
