library(data.table)
library(dplyr)
library(reshape2)
library(ggplot2)
library(ggdendro)
library(readxl)
library(ggpubr)
# Figure E4b ----------------------------------
dat <- read_excel("Fig E4 Source Data.xlsx", sheet = "E4b Prop of Mediation All")
# normalize
dat.dt <- dat
dat.dt$InflammationType <- sapply(strsplit(dat.dt$Group,"_",fixed = T),"[[",1 )
dat.dt$Path <- sapply(dat.dt$Group,
function(x){
parts <- strsplit(x, "_", fixed = T)[[1]]
paste(parts[2:length(parts)], collapse = "-")
})
dat.dt$Path <- factor(dat.dt$Path, levels = c("MetaG-MetaB","MetaB-Trans","Trans-Spuprot","Trans-Serprot"))
dat.dt$InflammationType <- factor(dat.dt$InflammationType,levels = c("NEU","EOS"))
dat.dt <- dat.dt %>% arrange(Path) %>% arrange(InflammationType)
dat.dt$Group <- factor(dat.dt$Group, levels = rev(unique(dat.dt$Group)))
FigE4b <- ggplot(dat.dt ,
aes(x=Group, y=Mediation_Proportion))+
geom_violin(aes(fill=Path),trim = T, scale = "width", alpha=0.5)+
geom_boxplot(width=0.1, outlier.shape = NA) +
scale_fill_manual(values = c("#F8766D","#CD9600","#7CAE00","#00BE67")) +
theme_bw()+ theme(panel.grid = element_blank(),
axis.text.x = element_blank())+
coord_flip()
FigE4b
# Figure E4c ----------------------------------
dat <- read_excel("Fig E4 Source Data.xlsx", sheet = "E4c Reverse Mediation")
plotDat <- dat %>%
mutate(Forward = ABC_Prop, Reverse = ACB_Prop) %>%
reshape2::melt(id.vars=c("Comparison","Type")) %>%
dplyr::filter(variable %in% c("Forward", "Reverse"))
plotDat$Type <- factor(plotDat$Type,
levels = c("MetaG-MetaB-NEU","MetaB-HostT-NEU","MetaG-MetaB-EOS","MetaB-HostT-EOS"))
FigE4c <- ggpaired(plotDat, x = "variable", y = "value",
color = "variable", line.color = "gray", line.size = 0.4)+
stat_compare_means(paired = TRUE) +
facet_wrap(vars(Type), scales = "free", ncol = 4)+
theme_bw()+theme(panel.grid = element_blank())
FigE4c
# Figure E4e ----------------------------------
# Fig E4e. LOSO density plot #######
rm(list = ls())
dat.hist <- read_excel("Fig E4 Source Data.xlsx4", sheet = "E4e LOSO")
colnames(dat.hist) <- c("Figs_map", "Figs_color","species","X1")
dat.hist <- dat.hist %>% mutate(X2=X1, Y1=-0.002, Y2=-0.009)
taxonomy_df <- read_excel("Fig E4 Source Data.xlsx4", sheet = "E4e taxonomy")
genus <- sapply(strsplit(dat.hist$species,"_", fixed = T),"[[", 1)
genus[!genus %in% taxonomy_df$Genus]
phylum <- sapply(genus,
function(x){
if(x %in% taxonomy_df$Genus) {
taxonomy_df$Phylum[which(taxonomy_df$Genus == x)[1]]
}else "Unclassified"
})
phylum_color_df <- cbind.data.frame(phylum=c("Bacteroidetes","Actinobacteria","TM7","Proteobacteria","Firmicutes","other"),
colors=c("#EF5656","#47B3DA","#9A8FC3","#F7A415","#2BB065","#BABABA"),
stringsAsFactors=F)
dat.hist$phylum <- phylum
dat.hist$phylum_other <- sapply(dat.hist$phylum, function(x)if(x %in% phylum_color_df$phylum) x else "other")
Fig.map_ymax_df <- cbind.data.frame(Fig.map = unique(dat.hist$Figs_map),
# ymax=c(0.04,0.16,0.03,0.015,0.035,0.038),
ymax = c(0.06,0.19, 0.06, 0.05),
stringsAsFactors=F)
for(Fig in unique(dat.hist$Figs_map)){
#Fig=unique(dat.hist$Figs_map)[1]
ymax = Fig.map_ymax_df$ymax[which(Fig.map_ymax_df$Fig.map == Fig)]
dat_sub <- dat.hist %>% filter(Figs_map %in% Fig)
phylum_ABC <- unique(dat_sub$phylum_other)[order(unique(dat_sub$phylum_other))]
phylum_rank <- c(phylum_ABC[phylum_ABC != "other"],"other")
dat_sub$phylum_other <- factor(dat_sub$phylum_other,
levels = phylum_rank)
Colors <- sapply(phylum_rank, function(x) phylum_color_df$colors[which(phylum_color_df$phylum ==x)])
histP <- ggplot(dat_sub) +
geom_density(aes(x=X1,y=(..count..)/sum(..count..)),fill = "gray") + ylab("Density") +
xlab("") +
theme_bw()+theme(panel.grid = element_blank()) +
ylim(-0.01,ymax) +
geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2, color=phylum_other )) +
scale_color_manual(values = Colors) +
theme(legend.position = "none") + ggtitle(Fig)
histP
assign(paste("histP_", Fig, sep = ""), histP, envir = .GlobalEnv)
}
histP_MetaG_M00044
histP_MetaG_P00250
histP_MetaG_P00380
histP_MetaG_P00564
library(ggpubr)
FigE4e.LOSO <- ggarrange(histP_MetaG_P00380, histP_MetaG_M00044, histP_MetaG_P00250, histP_MetaG_P00564, ncol = 1)
#ggsave(FigE4e.LOSO,device = "pdf", filename = "FigE4e.KOcontri.pdf", width = 3, height = 10)
# Fig E4e. KO contribution #######
zscore_df <- read_excel("Fig E4 Source Data.xlsx", sheet = "E4e KO contrib" )
colnames(zscore_df) <- c("K","sp","score")
tmp <- zscore.top3_df <- zscore_df %>%
group_by(K) %>%
top_n(n = 3, wt = score) %>% as.data.frame()
tmp <- tmp %>% arrange(desc(score)) %>% arrange(K)
zscore.top3_df <- tmp %>% #keep the top 3 with highest score
mutate(X = rep(c("X1","X2","X3"), length(unique(zscore_df$K)))) %>%
mutate(genus = sapply(strsplit(sp," ", fixed = T),"[[", 1)) %>%
mutate(phylum = sapply(genus,
function(x){
if(x %in% taxonomy_df$Genus){
taxonomy_df$Phylum[which(taxonomy_df$Genus == x)[1]]
}else "Unclassified"
} ) ) %>%
mutate(phylum_other = sapply(phylum, function(x) if(x %in% phylum_color_df$phylum) x else "other"))
zscore.top3_df$phylum_other <- factor(zscore.top3_df$phylum_other, levels = phylum_rank)
FigE4e.KOcontri <- ggplot(zscore.top3_df) +
geom_point(aes(x=X, y=K, size=score, fill=phylum_other), shape=21) +
scale_fill_manual(values = Colors) +
scale_size (range = c (3, 6)) +
theme(panel.grid = element_blank(), axis.title = element_blank(), panel.background = element_blank(),
axis.text.x = element_blank(), legend.position = "none", axis.ticks = element_blank())
FigE4e.KOcontri
Datasets
Models
