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seq_id_clustering.R
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seq_id_clustering.R
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# scripts to reproduce the analysis and figures from Bai et al., 2015
#
# originally by Ruben Garrido-Oter
# cleanup
rm(list = ls())
# load libraries
library(utils, quietly=T, warn.conflicts=F)
library(Biostrings, quietly=T, warn.conflicts=F)
library(grid, quietly=T, warn.conflicts=F)
library(gridExtra, quietly=T, warn.conflicts=F)
library(scales, quietly=T, warn.conflicts=F)
options(warn=-1)
# plotting stuff
source("plotting.functions.R")
main_theme <- theme(panel.background=element_blank(),
panel.grid=element_blank(),
axis.line=element_line(color="black"),
axis.ticks=element_line(color="black"),
axis.text=element_text(colour="black", size=10),
legend.position="top",
legend.background=element_blank(),
legend.key=element_blank(),
text=element_text(family="sans"))
# input data files
# load paths to project directories
source("paths.R")
mapping.file <- paste(data.dir, "mapping.txt", sep="")
taxonomy.file <- paste(data.dir, "wgs_taxonomy.txt", sep="")
amphora_list.file <- paste(amphora.dir, "amphora_list.txt", sep="")
functional.file <- paste(data.dir, "functional_profiles.txt", sep="")
# load data
mapping <- read.table(mapping.file, sep="\t", header=T, colClasses="character")
tax <- read.table(taxonomy.file, sep="\t", header=T)
func <- read.table(functional.file, sep="\t", header=T)
amphora_list <- read.table(amphora_list.file, sep="\t", header=F)[, 1]
colnames(func) <- gsub("^X", "", colnames(func))
### functional MDS
message("calculating functional PCoA...")
d <- 1 - cor(func)
d[d==0] <- .1
diag(d) <- 0
k <- 2
pcoa <- cmdscale(d, k=k, eig=T)
mds <- pcoa$points
eig <- pcoa$eig
mds <- as.data.frame(mds)
colnames(mds) <- c("x", "y")
mds$compartment <- mapping$compartment[match(rownames(mds), mapping$ID)]
tax$genome <- mapping$ID[match(tax$isolate_ID, mapping$Strain)]
mds$taxonomy <- tax$Phylum[match(rownames(mds), tax$genome)]
### clustering isolates by family
tree <- read.tree(paste(amphora.dir, "amphora_rooted_names.tree", sep=""))
groups_names <- rev(as.character(tax$Family[match(tree$tip.label, tax$isolate_ID)]))
groups_names <- match(groups_names, unique(groups_names))
names(groups_names) <- rev(tree$tip.label)
groups_ids <- groups_names
names(groups_ids) <- mapping$ID[match(names(groups_names), mapping$Strain)]
write.table(data.frame(ID=names(groups_names), cluster=groups_names),
file=paste(amphora.dir, "/clusters.txt", sep=""),
col.names=T, row.names=F, sep="\t", quote=F)
# read amphora MSA
amphora.msa <- readDNAStringSet(paste(amphora.dir, "amphora.msa", sep=""))
### functions needed to generate the plots
genTree <- function(k) {
# subset sequences from the amphora MSA
seqs <- amphora.msa[names(amphora.msa) %in% genomes]
writeXStringSet(seqs, paste(amphora.dir, "amphora_tmp.msa", sep=""))
# build sub-tree
system(paste("rm -f ", amphora.dir, "amphora_tmp.tree", sep=""))
system(paste("FastTree -nt -gtr ", amphora.dir, "amphora_tmp.msa >> ",
amphora.dir, "amphora_tmp.tree 2>> /dev/null", sep=""))
tree <- read.tree(paste(amphora.dir, "amphora_tmp.tree", sep=""))
# calculate pair-wise distances between all leaves of the tree
d <- cophenetic(tree)
compartment <- mapping$compartment[match(colnames(d), mapping$ID)]
idx.leaf <- compartment=="leaf"
d.leaf <- dist(d[idx.leaf, idx.leaf])
idx.root <- compartment=="root" | compartment=="soil"
d.root <- dist(d[idx.root, idx.root])
d <- dist(d)
df.all <- data.frame(distances=as.vector(d), compartment="all")
df.leaf <- if (sum(idx.leaf) > 1) data.frame(distances=as.vector(d.leaf), compartment="leaf") else NULL
df.root <- if (sum(idx.root) > 1) data.frame(distances=as.vector(d.root), compartment="root") else NULL
df <- rbind(df.all, df.leaf, df.root)
# plot unrooted tree with color labels
compartment <- mapping$compartment[match(tree$tip.label, mapping$ID)]
tips.col <- rep(NA, length(tree$tip.label))
tips.col[compartment=="leaf"] <- c_dark_green
tips.col[compartment=="root"] <- c_red
tips.col[compartment=="soil"] <- c_red
tips.shape <- rep(NA, length(tree$tip.label))
tips.shape[compartment=="leaf"] <- 24
tips.shape[compartment=="root"] <- 21
tips.shape[compartment=="soil"] <- 22
tips.cex <- rep(1, length(tree$tip.label))
tree$tip.label <- mapping$Strain[match(tree$tip.label, mapping$ID)]
plot_btree(tree, output.file=paste(figures.dir, "/amphora_tmp_tree.pdf", sep=""),
cex.labels=.7, label.offset=.03,
type="unrooted", use.edge.length=T, edge.width=1,
align.labels=T, color.labels="transparent", color.alg.lines="dark grey",
tips.col=tips.col, tips.shape=tips.shape, tips.cex=tips.cex,
margins=c(.1, 0, .2, 8), height=6.5, width=11.7)
return(df)
}
genDensities <- function(k, df, points) {
# plot boxplots
colors1 <- data.frame(compartment=c("all", "leaf", "root"),
cols=c("black", c_dark_green, c_red))
c1 <- as.character(colors1$cols[match(levels(df$compartment), colors1$compartment)])
p1 <- ggplot(df, aes(x=compartment, y=distances, color=compartment, shape=compartment)) +
geom_boxplot(alpha=1, outlier.size=.5, size=.7) +
geom_jitter(position=position_jitter(0.35), size=.7, alpha=.25) +
scale_shape_manual(values=c(1, 17, 16)) +
scale_colour_manual(values=c1) +
labs(x="", y="mutations per site", title="pair-wise tree distances") +
coord_flip() +
main_theme +
theme(legend.title=element_blank())
# plot MDS sub-plot
points$compartment <- as.character(points$compartment)
points$compartment[!rownames(points) %in% genomes] <- "all"
points$compartment <- factor(points$compartment, levels=c("all", "leaf", "root", "soil"))
points <- droplevels(points)
colors2 <- data.frame(compartment=c("all", "leaf", "root", "soil"),
cols=c("lightgrey", c_dark_green, c_red, c_red))
c2 <- as.character(colors2$cols[match(levels(points$compartment), colors2$compartment)])
p2 <- ggplot(points, aes(x=x, y=y, color=compartment, shape=taxonomy)) +
geom_point(alpha=.8) +
scale_shape_manual(values=c(16, 17, 7, 15, 3)) +
scale_colour_manual(values=c2, guide=F) +
labs(x=paste("PCoA 1 (", format(100 * eig[1] / sum(eig), digits=4), "%)", sep=""),
y=paste("PCoA 2 (", format(100 * eig[2] / sum(eig), digits=4), "%)", sep=""),
title="functional distances between isolates") +
main_theme +
theme(legend.title=element_blank(),
legend.text=element_text(size=8))
return(arrangeGrob(p1, p2, ncol=1, heights=c(3.4, 4.9), widths=6))
}
genPangenome <- function(k) {
# pan-genome histogram
ko.all <- data.frame(genome=NULL, ko=NULL)
for (g in genomes) {
ko <- read.table(paste(annotation.dir, g, ".ko", sep=""),
fill=T, header=F, sep="\t",
col.names=c("peg", "ko"))[, 2]
ko.genome <- data.frame(genome=g, ko=ko)
ko.all <- rbind(ko.all, ko.genome)
}
ko.table <- table(ko.all)
perc_annotated <- (sum(ko.table[, -1]) * 100) / sum(ko.table)
ko.table <- ko.table[, -1]
ko.table <- (ko.table > 0) * 1
ko.table <- as.vector(table(colSums(ko.table)))
df <- data.frame(n_genomes=1:length(ko.table), count=ko.table)
df$count <- df$count / sum(df$count)
df$component <- "shell"
df$component[1] <- "singletons"
df$component[df$n_genomes==length(genomes)] <- "core"
p3 <- ggplot(df, aes(x=n_genomes, y=count, fill=component)) +
geom_histogram(alpha=1, size=.5, stat="identity", color="black") +
scale_y_continuous(labels = percent) +
#~ scale_x_continuous(breaks=0:length(genomes)) +
scale_fill_manual(values=c("black", "grey", "white")) +
labs(x="number of genomes", y="annotated gene families",
title="pan-genome histogram") +
main_theme +
theme(legend.title=element_blank())
# pan-genome stacked barplot
df <- aggregate(df$count, by=list(df$component), FUN=sum)
colnames(df) <- c("component", "count")
df$blank <-"all"
p4 <- ggplot(df, aes(x=blank, y=count, fill=component)) +
geom_bar(alpha=1, size=.5, stat="identity", color="black", width=.4) +
scale_y_continuous(labels=percent) +
scale_fill_manual(values=c("black", "grey", "white")) +
labs(x="", y="annotated gene families",
title="pan-genome distribution") +
coord_flip() +
main_theme +
theme(legend.position="none",
axis.ticks.y=element_blank(),
axis.text.y=element_blank(),
axis.line.y=element_blank())
return(arrangeGrob(p3, p4, ncol=1, heights=c(6.3, 1.7), widths=5.7))
}
genPlot <- function(k) {
# blank place-holder for the tree and cluster name and taxonomy
taxonomy <- tax$Family[match(genomes[1], tax$genome)]
title <- paste("Cluster ", k, "\n(", taxonomy, ")", "\n", sep="")
bp <- ggplot() +
geom_blank(aes(1, 1)) +
labs(title=title) +
theme(plot.background=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.border=element_blank(),
panel.background=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank())
# arrange PDF
pg <- arrangeGrob(p.clustering, p.pangenome, ncol=2, nrow=1, heights=8.3, widths=c(6, 5.7))
pg2 <- arrangeGrob(bp, pg, ncol=2, nrow=1, heights=8.3, widths=c(5, 11.7))
ggsave(pg2, file=paste(figures.dir, "/amphora_tmp.pdf", sep=""), height=8.3, width=16.7)
system(paste("pdftk ", figures.dir, "/amphora_tmp_tree.pdf background ",
figures.dir, "/amphora_tmp.pdf output ",
figures.dir, "/cluster_", k, "_tmp.pdf", sep=""))
}
### cluster analysis plots
message("generating cluster analysis PDF...")
for (cluster in unique(groups_ids)) {
message(paste("Cluster ", cluster, sep=""))
genomes <- names(groups_ids[groups_ids==cluster])
if (length(genomes) >= 5) {
df <- genTree(cluster)
p.clustering <- genDensities(cluster, df, mds)
p.pangenome <- genPangenome(cluster)
genPlot(cluster)
}
}
# concatenate cluster analysis PDFs
groups_table <- table(groups_ids)
command <- gsub("$", "_tmp.pdf", names(groups_table)[groups_table >= 5])
command <- gsub("^", paste(figures.dir, "cluster_", sep=""), command)
command <- Reduce(command,f=paste)
system(paste("pdftk ", command, " output ", figures.dir, "cluster_analysis.pdf", sep=""))
# cleanup temporary files
system(paste("rm -f ", amphora.dir, "amphora_tmp*", sep=""))
system(paste("rm -f ", figures.dir, "amphora_tmp*", sep=""))
system(paste("rm -f ", figures.dir, "cluster_*_tmp.pdf", sep=""))