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script_MolEcol.R
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# Author : Arnaud Meng
# Loading lib
library(igraph)
library(ggplot2)
library(parallel)
library(gridExtra)
# Custom operator for "NOT IN"
`%ni%` <- Negate(`%in%`)
# Import vertices & edges files
v = read.table("matches-wo-selfhits.filtered_pident60_qcov80_scov80.blast.vertices", h=T, sep=";", quote=NULL)
e = read.table("matches-wo-selfhits.filtered_pident60_qcov80_scov80.blast.edges", h=T, sep=";")
# Import taxonomy info
info = read.table("SUPP/infoDino4-good.csv", h=T, sep=";", na.strings=c("NA","NaN", " ", ""))
colnames(info)=c("phylum",
"class",
"order",
"family",
"genus",
"specie",
"prefix",
"strain",
"MMETSP",
"chloroplast",
"phototrophy",
"mixotrophy",
"harmful.for.human",
"symbiont",
"kleptoplastidy",
"ichyotoxin",
"parasite",
"dmsp",
"thecate",
"cyst.formation",
"transcriptome.quality")
# Merging tables vertices(v) & info (info)
v.info = merge(x=v, y=info, by="prefix", all.x=TRUE)
v.info = v.info[!duplicated(v.info[,"name"]),]
v.info = v.info[,c("name",
"prefix",
"phylum",
"class",
"order",
"family",
"genus",
"specie",
"strain",
"MMETSP",
"chloroplast",
"phototrophy",
"mixotrophy",
"parasite",
"dmsp",
"kleptoplastidy",
"symbiont",
"harmful.for.human",
"ichyotoxin",
"thecate",
"cyst.formation",
"transcriptome.quality")]
# Import annotation table
a = read.table("SUPP/all.sorted.filled.tsv", h=F, sep=";")
colnames(a)=c("name", # col 0 : is the id of the input sequence.
"checksum", # col 1 : is the crc64 (checksum) of the protein sequence (supposed to be unique).
"seqlength", # col 2 : is the length of the sequence (in AA).
"anaysis_method", # col 3 : is the anaysis method launched.
"db_entry", # col 4 : is the database members entry for this match.
"db_desc", # col 5 : is the database member description for the entry.
"domain_start", # col 6 : is the start of the domain match.
"domain_end", # col 7 : is the end of the domain match.
"evalue", # col 8 : is the evalue of the match (reported by member database anayling method).
"matching_statut", # col 9 : is the status of the match (T: true, M: marginal).
"date", # col 10 : is the date of the run.
"interpro_entry", # col 11 : is the corresponding InterPro entry (if iprlookup requested by the user).
"interpro_desc", # col 12 : is the description of the InterPro entry.
"go") # col 13 : is the GO (gene ontology) description for the InterPro entry.
# Merging tables : vertices(v.info) & annotations (a)
c = merge(x=v.info, y=a, by=c("name"), all.x=TRUE)
# Sorting :
c = c[ order(c$name, c$go, c$interpro_desc, c$db_desc), ]
# Deleting duplicated annotations
c.uniq = c[!duplicated(c$name),]
# If needed : saving tables
# write.table(x=c.uniq, file="c.uniq.tsv", sep="\t", col.names=T, row.names=F, quote=F)
################################################################################
#
# Adding SLIM terms : CF script "go2slim.R"
#
# or load : my.slim = read.csv("my.slim.txt", h=T, sep=";")
#
################################################################################
my.slim = read.csv("SUPP/my.slim.txt", h=T, sep=";")
# Merging tables : vertices(c.uniq) & slim (my.slim)
c.uniq.slim = merge(x=c.uniq, y=my.slim, by=c("name", "go"), all.x=TRUE)
c.uniq.slim = c.uniq.slim[,c("name",
"prefix",
"phylum",
"class",
"order",
"family",
"genus",
"specie",
"strain",
"MMETSP",
"chloroplast",
"phototrophy",
"mixotrophy",
"harmful.for.human",
"symbiont",
"kleptoplastidy",
"ichyotoxin",
"parasite",
"dmsp",
"thecate",
"cyst.formation",
"transcriptome.quality",
"checksum",
"seqlength",
"anaysis_method",
"db_entry",
"db_desc",
"domain_start",
"domain_end",
"evalue",
"matching_statut",
"date",
"interpro_entry",
"interpro_desc",
"go",
"MFslim",
"BPslim",
"CCslim")]
# graph creation
g = graph.data.frame(e, directed=F, vertices=c.uniq.slim)
# IGRAPH UN-- 1283775 6213111 --
# Removing vertices from "bad quality" transcriptomes
transcriptome.bad = which(V(g)$transcriptome.quality=="bad")
g.good.only = delete.vertices(g, transcriptome.bad)
# IGRAPH UN-- 1275911 6142013 --
# Components (CCs) with >= 2 vertices
comp.good.only = decompose.graph(g.good.only, min.vertices = 2)
saveRDS(comp.good.only, file="comp.good.only.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
comp.good.only = readRDS("comp.good.only.rds")
# CCs sizes
comp.sizes = unlist(lapply(comp.good.only, function(x) vcount(x)))
# Saving
saveRDS(v, file="v.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(e, file="e.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(e.id60, file="e.id60.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(a, file="a.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(info, file="info.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(v.info, file="v.info.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(c, file="c.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(c.uniq, file="c.uniq.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(c.uniq.slim, file="c.uniq.slim.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(g, file="g.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(g.good.only, file="g.good.only.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
################################################################################
# ----------------------- ESTIMATE CCs ANNOTATIONS ----------------------------#
################################################################################
# Function to retrieve the list of CCs with "n" different annotations
# at annotation level "golevel"
cc.n.annotation = function(graph, n, golevel) {
# print(" -------------------------------------------------- new ")
# get and unlist go term for the CC
goterm = as.character(V(graph)$go)
goterm = strsplit(goterm, split='|', fixed=T)
goterm = unlist(goterm)
# to find which goterm do not exist in goslim.g
# for (go in goterm) { if (!is.na(go) & go %ni% V(goslim.g)$name) { print(paste(go, "NOT EXISTING in goslim.g")) } }
# get slim term corresponding to GO id within 'goterm' list and also corresponding to 'golevel'
slimterm = V(goslim.g)[which(V(goslim.g)$name %in% goterm & V(goslim.g)$Ontology==golevel)]$Term
if (length(slimterm)==n) { return(graph) }
}
# Function to retrieve the list of CCs with more than "n" annotations
cc.more.annotation = function(graph, n, golevel) {
# get and unlist go term for the CC
goterm = as.character(V(graph)$go)
goterm = strsplit(goterm, split='|', fixed=T)
goterm = unlist(goterm)
# get slim term corresponding to GO id within 'goterm' list and also corresponding to 'golevel'
slimterm = V(goslim.g)[which(V(goslim.g)$name %in% goterm & V(goslim.g)$Ontology==golevel)]$Term
if (length(slimterm)>n) { return(graph) }
}
# Function to retrieve the list of CCs "full NA" (fully unannotated)
cc.na.annotation = function(graph) {
# get and unlist go term for the CC
annotation.table = as.data.frame(table(V(graph)$interpro_desc, useNA=c("ifany")))
if (dim(annotation.table)[1]==1) {
if (is.na(annotation.table$Var1)==T) {
return(graph)
}
}
}
# Function to display categorized CCs
cc.new.annotation = function(graph, golevel) {
print(paste("---------------------------------------------", vcount(graph)))
# get and unlist go term for the CC
goterm = as.character(V(graph)$go)
goterm = strsplit(goterm, split='|', fixed=T)
goterm = unlist(goterm)
table = as.data.frame(table(goterm, useNA="ifany"), stringsAsFactors=F)
table[,"term"] = NA
# ccna
if (dim(table)[1]==1) { if(is.na(table$goterm)==T) { print("YES -- ccna") } }
else {
slimterm = V(goslim.g)[which(V(goslim.g)$name %in% goterm & V(goslim.g)$Ontology==golevel)]$Term
# unfound goID
if (identical(slimterm, character(0))==TRUE) { print("GOid not found") }
else {
# cc1
if (length(slimterm)==1) { print("YES ---- cc1") }
# cc2
if (length(slimterm)==2) { print("YES ------ cc2") }
# cc3
if (length(slimterm)==3) { print("YES -------- cc3") }
# cc3+
if (length(slimterm)>3) { print("YES ---------- cc3+") }
}
}
}
test = lapply(comp.good.only[1:50], cc.new.annotation, golevel="BP")
test = test[!sapply(test, is.null)]
# id60
cc.with.1.annotation.BP = mclapply(comp.good.only, cc.n.annotation, n=1, golevel="BP", mc.cores=4)
cc.with.2.annotation.BP = mclapply(comp.good.only, cc.n.annotation, n=2, golevel="BP", mc.cores=4)
cc.with.3.annotation.BP = mclapply(comp.good.only, cc.n.annotation, n=3, golevel="BP", mc.cores=4)
cc.with.na.annotation = mclapply(comp.good.only, cc.na.annotation, mc.cores=4)
cc.with.more3.annotation.BP = mclapply(comp.good.only, cc.more.annotation, n=3, golevel="BP", mc.cores=4)
cc.with.1.annotation.BP = cc.with.1.annotation.BP[!sapply(cc.with.1.annotation.BP, is.null)]
cc.with.2.annotation.BP = cc.with.2.annotation.BP[!sapply(cc.with.2.annotation.BP, is.null)]
cc.with.3.annotation.BP = cc.with.3.annotation.BP[!sapply(cc.with.3.annotation.BP, is.null)]
cc.with.na.annotation = cc.with.na.annotation[!sapply(cc.with.na.annotation, is.null)]
cc.with.more3.annotation.BP = cc.with.more3.annotation.BP[!sapply(cc.with.more3.annotation.BP, is.null)]
saveRDS(cc.with.1.annotation.BP, file="cc.with.1.annotation.BP.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(cc.with.2.annotation.BP, file="cc.with.2.annotation.BP.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(cc.with.3.annotation.BP, file="cc.with.3.annotation.BP.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(cc.with.na.annotation, file="cc.with.na.annotation.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
################################################################################
# ----------------------- CORE/ACCESSORY/PAN ----------------------------------#
################################################################################
# Get the number of sequences per "prefix"
cc.prefix.list = as.data.frame(table(unlist(sapply(comp.good.only, function(x) V(x)$prefix))))
colnames(cc.prefix.list) = c("prefix", "nbseq")
# Sort by the number of sequences (descending)
cc.prefix.list = cc.prefix.list[order(cc.prefix.list$nbseq, decreasing=T),]
# Barplot : number of sequences per transcriptome
pdf("nbseq.per.transcriptome.pdf")
par(mar=c(10, 4.1, 4.1, 2.1))
barplot(cc.prefix.list$nbseq, ylab="number of sequences", names=cc.prefix.list$prefix, las=2, cex.axis=0.6, cex.names=0.6)
abline(h=9000, col="red")
dev.off()
# Keeping transcriptomes with more than 9000 sequences
cc.prefix.list.9000 = cc.prefix.list[which(cc.prefix.list$nbseq>9000),]
list.vec = list() # transcriptomes vector
core.size = vector() # core vector
pan.size = vector() # pan vector
acc.size = vector() # accessory vector
count = length(cc.prefix.list.9000$prefix)
for (transcriptome in cc.prefix.list.9000$prefix) {
list.vec = unlist(c(list.vec, transcriptome))
print(paste("remaining : ", count))
# 1 transcriptome
if (length(list.vec)==1) {
core.size = NA
pan.size = NA
acc.size = NA
}
# at least 2 transcriptomes
else if (length(list.vec)>=2) {
v.spec = c.uniq.slim[which(c.uniq.slim$prefix %in% list.vec),]
v.spec.names = unique(v.spec$name)
e.spec = e[which(e$from %in% v.spec.names & e$to %in% v.spec.names),]
g.spec = graph.data.frame(e.spec, directed=F, vertices=v.spec)
comp.spec = decompose.graph(g.spec, min.vertices=2)
comp.prefix.list = lapply(comp.spec, function(x) return(unique(V(x)$prefix)))
comp.size = unlist(lapply(comp.prefix.list, function(x) length(x)))
core.size = c(core.size, length(which(comp.size==length(list.vec))))
acc.size = c(acc.size, length(which(comp.size==1)))
pan.size = c(pan.size, length(comp.spec))
print(core.size)
print(acc.size)
print(pan.size)
}
count = count - 1
}
# Retrieve CCs : core / acc / pan
fct = function (g, n) { if ( length(unique(V(g)$prefix))==n ) { return (g) }}
core.comp = lapply(comp.spec, fct, n=length(list.vec))
core.comp = core.comp[!sapply(core.comp, is.null)]
acc.comp = lapply(comp.spec, fct, n=1)
acc.comp = acc.comp[!sapply(acc.comp, is.null)]
pan.comp = comp.spec
saveRDS(core.comp, file="core.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(acc.comp, file="acc.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(pan.comp, file="pan.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(core.size, file="core.size.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(acc.size, file="acc.size.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(pan.size, file="pan.size.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
# Tables creation
core.pan.table = cbind(seq(1,length(core.size),1), core.size, acc.size, pan.size)
colnames(core.pan.table) = c("nbtranscriptome","core","accesory", "pan")
core.pan.table = as.data.frame(core.pan.table)
saveRDS(core.pan.table, file="core.pan.table.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
# Plot
pdf("core.pan.genome_CC_NEW-prefix.pdf")
lp = ggplot(core.pan.table, aes(nbtranscriptome))
lp = lp + geom_line(aes(y = core, colour = "Core")) + geom_point(aes(y = core, colour = "Core"))
lp = lp + geom_line(aes(y = accesory, colour = "Accesory")) + geom_point(aes(y = accesory, colour = "Accesory"))
lp = lp + geom_line(aes(y = pan, colour = "Pan")) + geom_point(aes(y = pan, colour = "Pan"))
lp = lp + geom_text(aes(y = core, label=core), vjust=2, hjust=1.5, size=1.5, angle=90)
lp = lp + geom_text(aes(y = accesory, label=accesory), vjust=2, hjust=1.5, size=1.5, angle=90)
lp = lp + geom_text(aes(y = pan, label=pan), vjust=2, hjust=1.5, size=1.5, angle=90)
lp = lp + xlab("number of transcriptomes")
lp = lp + ylab("number of connected components")
lp = lp + theme(legend.title=element_blank())
lp
dev.off()
pdf("core.pan.genome_CC_NEW-prefix_LOG.pdf")
lp = ggplot(core.pan.table, aes(nbtranscriptome))
lp = lp + geom_line(aes(y = core.log, colour = "Core")) + geom_point(aes(y = core.log, colour = "Core"))
lp = lp + geom_line(aes(y = accesory.log, colour = "Accesory")) + geom_point(aes(y = accesory.log, colour = "Accesory"))
lp = lp + geom_line(aes(y = pan.log, colour = "Pan")) + geom_point(aes(y = pan.log, colour = "Pan"))
lp = lp + geom_text(aes(y = core.log, label=core), vjust=2, hjust=1.5, size=1.5, angle=90)
lp = lp + geom_text(aes(y = accesory.log, label=accesory), vjust=2, hjust=1.5, size=1.5, angle=90)
lp = lp + geom_text(aes(y = pan.log, label=pan), vjust=2, hjust=1.5, size=1.5, angle=90)
lp = lp + xlab("number of transcriptomes")
lp = lp + ylab("number of connected components")
lp = lp + theme(legend.title=element_blank())
lp
dev.off()
# Predicting model core/accessory/pan of dinoflagellate proteome
# Purpose : try to predict how this proteome size could evolve
# source : http://stackoverflow.com/questions/15535877/extrapolate-in-r-for-a-time-series-data
core.pan.table = readRDS("../RDS/core.pan.table.rds")
core.pan.table = core.pan.table[-1,]
x = core.pan.table$nbtranscriptome
y = core.pan.table$core
# Noise creation
noise = rnorm(length(x), mean=10, sd=80)
noisy.y = y + noise
plot(x,noisy.y,col='deepskyblue4',xlab='q',main='Observed data')
lines(x,y,col='firebrick1',lwd=3)
# According to my guess, the possible function could be :
# y = N / x
# with y the number of core genes, N the number of genes for the first point,
# and x the number of transcriptome
# the more transcriptomes are involved, the more we could reach a "plateau"
# that means : we reach a maximum number of core genes existing in the nature
dat = cbind(x, y, noisy.y)
colnames(dat) = c("nbtrans","nbcore","noisy_y")
dat = as.data.frame(dat)
dat$pred1 = predict(nls(nbcore~a/nbtrans, data=dat))
dat$pred2 = predict(nls(noisy_y~a/nbtrans, data=dat))
# pvalue of the a parameter :
mod = nls(noisy_y~a/nbtrans, data=dat)
summary(mod)
# Parameters:
# Estimate Std. Error t value Pr(>|t|)
# a 9008.3 318.1 28.32 <2e-16 ***
# correlation : prediction vs data
cor(dat$nbcore, dat$pred1)
# [1] 0.976478
## extrapolate based on model
pred = data.frame(nbtrans=2:50)
mod = nls(nbcore~a/nbtrans, data=dat)
mod_noisy = nls(noisy_y~a/nbtrans, data=dat)
mod_noisy = nls(noisy_y~a/(nbtrans+b), data=dat, start=c(a=1, b=2))
pred$nbcore = predict(mod, newdata=pred)
pred$nbcore_noisy = predict(mod_noisy, newdata=pred)
p1 = ggplot(dat, aes(x=nbtrans, y=noisy_y)) +
geom_point(color="darkgrey") +
geom_line(aes(y=nbcore, color="nbcore"), color="black" ,size=1) +
geom_point(aes(y=nbcore, color="nbcore"), color="black") +
geom_hline(aes(yintercept=0)) +
geom_vline(aes(xintercept=0)) +
geom_line(aes(y=pred1, colour="data_model"), size=1) +
geom_line(data=pred, aes(x=nbtrans, y=nbcore_noisy, colour="prediction"), color="blue", linetype=2)
print(p1)
pdf("extrapolate_core_300.pdf")
print(p1)
dev.off()
write.table(pred, file="extrapolate_core.txt",row.names=F, quote=F, sep="\t")
# function to create random string of length = 12
MHmakeRandomString <- function(n=1, lenght=12) {
# initialize vector
randomString <- c(1:n)
for (i in 1:n) { randomString[i] <- paste(sample(c(0:9, letters, LETTERS), lenght, replace=TRUE), collapse="") }
return(randomString)
}
# function to extract sequences from each components of a list
extract.cc.sequence = function(cc) {
cc.nodes.index = ""
# create component index
index_name = MHmakeRandomString()
index_nodes = vcount(cc)
index_edges = ecount(cc)
cc.nodes.index = as.character(paste(index_nodes, "_", index_edges, "_", index_name, sep=""))
# recupere les noms des sequences
# cc.nodes.names = as.data.frame(V(core.comp[[1]])$name)
cc.nodes.names = as.data.frame(V(cc)$name)
write.table(cc.nodes.names,
file=paste(cc.nodes.index, ".txt", sep=""),
append=FALSE,
sep="\n",
row.names=FALSE,
col.names=FALSE,
quote=FALSE)
return(cc.nodes.names)
}
# Retrieving IDs for core CCs in .txt files
lapply(core.comp, extract.cc.sequence)
# SHELL
#
# Concatenate ID files
# cat *.txt > core.seq
#
# Extracting core sequence based on their IDs
# perl extractFromFasta.pl ALL.clean.pep list core.seq > core.fasta
#
core.blastp.nr = read.table("core.vs.nr.diamond.1e-25.blastp", h=F)
colnames(core.blastp.nr) = c("name","nr.subject","nr.id_perc","nr.align_len","nr.mismatches","nr.gap_openings","nr.qstart","nr.qend","nr.sstart","nr.send","nr.evalue","nr.bitscore")
comp.good.only_ccindex = readRDS("../../RDS/comp.good.only_ccindex.rds")
core.comp = readRDS("../../RDS/core.core.rds")
core.comp.seq = as.data.frame(unlist(lapply(core.comp, function(x) return(V(x)$name))))
colnames(core.comp.seq) = "name"
c.uniq.slim = readRDS("../../RDS/c.uniq.slim.rds")
a = merge(x=core.comp.seq, y=comp.good.only_ccindex, by="name", all.x=T)
b = merge(x=a, y=c.uniq.slim, by="name", all.x=T)
c = merge(x=b, y=core.blastp.nr, by="name", all.x=T)
c = c[!duplicated(c[,"name"]),]
cclist = list()
for(cc in c$cc_index){
seq = c[which(c$cc_index==cc),]
a = as.numeric(dim(seq[which(!is.na(seq$nr.subject)),])[1])
u = as.numeric(dim(seq[which(is.na(seq$nr.subject)),])[1])
if(a!=0 & u!=0) {
prop = a/u
if (prop>=1.0) { cclist = c(cclist,cc) }
}
}
cclist = as.data.frame(unique(unlist(cclist)))
colnames(cclist) = "cc_index"
res = merge(x=cclist, y=a, by="cc_index", all.x=T)
res2 = merge(x=res, y=core.blastp.nr, by="name", all.x=T)
res2 = res2[!duplicated(res2[,"name"]),]
res2_NA = res2[which(is.na(res2$nr.subject)),]
res2_NA = res2_NA[!duplicated(res2_NA[,"name"]),]
# Comparisons to nr / sp / BUSCO
core.blastp.nr = read.table("core.vs.nr.diamond.1e-25.blastp", h=F)
core.blastp.sp = read.table("core.vs.sp.diamond.1e-25.blastp", h=F)
core.blastp.busco = read.table("core.vs.busco.busco.txt", fill=T)
colnames(core.blastp.nr) = c("name","nr.subject","nr.id_perc","nr.align_len","nr.mismatches","nr.gap_openings","nr.qstart","nr.qend","nr.sstart","nr.send","nr.evalue","nr.bitscore")
colnames(core.blastp.sp) = c("name","sp.subject","sp.id_perc","sp.align_len","sp.mismatches","sp.gap_openings","sp.qstart","sp.qend","sp.sstart","sp.send","sp.evalue","sp.bitscore")
colnames(core.blastp.busco) = c("busco_group","status","scaffold","bitscore","length")
core.blastp.nr.seq = as.data.frame(unique(core.blastp.nr$name), stringsAsFactors=FALSE)
core.blastp.sp.seq = as.data.frame(unique(core.blastp.sp$name), stringsAsFactors=FALSE)
core.blastp.busco.seq = as.data.frame(unique(core.blastp.busco$scaffold), stringsAsFactors=FALSE)
colnames(core.blastp.nr.seq) = "name"
colnames(core.blastp.sp.seq) = "name"
colnames(core.blastp.busco.seq) = "name"
# function to number sequences to each components of a list
number.cc.sequence = function(cc) {
# create component index of length N
n=1
lenght=12
# initialize vector
randomString = c(1:n)
for (i in 1:n) { randomString[i] = paste(sample(c(0:9, letters, LETTERS), lenght, replace=TRUE), collapse="") }
seq.table = as.data.frame(V(cc)$name)
seq.table$cc_index = rep(randomString, dim(seq.table)[1])
colnames(seq.table) = c("name","cc_index")
return(seq.table)
}
# Retrieving CORE sequences with their CCs association ID
core.comp = readRDS("RDS/core.comp.rds")
core.seq = mclapply(core.comp, number.cc.sequence, mc.cores=8)
core.seq = do.call("rbind", core.seq)
# Merging tables
core.comp.nr.blastp = merge(x=core.blastp.nr.seq, y=core.seq, all.x=TRUE)
core.comp.sp.blastp = merge(x=core.blastp.sp.seq, y=core.seq, all.x=TRUE)
core.comp.busco.blastp = merge(x=core.blastp.busco.seq, y=core.seq, all.x=TRUE)
matched.seq = rbind(core.comp.nr.blastp, core.comp.sp.blastp, core.comp.busco.blastp)
unmatched.seq = core.seq[which(core.seq$name %ni% matched.seq$name),]
# How many core full NA CCs ?
core.full.na = lapply(core.comp, cc.na.annotation)
core.full.na = core.full.na[!sapply(core.full.na, is.null)]
core.full.na.seq = lapply(core.full.na, number.cc.sequence)
core.full.na.seq = do.call("rbind", core.full.na.seq)
core.no.hit = merge(x=unmatched.seq, y=core.full.na.seq, by="name")
# Get the CCs IDs
unique(core.no.hit$cc_index.y)
# [1] "RXJHlIhcw9Cm" "5tQiTiLhfS48" "AAe3pgnSyLlU" "DyqXnkvAU9x4" "ZXNkylzG7wZZ"
# [6] "9Gz7Y7C3Imgd" "radACsCnwOri" "Ik38rtFb8FBM" "kVCzD7jEKtA6" "tVXQSIcySEnl"
# [11] "V6vuicCBoxfx" "1MDzFe4QWHhn" "48FcxWMbaivn" "8toCsU0zFozp" "C7Utibrxnf8y"
# [16] "6QcuqfoUXL6O"
nacore1 = core.full.na.seq[which(core.full.na.seq$cc_index=="RXJHlIhcw9Cm"),]
nacore2 = core.full.na.seq[which(core.full.na.seq$cc_index=="5tQiTiLhfS48"),]
nacore3 = core.full.na.seq[which(core.full.na.seq$cc_index=="AAe3pgnSyLlU"),]
nacore4 = core.full.na.seq[which(core.full.na.seq$cc_index=="DyqXnkvAU9x4"),]
nacore5 = core.full.na.seq[which(core.full.na.seq$cc_index=="ZXNkylzG7wZZ"),]
nacore6 = core.full.na.seq[which(core.full.na.seq$cc_index=="9Gz7Y7C3Imgd"),]
nacore7 = core.full.na.seq[which(core.full.na.seq$cc_index=="radACsCnwOri"),]
nacore8 = core.full.na.seq[which(core.full.na.seq$cc_index=="Ik38rtFb8FBM"),]
nacore9 = core.full.na.seq[which(core.full.na.seq$cc_index=="kVCzD7jEKtA6"),]
nacore10 = core.full.na.seq[which(core.full.na.seq$cc_index=="tVXQSIcySEnl"),]
nacore11 = core.full.na.seq[which(core.full.na.seq$cc_index=="V6vuicCBoxfx"),]
nacore12 = core.full.na.seq[which(core.full.na.seq$cc_index=="1MDzFe4QWHhn"),]
nacore13 = core.full.na.seq[which(core.full.na.seq$cc_index=="48FcxWMbaivn"),]
nacore14 = core.full.na.seq[which(core.full.na.seq$cc_index=="8toCsU0zFozp"),]
nacore15 = core.full.na.seq[which(core.full.na.seq$cc_index=="C7Utibrxnf8y"),]
nacore16 = core.full.na.seq[which(core.full.na.seq$cc_index=="6QcuqfoUXL6O"),]
write(as.character(nacore1$name), file="nacore1.txt")
write(as.character(nacore2$name), file="nacore2.txt")
write(as.character(nacore3$name), file="nacore3.txt")
write(as.character(nacore4$name), file="nacore4.txt")
write(as.character(nacore5$name), file="nacore5.txt")
write(as.character(nacore6$name), file="nacore6.txt")
write(as.character(nacore7$name), file="nacore7.txt")
write(as.character(nacore8$name), file="nacore8.txt")
write(as.character(nacore9$name), file="nacore9.txt")
write(as.character(nacore10$name), file="nacore10.txt")
write(as.character(nacore11$name), file="nacore11.txt")
write(as.character(nacore12$name), file="nacore12.txt")
write(as.character(nacore13$name), file="nacore13.txt")
write(as.character(nacore14$name), file="nacore14.txt")
write(as.character(nacore15$name), file="nacore15.txt")
write(as.character(nacore16$name), file="nacore16.txt")
# Core CCs fonctions summary (INTERPRO - GOslim)
core.interpro = merge(x=core.seq, y=c.uniq.slim, all.x=T)
core.interpro.MFslim = as.data.frame(table(core.interpro$MFslim))
core.interpro.BPslim = as.data.frame(table(core.interpro$BPslim))
core.interpro.CCslim = as.data.frame(table(core.interpro$CCslim))
core.interpro.MFslim = core.interpro.MFslim[order(core.interpro.MFslim$Freq, decreasing=T),]
core.interpro.BPslim = core.interpro.BPslim[order(core.interpro.BPslim$Freq, decreasing=T),]
core.interpro.CCslim = core.interpro.CCslim[order(core.interpro.CCslim$Freq, decreasing=T),]
core.interpro.MFslim = cbind(head(core.interpro.MFslim, 10), rep("MF",10))
core.interpro.BPslim = cbind(head(core.interpro.BPslim, 10), rep("BP",10))
core.interpro.CCslim = cbind(head(core.interpro.CCslim, 10), rep("CC",10))
colnames(core.interpro.MFslim) = c("annotation","Freq","level")
colnames(core.interpro.BPslim) = c("annotation","Freq","level")
colnames(core.interpro.CCslim) = c("annotation","Freq","level")
core.interpro.go = rbind(core.interpro.MFslim, core.interpro.BPslim, core.interpro.CCslim)
core.interpro.go$annotation = factor(core.interpro.go$annotation, levels=core.interpro.go$annotation)
pdf("core_interpro_annotation_top10.pdf")
ggplot(core.interpro.go, aes(x=annotation, y=Freq, fill=level)) +
geom_bar(stat="identity", position="dodge") +
scale_x_discrete(labels=core.interpro.go$annotation) +
theme(axis.text=element_text(size=8)) +
coord_flip()
dev.off()
# STATISTIC : is there some under-represented core CCs ?
mat = lapply(core.comp, function(x) as.data.frame(as.numeric(table(V(x)$prefix))))
mat = lapply(mat, function(x) t(x))
div.shannon.prefix = unlist(lapply(mat, function(x) round(diversity(x, "shannon"),3)))
hmax = log(43) # maximum number of species
div.pielou.prefix = div.shannon.prefix/hmax
################################################################################
# ------------------------- FUNCTIONAL TRAITS ---------------------------------#
################################################################################
kleptoplastidy.comp = function(graph, param) {
res = as.data.frame(table(V(graph)$kleptoplastidy, useNA="ifany"))
if (dim(res)[1]==1) { if (is.na(res$Var1)==F & res$Var1==param) { return(graph) }}
}
mixotrophy.comp = function(graph, param) {
res = as.data.frame(table(V(graph)$mixotrophy, useNA="ifany"))
if (dim(res)[1]==1) { if (is.na(res$Var1)==F & res$Var1==param) { return(graph) }}
}
symbionte.comp = function(graph, param) {
res = as.data.frame(table(V(graph)$symbiont, useNA="ifany"))
if (dim(res)[1]==1) { if (is.na(res$Var1)==F & res$Var1==param) { return(graph) }}
}
ichyotoxin.comp = function(graph, param) {
res = as.data.frame(table(V(graph)$ichyotoxin, useNA="ifany"))
if (dim(res)[1]==1) { if (is.na(res$Var1)==F & res$Var1==param) { return(graph) }}
}
parasite.comp = function(graph, param) {
res = as.data.frame(table(V(graph)$parasite, useNA="ifany"))
if (dim(res)[1]==1) { if (is.na(res$Var1)==F & res$Var1==param) { return(graph) }}
}
dmsp.comp = function(graph, param) {
res = as.data.frame(table(V(graph)$dmsp, useNA="ifany"))
if (dim(res)[1]==1) { if (is.na(res$Var1)==F & res$Var1==param) { return(graph) }}
}
thecate.comp = function(graph, param) {
res = as.data.frame(table(V(graph)$thecate, useNA="ifany"))
if (dim(res)[1]==1) { if (is.na(res$Var1)==F & res$Var1==param) { return(graph) }}
}
cyst.forming.comp = function(graph, param) {
res = as.data.frame(table(V(graph)$cyst.formation, useNA="ifany"))
if (dim(res)[1]==1) { if (is.na(res$Var1)==F & res$Var1==param) { return(graph) }}
}
phototrophy.comp = function(graph, param) {
res = as.data.frame(table(V(graph)$phototrophy, useNA="ifany"))
if (dim(res)[1]==1) { if (is.na(res$Var1)==F & res$Var1==param) { return(graph) }}
}
chloroplast.comp = function(graph) {
res = as.data.frame(table(V(graph)$chloroplast, useNA="ifany"))
if (dim(res)[1]>=1) {
if ("R" %ni% res$Var1 & NA %ni% res$Var1) {
return(graph)
}
}
}
harmful4humans.comp = function(graph) {
res = as.data.frame(table(V(graph)$harmful.for.human, useNA="ifany"))
if (dim(res)[1]>=1) {
if ("no" %ni% res$Var1 & NA %ni% res$Var1) {
return(graph)
}
}
}
# Full CCs defintion
klepto.comp = lapply(comp.good.only, kleptoplastidy.comp, param="yes")
klepto.comp = klepto.comp[!sapply(klepto.comp, is.null)]
mixo.comp = lapply(comp.good.only, mixotrophy.comp, param="yes")
mixo.comp = mixo.comp[!sapply(mixo.comp, is.null)]
symb.comp = lapply(comp.good.only, symbionte.comp, param="yes")
symb.comp = symb.comp[!sapply(symb.comp, is.null)]
h4h.comp = sapply(comp.good.only, harmful4humans.comp)
h4h.comp = h4h.comp[!sapply(h4h.comp, is.null)]
ichyo.comp = lapply(comp.good.only, ichyotoxin.comp, param="yes")
ichyo.comp = ichyo.comp[!sapply(ichyo.comp, is.null)]
parasite.comp = lapply(comp.good.only, parasite.comp, param="yes")
parasite.comp = parasite.comp[!sapply(parasite.comp, is.null)]
dmsp.comp = lapply(comp.good.only, dmsp.comp, param="yes")
dmsp.comp = dmsp.comp[!sapply(dmsp.comp, is.null)]
thecate.comp = lapply(comp.good.only, thecate.comp, param="yes")
thecate.comp = thecate.comp[!sapply(thecate.comp, is.null)]
cyst.forming.comp = lapply(comp.good.only, cyst.forming.comp, param="yes")
cyst.forming.comp = cyst.forming.comp[!sapply(cyst.forming.comp, is.null)]
chloro.comp = lapply(comp.good.only, chloroplast.comp)
chloro.comp = chloro.comp[!sapply(chloro.comp, is.null)]
phototrophy.comp = lapply(comp.good.only, phototrophy.comp, param="yes")
phototrophy.comp = phototrophy.comp[!sapply(phototrophy.comp, is.null)]
saveRDS(klepto.comp, file="klepto.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(h4h.comp, file="h4h.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(mixo.comp, file="mixo.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(symb.comp, file="symb.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(ichyo.comp, file="ichyo.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(parasite.comp, file="parasite.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(dmsp.comp, file="dmsp.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(thecate.comp, file="thecate.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(cyst.forming.comp, file="cyst.forming.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
saveRDS(chloro.comp, file="chloro.comp.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
# Extracting h4h CCs
h4h.seq = unlist(lapply(h4h.comp, function(x) return(V(x)$name)))
write(h4h.seq, file="h4h.seq")
# Extracting non-h4h CCs
all.seq = unlist(V(g.goog.only)$name)
all.wo.h4h.seq = all.seq[which(all.seq %ni% h4h.seq)]
write(all.wo.h4h.seq, file="all.wo.h4h.seq")
# How many transcriptomes per functional trait ?
info = readRDS("../RDS/info.rds")
symb.pre = length(unique(info[which(info$symbiont=="yes" & info$transcriptome.quality=="good"),"prefix"]))
chlo.pre = length(unique(info[which(info$chloroplast!="R" & info$transcriptome.quality=="good"),"prefix"]))
mixo.pre = length(unique(info[which(info$mixo=="yes" & info$transcriptome.quality=="good"),"prefix"]))
h4h.pre = length(unique(info[which(info$harmful.for.human!="no" & info$transcriptome.quality=="good"),"prefix"]))
klep.pre = length(unique(info[which(info$kleptoplastidy=="yes" & info$transcriptome.quality=="good"),"prefix"]))
ichy.pre = length(unique(info[which(info$ichyotoxin=="yes" & info$transcriptome.quality=="good"),"prefix"]))
para.pre = length(unique(info[which(info$parasite=="yes" & info$transcriptome.quality=="good"),"prefix"]))
dmsp.pre = length(unique(info[which(info$dmsp=="yes" & info$transcriptome.quality=="good"),"prefix"]))
thec.pre = length(unique(info[which(info$thecate=="yes" & info$transcriptome.quality=="good"),"prefix"]))
cyst.pre = length(unique(info[which(info$cyst.formation=="yes" & info$transcriptome.quality=="good"),"prefix"]))
# max symbiont transcriptomes = 12
# max chloroplast transcriptomes = 43
# max mixotrophy transcriptomes = 18
# max harmful4humans transcriptomes = 14
# max kleptoplastidy transcriptomes = 2
# max ichyotoxin transcriptomes = 3
# max parasite transcriptomes = 1
# max dmsp transcriptomes = 25
# max thecate transcriptomes = 28
# max cyst transcriptomes = 16
# Seeking full NA CCs for each functional trait
chloro.na.cc = mclapply(chloro.comp, cc.na.annotation, mc.cores=4)
chloro.na.cc = chloro.na.cc[!sapply(chloro.na.cc, is.null)]
saveRDS(chloro.na.cc, file="chloro.na.cc.rds", ascii=FALSE, version=NULL, compress=TRUE, refhook=NULL)
# CC chloro - full NA = 220,081
# CC symbiont - full NA = 64,203
# CC thecate - full NA = 111,538
# CC DMSP - full NA = 99,906
# CC mixotrophy - full NA = 40,042
# CC h4h - full NA = 31,496
# CC klepto - full NA = 5,948
# CC ichyo - full NA = 3,404
# CC cyst - full NA = 54,796
# CC parasite - full NA = 640
# Looking for CCs having 1/2/3... TRANSCRIPTOMES for each functional trait
symb.comp = readRDS("../RDS/symb.comp.rds")
chloro.comp = readRDS("../RDS/chloro.comp.rds")
mixo.comp = readRDS("../RDS/mixo.comp.rds")
h4h.comp = readRDS("../RDS/h4h.comp.rds")
klepto.comp = readRDS("../RDS/klepto.comp.rds")
ichyo.comp = readRDS("../RDS/ichyo.comp.rds")
para.comp = readRDS("../RDS/para.comp.rds")
dmsp.comp = readRDS("../RDS/dmsp.comp.rds")
thecate.comp = readRDS("../RDS/thecate.comp.rds")
cyst.comp = readRDS("../RDS/cyst.comp.rds")
# Function to retrieve the list of CCs with "n" different transcriptomes
cc.n.transcriptome = function(graph, n) {
# get, unlist and count the number of prefix found in the graph
pre = as.character(V(graph)$prefix)
pre = unlist(pre)
pre = unique(pre)
nbpre = length(pre)
if (nbpre==n) { return(graph) }
}
# symb CCs (DO THE SAME THING FOR OTHER FUNCTIONAL TRAITS)
symb.comp.12.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 12), mc.cores=8) ; symb.comp.12.prefix = symb.comp.12.prefix[!sapply(symb.comp.12.prefix, is.null)] ; saveRDS(symb.comp.12.prefix, file="symb.comp.12.prefix.rds")
symb.comp.11.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 11), mc.cores=8) ; symb.comp.11.prefix = symb.comp.11.prefix[!sapply(symb.comp.11.prefix, is.null)] ; saveRDS(symb.comp.11.prefix, file="symb.comp.11.prefix.rds")
symb.comp.10.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 10), mc.cores=8) ; symb.comp.10.prefix = symb.comp.10.prefix[!sapply(symb.comp.10.prefix, is.null)] ; saveRDS(symb.comp.10.prefix, file="symb.comp.10.prefix.rds")
symb.comp.9.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 9), mc.cores=8) ; symb.comp.9.prefix = symb.comp.9.prefix[!sapply(symb.comp.9.prefix, is.null)] ; saveRDS(symb.comp.9.prefix, file="symb.comp.9.prefix.rds")
symb.comp.8.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 8), mc.cores=8) ; symb.comp.8.prefix = symb.comp.8.prefix[!sapply(symb.comp.8.prefix, is.null)] ; saveRDS(symb.comp.8.prefix, file="symb.comp.8.prefix.rds")
symb.comp.7.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 7), mc.cores=8) ; symb.comp.7.prefix = symb.comp.7.prefix[!sapply(symb.comp.7.prefix, is.null)] ; saveRDS(symb.comp.7.prefix, file="symb.comp.7.prefix.rds")
symb.comp.6.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 6), mc.cores=8) ; symb.comp.6.prefix = symb.comp.6.prefix[!sapply(symb.comp.6.prefix, is.null)] ; saveRDS(symb.comp.6.prefix, file="symb.comp.6.prefix.rds")
symb.comp.5.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 5), mc.cores=8) ; symb.comp.5.prefix = symb.comp.5.prefix[!sapply(symb.comp.5.prefix, is.null)] ; saveRDS(symb.comp.5.prefix, file="symb.comp.5.prefix.rds")
symb.comp.4.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 4), mc.cores=8) ; symb.comp.4.prefix = symb.comp.4.prefix[!sapply(symb.comp.4.prefix, is.null)] ; saveRDS(symb.comp.4.prefix, file="symb.comp.4.prefix.rds")
symb.comp.3.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 3), mc.cores=8) ; symb.comp.3.prefix = symb.comp.3.prefix[!sapply(symb.comp.3.prefix, is.null)] ; saveRDS(symb.comp.3.prefix, file="symb.comp.3.prefix.rds")
symb.comp.2.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 2), mc.cores=8) ; symb.comp.2.prefix = symb.comp.2.prefix[!sapply(symb.comp.2.prefix, is.null)] ; saveRDS(symb.comp.2.prefix, file="symb.comp.2.prefix.rds")
symb.comp.1.prefix = mclapply(symb.comp, function(x) cc.n.transcriptome(x, 1), mc.cores=8) ; symb.comp.1.prefix = symb.comp.1.prefix[!sapply(symb.comp.1.prefix, is.null)] ; saveRDS(symb.comp.1.prefix, file="symb.comp.1.prefix.rds")
table = cbind(seq(1,12,1),c(length(symb.comp.1.prefix),
length(symb.comp.2.prefix),
length(symb.comp.3.prefix),
length(symb.comp.4.prefix),
length(symb.comp.5.prefix),
length(symb.comp.6.prefix),
length(symb.comp.7.prefix),
length(symb.comp.8.prefix),
length(symb.comp.9.prefix),
length(symb.comp.10.prefix),
length(symb.comp.11.prefix),
length(symb.comp.12.prefix)))
colnames(table) = c("nb_prefix","nb_comp")
write.table(table, file="symb.table.txt", row.names=F, quote=F)
# Plot
tab = read.table("symb.table.txt", h=T)
pdf("symb_bp.pdf")
bp = barplot(tab$nb_comp,
names.arg=tab$nb_prefix,
ylim=c(0,max(tab$nb_comp+2000)))
text(x=bp, y=tab$nb_comp, label=tab$nb_comp, pos=3, cex=0.8)
dev.off()
pdf("symb_rose.pdf")
bp = ggplot(tab, aes(factor(nb_prefix), log(nb_comp)))
bp = bp + geom_bar(stat="identity", width=0.8, color="black", fill="#999999")
bp = bp + scale_y_continuous(breaks=0:10)
bp = bp + coord_polar()
bp = bp + labs(x = "", y = "")
bp = bp + geom_text(aes(label=paste("(",nb_comp,")",sep=""), y=log(nb_comp)+3), size=2.2)
bp = bp + theme(panel.grid.minor = element_blank())
bp
dev.off()
# Function to draw the graph
plot.my.graph = function(net) {
par(mfrow=c(3, 3))
# assign the "genus" attribute as the vertex color
vpref = unique(V(net)$genus)
vcol = rainbow(length(vpref))[rank(vpref)]
color.vertex = data.frame(v1=unique(vpref), v2=unique(vcol))
colnames(color.vertex) = c("genus", "color")
# assign color to each species
V(net)$color=V(net)$genus
for (i in 1:dim(color.vertex)[1]) { V(net)$color=gsub(color.vertex[i,1], color.vertex[i,2], V(net)$color) }
# plot the network
network_plot = plot(net,
vertex.label = "",
vertex.label.color='black',
vertex.label.cex=0.4,
vertex.frame.color='white',
vertex.size=15,
edge.color='grey',
edge.curved=FALSE,
layout=layout.lgl,
main=paste("Component : ",vcount(net),"v",ecount(net),"e", sep=""))
# plot the genus distribution
genus.distr = as.data.frame(table(V(net)$genus))
colnames(genus.distr) = c("genus", "Freq")
genus.distr = merge(x=genus.distr, y=color.vertex, all.x=TRUE, by="genus")
rownames(genus.distr) = genus.distr$genus
bp = barplot(genus.distr$Freq,
names.arg=genus.distr$genus,
cex.names=0.8,
las=2,
col=as.character(genus.distr$color),
main="Number of vertices per genus",
ylab="Number of vertices")
# plot the toppology indexes
# assortativity
assortativity = round(assortativity.degree(net, directed=F),3)
# calculate diversity at genus level
mat = as.data.frame(table(V(net)$genus))
colnames(mat) = c("genus", "Freq")
rownames(mat) = mat$genus
mat$genus = NULL
mat = t(mat)
div.shannon.genus = round(diversity(mat, "shannon"),3)
div.simpson.genus = round(diversity(mat, "simpson"),3)
divplot = plot(1, type="n", axes=F, xlab="", ylab="")
text(x = 1,
y = 1,
cex = 0.8,
col = "black",
paste("Assortativity = ", assortativity, "\n",
"Shannon diversity (at genus level) = ", div.shannon.genus, "\n",
"Simpson diversity (at genus level) = ", div.simpson.genus))
# annotation
MFslim = as.data.frame(table(V(net)$MFslim, useNA='ifany'))
BPslim = as.data.frame(table(V(net)$BPslim, useNA='ifany'))
CCslim = as.data.frame(table(V(net)$CCslim, useNA='ifany'))
MFslim = MFslim[order(MFslim$Freq, decreasing=T),]
BPslim = BPslim[order(BPslim$Freq, decreasing=T),]
CCslim = CCslim[order(CCslim$Freq, decreasing=T),]
colnames(MFslim) = c("annotation","Freq")
colnames(BPslim) = c("annotation","Freq")
colnames(CCslim) = c("annotation","Freq")
# MFslim[is.na(MFslim$annotation),"annotation"] = "na"
barplot(MFslim$Freq, names.arg=MFslim$annotation, cex.names=0.8, las=2, main="MF GOslim", ylab="Number of vertices")
barplot(BPslim$Freq, names.arg=BPslim$annotation, cex.names=0.8, las=2, main="BP GOslim", ylab="Number of vertices")
barplot(CCslim$Freq, names.arg=CCslim$annotation, cex.names=0.8, las=2, main="CC GOslim", ylab="Number of vertices")
}
symb.comp.8.prefix = readRDS("symb.comp.8.prefix.rds")
pdf("symb_comp8.pdf")
lapply(symb.comp.8.prefix, plot.my.graph)
dev.off()
# top 10 annotations for each trait
symb.annot.mf = as.data.frame(table(unlist(lapply(symb.comp, function(x) return(V(x)$MFslim)))))
symb.annot.bp = as.data.frame(table(unlist(lapply(symb.comp, function(x) return(V(x)$BPslim)))))
symb.annot.cc = as.data.frame(table(unlist(lapply(symb.comp, function(x) return(V(x)$CCslim)))))
symb.annot.mf = symb.annot.mf[order(symb.annot.mf$Freq, decreasing=T),]
symb.annot.bp = symb.annot.bp[order(symb.annot.bp$Freq, decreasing=T),]
symb.annot.cc = symb.annot.cc[order(symb.annot.cc$Freq, decreasing=T),]
symb.annot.mf = cbind(head(symb.annot.mf, 10), rep("MF",10))