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8_omcl_comparison.R
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library(ggplot2)
library(pheatmap)
library(reshape)
library(ggplot2)
library(gridExtra)
library(RSvgDevice)
library(xtable)
Ef.nRC <- read.table("output_data/Ef_norm_counts.csv", sep=",", header=TRUE)
Ef.nRC <- matrix(unlist(Ef.nRC), nrow=nrow(Ef.nRC), ncol=ncol(Ef.nRC),
dimnames=list(rownames(Ef.nRC), colnames(Ef.nRC)))
### ORTHOMCL categories
read.omcl <- function(path="data/Omcl_groups.txt"){
omcl <- readLines(path)
omcl <- lapply(omcl, strsplit, " ")
omcl <- lapply(omcl, function(x) x[[1]])
names(omcl) <- lapply(omcl, function(x) gsub("\\:", "", x[1]))
omcl <- lapply(omcl, function(x) x[-1])
## make gene-ID identifiers used in the omcl clsters
omcl <- lapply(omcl, function (x)
gsub("(Efa|)NODE_(.*_\\d+.g\\d+).t\\d+","\\1EfaB_\\2", x))
omcl <- lapply(omcl, function (x) x[!duplicated(x)])
omcl <- omcl[!unlist(lapply(omcl, length))==1]
omcl <- omcl[!names(omcl)%in%"api10278"] ## exclude a cluster... wonder now why that ;-)
}
omcl <- read.omcl()
get.genes.4.clusters <- function (clusters) {
gsub("Efa\\|", "", grep("Efa\\|",
unlist(clusters),
value=TRUE))
}
get.omcl.categories <- function(ortho.obj){
## Orthomcl
get.presence.omcl <- function(ortho.obj){
omcl.tab <- lapply(ortho.obj, function (x) table(substr(x, 1, 3)))
spp.inf <- unique(unlist(lapply(omcl.tab, names)))
dat <- data.frame()
for(i in seq(along=omcl.tab)) for(j in names(omcl.tab[[i]])){
dat[i,j] <- omcl.tab[[i]][j]}
rownames(dat) <- names(omcl.tab)
dat[is.na(dat)] <- 0
ortholog.presence <- as.data.frame(apply(dat, 2, function (x) as.numeric(x>0)))
rownames(ortholog.presence) <- rownames(dat)
return(ortholog.presence)
}
ortholog.presence <- get.presence.omcl(omcl)
Ef.only.clusters <- names(omcl[ortholog.presence$Efa==1 & rowSums(ortholog.presence)==1 ])
Ef.only.genes <- get.genes.4.clusters(omcl[Ef.only.clusters])
Eimeria <- c("Efa", "Ema", "Ete")
Sarcocystida <- c("Tgo", "Nca")
Coccidia <- c(Eimeria, Sarcocystida)
Piroplasma <- c("Bbo", "Tan")
Haemosporidia <- c("Pfa", "Pbe")
Aconoidasida <- c(Piroplasma, Haemosporidia)
Apicomplexa <- c(Coccidia, Aconoidasida)
ApicomplexaC <- c(Apicomplexa, "Cho")
## use x=1 to restrict to multiple species clusters at terminal nodes
select.deeper.nodes <- function (Node, x=1){
rowSums(ortholog.presence[, Node])>x &
rowSums(ortholog.presence[,!names(ortholog.presence)
%in%Node])==0
}
Eimeria.clusters <- rownames(ortholog.presence[select.deeper.nodes(Eimeria), ])
Eimeria.clusters <- Eimeria.clusters[!Eimeria.clusters%in%Ef.only.clusters]
Eimeria.genes <- get.genes.4.clusters(omcl[Eimeria.clusters])
Sarcocystida.clusters <- rownames(ortholog.presence[select.deeper.nodes(Sarcocystida), ])
Piroplasma.clusters <- rownames(ortholog.presence[select.deeper.nodes(Piroplasma), ])
#Haemosporidia.clusters <- rownames(ortholog.presence[select.deeper.nodes(Haemosporidia, ), ])
## with or without comma?????????
Haemosporidia.clusters <- rownames(ortholog.presence[select.deeper.nodes(Haemosporidia), ])
Coccidia.clusters <- rownames(ortholog.presence[select.deeper.nodes(Coccidia), ])
Coccidia.clusters <- Coccidia.clusters[!Coccidia.clusters%in%Eimeria.clusters &
!Coccidia.clusters%in%Sarcocystida.clusters]
Coccidia.genes <- get.genes.4.clusters(omcl[Coccidia.clusters])
Aconoidasida.clusters <- rownames(ortholog.presence[select.deeper.nodes(Aconoidasida), ])
Aconoidasida.clusters <- Aconoidasida.clusters[!Aconoidasida.clusters%in%Piroplasma.clusters &
!Aconoidasida.clusters%in%Haemosporidia.clusters]
Apicomplexa.clusters <- rownames(ortholog.presence[select.deeper.nodes(Apicomplexa), ])
Apicomplexa.clusters <- Apicomplexa.clusters[!Apicomplexa.clusters%in%Aconoidasida.clusters &
!Apicomplexa.clusters%in%Coccidia.clusters &
!Apicomplexa.clusters%in%Eimeria.clusters &
!Apicomplexa.clusters%in%Ef.only.clusters]
Apicomplexa.genes <- get.genes.4.clusters(omcl[Apicomplexa.clusters])
ApicomplexaC.clusters <- rownames(ortholog.presence[select.deeper.nodes(ApicomplexaC), ])
ApicomplexaC.clusters <- ApicomplexaC.clusters[!ApicomplexaC.clusters%in%Apicomplexa.clusters&
!ApicomplexaC.clusters%in%Coccidia.clusters &
!ApicomplexaC.clusters%in%Eimeria.clusters &
!ApicomplexaC.clusters%in%Ef.only.clusters]
ApicomplexaC.genes <- get.genes.4.clusters(omcl[ApicomplexaC.clusters])
cluster.grouping <- melt(list(
Efalciformis=Ef.only.clusters,
Eimeria=Eimeria.clusters,
Sarcocystida=Sarcocystida.clusters,
Coccidia=Coccidia.clusters,
Haemosporidi=Haemosporidia.clusters, #
Piroplasma=Piroplasma.clusters, # Aplicomplexan parasite
Aconoidasida=Aconoidasida.clusters, # Aplicomplexan parasite
Apicomplexa=Apicomplexa.clusters,
ApicomplexaC=ApicomplexaC.clusters))
names(cluster.grouping) <- c("cluster", "grouping")
Ef.gene.ortho.list <- lapply(omcl, get.genes.4.clusters)
Ef.gene.ortho.list <- Ef.gene.ortho.list[unlist(lapply(Ef.gene.ortho.list, function (x) length(x)>0))]
Ef.gene.ortho.df <- melt(Ef.gene.ortho.list)
names(Ef.gene.ortho.df) <- c("gene", "cluster")
gene.clus.cat <- merge(Ef.gene.ortho.df, cluster.grouping, all.x=TRUE)
gene.clus.cat$grouping[is.na(gene.clus.cat$grouping)] <- "conserved"
return(gene.clus.cat)
}
gene.cluster.category <- get.omcl.categories(omcl)
write.table(gene.cluster.category, "output_data/gene_family_category.csv", sep=",")
## testing for overrepresentation in conservating grouping
hcluster <- list()
hcluster[["Mm"]] <- read.table("output_data/Mm_hclustered_cycle.csv", sep=",",
header=TRUE)
hcluster[["Ef"]] <- read.table("output_data/Ef_hclustered_cycle.csv", sep=",",
header=TRUE)
###
lots.of.f <- lapply(unique(gene.cluster.category$grouping), function (cons.group){
inner <-
lapply(unique(hcluster[["Ef"]]$Cluster), function (hier.cluster){
fisher.test(rownames(Ef.nRC)%in%
gene.cluster.category[gene.cluster.category$grouping%in%cons.group,"gene"],
rownames(Ef.nRC)%in%
rownames(hcluster[["Ef"]])[hcluster[["Ef"]]$Cluster%in%hier.cluster])[c("p.value",
"estimate")]
})
names(inner) <- paste("Cluster", unique(hcluster[["Ef"]]$Cluster), sep="_")
return(inner)
})
names(lots.of.f) <- unique(gene.cluster.category$grouping)
lots.of.f <- melt(lots.of.f)
lots.of.f <- reshape(lots.of.f, idvar = c("L2", "L1"), timevar="L3", direction="wide")
lots.of.f$FDR <- p.adjust(lots.of.f$value.p.value, method="BH")
lots.signif <- lots.of.f[ lots.of.f[, "FDR"]<0.01, ]
names(lots.signif) <- c("Ef_Cluster", "conservation", "p-value", "odds.ratio", "FDR")
tabl.lots <-
xtable(lots.signif[, c("Ef_Cluster", "conservation",
"odds.ratio", "p-value", "FDR")],
digits=c(NA, NA, NA, 2, -2, -2))
print(tabl.lots,
type = "html", file = "tables/Table3_Conserv_Cluster.html", include.rownames = F,
format.args = list(big.mark = ",", decimal.mark = "."))
#################################################
## Ortholog expression analysis############
#########################################
## new 2016: Get for every cluster the E. falciformis and E. tenella gene
get.all.pairedE.orthos <- function (){
Efa.Ete <- lapply(omcl, function (x) {
x[grep("Efa\\||Ete\\|", x)]
})
## to be a cluster still
Efa.Ete <- Efa.Ete[unlist(lapply(Efa.Ete, length))>1]
## and contain genes from both
Efa.Ete <- Efa.Ete[unlist(lapply(Efa.Ete, function (x){ any(grepl("Efa\\|", x)) &
any(grepl("Ete\\|", x)) }))]
## split them appart again
Efa.list <-lapply(Efa.Ete, function (x) x[grep("Efa\\|", x)])
Ete.list <- lapply(Efa.Ete, function (x) x[grep("Ete\\|", x)])
## Do all combinations of each Efa gene with with each Ete gene in a cluster
cobn.list <- lapply(1:length(Efa.list), function(i){
exp <- rep(Efa.list[[i]], times=length(Ete.list[[i]]))
cbind(exp, Ete.list[[i]])
})
all.orthologs <- as.data.frame(do.call("rbind", cobn.list))
names(all.orthologs) <- c("Efa", "Ete")
all.orthologs$Ete <- gsub("Ete\\|", "", all.orthologs$Ete)
all.orthologs$Efa <- gsub("Efa\\|", "", all.orthologs$Efa)
return(all.orthologs)
}
all.orthologs <- get.all.pairedE.orthos()
get.all.pairedT.orthos <- function (){
Efa.Tgo <- lapply(omcl, function (x) {
x[grep("Efa\\||Tgo\\|", x)]
})
## has to be a cluster still
Efa.Tgo <- Efa.Tgo[unlist(lapply(Efa.Tgo, length))>1]
## and contain genes from both
Efa.Tgo <- Efa.Tgo[unlist(lapply(Efa.Tgo, function (x){ any(grepl("Efa\\|", x)) &
any(grepl("Tgo\\|", x)) }))]
## split them appart again
Efa.list <-lapply(Efa.Tgo, function (x) x[grep("Efa\\|", x)])
Tgo.list <- lapply(Efa.Tgo, function (x) x[grep("Tgo\\|", x)])
## Do all combinations of each Efa gene with with each Tgo gene in a cluster
cobn.list <- lapply(1:length(Efa.list), function(i){
exp <- rep(Efa.list[[i]], times=length(Tgo.list[[i]]))
cbind(exp, Tgo.list[[i]])
})
all.orthologs <- as.data.frame(do.call("rbind", cobn.list))
names(all.orthologs) <- c("Efa", "Tgo")
all.orthologs$Tgo <- gsub("Tgo\\|", "", all.orthologs$Tgo)
all.orthologs$Efa <- gsub("Efa\\|", "", all.orthologs$Efa)
return(all.orthologs)
}
all.orthologs <- merge(all.orthologs, get.all.pairedT.orthos(), all.x=TRUE)
## downloaded from ToxoDB v. 28
## Walker data
Walker.RC <- read.delim("data/Walker_profiles.min.pct")
names(Walker.RC) <- c("Ete", "Walker.Gametocytes",
"Walker.Merozoites", "Walker.Sporozoites")
Walker.RC <- merge(Walker.RC, all.orthologs)
## More from Reid et al via ToxoDB 28
Reid.RC <- read.delim("data/Reid_profiles.min.pct")
names(Reid.RC)[1] <- "Ete"
names(Reid.RC)[2:ncol(Reid.RC)] <-
paste("Reid", names(Reid.RC)[2:ncol(Reid.RC)], sep=".")
Reid.RC <- merge(Reid.RC, all.orthologs)
Hehl.RC <- read.delim("data/Hehl_profiles.min.pct")
names(Hehl.RC)[1] <- "Tgo_Now"
names(Hehl.RC)[2:ncol(Hehl.RC)] <-
paste("Hehl", names(Hehl.RC)[2:ncol(Hehl.RC)], sep=".")
ID.toxo <- read.delim("data/ID_mapping_toxo.txt")
Tg.prev <- strsplit(as.character(ID.toxo$X.Previous.ID.s..), ", ")
Tg.prev <- lapply(Tg.prev, function (x) grep("^TGME49_\\d", x, value=TRUE))
Tg.prev <- lapply(Tg.prev, "[", 1)
Tg.prev <- unlist(Tg.prev)
Tgo <- gsub("<br>", "", Tg.prev)
Tg.link <- as.data.frame(cbind(as.character(ID.toxo$X.Gene.ID.), Tgo))
Hehl.RC <- merge(Hehl.RC, Tg.link, by.x = "Tgo_Now", by.y = "V1")
Hehl.RC$Tgo_Now <- NULL
Hehl.RC <- Hehl.RC[!is.na(Hehl.RC$Tgo),]
Hehl.RC <- merge(Hehl.RC, all.orthologs)
## Work with normalized counts
get.ortholog.RC <- function(){
Ef.counts <- log2(Ef.nRC)
Ef.counts[is.infinite(Ef.counts)] <- 0
Ef.counts <- as.data.frame(Ef.counts)
Ef.counts$Efa <- rownames(Ef.counts)
foo <- merge(Hehl.RC, Ef.counts)
foo <- merge(foo, Walker.RC)
foo <- merge(foo, Reid.RC)
bar <- unique(foo)
bar[, 3:ncol(bar)] <- apply(bar[, 3:ncol(bar)], 2,
function (x) as.numeric(as.character(x)))
return(bar)
}
RC.ortho <- get.ortholog.RC()
mean.columns <- function(x){
reps <- as.factor(gsub("_rep\\d", "", names(x)))
y <- do.call(rbind, by(t(x), reps, colMeans))
t(y)
}
RC.ortho.mean <- as.data.frame(cbind(RC.ortho[, 1:13],
mean.columns(RC.ortho[, 14:ncol(RC.ortho)])))
non.one.to.one <- RC.ortho.mean$Efa[duplicated(RC.ortho.mean$Efa)]
RC.ortho.oneONE <- RC.ortho[!RC.ortho$Efa%in%non.one.to.one, ]
RC.ortho.mean.oneONE <- RC.ortho.mean[!RC.ortho.mean$Efa%in%non.one.to.one, ]
pdf("figures/Figure4_RC_mean_correlation_Efal_Ete_Tgo_ONEONE.pdf", width=10, height=10, onefile=FALSE)
pheatmap(cor(RC.ortho.mean.oneONE[,4:ncol(RC.ortho.mean.oneONE)], method="spearman"),
display_numbers=TRUE, scale="none")
dev.off()