In vtorda/CompareTools: Post processing of COMPARE analysis
knitr::opts_chunk$set(echo = TRUE)
Import data
library(CompareTools)
library(ape)
tree_path <- "~/Documents/MUNKA/PhD/CompareTools/inst/extdata/71g_species.tree"
dollo_path <- "~/Documents/MUNKA/PhD/CompareTools/inst/extdata/Dolloout_all_cluster"
First the tree should be imported by an ape function
Then the dollo file can be imported. It is good to ladderize the tree, which can be done inside the function.
tree <- read.tree(tree_path)
comp_obj <- DolloImport3(tree = ladderize(tree), files = dollo_path)
change tip labels
species_df <- read_tsv("~/Documents/MUNKA/PhD/CompareTools/inst/extdata/species_list.txt")
any(comp_obj$tree$tip.label %in% species_df$Species_Short_Name == FALSE)
index <- which(comp_obj$tree$tip.label %in% species_df$Species_Short_Name == FALSE)
comp_obj$tree$tip.label[index]
index2 <- which(species_df$Species_Short_Name %in% comp_obj$tree$tip.label == FALSE)
comp_obj$tree$tip.label[index]
unlist(species_df[index2,2])
comp_obj$tree$tip.label[index] <- c(unlist(species_df[index2,2]))
mt_index <- match(comp_obj$tree$tip.label, species_df$Species_Short_Name)
comp_obj$tree$tip.label <- species_df$Species_Name[mt_index]
comp_obj$tree$tip.label
comp_obj <- GroupComp(compare = comp_obj, event_data = "event_data",
groups = data.frame(cl = colnames(comp_obj$event_data$gains),
groups = rep("all", ncol(comp_obj$event_data$gains))))
pdf(file = "sum_all_1.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "standard", comp.size = c(1, max(comp_obj$event_data_GROUPED$CumGain)), size = 18, tip_pch_opt = list(pch = 16, col = "red", adj = c(1, 0.5)))
dev.off()
pdf(file = "sum_all_2.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "standard", comp.size = c(1, max(comp_obj$event_data_GROUPED$CumGain)), size = 10)
dev.off()
pdf(file = "sum_all_5.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1, max(comp_obj$event_data_GROUPED$gains)), size = 20, tip_pch_opt = FALSE, tip_text_opt = FALSE)
dev.off()
pdf(file = "sum_all_6.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(-0.8,0.5), bg = NULL, frame = "none"), labels.offset = 1)
dev.off()
pdf(file = "sum_all_7.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(-0.4,0.1), bg = NULL, frame = "none"), label.offset = 2, edge.width = 5)
dev.off()
pdf(file = "sum_all_9.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(100, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0,0), bg = NULL, frame = "r"), label.offset = 2, edge.width = 5)
dev.off()
pdf(file = "sum_all_10.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1000, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = NULL, frame = "r"), label.offset = 2, edge.width = 5)
dev.off()
makeTransparent<-function(someColor, alpha=100)
{
newColor<-col2rgb(someColor)
apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}
makeTransparent("grey", 10)
pdf(file = "sum_all_11.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1000, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("black", 100), frame = "r"), label.offset = 2, edge.width = 5)
dev.off()
pdf(file = "sum_all_12.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1000, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,-0.8), bg = makeTransparent("deepskyblue", 100), frame = "r"), label.offset = 2, edge.width = 5)
dev.off()
pdf(file = "sum_all_13.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(10000, 1000), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,-0.8), bg = makeTransparent("deepskyblue", 100), frame = "r"), label.offset = 2, edge.width = 5)
dev.off()
pdf(file = "sum_all_14.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", comp.size = c(10000, 1000), size = 2, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 2, edge.width = 5)
dev.off()
pdf(file = "sum_all_15.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", comp.size = c(10000, 1000), size = 2, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5)
dev.off()
pdf(file = "sum_all_16.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", size = 2, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5)
dev.off()
pdf(file = "sum_all_17.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 2, size = 0.5, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5)
dev.off()
pdf(file = "sum_all_18.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 20, size = 3, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5)
dev.off()
pdf(file = "sum_all_19.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 50, size = 4, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5)
dev.off()
pdf(file = "sum_all_20.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 5, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5)
dev.off()
pdf(file = "sum_all_23.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 5, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
dev.off()
pdf(file = "sum_all_24.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 4, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
dev.off()
pdf(file = "sum_all_27.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 4, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200),
cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 100) * 4))
dev.off()
pdf(file = "sum_all_28.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 4, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200),
cex = 1, pt.cex = (log(c(10, 100, 1000, 5000), 100) * 4))
dev.off()
pdf(file = "sum_all_29.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 10, size = 1, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200),
cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 10) * 1))
dev.off()
pdf(file = "sum_all_30.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 10, size = 3, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200),
cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 10) * 3))
dev.off()
pdf(file = "sum_all_31.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 10, size = 2.5, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200),
cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 10) * 2.5))
dev.off()
pdf(file = "sum_all_32.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "standard", base = 10, size = 20, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200),
cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 10) * 20))
dev.off()
group clusters
dendro <- HclustComp(compare = comp_obj$event_data$gains)
save(dendro, file = "./dendro.RData")
cl_groups <- CutDendroComp(dendro = dendro, n_groups = 30, plot = TRUE, min_num = 20)
pdf(file = "dendro_1.pdf", width = 40, height = 20)
CutDendroComp(dendro = dendro, n_groups = 30, plot = TRUE, min_num = 20)
dev.off()
pdf(file = "dendro_2.pdf", width = 40, height = 20)
CutDendroComp(dendro = dendro, n_groups = 30, plot = TRUE, min_num = 20, labels = FALSE)
dev.off()
cl_groups <- CutDendroComp(dendro = dendro, n_groups = 30, plot = FALSE, min_num = 20)
comp_obj2 <- GroupComp(compare = comp_obj, groups = cl_groups, event_data = "event_data")
comp_obj2 <- comp_obj2[-3]
PlotEvent(compare = comp_obj2, dataset = "event_data_GROUPED", stat = "gains", data = 1:13, cladogram = TRUE, sizemeth = "log", base = 10, size = 4, comp.size = c(0,7000), tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
v <- cl_groups$groups
names(v) <- cl_groups$clusters
table(v)
pdf(file = "groups_all_1.pdf", width = 24, height = 24)
PlotEvent(compare = comp_obj2, dataset = "event_data_GROUPED", stat = "gains", data = 1:13, cladogram = TRUE, sizemeth = "log", base = 10, size = 4, comp.size = c(0,7000), tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
dev.off()
mi van ha tobb csoportra nyomom?
cl_groups <- CutDendroComp(dendro = dendro, n_groups = 70, plot = TRUE, min_num = 20)
comp_obj2 <- GroupComp(compare = comp_obj, groups = cl_groups, event_data = "event_data")
comp_obj2 <- comp_obj2[-3]
pdf(file = "dendro_3.pdf", width = 40, height = 20)
CutDendroComp(dendro = dendro, n_groups = 70, plot = TRUE, min_num = 20, labels = FALSE)
dev.off()
PlotEvent(compare = comp_obj2, dataset = "event_data_GROUPED", stat = "gains", data = 1:21, cladogram = TRUE, sizemeth = "log", base = 10, size = 4, comp.size = c(0,7000), tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
A <- GetNodes(comp_obj2, c("Rozella allomycis", "Laccaria bicolor"), tips = FALSE)
all_n <- c(1:141)
all_n2 <- all_n[!all_n %in% A]
all_n3 <- all_n2[!all_n2 %in% 1:71]
plot.phylo(comp_obj2$tree)
nodelabels(node = all_n3, pch = 16, col = "blue")
aov_groups <- c(A, all_n3)
names(aov_groups) <- c(rep("Fungi", length(A)), rep("Animals_Protists", length(all_n3)))
RandomAovTest(comp_obj2, dataset = "event_data_GROUPED", "gains", aov_groups = aov_groups, 100)
comp_obj3 <- SliceComp(compare = comp_obj, clusters = cl_groups$clusters[as.character(cl_groups$groups) %in% "7"])
seventh_cl <- RandomAovTest(comp_obj3, dataset = "event_data_SLICED", "gains", aov_groups = aov_groups, 100)
B <- GetNodes(comp_obj2, c("Schizosaccharomyces pombe", "Laccaria bicolor"), tips = FALSE)
LowerFungi <- A[!A %in% B]
aov_groups <- c(B, LowerFungi, all_n3)
names(aov_groups) <- c(rep("HigherFungi", length(B)), rep("LowerFungi", length(LowerFungi)),
rep("Animals_Protists", length(all_n3)))
plot.phylo(comp_obj2$tree)
nodelabels(node = aov_groups, pch = 16, col = c(rep("blue", length(B)),rep("red", length(LowerFungi)),
rep("green", length(all_n3))))
RandomAovTest(comp_obj2, dataset = "event_data_GROUPED", "gains", aov_groups = aov_groups, 100)
seventh_cl <- RandomAovTest(comp_obj3, dataset = "event_data_SLICED", "gains", aov_groups = aov_groups, 100)
A <- GetNodes(comp_obj2, c("Rozella allomycis", "Laccaria bicolor"), tips = FALSE)
all_n <- c(1:141)
all_n2 <- all_n[!all_n %in% A]
all_n3 <- all_n2[!all_n2 %in% 1:71]
aov_groups <- c(A, all_n3)
names(aov_groups) <- c(rep("Fungi", length(A)), rep("Animals_Protists", length(all_n3)))
all_cl <- RandomAovTest(comp_obj, dataset = "event_data", "gains", aov_groups = aov_groups, 100)
head(all_cl)
head(all_cl[,5] < 0.05)
index <- all_cl[,5] < 0.05
sum(index, na.rm = TRUE)
index2 <- which(index == TRUE)
sig_clusters <- all_cl[index2,]
sig_cl_names <- rownames(sig_clusters)
comp_obj_sign <- SliceComp(compare = comp_obj, clusters = sig_cl_names, new_name_suf = "all_sig", overwrite = FALSE)
# divide to clades
animal_nodes <- aov_groups[names(aov_groups) %in% "Animals_Protists"]
animal_gains <- comp_obj_sign$event_data_all_sig$gains[animal_nodes,]
fungi_nodes <- aov_groups[names(aov_groups) %in% "Fungi"]
fungi_gains <- comp_obj_sign$event_data_all_sig$gains[fungi_nodes,]
bigger_fun <- vector()
for(i in 1:ncol(animal_gains)){
fun_mean <- mean(fungi_gains[,i])
animal_mean <- mean(animal_gains[,i])
bigger_fun[i] <- fun_mean > animal_mean
}
sum(bigger_fun)
hat a rootot lehet hogy ki kene zarni...
root <- getMRCA(comp_obj$tree, comp_obj$tree$tip.label)
animal_nodes2 <- animal_nodes[-1]
animal_gains <- comp_obj_sign$event_data_all_sig$gains[animal_nodes2,]
fungi_nodes <- aov_groups[names(aov_groups) %in% "Fungi"]
fungi_gains <- comp_obj_sign$event_data_all_sig$gains[fungi_nodes,]
bigger_fun <- vector()
for(i in 1:ncol(animal_gains)){
fun_mean <- mean(fungi_gains[,i])
animal_mean <- mean(animal_gains[,i])
bigger_fun[i] <- fun_mean > animal_mean
}
sum(bigger_fun)
which(bigger_fun == TRUE)
bigger_fungi_animal <- animal_gains[,which(bigger_fun == TRUE)]
bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == TRUE)]
ncol(fungi_gains)
ncol(animal_gains)
fun_mean <- vector()
animal_mean <- vector()
for(i in 1:ncol(bigger_fungi_animal)){
fun_mean[i] <- mean(bigger_fungi_animal[,i])
animal_mean[i] <- mean(bigger_fungi_fungi[,i])
}
bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean))))
library(ggplot2)
ggplot(bigger_fun_data, aes(x = groups, y = means)) +
geom_boxplot()
bigger_fungi_animal <- animal_gains[,which(bigger_fun == FALSE)]
bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == FALSE)]
fun_mean <- vector()
animal_mean <- vector()
for(i in 1:ncol(bigger_fungi_animal)){
fun_mean[i] <- mean(bigger_fungi_animal[,i])
animal_mean[i] <- mean(bigger_fungi_fungi[,i])
}
bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean))))
ggplot(bigger_fun_data, aes(x = groups, y = means)) +
geom_boxplot()
comp_obj_3_7 <- SliceComp(compare = comp_obj, clusters = cl_groups$clusters[cl_groups$groups %in% c(3,7)], new_name_suf = "three_seven")
animal_nodes <- aov_groups[names(aov_groups) %in% "Animals_Protists"]
animal_gains <- comp_obj_3_7$event_data_three_seven$gains[animal_nodes,]
fungi_nodes <- aov_groups[names(aov_groups) %in% "Fungi"]
fungi_gains <- comp_obj_3_7$event_data_three_seven$gains[fungi_nodes,]
animal_nodes2 <- animal_nodes[-1]
animal_gains <- comp_obj_3_7$event_data_three_seven$gains[animal_nodes2,]
bigger_fun <- vector()
for(i in 1:ncol(animal_gains)){
fun_mean <- mean(fungi_gains[,i])
animal_mean <- mean(animal_gains[,i])
bigger_fun[i] <- fun_mean > animal_mean
}
sum(bigger_fun)
which(bigger_fun == TRUE)
bigger_fungi_animal <- animal_gains[,which(bigger_fun == TRUE)]
bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == TRUE)]
fun_mean <- vector()
animal_mean <- vector()
for(i in 1:ncol(bigger_fungi_animal)){
fun_mean[i] <- mean(bigger_fungi_animal[,i])
animal_mean[i] <- mean(bigger_fungi_fungi[,i])
}
bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean))))
ggplot(bigger_fun_data, aes(x = groups, y = means)) +
geom_boxplot(outlier.shape = NA)
p1 <- ggplot(bigger_fun_data, aes(x = groups, y = means)) +
geom_boxplot(outlier.shape = NA)
ylim1 <- boxplot.stats(bigger_fun_data$means)$stats[c(1, 5)]
p2 <- p1 + coord_cartesian(ylim = ylim1*2)
p2
p1 <- ggplot(bigger_fun_data, aes(x = groups, y = means)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.1)
ylim1 <- boxplot.stats(bigger_fun_data$means)$stats[c(1, 5)]
p2 <- p1 + coord_cartesian(ylim = ylim1*2)
p2
bigger_fungi_animal <- animal_gains[,which(bigger_fun == FALSE)]
bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == FALSE)]
fun_mean <- vector()
animal_mean <- vector()
for(i in 1:ncol(bigger_fungi_animal)){
fun_mean[i] <- mean(bigger_fungi_animal[,i])
animal_mean[i] <- mean(bigger_fungi_fungi[,i])
}
bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean))))
p1 <- ggplot(bigger_fun_data, aes(x = groups, y = means)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.05, aes(col = groups), alpha = 0.7)
ylim1 <- c(0,0,6)
p2 <- p1 + coord_cartesian(ylim = ylim1*0.7)
p2
pdf(file = "boxplot3.pdf")
p2
dev.off()
bigger_fungi_animal <- animal_gains[,which(bigger_fun == TRUE)]
bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == TRUE)]
fun_mean <- vector()
animal_mean <- vector()
for(i in 1:ncol(bigger_fungi_animal)){
fun_mean[i] <- mean(bigger_fungi_animal[,i])
animal_mean[i] <- mean(bigger_fungi_fungi[,i])
}
bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean))))
p1 <- ggplot(bigger_fun_data, aes(x = groups, y = means)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.05, aes(col = groups), alpha = 0.7)
ylim1 <- boxplot.stats(bigger_fun_data$means)$stats[c(1, 5)]
p2 <- p1 + coord_cartesian(ylim = ylim1*2)
p2
pdf(file = "boxplot4.pdf")
p2
dev.off()
save(comp_obj, file = "comp_obj.RData")
load("~/Documents/MUNKA/PhD/CompareTools/benchmark/all_aov_test.RData")
sum()
head(all_cl)
head(all_cl[,5] < 0.05)
index <- all_cl[,5] < 0.05
sum(index, na.rm = TRUE)
index2 <- which(index == TRUE)
length(index2)
sig_clusters <- all_cl[index2,]
sig_cl_names <- rownames(sig_clusters)
comp_obj_sign <- SliceComp(compare = comp_obj, clusters = sig_cl_names, new_name_suf = "all_sig", overwrite = FALSE)
# divide to clades
animal_nodes <- aov_groups[names(aov_groups) %in% "Animals_Protists"]
animal_gains <- comp_obj_sign$event_data_all_sig$gains[animal_nodes,]
fungi_nodes <- aov_groups[names(aov_groups) %in% "Fungi"]
fungi_gains <- comp_obj_sign$event_data_all_sig$gains[fungi_nodes,]
bigger_fun <- vector()
for(i in 1:ncol(animal_gains)){
fun_mean <- mean(fungi_gains[,i])
animal_mean <- mean(animal_gains[,i])
bigger_fun[i] <- fun_mean > animal_mean
}
sum(bigger_fun)
vtorda/CompareTools documentation built on Nov. 6, 2019, 9:07 p.m.
R Package Documentation
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knitr::opts_chunk$set(echo = TRUE)
Import data
library(CompareTools) library(ape)
tree_path <- "~/Documents/MUNKA/PhD/CompareTools/inst/extdata/71g_species.tree" dollo_path <- "~/Documents/MUNKA/PhD/CompareTools/inst/extdata/Dolloout_all_cluster"
First the tree should be imported by an ape function
Then the dollo file can be imported. It is good to ladderize the tree, which can be done inside the function.
tree <- read.tree(tree_path) comp_obj <- DolloImport3(tree = ladderize(tree), files = dollo_path)
change tip labels
species_df <- read_tsv("~/Documents/MUNKA/PhD/CompareTools/inst/extdata/species_list.txt") any(comp_obj$tree$tip.label %in% species_df$Species_Short_Name == FALSE) index <- which(comp_obj$tree$tip.label %in% species_df$Species_Short_Name == FALSE) comp_obj$tree$tip.label[index] index2 <- which(species_df$Species_Short_Name %in% comp_obj$tree$tip.label == FALSE) comp_obj$tree$tip.label[index] unlist(species_df[index2,2]) comp_obj$tree$tip.label[index] <- c(unlist(species_df[index2,2])) mt_index <- match(comp_obj$tree$tip.label, species_df$Species_Short_Name) comp_obj$tree$tip.label <- species_df$Species_Name[mt_index] comp_obj$tree$tip.label
comp_obj <- GroupComp(compare = comp_obj, event_data = "event_data", groups = data.frame(cl = colnames(comp_obj$event_data$gains), groups = rep("all", ncol(comp_obj$event_data$gains))))
pdf(file = "sum_all_1.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "standard", comp.size = c(1, max(comp_obj$event_data_GROUPED$CumGain)), size = 18, tip_pch_opt = list(pch = 16, col = "red", adj = c(1, 0.5))) dev.off()
pdf(file = "sum_all_2.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "standard", comp.size = c(1, max(comp_obj$event_data_GROUPED$CumGain)), size = 10) dev.off()
pdf(file = "sum_all_5.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1, max(comp_obj$event_data_GROUPED$gains)), size = 20, tip_pch_opt = FALSE, tip_text_opt = FALSE) dev.off()
pdf(file = "sum_all_6.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(-0.8,0.5), bg = NULL, frame = "none"), labels.offset = 1) dev.off()
pdf(file = "sum_all_7.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(-0.4,0.1), bg = NULL, frame = "none"), label.offset = 2, edge.width = 5) dev.off()
pdf(file = "sum_all_9.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(100, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0,0), bg = NULL, frame = "r"), label.offset = 2, edge.width = 5) dev.off()
pdf(file = "sum_all_10.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1000, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = NULL, frame = "r"), label.offset = 2, edge.width = 5) dev.off()
makeTransparent<-function(someColor, alpha=100) { newColor<-col2rgb(someColor) apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2], blue=curcoldata[3],alpha=alpha, maxColorValue=255)}) } makeTransparent("grey", 10)
pdf(file = "sum_all_11.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1000, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("black", 100), frame = "r"), label.offset = 2, edge.width = 5) dev.off()
pdf(file = "sum_all_12.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(1000, max(comp_obj$event_data_GROUPED$gains)), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,-0.8), bg = makeTransparent("deepskyblue", 100), frame = "r"), label.offset = 2, edge.width = 5) dev.off()
pdf(file = "sum_all_13.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "comparable", comp.size = c(10000, 1000), size = 25, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,-0.8), bg = makeTransparent("deepskyblue", 100), frame = "r"), label.offset = 2, edge.width = 5) dev.off()
pdf(file = "sum_all_14.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", comp.size = c(10000, 1000), size = 2, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 2, edge.width = 5) dev.off()
pdf(file = "sum_all_15.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", comp.size = c(10000, 1000), size = 2, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5) dev.off()
pdf(file = "sum_all_16.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", size = 2, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5) dev.off()
pdf(file = "sum_all_17.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 2, size = 0.5, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5) dev.off()
pdf(file = "sum_all_18.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 20, size = 3, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5) dev.off()
pdf(file = "sum_all_19.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 50, size = 4, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5) dev.off()
pdf(file = "sum_all_20.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 5, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), label.offset = 1, edge.width = 5) dev.off()
pdf(file = "sum_all_23.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 5, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5) dev.off()
pdf(file = "sum_all_24.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 4, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5) dev.off()
pdf(file = "sum_all_27.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 4, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5) legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200), cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 100) * 4)) dev.off()
pdf(file = "sum_all_28.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 100, size = 4, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5) legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200), cex = 1, pt.cex = (log(c(10, 100, 1000, 5000), 100) * 4)) dev.off()
pdf(file = "sum_all_29.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 10, size = 1, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5) legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200), cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 10) * 1)) dev.off()
pdf(file = "sum_all_30.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 10, size = 3, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5) legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200), cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 10) * 3)) dev.off()
pdf(file = "sum_all_31.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "log", base = 10, size = 2.5, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5) legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200), cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 10) * 2.5)) dev.off()
pdf(file = "sum_all_32.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj, dataset = "event_data_GROUPED", stat = "gains", data = 1, cladogram = TRUE, sizemeth = "standard", base = 10, size = 20, tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5) legend("bottomleft", legend = c(10, 100, 1000, 5000), pch = 16, col = makeTransparent("red", 200), cex = 3, pt.cex = (log(c(10, 100, 1000, 5000), 10) * 20)) dev.off()
group clusters
dendro <- HclustComp(compare = comp_obj$event_data$gains) save(dendro, file = "./dendro.RData")
cl_groups <- CutDendroComp(dendro = dendro, n_groups = 30, plot = TRUE, min_num = 20)
pdf(file = "dendro_1.pdf", width = 40, height = 20) CutDendroComp(dendro = dendro, n_groups = 30, plot = TRUE, min_num = 20) dev.off()
pdf(file = "dendro_2.pdf", width = 40, height = 20) CutDendroComp(dendro = dendro, n_groups = 30, plot = TRUE, min_num = 20, labels = FALSE) dev.off()
cl_groups <- CutDendroComp(dendro = dendro, n_groups = 30, plot = FALSE, min_num = 20) comp_obj2 <- GroupComp(compare = comp_obj, groups = cl_groups, event_data = "event_data") comp_obj2 <- comp_obj2[-3]
PlotEvent(compare = comp_obj2, dataset = "event_data_GROUPED", stat = "gains", data = 1:13, cladogram = TRUE, sizemeth = "log", base = 10, size = 4, comp.size = c(0,7000), tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
v <- cl_groups$groups names(v) <- cl_groups$clusters table(v)
pdf(file = "groups_all_1.pdf", width = 24, height = 24) PlotEvent(compare = comp_obj2, dataset = "event_data_GROUPED", stat = "gains", data = 1:13, cladogram = TRUE, sizemeth = "log", base = 10, size = 4, comp.size = c(0,7000), tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5) dev.off()
mi van ha tobb csoportra nyomom?
cl_groups <- CutDendroComp(dendro = dendro, n_groups = 70, plot = TRUE, min_num = 20) comp_obj2 <- GroupComp(compare = comp_obj, groups = cl_groups, event_data = "event_data") comp_obj2 <- comp_obj2[-3]
pdf(file = "dendro_3.pdf", width = 40, height = 20) CutDendroComp(dendro = dendro, n_groups = 70, plot = TRUE, min_num = 20, labels = FALSE) dev.off()
PlotEvent(compare = comp_obj2, dataset = "event_data_GROUPED", stat = "gains", data = 1:21, cladogram = TRUE, sizemeth = "log", base = 10, size = 4, comp.size = c(0,7000), tip_pch_opt = FALSE, tip_text_opt = FALSE, node_text_opt = list(adj = c(0.5,0.5), bg = makeTransparent("grey", 100), frame = "n"), node_pch_opt = list(pch = 16, col = makeTransparent("red", 200)), label.offset = 1, edge.width = 5)
A <- GetNodes(comp_obj2, c("Rozella allomycis", "Laccaria bicolor"), tips = FALSE) all_n <- c(1:141) all_n2 <- all_n[!all_n %in% A] all_n3 <- all_n2[!all_n2 %in% 1:71] plot.phylo(comp_obj2$tree) nodelabels(node = all_n3, pch = 16, col = "blue") aov_groups <- c(A, all_n3) names(aov_groups) <- c(rep("Fungi", length(A)), rep("Animals_Protists", length(all_n3))) RandomAovTest(comp_obj2, dataset = "event_data_GROUPED", "gains", aov_groups = aov_groups, 100)
comp_obj3 <- SliceComp(compare = comp_obj, clusters = cl_groups$clusters[as.character(cl_groups$groups) %in% "7"]) seventh_cl <- RandomAovTest(comp_obj3, dataset = "event_data_SLICED", "gains", aov_groups = aov_groups, 100)
B <- GetNodes(comp_obj2, c("Schizosaccharomyces pombe", "Laccaria bicolor"), tips = FALSE) LowerFungi <- A[!A %in% B] aov_groups <- c(B, LowerFungi, all_n3) names(aov_groups) <- c(rep("HigherFungi", length(B)), rep("LowerFungi", length(LowerFungi)), rep("Animals_Protists", length(all_n3))) plot.phylo(comp_obj2$tree) nodelabels(node = aov_groups, pch = 16, col = c(rep("blue", length(B)),rep("red", length(LowerFungi)), rep("green", length(all_n3))))
RandomAovTest(comp_obj2, dataset = "event_data_GROUPED", "gains", aov_groups = aov_groups, 100)
seventh_cl <- RandomAovTest(comp_obj3, dataset = "event_data_SLICED", "gains", aov_groups = aov_groups, 100)
A <- GetNodes(comp_obj2, c("Rozella allomycis", "Laccaria bicolor"), tips = FALSE) all_n <- c(1:141) all_n2 <- all_n[!all_n %in% A] all_n3 <- all_n2[!all_n2 %in% 1:71] aov_groups <- c(A, all_n3) names(aov_groups) <- c(rep("Fungi", length(A)), rep("Animals_Protists", length(all_n3))) all_cl <- RandomAovTest(comp_obj, dataset = "event_data", "gains", aov_groups = aov_groups, 100)
head(all_cl) head(all_cl[,5] < 0.05) index <- all_cl[,5] < 0.05 sum(index, na.rm = TRUE) index2 <- which(index == TRUE) sig_clusters <- all_cl[index2,] sig_cl_names <- rownames(sig_clusters) comp_obj_sign <- SliceComp(compare = comp_obj, clusters = sig_cl_names, new_name_suf = "all_sig", overwrite = FALSE)
# divide to clades animal_nodes <- aov_groups[names(aov_groups) %in% "Animals_Protists"] animal_gains <- comp_obj_sign$event_data_all_sig$gains[animal_nodes,] fungi_nodes <- aov_groups[names(aov_groups) %in% "Fungi"] fungi_gains <- comp_obj_sign$event_data_all_sig$gains[fungi_nodes,] bigger_fun <- vector() for(i in 1:ncol(animal_gains)){ fun_mean <- mean(fungi_gains[,i]) animal_mean <- mean(animal_gains[,i]) bigger_fun[i] <- fun_mean > animal_mean } sum(bigger_fun)
hat a rootot lehet hogy ki kene zarni...
root <- getMRCA(comp_obj$tree, comp_obj$tree$tip.label) animal_nodes2 <- animal_nodes[-1] animal_gains <- comp_obj_sign$event_data_all_sig$gains[animal_nodes2,] fungi_nodes <- aov_groups[names(aov_groups) %in% "Fungi"] fungi_gains <- comp_obj_sign$event_data_all_sig$gains[fungi_nodes,] bigger_fun <- vector() for(i in 1:ncol(animal_gains)){ fun_mean <- mean(fungi_gains[,i]) animal_mean <- mean(animal_gains[,i]) bigger_fun[i] <- fun_mean > animal_mean } sum(bigger_fun) which(bigger_fun == TRUE)
bigger_fungi_animal <- animal_gains[,which(bigger_fun == TRUE)] bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == TRUE)] ncol(fungi_gains) ncol(animal_gains) fun_mean <- vector() animal_mean <- vector() for(i in 1:ncol(bigger_fungi_animal)){ fun_mean[i] <- mean(bigger_fungi_animal[,i]) animal_mean[i] <- mean(bigger_fungi_fungi[,i]) } bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean)))) library(ggplot2) ggplot(bigger_fun_data, aes(x = groups, y = means)) + geom_boxplot() bigger_fungi_animal <- animal_gains[,which(bigger_fun == FALSE)] bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == FALSE)] fun_mean <- vector() animal_mean <- vector() for(i in 1:ncol(bigger_fungi_animal)){ fun_mean[i] <- mean(bigger_fungi_animal[,i]) animal_mean[i] <- mean(bigger_fungi_fungi[,i]) } bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean)))) ggplot(bigger_fun_data, aes(x = groups, y = means)) + geom_boxplot()
comp_obj_3_7 <- SliceComp(compare = comp_obj, clusters = cl_groups$clusters[cl_groups$groups %in% c(3,7)], new_name_suf = "three_seven") animal_nodes <- aov_groups[names(aov_groups) %in% "Animals_Protists"] animal_gains <- comp_obj_3_7$event_data_three_seven$gains[animal_nodes,] fungi_nodes <- aov_groups[names(aov_groups) %in% "Fungi"] fungi_gains <- comp_obj_3_7$event_data_three_seven$gains[fungi_nodes,] animal_nodes2 <- animal_nodes[-1] animal_gains <- comp_obj_3_7$event_data_three_seven$gains[animal_nodes2,] bigger_fun <- vector() for(i in 1:ncol(animal_gains)){ fun_mean <- mean(fungi_gains[,i]) animal_mean <- mean(animal_gains[,i]) bigger_fun[i] <- fun_mean > animal_mean } sum(bigger_fun) which(bigger_fun == TRUE)
bigger_fungi_animal <- animal_gains[,which(bigger_fun == TRUE)] bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == TRUE)] fun_mean <- vector() animal_mean <- vector() for(i in 1:ncol(bigger_fungi_animal)){ fun_mean[i] <- mean(bigger_fungi_animal[,i]) animal_mean[i] <- mean(bigger_fungi_fungi[,i]) } bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean)))) ggplot(bigger_fun_data, aes(x = groups, y = means)) + geom_boxplot(outlier.shape = NA)
p1 <- ggplot(bigger_fun_data, aes(x = groups, y = means)) + geom_boxplot(outlier.shape = NA) ylim1 <- boxplot.stats(bigger_fun_data$means)$stats[c(1, 5)] p2 <- p1 + coord_cartesian(ylim = ylim1*2) p2
p1 <- ggplot(bigger_fun_data, aes(x = groups, y = means)) + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.1) ylim1 <- boxplot.stats(bigger_fun_data$means)$stats[c(1, 5)] p2 <- p1 + coord_cartesian(ylim = ylim1*2) p2
bigger_fungi_animal <- animal_gains[,which(bigger_fun == FALSE)] bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == FALSE)] fun_mean <- vector() animal_mean <- vector() for(i in 1:ncol(bigger_fungi_animal)){ fun_mean[i] <- mean(bigger_fungi_animal[,i]) animal_mean[i] <- mean(bigger_fungi_fungi[,i]) } bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean)))) p1 <- ggplot(bigger_fun_data, aes(x = groups, y = means)) + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.05, aes(col = groups), alpha = 0.7) ylim1 <- c(0,0,6) p2 <- p1 + coord_cartesian(ylim = ylim1*0.7) p2
pdf(file = "boxplot3.pdf") p2 dev.off()
bigger_fungi_animal <- animal_gains[,which(bigger_fun == TRUE)] bigger_fungi_fungi <- fungi_gains[,which(bigger_fun == TRUE)] fun_mean <- vector() animal_mean <- vector() for(i in 1:ncol(bigger_fungi_animal)){ fun_mean[i] <- mean(bigger_fungi_animal[,i]) animal_mean[i] <- mean(bigger_fungi_fungi[,i]) } bigger_fun_data <- data.frame(means = c(fun_mean, animal_mean), groups = c(rep("Fungi", length(fun_mean)), rep("Animals&Protists", length(animal_mean)))) p1 <- ggplot(bigger_fun_data, aes(x = groups, y = means)) + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.05, aes(col = groups), alpha = 0.7) ylim1 <- boxplot.stats(bigger_fun_data$means)$stats[c(1, 5)] p2 <- p1 + coord_cartesian(ylim = ylim1*2) p2 pdf(file = "boxplot4.pdf") p2 dev.off()
save(comp_obj, file = "comp_obj.RData")
load("~/Documents/MUNKA/PhD/CompareTools/benchmark/all_aov_test.RData") sum()
head(all_cl) head(all_cl[,5] < 0.05) index <- all_cl[,5] < 0.05 sum(index, na.rm = TRUE) index2 <- which(index == TRUE) length(index2) sig_clusters <- all_cl[index2,] sig_cl_names <- rownames(sig_clusters) comp_obj_sign <- SliceComp(compare = comp_obj, clusters = sig_cl_names, new_name_suf = "all_sig", overwrite = FALSE)
# divide to clades animal_nodes <- aov_groups[names(aov_groups) %in% "Animals_Protists"] animal_gains <- comp_obj_sign$event_data_all_sig$gains[animal_nodes,] fungi_nodes <- aov_groups[names(aov_groups) %in% "Fungi"] fungi_gains <- comp_obj_sign$event_data_all_sig$gains[fungi_nodes,] bigger_fun <- vector() for(i in 1:ncol(animal_gains)){ fun_mean <- mean(fungi_gains[,i]) animal_mean <- mean(animal_gains[,i]) bigger_fun[i] <- fun_mean > animal_mean } sum(bigger_fun)
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