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example_script.R
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#Load in packages
devtools::install_github("chutter/AstralPlane")
library(AstralPlane)
library(data.table)
#To do:
### Make name simpler
### Use hyphen
#### Use astral results to calcualte anomaly zone across everything
#### Collect node data from anomaly zone
######################################################################################
##### Testing the anomaly zone
work.dir = "/Volumes/Armored/Hylidae/Dataset-single"
astral.path = "/usr/local/bin/Astral-5-14/astral.5.14.2.jar"
dir.create(work.dir)
setwd(work.dir)
tree.file = "/Users/chutter/Dropbox/Research/1_Main-Projects/0_Working-Projects/Hylidae/Trees/Tree_Grid/Unfiltered/Astral/uce.tre"
outgroup.taxa = c("Phyllomedusa_tomopterna_WED_55506", "Nyctimystes_infrafrenatus_SLT_771")
uce.tree = ape::read.tree(tree.file)
anom.data = anomalyZone(tree = uce.tree,
outgroups = outgroup.taxa)
plot.anomalyZone(tree = uce.tree,
data = anom.data,
outgroups = outgroup.taxa,
save.file = NULL,
tip.label.size = 0.5,
node.label.size = 1,
edge.width = 3)
######################################################################################
##### Filtering demonstration
#Set up your directories
align.dir = "/Volumes/Armored/Hylidae/Alignments/all-markers_trimmed"
tree.dir = "/Users/chutter/Dropbox/Research/1_Main-Projects/0_Working-Projects/Hylidae/Trees/Gene_Trees/all-markers_trimmed"
astral.path = "/usr/local/bin/Astral-5-14/astral.5.14.2.jar"
dir.create(work.dir)
setwd(work.dir)
#Set up your filters
filter.length = c(100, 200, 300, 400, 500, 600, 700, 800, 900, 1000,
1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000,
2100, 2200, 2300, 2400, 2500) #number of base pairs
filter.sample = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1) #proportion samples
filter.prop.pis = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1) #proportion pis
filter.count.pis = c(10, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500) #count of pis
#Alignment summary function [20 minutes]
align.summary = summarizeAlignments(alignment.path = align.dir,
file.export = "Hylidae_Alignment_stats",
alignment.format = "phylip",
dataset.name = "Hylidae",
overwrite = TRUE)
#Apply filters and create summary table of filters [< 1 min]
filt.summary = filterSummary(alignment.data = align.summary,
alignment.folder = align.dir,
dataset.name = "Hylidae",
file.out = "filter_summary",
length.filters = filter.length,
sample.filters = filter.sample,
prop.pis.filters = filter.prop.pis,
count.pis.filters = filter.count.pis,
overwrite = T)
#Make filtered alignments datasets [10 minutes]
filterAlignments(filter.summary = filt.summary,
alignment.data = align.summary,
alignment.folder = align.dir,
format = "concatenated",
min.alignments = 5,
overwrite = TRUE)
#Make filtered gene trees datasets [5 minutes]
filterGeneTrees(filter.summary = filt.summary,
alignment.data = align.summary,
genetree.folder = tree.dir,
format = "concatenated",
overwrite = TRUE,
taxa.remove = NULL,
min.trees = 5,
min.n.samples = 4,
min.sample.prop = NULL,
make.polytomy = TRUE,
polytomy.limit = 10,
remove.node.labels = FALSE)
#Runs astral across all filtered gene tree sets
AstralPlane::astralRunner(concat.genetree.folder = "filtered-genetrees-concatenated",
output.dir = "filtered-Astral",
overwrite = FALSE,
astral.path = astral.path,
astral.t = 2,
quiet = FALSE,
multi.thread = TRUE,
memory = "8g")
#Concordance factor analysis across all filtered datasets
AstralPlane::concordanceRunner(alignment.dir = "filtered-alignments-concatenated",
species.tree.dir = "filtered-Astral",
genetree.dir = "filtered-genetrees-concatenated",
output.dir = "concordance-factors",
overwrite = FALSE,
quiet = TRUE,
threads = 6)
######################################################################################
##### Testing the effectiveness of the anomaly zone
#Set up your directories
work.dir = "/Volumes/Armored/Hylidae/Dataset-single"
align.dir = "/Volumes/Armored/Hylidae/Alignments/all-markers_trimmed"
tree.dir = "/Users/chutter/Dropbox/Research/1_Main-Projects/0_Working-Projects/Hylidae/Trees/Gene_Trees/all-markers_trimmed"
setwd(work.dir)
align.summary = read.csv("Hylidae_Alignment_stats.csv")
filt.summary = read.csv("filter_summary.csv")
#Set up a list of your clades of interest
outgroup.taxa = c("Phyllomedusa_tomopterna_WED_55506", "Nyctimystes_infrafrenatus_SLT_771")
taxa.set = list()
taxa.set[[1]] = c("Colomascirtus_staufferorum_LAC_2153", "Hyloscirtus_phyllognathus_WED_58378", "Hypsiboas_boans_WED_57877")
taxa.set[[2]] = c("Sphaenorhynchus_lacteus_WED_54090", "Scinax_garbei_LAC_1787", "Scinax_ruber_WED_56140" )
taxa.set[[3]] = c("Trachycephalus_jordani_WED_53658", "Phyrnohyas_venulosa_WED_55450", "Osteocephalus_taurinus_WED_55452")
taxa.set[[4]] = c("Scarthyla_goinorum_WED_58246" , "Lysapsus_laevis_CAS_257655", "Pseudis_paradoxus_CAS_245053")
taxa.set[[5]] = c("Dendropsophus_leucophyllatus_WED_59288", "Dendropsophus_parviceps_MZUTI_1357", "Dendropsophus_koechlini_WED_57879")
taxa.set[[6]] = c("Plectrohyla_quecchi_MVZ_251534" , "Ptychohyla_salvadorensis_EBG_518","Smilisca_phaeota_LAC_2299",
"Hyla_sarda_WED_54544", "Hyla_walkeri_MVZ_263408", "Dryophtes_cinerea_WED_56355")
#taxa.set[[7]] = c("Acris_blanchardi_DSM_2012", "Pseudacris_triseriata_BLO_006","Hyliola_cadaverina_WED_54461")
names(taxa.set) = c("node1", "node2", "node3", "node4", "node5", "node6")
#### Function start
anomaly.data = filterAnomalies(astral.directory = "filtered-Astral",
outgroups = outgroup.taxa,
filter.data = filt.summary)
#Obtains the concordance factors data for all filtered replicates
concord.data = filterConcordance(input.dir = "concordance-factors",
clade.list = taxa.set,
outgroups = outgroup.taxa)
#### Pull out best tree
bestFilterTrees(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
output.dir = "best-trees",
min.trees = 20,
fewest.anomaly.zones = TRUE,
highest.gene.cf = TRUE,
highest.post.prob = TRUE,
all.datasets = TRUE,
top.best = 1)
# General plotting function to get a sense of the results without specific node
plot.filterZone(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
save.plots = TRUE,
output.dir = "Filter-Plots",
dataset.name = "all",
plot.gcf = TRUE,
plot.scf = TRUE,
az.colors = c("#7BC143", "#DE3293"),
m.shape = c(22, 21),
min.trees = 10)
#Plot alignment length, node 2
plot.filterNode(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
output.dir = "Filter-Plots",
focal.node = "node2",
filter.name = "alignment_length",
dataset.name = "all",
plot.gcf = TRUE,
plot.scf = TRUE,
az.colors = c("#7BC143", "#DE3293"),
m.shape = c(22, 21),
min.trees = 10 )
#Plot alignment length, node 6
plot.filterNode(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
output.dir = "Filter-Plots",
focal.node = "node6",
filter.name = "alignment_length",
dataset.name = "all",
plot.gcf = TRUE,
plot.scf = TRUE,
az.colors = c("#7BC143", "#DE3293"),
m.shape = c(22, 21),
min.trees = 10 )
######################################################################################
######################################################################################
######################################################################################
######################################################################################
######################################################################################
##### Filtering demonstration with multi-dataset loop
#Load in packages
devtools::install_github("chutter/AstralPlane")
library(AstralPlane)
library(data.table)
#Set up your directories
align.dir = "/Volumes/Armored/Hylidae/Alignments"
tree.dir = "/Users/chutter/Dropbox/Research/1_Main-Projects/0_Working-Projects/Hylidae/Trees/Gene_Trees"
work.dir = "/Volumes/Armored/Hylidae/Dataset-filtering"
astral.path = "/usr/local/bin/Astral-5-14/astral.5.14.2.jar"
dir.create(work.dir)
setwd(work.dir)
#If you have many different datasets to set up and run, you can give it the folder
#of various datasets.
batchAstral(genetree.datasets = tree.dir,
astral.t = 2,
output.dir = "test-dataset",
min.n.samples = 4,
min.sample.prop = 0.1,
taxa.remove = NULL,
overwrite = TRUE,
quiet = F,
astral.path = astral.path,
make.polytomy = TRUE,
polytomy.limit = 10,
multi.thread = TRUE,
memory = "8g")
#Obtains dataset names
datasets = list.dirs(tree.dir, full.names = F, recursive = F)
for (i in 1:length(datasets)){
#Read in the astral data and tree and organize it into different slots
dataset.name = paste0("test-dataset/", datasets[i], "_astral.tre")
astral.data = createAstralPlane(astral.tree = dataset.name,
outgroups = outgroups,
tip.length = 1)
#Plots the astral data
astralProjection(astral.plane = astral.data,
local.posterior = TRUE,
pie.plot = TRUE,
pie.data = "qscore",
save.file = paste0("test-dataset/", datasets[i], ".pdf"),
pie.colors = c("purple", "blue", "green"),
node.color.text = c("white"),
node.color.bg = c("black"),
tip.label.size = 0.75,
pie.chart.size = 1)
species.tree = ape::read.tree(paste0(astral.dir, "/", astral.trees[i]))
anom.data = anomalyZone(tree = species.tree,
outgroups = outgroup.taxa)
plot.anomalyZone(tree = species.tree,
data = anom.data,
outgroups = outgroup.taxa,
save.file = paste0(out.dir, "/", astral.trees[i],"_anomaly_zone.pdf"),
tip.label.size = 0.4,
node.label.size = 0.3,
edge.width = 2)
}#end i loop
##### Filtering demonstration with multi-dataset loop
#######
#Set up your filters
filter.length = c(100, 200, 300, 400, 500, 600, 700, 800, 900, 1000,
1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000,
2100, 2200, 2300, 2400, 2500) #number of base pairs
filter.sample = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1) #proportion samples
filter.prop.pis = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1) #proportion pis
filter.count.pis = c(10, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500) #count of pis
#Gather datasets
datasets = c("all-markers_trimmed", "exon-only_trimmed",
"intron-only_trimmed", "legacy-markers_trimmed",
"locus-combined", "uce_trimmed")
master.align.summary = data.table()
master.filt.summary = data.table()
for (i in 1:length(datasets)){
#makes the alignment directory name from the dataset
dataset.align = paste0(align.dir, "/", datasets[i])
dataset.trees = paste0(tree.dir, "/", datasets[i])
#Alignment summary function [20 minutes]
#Do not want to save a file. Do this at the end.
#Use datasetalign here
align.summary = summarizeAlignments(alignment.path = dataset.align,
dataset.name = datasets[i],
file.export = datasets[i],
alignment.format = "phylip",
overwrite = FALSE)
#Apply filters and create summary table of filters [< 1 min]
#Do not want to save a file. Do this at the end.
filt.summary = filterSummary(alignment.data = align.summary,
alignment.folder = align.dir,
dataset.name = datasets[i],
file.out = NULL,
length.filters = filter.length,
sample.filters = filter.sample,
prop.pis.filters = filter.prop.pis,
count.pis.filters = filter.count.pis)
#Make filtered alignments datasets [10 minutes]
#Use datasetalign here; overwrite = FALSE
filterAlignments(filter.summary = filt.summary,
alignment.data = align.summary,
alignment.folder = dataset.align,
format = "concatenated",
min.alignments = 5,
min.n.samples = 4,
overwrite = FALSE)
#Make filtered gene trees datasets [5 minutes]
#Use datasetalign here; overwrite = FALSE
filterGeneTrees(filter.summary = filt.summary,
alignment.data = align.summary,
genetree.folder = dataset.trees,
format = "concatenated",
overwrite = FALSE,
taxa.remove = NULL,
min.trees = 5,
min.n.samples = 5,
min.sample.prop = NULL,
make.polytomy = TRUE,
polytomy.limit = 10,
remove.node.labels = FALSE)
master.align.summary = rbind(master.align.summary, align.summary)
master.filt.summary = rbind(master.filt.summary, filt.summary)
print(paste0(datasets[i], " complete!"))
}#end i loop
##### Save the files here since it too awhile to generate
write.csv(master.align.summary, file = "master_alignment_summary.csv", row.names = F)
write.csv(master.filt.summary, file = "master_filter_summary.csv", row.names = F)
#Runs astral across all filtered gene tree sets
AstralPlane::astralRunner(concat.genetree.folder = "filtered-genetrees-concatenated",
output.dir = "filtered-astral",
overwrite = TRUE,
astral.path = astral.path,
astral.t = 2,
quiet = FALSE,
multi.thread = TRUE,
memory = "8g")
#Concordance factor analysis across all filtered datasets
AstralPlane::concordanceRunner(alignment.dir = paste0(work.dir, "/filtered-alignments-concatenated"),
species.tree.dir = paste0(work.dir, "/filtered-astral"),
genetree.dir = paste0(work.dir, "/filtered-genetrees-concatenated"),
output.dir = "concordance-factors",
overwrite = TRUE,
quiet = TRUE,
threads = 6)
######################################################################################
######################################################################################
######################################################################################
######################################################################################
######################################################################################
##### Analyze data all together
#Load in packages
devtools::install_github("chutter/AstralPlane")
library(AstralPlane)
library(data.table)
#Set up your directories
align.dir = "/Volumes/Armored/Hylidae/Alignments"
work.dir = "/Volumes/Armored/Hylidae/Dataset-filtering"
astral.dir = "filtered-astral"
setwd(work.dir)
#outgroups
outgroup.taxa = c("Phyllomedusa_tomopterna_WED_55506", "Nyctimystes_infrafrenatus_SLT_771")
#Set up a list of your clades of interest
taxa.set = list()
taxa.set[[1]] = c("Colomascirtus_staufferorum_LAC_2153", "Hyloscirtus_phyllognathus_WED_58378", "Hypsiboas_boans_WED_57877")
taxa.set[[2]] = c("Sphaenorhynchus_lacteus_WED_54090", "Scinax_garbei_LAC_1787", "Scinax_ruber_WED_56140" )
taxa.set[[3]] = c("Trachycephalus_jordani_WED_53658", "Phyrnohyas_venulosa_WED_55450", "Osteocephalus_taurinus_WED_55452")
taxa.set[[4]] = c("Scarthyla_goinorum_WED_58246" , "Lysapsus_laevis_CAS_257655", "Pseudis_paradoxus_CAS_245053")
taxa.set[[5]] = c("Dendropsophus_leucophyllatus_WED_59288", "Dendropsophus_parviceps_MZUTI_1357", "Dendropsophus_koechlini_WED_57879")
taxa.set[[6]] = c("Plectrohyla_quecchi_MVZ_251534" , "Ptychohyla_salvadorensis_EBG_518","Smilisca_phaeota_LAC_2299",
"Hyla_sarda_WED_54544", "Hyla_walkeri_MVZ_263408", "Dryophtes_cinerea_WED_56355")
taxa.set[[7]] = c("Pseudacris_triseriata_BLO_006","Hyliola_cadaverina_WED_54461")
names(taxa.set) = c("node1", "node2", "node3", "node4", "node5", "node6", "node7")
align.summary = read.csv("master_alignment_summary.csv")
filt.summary = read.csv("master_filter_summary.csv")
#### Collect anamaly zone data from all filtered replicates
anomaly.data = filterAnomalies(astral.directory = astral.dir,
outgroups = outgroup.taxa,
filter.data = filt.summary)
#Obtains the concordance factors data for all filtered replicates
concord.data = filterConcordance(input.dir = "concordance-factors",
clade.list = taxa.set,
outgroups = outgroup.taxa)
# General plotting function to get a sense of the results without specific node
plot.filterZone(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
save.plots = TRUE,
output.dir = "Filter-Plots",
dataset.name = "all",
plot.gcf = TRUE,
plot.scf = TRUE,
az.colors = c("#7BC143", "#DE3293"),
m.shape = c(22, 21),
min.trees = 10)
#### Pull out best tree
bestFilterTrees(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
output.dir = "best-trees",
min.trees = 20,
fewest.anomaly.zones = TRUE,
highest.gene.cf = TRUE,
highest.post.prob = TRUE,
all.datasets = TRUE,
top.best = 1)
#Plot alignment length, node 2
plot.filterNode(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
output.dir = "Filter-Plots",
focal.node = "node2",
filter.name = "alignment_length",
dataset.name = "all",
plot.gcf = TRUE,
plot.scf = TRUE,
az.colors = c("#7BC143", "#DE3293"),
m.shape = c(22, 21),
min.trees = 10 )
#Plot alignment length, node 6
plot.filterNode(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
output.dir = "Filter-Plots",
focal.node = "node6",
filter.name = "alignment_length",
dataset.name = "all",
plot.gcf = TRUE,
plot.scf = TRUE,
az.colors = c("#7BC143", "#DE3293"),
m.shape = c(22, 21),
min.trees = 10 )
#Plot count PIS node 2
plot.filterNode(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
output.dir = "Filter-Plots",
focal.node = "node2",
filter.name = "count_pis",
dataset.name = "all",
plot.gcf = TRUE,
plot.scf = TRUE,
az.colors = c("#7BC143", "#DE3293"),
m.shape = c(22, 21),
min.trees = 10 )
#Plot count PIS node 6
plot.filterNode(anomaly.zone.data = anomaly.data,
concordance.factors.data = concord.data,
output.dir = "Filter-Plots",
focal.node = "node6",
filter.name = "count_pis",
dataset.name = "all",
plot.gcf = TRUE,
plot.scf = TRUE,
az.colors = c("#7BC143", "#DE3293"),
m.shape = c(22, 21),
min.trees = 10 )
###############################################################################
###############################################################################
############### PLOT THE NODES OF INTEREST! ########################
###############################################################################
###############################################################################
#Set up your directories
align.dir = "/Volumes/Armored/Hylidae/Alignments"
species.tree.path = "/Users/chutter/Dropbox/Research/1_Main-Projects/0_Working-Projects/Hylidae/Trees/Astral/Astral_everything.tre"
species.tree.path = "/Users/chutter/Dropbox/Research/1_Main-Projects/0_Working-Projects/Hylidae/Trees/Concat/all-markers_trimmed_50.phy.tre"
tree.dir = "/Users/chutter/Dropbox/Research/1_Main-Projects/0_Working-Projects/Hylidae/Trees/Gene_Trees/all-markers_trimmed"
work.dir = "/Volumes/Armored/Hylidae/Dataset-filtering"
setwd(work.dir)
align.summary = read.csv("master_alignment_summary.csv")
filt.summary = read.csv("master_filter_summary.csv")
outgroups = c("Phyllomedusa_tomopterna_WED_55506", "Nyctimystes_infrafrenatus_SLT_771")
### discordance versus alignment length
erroneousZone = function(species.tree.file = NULL,
outgroup.taxa = NULL,
genetree.directory = NULL,
alignment.summary = align.summary,
low.support.value = 94,
remove.taxa = NULL,
output.dir = "Erroneous-Zone",
dataset.name = "all",
filter.name = c("alignment_length", "count_pis", "proportion_pis", "proportion_sample"),
zone.colors = c("#7BC143", "#DE3293") ) {
#Debug
remove.taxa = c("Phyllomedusa_tomopterna_WED_55506", "Nyctimystes_infrafrenatus_SLT_771")
low.support.value = 94
outgroup.taxa = outgroups
species.tree.file = species.tree.path
genetree.directory = tree.dir
alignment.summary = align.summary
alignment.summary = alignment.summary[alignment.summary$dataset %in% "all-markers_trimmed",]
output.dir = "Erroneous-Zone"
dataset.name = "all"
filter.name = c("alignment_length", "count_pis", "proportion_pis", "proportion_sample")
zone.colors = c("#7BC143", "#DE3293")
#nitial checks
if (is.null(dataset.name) == TRUE){ stop("Error: a dataset name is needed.")}
if (is.null(filter.name) == TRUE){ stop("Error: filter choice in filter.name is needed.")}
#if (length(filter.name) != 1){ stop("Error: Only 1 filter can be plotted at a time.")}
if (is.null(dataset.name) == TRUE){ stop("Error: a dataset name is needed.")}
#Create working directory
if(file.exists(output.dir) == F) { dir.create(output.dir) }
#Get trees
gene.trees = list.files(genetree.directory)
#Read in species tree
species.tree = ape::read.tree(species.tree.file)
species.tree = ape::drop.tip(species.tree, remove.taxa)
species.tree = ape::unroot(species.tree)
#CReates empty datasets
header.data = c("file", "marker_name", "rf_dist_abs", "rf_dist_norm", "tree_discordant",
"tree_n_samples", "mean_support", "min_support", "max_support",
"n_low_support", "prop_low_support",
"align_n_samples", "align_proportion_samples", "alignment_length",
"alignment_count_pis", "alignment_proportion_pis", "alignment_count_missing_bp",
"alignment_proportion_missing_bp")
collect.data = data.table(matrix(as.numeric(0),
nrow = length(gene.trees),
ncol = length(header.data)))
setnames(collect.data, header.data)
collect.data[, file:=as.character(file)]
collect.data[, marker_name:=as.character(marker_name)]
index.val = 1
for (i in 1:length(gene.trees)){
#Read in gene tree
tree.gene = ape::read.tree(paste0(genetree.directory, "/", gene.trees[i]) )
tree.gene = ape::drop.tip(tree.gene, remove.taxa)
tree.gene = ape::unroot(tree.gene)
gene.name = gsub(".phy.*", "", gene.trees[i])
#Drop taxa from each to make the same tree
drop.taxa = species.tree$tip.label[!species.tree$tip.label %in% tree.gene$tip.label]
red.tree = ape::drop.tip(species.tree, drop.taxa)
drop.taxa = tree.gene$tip.label[!tree.gene$tip.label %in% species.tree$tip.label]
tree.gene = ape::drop.tip(tree.gene, drop.taxa)
#Skips if removed too much
if (length(tree.gene$tip.label) <= 3){ next }
#Obtains alignment summary data
gene.summary = alignment.summary[gsub("\\..*", "", alignment.summary$file) %in% gene.name,]
#Gets gene tree stats
gene.node.mean = mean(as.numeric(tree.gene$node.label), na.rm = T)
gene.node.low = as.numeric(tree.gene$node.label)[as.numeric(tree.gene$node.label) <= low.support.value]
gene.node.low = length(gene.node.low[is.na(gene.node.low) != T])
gene.node.min = min(as.numeric(tree.gene$node.label), na.rm = T)
gene.node.max = max(as.numeric(tree.gene$node.label), na.rm = T)
#Gets rf distances
gene.rf.norm = phangorn::RF.dist(red.tree, tree.gene, normalize = T)
gene.rf.abs = phangorn::RF.dist(red.tree, tree.gene, normalize = F)
discord.value = 0
if (gene.rf.norm != 0){ discord.value = 1 }
#Saves data
set(collect.data, i = as.integer(index.val), j = match("file", header.data), value = gene.trees[i] )
set(collect.data, i = as.integer(index.val), j = match("marker_name", header.data), value = gene.name )
set(collect.data, i = as.integer(index.val), j = match("rf_dist_abs", header.data), value = gene.rf.abs )
set(collect.data, i = as.integer(index.val), j = match("rf_dist_norm", header.data), value = gene.rf.norm )
set(collect.data, i = as.integer(index.val), j = match("tree_discordant", header.data), value = discord.value )
set(collect.data, i = as.integer(index.val), j = match("tree_n_samples", header.data), value = length(tree.gene$tip.label) - length(remove.taxa) )
set(collect.data, i = as.integer(index.val), j = match("mean_support", header.data), value = gene.node.mean )
set(collect.data, i = as.integer(index.val), j = match("min_support", header.data), value = gene.node.min )
set(collect.data, i = as.integer(index.val), j = match("max_support", header.data), value = gene.node.max )
set(collect.data, i = as.integer(index.val), j = match("n_low_support", header.data), value = gene.node.low )
set(collect.data, i = as.integer(index.val), j = match("prop_low_support", header.data), value = gene.node.low/(length(tree.gene$node.label)-1) )
set(collect.data, i = as.integer(index.val), j = match("align_n_samples", header.data), value = gene.summary$number_samples )
set(collect.data, i = as.integer(index.val), j = match("align_proportion_samples", header.data), value = gene.summary$proportion_samples )
set(collect.data, i = as.integer(index.val), j = match("alignment_length", header.data), value = gene.summary$alignment_length )
set(collect.data, i = as.integer(index.val), j = match("alignment_count_pis", header.data), value = gene.summary$count_pis )
set(collect.data, i = as.integer(index.val), j = match("alignment_proportion_pis", header.data), value = gene.summary$proportion_pis )
set(collect.data, i = as.integer(index.val), j = match("alignment_count_missing_bp", header.data), value = gene.summary$count_missing_bp )
set(collect.data, i = as.integer(index.val), j = match("alignment_proportion_missing_bp", header.data), value = gene.summary$proportion_missing_bp )
#Adds oup counter
index.val = index.val + 1
}#end i loop
#All regression
summary(lm((collect.data$rf_dist_norm) ~ collect.data$alignment_length ))
#concordant = 0
c.data = collect.data[collect.data$rf_dist_norm == 0,]
c.data$tree_discordant = factor(c.data$tree_discordant)
c.data = c.data[is.na(c.data$tree_discordant) != T,]
#Discordant = 1
d.data = collect.data[collect.data$rf_dist_norm >= 0.5,]
d.data$tree_discordant = factor(d.data$tree_discordant)
d.data = d.data[is.na(d.data$tree_discordant) != T,]
#Box plot and t-test
boxplot(c.data$alignment_length, d.data$alignment_length)
og.model = t.test(c.data$alignment_length, d.data$alignment_length, var.equal = F)
og.model
#cohensD(red.orig$rfDist, red.exon$rfDist)
plot.data = collect.data
plot.data = plot.data[plot.data$tree_n_samples >= 21,]
plot.data$tree_discordant = factor(plot.data$tree_discordant)
ggplot(plot.data, aes(x=alignment_length, color = tree_discordant)) +
geom_density()
boxplot(plot.data$alignment_length~plot.data$tree_discordant)
model = glm(Discord ~ alignment_length, data = plot.data, family = "binomial")
summary(model)
exp(cbind(Odds=coef(model), confint(model)))
}#END FUNCTION
#Loop through each gene tree and compare exon trees
index.val = 1
for (i in 1:length(gene.trees)){
gene.name = gsub(".phy.*", "", gene.trees[i])
gene.exons = exon.trees[grep(gene.name, exon.trees)]
tree.gene = read.tree(paste0(gene.tree.dir, "/", gene.trees[i]) )
tree.gene = drop.tip(tree.gene, outgroups)
if (length(tree.gene$tip.label) <= 3){ next }
gene.node.mean = mean(as.numeric(tree.gene$node.label), na.rm = T)
gene.low.node = as.numeric(tree.gene$node.label)[as.numeric(tree.gene$node.label) <= 94]
gene.low.node = gene.low.node[is.na(gene.low.node) != T]
drop.taxa = species.tree$tip.label[!species.tree$tip.label %in% tree.gene$tip.label]
red.tree = drop.tip(species.tree, drop.taxa)
red.tree = unroot(red.tree)
gene.dist = RF.dist(red.tree, tree.gene, normalize = T)
if (length(gene.exons) == 0){ next }
#Loops through each exon
for (j in 1:length(gene.exons)){
tree.exon = read.tree(paste0(exon.tree.dir, "/", gene.exons[j]) )
tree.exon = drop.tip(tree.exon, outgroups)
if (length(tree.exon$tip.label) <= 3){ next }
drop.taxa = tree.gene$tip.label[!tree.gene$tip.label %in% tree.exon$tip.label]
red.tree = drop.tip(tree.gene, drop.taxa)
#Drops taxa not on the exon tree but on the gene tree
drop.taxa = tree.exon$tip.label[!tree.exon$tip.label %in% red.tree$tip.label]
red.exon = drop.tip(tree.exon, drop.taxa)
exon.dist = RF.dist(red.tree, red.exon, normalize = T)
#Gets bootstrap
exon.node.mean = mean(as.numeric(tree.exon$node.label), na.rm = T)
exon.low.node = as.numeric(tree.exon$node.label)[as.numeric(tree.exon$node.label) <= 94]
exon.low.node = exon.low.node[is.na(exon.low.node) != T]
#Gets species tree data
drop.taxa = species.tree$tip.label[!species.tree$tip.label %in% tree.exon$tip.label]
red.species = drop.tip(species.tree, drop.taxa)
red.species = unroot(red.species)
spp.dist = RF.dist(red.species, tree.exon, normalize = T)
#Saves data
exon.name = gsub(".phy.*", "", gene.exons[j])
set(collect.data, i = as.integer(index.val), j = match("Exon", header.data), value = exon.name )
set(collect.data, i = as.integer(index.val), j = match("Gene", header.data), value = gene.name )
set(collect.data, i = as.integer(index.val), j = match("nSamples", header.data), value = length(red.species$tip.label) )
set(collect.data, i = as.integer(index.val), j = match("rfDist_Exon_Gene", header.data), value = exon.dist )
set(collect.data, i = as.integer(index.val), j = match("rfDist_Exon_Spp", header.data), value = spp.dist )
set(collect.data, i = as.integer(index.val), j = match("rfDist_Gene_Spp", header.data), value = gene.dist )
set(collect.data, i = as.integer(index.val), j = match("Gene_lowBS_prop", header.data), value = length(gene.low.node)/(length(tree.gene$node.label)-1) )
set(collect.data, i = as.integer(index.val), j = match("Gene_meanBS", header.data), value = gene.node.mean )
set(collect.data, i = as.integer(index.val), j = match("Exon_lowBS_prop", header.data), value = length(exon.low.node)/(length(tree.exon$node.label)-1) )
set(collect.data, i = as.integer(index.val), j = match("Exon_meanBS", header.data), value = exon.node.mean )
index.val = index.val + 1
} #end j loop
} #end i loop
###############
#Start function
concord.red = concordance.factors.data[concordance.factors.data$clade %in% focal.node,]
filter.datasets = unique(concord.red$dataset)
collect.data = c()
for (x in 1:length(filter.datasets)){
temp.a = anomaly.zone.data[anomaly.zone.data$filter_file %in% filter.datasets[x],]
temp.c = concord.red[concord.red$dataset %in% filter.datasets[x],]
mono.p = unique(temp.c$monophyletic)
mono.p = mono.p[is.na(mono.p) != T]
temp.c = temp.c[temp.c$gCF %in% min(temp.c$gCF),]
temp.c$monophyletic = mono.p
if (length(unique(temp.a$anomaly_zone)) == 1){
temp.collect = cbind(temp.c, temp.a[1,])
collect.data = rbind(collect.data, temp.collect)
next
}
#Match up anomaly zone or not
node.data1 = temp.a[temp.a$parent_node %in% temp.c$node,]
node.data2 = temp.a[temp.a$child_node %in% temp.c$node,]
node.data3 = temp.a[temp.a$parent_node %in% node.data1$child_node,]
node.data = rbind(node.data1, node.data2, node.data3)
az.data = node.data[node.data$anomaly_zone == 1,]
#Picks the best
if (nrow(az.data) >= 2){ az.data = az.data[1,] }
if (nrow(az.data) == 0){ az.data = node.data[1,] }
temp.collect = cbind(temp.c, az.data)
collect.data = rbind(collect.data, temp.collect)
}#x loop end
###################################################
#Filters down to the target dataset and filter set
###################################################
sub.data = collect.data[collect.data$filter_name %in% filter.name,]
if (filter.name == "alignment_length"){ sub.data$final_filter = sub.data$filter_length }
if (filter.name == "count_pis"){ sub.data$final_filter = sub.data$filter_count_pis }
if (filter.name == "proportion_pis"){ sub.data$final_filter = sub.data$filter_prop_pis }
if (filter.name == "proportion_sample"){ sub.data$final_filter = sub.data$filter_sample }
if (dataset.name != "all"){
sub.data = sub.data[sub.data$align_dataset %in% dataset.name,]
dataset.names = dataset.name
} else{
dataset.names = unique(sub.data$align_dataset)
}
plot.list = vector("list", length(dataset.names))
for (x in 1:length(dataset.names)){
red.data = sub.data[sub.data$align_dataset %in% dataset.names[x],]
if (plot.gcf == TRUE){
plot.data = data.frame(Type = "gCF",
CF = red.data$gCF,
Trees = red.data$no_trees,
Filter = red.data$final_filter,
AZ = red.data$anomaly_zone,
M = red.data$monophyletic)
}
if (plot.scf == TRUE){
plot.data1 = data.frame(Type = "sCF",
CF = red.data$sCF,
Trees = red.data$no_trees,
Filter = red.data$final_filter,
AZ = red.data$anomaly_zone,
M = red.data$monophyletic)
plot.data = rbind(plot.data, plot.data1)
}
#plot.data = rbind(plot1.data, plot2.data)
plot.data = plot.data[plot.data$Trees > min.trees,]
plot.data = plot.data[plot.data$Filter != 0,]
plot.data = plot.data[is.na(plot.data$CF) != T,]
plot.data$Filter = factor(plot.data$Filter)
plot.data$M = factor(plot.data$M)
plot.data$AZ = factor(plot.data$AZ)
#Sets up colors in case there is just one
if (length(unique(plot.data$AZ)) == 1){
if(unique(plot.data$AZ) == 1){
temp.az.colors = az.colors[2]
}
if(unique(plot.data$AZ) == 0){
temp.az.colors = az.colors[1]
}
} else {temp.az.colors = az.colors }
#Sets up shapes in case there is just one
if (length(unique(plot.data$M)) == 1){
if(unique(plot.data$M) == TRUE){
temp.m.shape = m.shape[2]
}
if(unique(plot.data$M) == FALSE){
temp.m.shape = m.shape[1]
}
} else { temp.m.shape = m.shape }
#Have to display 1 state correctly for each of the four
#Pick a better shape?
#Plot gene concordance factor
if (plot.gcf == TRUE){
gcf.plot = plot.data[plot.data$Type == "gCF",]
#Getst the best y limit
best.y = signif(max(gcf.plot$CF), digits = 0)
if (best.y >= 80){
best.y = best.y + 10
#If greater than 100
if(best.y >= 100){ best.y = 100 }
} else { best.y = 80 }
#End the y limit
#Final one for paper
main.plot = ggplot2::ggplot(gcf.plot, ggplot2::aes(x=Filter, y=CF, fill = AZ, shape = M)) +
ggplot2::geom_point(ggplot2::aes(color = AZ, shape = M), size = 10, color = "black") +
ggplot2::ylim(limits=c(0, best.y)) +
ggplot2::scale_shape_manual(values = temp.m.shape) +
ggplot2::scale_fill_manual(values=temp.az.colors)
main.plot = main.plot +
ggplot2::theme_minimal() +
ggplot2::theme(legend.title = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank())
main.plot
#Saves the plot
if(is.null(save.plots) != TRUE) {
ggplot2::ggsave(paste0(output.dir, "/GCF_", focal.node, "_", filter.name, "_", dataset.names[x], ".pdf"),
width = 8, height = 4, device = "pdf")
print(paste0(output.dir, "/GCF_", focal.node, "_", filter.name, "_", dataset.names[x], ".pdf",
" saved to file!"))
}#end if
}#end GCF plot
#Plot site concordance factor
if (plot.scf == TRUE){
scf.plot = plot.data[plot.data$Type == "sCF",]
#Getst the best y limit
best.y = signif(max(scf.plot$CF), digits = 0)
if (best.y >= 80){
best.y = best.y + 10
#If greater than 100
if(best.y >= 100){ best.y = 100 }
} else { best.y = 80 }
#End the y limit
#Final one for paper
main.plot = ggplot2::ggplot(scf.plot, ggplot2::aes(x=Filter, y=CF, fill = AZ, shape = M)) +
ggplot2::geom_point(ggplot2::aes(color = AZ, shape = M), size = 10, color = "black") +
ggplot2::ylim(limits=c(0, best.y)) +
ggplot2::scale_shape_manual(values = temp.m.shape) +
ggplot2::scale_fill_manual(values= temp.az.colors)
main.plot = main.plot +
ggplot2::theme_minimal() +
ggplot2::theme(legend.title = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank())
main.plot
plot.list[[x]] = main.plot
#Saves the plot
if(is.null(save.plots) != TRUE) {
ggplot2::ggsave(paste0(output.dir, "/SCF_", focal.node, "_", filter.name, "_", dataset.names[x], ".pdf"),
width = 8, height = 4, device = "pdf") }#end if
print(paste0(output.dir, "/SCF_", focal.node, "_", filter.name, "_", dataset.names[x], ".pdf",
" saved to file!"))
}#end GCF plot
}#end dataset loop
#### COMBINE INTO 1 FIGURE
#ggpubr::ggarrange(plot.list[[1]], plot.list[[2]], plot.list[[3]], plot.list[[4]], plot.list[[5]], nrow = 2)
#ggsave(paste(output.dir, "/GCF_", focal.node, "_", filter.name, "_ALL.pdf"), width = 28, height = 4, device = "pdf")
}#end function
### discordance versus alignment length after filtration
###############################################################################
###############################################################################
############### PLOT THE NODES OF INTEREST! ########################
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#### Ideas: Randomization test
#Using concordance factors, we develop an alternative test of whether the species tree
#is in the erroneous zone, predicting that gCF will be less than sCF because EGTs will be
#unable estimate the true species tree because of insufficient informative sites.
#Conversely, to support the anomaly zone, we would expect gCF and sCF to be about equal
#because ILS would occur on the underlying sites and be reflected on gene trees given accurate
#estimation.