# Loads newick tree
phylo = phyloxml.PhyloxmlTree(newick=sys.argv[1])
# Set basic tree info as a phyloxml phylogeny object
phylo.phyloxml_phylogeny.set_name("test_tree")
if len(phylo.children) <= 2:
phylo.phyloxml_phylogeny.set_rooted("true")
else:
phylo.phyloxml_phylogeny.set_rooted("false")
# Add the tree to the phyloxml project
project.add_phylogeny(phylo)
# Export phyloxml document
OUTPUT = StringIO()
project.export(OUTPUT)
# Some ad-hoc changes to the phyloxml formatted document to meet the schema definition
text = OUTPUT.getvalue()
text = text.replace("phy:", "")
text = re.sub('branch_length_attr="[^"]+"', "", text)
header = """
<phyloxml xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.phyloxml.org http://www.phyloxml.org/1.10/phyloxml.xsd"
xmlns="http://www.phyloxml.org">
"""
text = re.sub('<Phyloxml[^>]+>', header, text)
text = text.replace('Phyloxml', 'phyloxml')
# Save result
open(sys.argv[1]+".phyloxml", "w").write(text)
def MakePlot(x, org_names, ckm30, ckm50, outgroup, outfile, outfilexml, sum_x):
#Make sure names are unique
names = org_names
for name in names:
if names.count(name)>1:
temp_name = name
i=1
for dummy in range(0,names.count(name)-1): #Don't change the last one, just to make sure we don't conflict with the outgroup
names[names.index(temp_name)] = temp_name + "_" + str(i)
i = i +1
#Normalize the x vector
x = map(lambda y: y/sum(x),x)
ckm30_norm = np.multiply(ckm30,1/np.diag(ckm30))
ckm50_norm = np.multiply(ckm50,1/np.diag(ckm50))
num_rows = ckm30_norm.shape[0]
num_cols = ckm30_norm.shape[1]
matrix=list()
for i in range(num_rows):
matrix.append([.5*(1-.5*ckm30_norm[i,j]-.5*ckm30_norm[j,i])+.5*(1-.5*ckm50_norm[i,j]-.5*ckm50_norm[j,i]) for j in range(i+1)])
#Make the list of distances (ave of the two ckm matrices)
ckm_ave_train = .5*ckm30_norm+.5*ckm50_norm
ckm_ave_train_dist = dict()
for i in range(len(org_names)):
ckm_ave_train_dist[org_names[i]] = [.5*ckm_ave_train[i,j]+.5*ckm_ave_train[j,i] for j in range(len(org_names))]
#Construct the tree. Note I could use RapidNJ here, but a few tests have shown that the trees that RapidNJ creates are rubbish.
dm = _DistanceMatrix(names, matrix)
constructor = DistanceTreeConstructor()
tree = constructor.nj(dm)
t=Tree(tree.format('newick'),format=1)
#tree.format('newick')
#Phylo.draw_ascii(tree)
#Now I will put internal nodes in a certain phylogenetic distance between the root and a given node.
#Function to insert a node at a given distance
def insert_node(t, name_to_insert, insert_above, dist_along):
insert_at_node = t.search_nodes(name=insert_above)[0]
parent = (t&insert_above).up
orig_branch_length = t.get_distance(insert_at_node,parent)
if orig_branch_length < dist_along:
raise ValueError("error: dist_along larger than orig_branch_length in PlotPackage.py")
removed_node = insert_at_node.detach()
removed_node.dist = orig_branch_length - dist_along
added_node = parent.add_child(name=name_to_insert, dist=dist_along)
added_node.add_child(removed_node)
#Function to insert a node some % along a branch, taking into account the ckm distances and nodes already created in the NJ tree (and what distance their descendants are from everyone else)
def insert_hyp_node(t, leaf_name, percent, ckm_ave_train_dist, org_names):
dists = map(lambda y: abs(y-percent), ckm_ave_train_dist[leaf_name])
nearby_indicies = list()
#Add all the organisms that are within 0.05 of the given percent
# for i in range(len(dists)):
# if dists[i]<=.05:
# nearby_indicies.append(i)
nearby_names = list()
#If there are no nearby indicies, add the closest organism to the given percent
if nearby_indicies==[]:
nearby_names.append(org_names[dists.index(min(dists))])
else:
for i in range(len(nearby_indicies)):
nearby_names.append(org_names[i])
mean_dist = np.mean(map(lambda y: ckm_ave_train_dist[leaf_name][org_names.index(y)],nearby_names))
nearby_names.append(leaf_name)
LCA = t.get_common_ancestor(nearby_names)
LCA_to_leaf_dist = t.get_distance(LCA,leaf_name)
#divide the dist to the right/left of the LCA node by the number of percentage points in there
if LCA.name==t.name:
percent_dist = percent*LCA_to_leaf_dist
if mean_dist <= percent:
child_node = (t&leaf_name)
else:
child_node = (t&nearby_names[0])#This means "go up from root" in the direction of the nearest guy
ancestor_node = (t&child_node.name).up
elif mean_dist <= percent:
percent_dist = t.get_distance(LCA) + abs(percent-mean_dist)*(LCA_to_leaf_dist)/(1-mean_dist)
child_node = (t&leaf_name)
ancestor_node = (t&child_node.name).up
else:
percent_dist = t.get_distance(LCA) - abs(percent-mean_dist)*(t.get_distance(LCA))/(mean_dist)
child_node = (t&leaf_name)
ancestor_node = (t&child_node.name).up
while t.get_distance(t.name, ancestor_node) > percent_dist:
child_node = ancestor_node
ancestor_node = (t&child_node.name).up
insert_node(t, leaf_name+"_"+str(percent), child_node.name, percent_dist-t.get_distance(t.name, ancestor_node))
#Set outgroup
if outgroup in names:
t.set_outgroup(t&outgroup) #I will need to check that this outgroup is actually one of the names...
else:
print("WARNING: the chosen outgroup " + outgroup + " is not in the given taxonomy: ")
print(names)
print("Proceeding without setting an outgroup. This may cause results to be uninterpretable.")
#Insert hypothetical nodes
hyp_node_names = dict()
cutoffs = [.9,.8,.7,.6,.5,.4,.3,.2,.1]
cutoffs = [-.5141*(val**3)+1.0932*(val**2)+0.3824*val for val in cutoffs]
for i in range(len(org_names)):
xi = x[i:len(x):len(org_names)]
for j in range(1,len(cutoffs)+1):
if xi[j]>0:
insert_hyp_node(t, org_names[i], cutoffs[j-1],ckm_ave_train_dist, org_names)
hyp_node_names[org_names[i]+"_"+str(cutoffs[j-1])] = [org_names[i], cutoffs[j-1], j-1] #in case there are "_" in the file names
size_factor=250
font_size=55
#Now put the bubbles on the nodes
def layout(node):
node_style = NodeStyle()
node_style["hz_line_width"] = 10
node_style["vt_line_width"] = 10
node.set_style(node_style)
#print(node)
if node.is_leaf():
if node.name in org_names:
#make reconstructed bubble
size = x[org_names.index(node.name)]
F = CircleFace(radius=size_factor*math.sqrt(size), color="RoyalBlue", style="sphere")
F.border.width = None
F.opacity = 0.6
faces.add_face_to_node(F,node, 0, position="branch-right")
#Denote that this was a training organism
nameFace = AttrFace("name", fsize=font_size, fgcolor='black')
faces.add_face_to_node(nameFace, node, 0, position="branch-right")
elif node.name in hyp_node_names: #Otherwise it's a hypothetical node, just use recon x
node_base_name = hyp_node_names[node.name][0]
percent = hyp_node_names[node.name][1]
if node_base_name in org_names:
idx = hyp_node_names[node.name][2]
size = x[org_names.index(node_base_name)+(idx+1)*len(org_names)]
F = CircleFace(radius=size_factor*math.sqrt(size), color="RoyalBlue", style="sphere")
F.border.width = None
F.opacity = 0.6
faces.add_face_to_node(F,node, 0, position="branch-right")
#This is if I want the names of the hypothetical nodes to be printed as well
#nameFace = AttrFace("name", fsize=font_size, fgcolor='black')
#faces.add_face_to_node(nameFace, node, 0, position="branch-right")
else:
size=0
else:
size=0
ts = TreeStyle()
ts.layout_fn = layout
ts.mode = "r"
#ts.mode = "c"
ts.scale = 2*1000
ts.show_leaf_name = False
ts.min_leaf_separation = 50
F = CircleFace(radius=.87*size_factor, color="RoyalBlue", style="sphere")
F.border.width = None
F.opacity = 0.6
ts.legend.add_face(F,0)
ts.legend.add_face(TextFace(" Inferred relative abundance",fsize=1.5*font_size,fgcolor="Blue"),1)
ts.legend.add_face(TextFace(" Total absolute abundance depicted " + str(sum_x)[0:8], fsize=1.5*font_size,fgcolor="Black"),1)
ts.legend_position=4
#t.show(tree_style=ts)
t.render(outfile, w=550, units="mm", tree_style=ts)
#Redner the XML file
project = Phyloxml()
phylo = phyloxml.PhyloxmlTree(newick=t.write(format=0, features=[]))
project.add_phylogeny(phylo)
project.export(open(outfilexml,'w'))
def main(argv):
input_file=''
title='Title'
label_internal_nodes = False
label_leaves = False
out_file=''
width=750
out_file_xml=''
plot_rectangular = False
common_kmer_data_path=''
taxonomic_names_on_leaves = False
try:
opts, args = getopt.getopt(argv,"h:i:lnrto:w:x:D:",["Help=","InputCommonKmerXFile=","LabelLeaves=", "LabelInternalNodes=","Rectangular=", "TaxonomicNamesOnLeaves=", "OutFile=","Width=","OutFileXML=","CommonKmerDataPath="])
except getopt.GetoptError:
print 'Unknown option, call using: ./PlotNJTree.py -i <InputCommonKmerXFile> -D <CommonKmerDataPath> -l <LabelLeavesFlag> -n <LabelInternalNodesFlag> -r <RectangularPlotFlag> -t <TaxonomicNamesOnLeavesFlag> -o <OutFile.png> -x <Outfile.xml> -w <Width>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print './PlotNJTree.py -i <InputCommonKmerXFile> -D <CommonKmerDataPath> -l <LabelLeavesFlag> -n <LabelInternalNodesFlag> -r <RectangularPlotFlag> -t <TaxonomicNamesOnLeavesFlag> -o <OutFile.png> -x <Outfile.xml> -w <Width>'
sys.exit(2)
elif opt in ("-i", "--InputCommonKmerXFile"):
input_file = arg
elif opt in ("-l", "--LabelLeaves"):
label_leaves = True
elif opt in ("-n","--LabelInternalNodes"):
label_internal_nodes = True
elif opt in ("-o", "--OutFile"):
out_file = arg
elif opt in ("-w", "--Width"):
width = int(arg)
elif opt in ("-x", "--OutFileXML"):
out_file_xml = arg
elif opt in ("-D", "--CommonKmerDataPath"):
common_kmer_data_path = arg
elif opt in ("-r", "--Rectangular"):
plot_rectangular = True
elif opt in ("-t", "--TaxonomicNamesOnLeaves"):
taxonomic_names_on_leaves = True
#Read in the x vector
fid = open(input_file,'r')
x = map(lambda y: float(y),fid.readlines())
fid.close()
#Normalize the x vector
#x = map(lambda y: y/sum(x),x)
#Read in the taxonomy
taxonomy = list()
fid = open(os.path.join(common_kmer_data_path,"Taxonomy.txt"),'r')
for line in fid:
taxonomy.append('_'.join(line.split()[0].split("_")[1:])) #Just take the first line of the taxonomy (erasing the taxID)
fid.close()
#Read in the basis for the ckm matrices
x_file_names = list()
fid = open(os.path.join(common_kmer_data_path,"FileNames.txt"),'r')
for line in fid:
x_file_names.append(os.path.basename(line.strip()))
fid.close()
#Read in the common kmer matrix
f=h5py.File(os.path.join(common_kmer_data_path,'CommonKmerMatrix-30mers.h5'),'r')
ckm30=np.array(f['common_kmers'],dtype=np.float64)
f.close()
f=h5py.File(os.path.join(common_kmer_data_path,'CommonKmerMatrix-50mers.h5'),'r')
ckm50=np.array(f['common_kmers'],dtype=np.float64)
f.close()
ckm30_norm = np.multiply(ckm30,1/np.diag(ckm30))
ckm50_norm = np.multiply(ckm50,1/np.diag(ckm50))
num_rows = ckm30_norm.shape[0]
num_cols = ckm30_norm.shape[1]
names = x_file_names
matrix=list()
for i in range(num_rows):
matrix.append([.5*(1-.5*ckm30_norm[i,j]-.5*ckm30_norm[j,i])+.5*(1-.5*ckm50_norm[i,j]-.5*ckm50_norm[j,i]) for j in range(i+1)])
#Construct the tree. Note I could use RapidNJ here, but a few tests have shown that the trees that RapidNJ creates are rubbish.
dm = _DistanceMatrix(names, matrix)
constructor = DistanceTreeConstructor()
tree = constructor.nj(dm)
t=Tree(tree.format('newick'),format=1)
#tree.format('newick')
#Phylo.draw_ascii(tree)
#Now I will put internal nodes in a certain phylogenetic distance between the root and a given node.
#Function to insert a node at a given distance
def insert_node(t, name_to_insert, insert_above, dist_along):
insert_at_node = t.search_nodes(name=insert_above)[0]
parent = (t&insert_above).up
orig_branch_length = t.get_distance(insert_at_node,parent)
if orig_branch_length < dist_along:
raise ValueError("error: dist_along larger than orig_branch_length")
removed_node = insert_at_node.detach()
removed_node.dist = orig_branch_length - dist_along
added_node = parent.add_child(name=name_to_insert, dist=dist_along)
added_node.add_child(removed_node)
#Function to insert a node some % along a branch
def insert_hyp_node(t, leaf_name, percent):
total_dist = t.get_distance(t.name,leaf_name)
percent_dist = percent*total_dist
child_node = (t&leaf_name)
ancestor_node = (t&child_node.name).up
while t.get_distance(t.name, ancestor_node) > percent_dist:
child_node = ancestor_node
ancestor_node = (t&child_node.name).up
insert_node(t, leaf_name+"_"+str(percent), child_node.name, percent_dist-t.get_distance(t.name, ancestor_node))
#Insert hypothetical nodes
hyp_node_names = dict()
cutoffs = [.9,.8,.7,.6,.5,.4,.3,.2,.1]
cutoffs = map(lambda y: y**1.5,cutoffs)
for i in range(len(x_file_names)):
xi = x[i:len(x):len(x_file_names)]
for j in range(1,len(cutoffs)+1):
if xi[j]>0:
insert_hyp_node(t, x_file_names[i], cutoffs[j-1])
hyp_node_names[x_file_names[i]+"_"+str(cutoffs[j-1])] = [x_file_names[i], cutoffs[j-1], j-1] #in case there are "_" in the file names
#insert_hyp_node(t, x_file_names[i],.5/t.get_distance(t.name,t&x_file_names[i])*cutoffs[j])
#Now put the bubbles on the nodes
def layout(node):
#print(node)
if node.is_leaf():
if node.name in x_file_names:
#make reconstructed bubble
size = x[x_file_names.index(node.name)]
F = CircleFace(radius=500*math.sqrt(size), color="RoyalBlue", style="sphere")
F.border.width = None
F.opacity = 0.6
faces.add_face_to_node(F,node, 0, position="branch-right")
if taxonomic_names_on_leaves:
nameFace = AttrFace("name", fsize=25, fgcolor='black',text_suffix="_"+taxonomy[x_file_names.index(node.name)])
faces.add_face_to_node(nameFace, node, 0, position="branch-right")
else:
nameFace = AttrFace("name", fsize=25, fgcolor='black')
faces.add_face_to_node(nameFace, node, 0, position="branch-right")
elif node.name in hyp_node_names: #Otherwise it's a hypothetical node, just use recon x
node_base_name = hyp_node_names[node.name][0]
percent = hyp_node_names[node.name][1]
if node_base_name in x_file_names:
idx = hyp_node_names[node.name][2]
size = x[x_file_names.index(node_base_name)+(idx+1)*len(x_file_names)]
F = CircleFace(radius=500*math.sqrt(size), color="RoyalBlue", style="sphere")
F.border.width = None
F.opacity = 0.6
faces.add_face_to_node(F,node, 0, position="branch-right")
#print node
#print size
else:
size=0
else:
size=0
#print(size)
ts = TreeStyle()
ts.layout_fn = layout
if plot_rectangular:
ts.mode = "r"
else:
ts.mode = "c"
ts.show_leaf_name = False
ts.min_leaf_separation = 50
#Export the tree to a png image
t.render(out_file, w=width, units="mm", tree_style=ts)
#Export the xml file
project = Phyloxml()
phylo = phyloxml.PhyloxmlTree(newick=t.write(format=0, features=[]))
phylo.phyloxml_phylogeny.set_name(title)
project.add_phylogeny(phylo)
project.export(open(out_file_xml,'w'))
# /-iajom # /---| # | \-wiszh #----| # | /-xrygw # \---| # | /-gjlwx # \---| # \-ijvnk # Trees can be operated as normal ETE trees phylo.show() # Export the project as phyloXML format project.export() # <phy:Phyloxml xmlns:phy="http://www.phyloxml.org/1.10/phyloxml.xsd"> # <phy:phylogeny> # <phy:name>test_tree</phy:name> # <phy:clade> # <phy:name>NoName</phy:name> # <phy:branch_length>0.000000e+00</phy:branch_length> # <phy:confidence type="branch_support">1.0</phy:confidence> # <phy:clade> # <phy:name>NoName</phy:name> # <phy:branch_length>1.665083e-01</phy:branch_length> # <phy:confidence type="branch_support">0.938507980435</phy:confidence> # <phy:clade> # <phy:name>NoName</phy:name> # <phy:branch_length>1.366655e-01</phy:branch_length>
def MakePlot(x, org_names, ckm30, ckm50, outgroup, outfile, outfilexml, sum_x):
#Make sure names are unique
names = org_names
for name in names:
if names.count(name) > 1:
temp_name = name
i = 1
for dummy in range(
0,
names.count(name) - 1
): #Don't change the last one, just to make sure we don't conflict with the outgroup
names[names.index(temp_name)] = temp_name + "_" + str(i)
i = i + 1
#Normalize the x vector
x = map(lambda y: y / sum(x), x)
ckm30_norm = np.multiply(ckm30, 1 / np.diag(ckm30))
ckm50_norm = np.multiply(ckm50, 1 / np.diag(ckm50))
num_rows = ckm30_norm.shape[0]
num_cols = ckm30_norm.shape[1]
matrix = list()
for i in range(num_rows):
matrix.append([
.5 * (1 - .5 * ckm30_norm[i, j] - .5 * ckm30_norm[j, i]) + .5 *
(1 - .5 * ckm50_norm[i, j] - .5 * ckm50_norm[j, i])
for j in range(i + 1)
])
#Make the list of distances (ave of the two ckm matrices)
ckm_ave_train = .5 * ckm30_norm + .5 * ckm50_norm
ckm_ave_train_dist = dict()
for i in range(len(org_names)):
ckm_ave_train_dist[org_names[i]] = [
.5 * ckm_ave_train[i, j] + .5 * ckm_ave_train[j, i]
for j in range(len(org_names))
]
#Construct the tree. Note I could use RapidNJ here, but a few tests have shown that the trees that RapidNJ creates are rubbish.
dm = _DistanceMatrix(names, matrix)
constructor = DistanceTreeConstructor()
tree = constructor.nj(dm)
t = Tree(tree.format('newick'), format=1)
#tree.format('newick')
#Phylo.draw_ascii(tree)
#Now I will put internal nodes in a certain phylogenetic distance between the root and a given node.
#Function to insert a node at a given distance
def insert_node(t, name_to_insert, insert_above, dist_along):
insert_at_node = t.search_nodes(name=insert_above)[0]
parent = (t & insert_above).up
orig_branch_length = t.get_distance(insert_at_node, parent)
if orig_branch_length < dist_along:
raise ValueError(
"error: dist_along larger than orig_branch_length in PlotPackage.py"
)
removed_node = insert_at_node.detach()
removed_node.dist = orig_branch_length - dist_along
added_node = parent.add_child(name=name_to_insert, dist=dist_along)
added_node.add_child(removed_node)
#Function to insert a node some % along a branch, taking into account the ckm distances and nodes already created in the NJ tree (and what distance their descendants are from everyone else)
def insert_hyp_node(t, leaf_name, percent, ckm_ave_train_dist, org_names):
dists = map(lambda y: abs(y - percent), ckm_ave_train_dist[leaf_name])
nearby_indicies = list()
#Add all the organisms that are within 0.05 of the given percent
# for i in range(len(dists)):
# if dists[i]<=.05:
# nearby_indicies.append(i)
nearby_names = list()
#If there are no nearby indicies, add the closest organism to the given percent
if nearby_indicies == []:
nearby_names.append(org_names[dists.index(min(dists))])
else:
for i in range(len(nearby_indicies)):
nearby_names.append(org_names[i])
mean_dist = np.mean(
map(lambda y: ckm_ave_train_dist[leaf_name][org_names.index(y)],
nearby_names))
nearby_names.append(leaf_name)
LCA = t.get_common_ancestor(nearby_names)
LCA_to_leaf_dist = t.get_distance(LCA, leaf_name)
#divide the dist to the right/left of the LCA node by the number of percentage points in there
if LCA.name == t.name:
percent_dist = percent * LCA_to_leaf_dist
if mean_dist <= percent:
child_node = (t & leaf_name)
else:
child_node = (
t & nearby_names[0]
) #This means "go up from root" in the direction of the nearest guy
ancestor_node = (t & child_node.name).up
elif mean_dist <= percent:
percent_dist = t.get_distance(LCA) + abs(percent - mean_dist) * (
LCA_to_leaf_dist) / (1 - mean_dist)
child_node = (t & leaf_name)
ancestor_node = (t & child_node.name).up
else:
percent_dist = t.get_distance(LCA) - abs(percent - mean_dist) * (
t.get_distance(LCA)) / (mean_dist)
child_node = (t & leaf_name)
ancestor_node = (t & child_node.name).up
while t.get_distance(t.name, ancestor_node) > percent_dist:
child_node = ancestor_node
ancestor_node = (t & child_node.name).up
insert_node(t, leaf_name + "_" + str(percent), child_node.name,
percent_dist - t.get_distance(t.name, ancestor_node))
#Set outgroup
if outgroup in names:
t.set_outgroup(
t & outgroup
) #I will need to check that this outgroup is actually one of the names...
else:
print("WARNING: the chosen outgroup " + outgroup +
" is not in the given taxonomy: ")
print(names)
print(
"Proceeding without setting an outgroup. This may cause results to be uninterpretable."
)
#Insert hypothetical nodes
hyp_node_names = dict()
cutoffs = [.9, .8, .7, .6, .5, .4, .3, .2, .1]
cutoffs = [
-.5141 * (val**3) + 1.0932 * (val**2) + 0.3824 * val for val in cutoffs
]
for i in range(len(org_names)):
xi = x[i:len(x):len(org_names)]
for j in range(1, len(cutoffs) + 1):
if xi[j] > 0:
insert_hyp_node(t, org_names[i], cutoffs[j - 1],
ckm_ave_train_dist, org_names)
hyp_node_names[org_names[i] + "_" + str(cutoffs[j - 1])] = [
org_names[i], cutoffs[j - 1], j - 1
] #in case there are "_" in the file names
size_factor = 250
font_size = 55
#Now put the bubbles on the nodes
def layout(node):
node_style = NodeStyle()
node_style["hz_line_width"] = 10
node_style["vt_line_width"] = 10
node.set_style(node_style)
#print(node)
if node.is_leaf():
if node.name in org_names:
#make reconstructed bubble
size = x[org_names.index(node.name)]
F = CircleFace(radius=size_factor * math.sqrt(size),
color="RoyalBlue",
style="sphere")
F.border.width = None
F.opacity = 0.6
faces.add_face_to_node(F, node, 0, position="branch-right")
#Denote that this was a training organism
nameFace = AttrFace("name", fsize=font_size, fgcolor='black')
faces.add_face_to_node(nameFace,
node,
0,
position="branch-right")
elif node.name in hyp_node_names: #Otherwise it's a hypothetical node, just use recon x
node_base_name = hyp_node_names[node.name][0]
percent = hyp_node_names[node.name][1]
if node_base_name in org_names:
idx = hyp_node_names[node.name][2]
size = x[org_names.index(node_base_name) +
(idx + 1) * len(org_names)]
F = CircleFace(radius=size_factor * math.sqrt(size),
color="RoyalBlue",
style="sphere")
F.border.width = None
F.opacity = 0.6
faces.add_face_to_node(F, node, 0, position="branch-right")
#This is if I want the names of the hypothetical nodes to be printed as well
#nameFace = AttrFace("name", fsize=font_size, fgcolor='black')
#faces.add_face_to_node(nameFace, node, 0, position="branch-right")
else:
size = 0
else:
size = 0
ts = TreeStyle()
ts.layout_fn = layout
ts.mode = "r"
#ts.mode = "c"
ts.scale = 2 * 1000
ts.show_leaf_name = False
ts.min_leaf_separation = 50
F = CircleFace(radius=.87 * size_factor, color="RoyalBlue", style="sphere")
F.border.width = None
F.opacity = 0.6
ts.legend.add_face(F, 0)
ts.legend.add_face(
TextFace(" Inferred relative abundance",
fsize=1.5 * font_size,
fgcolor="Blue"), 1)
ts.legend.add_face(
TextFace(" Total absolute abundance depicted " + str(sum_x)[0:8],
fsize=1.5 * font_size,
fgcolor="Black"), 1)
ts.legend_position = 4
#t.show(tree_style=ts)
t.render(outfile, w=550, units="mm", tree_style=ts)
#Redner the XML file
project = Phyloxml()
phylo = phyloxml.PhyloxmlTree(newick=t.write(format=0, features=[]))
project.add_phylogeny(phylo)
project.export(open(outfilexml, 'w'))
def main(argv):
input_file=''
title='Title'
label_internal_nodes = False
label_leaves = False
out_file=''
width=750
out_file_xml=''
try:
opts, args = getopt.getopt(argv,"h:i:t:lno:w:x:",["Help=","InputFile=","Title=","LabelLeaves=", "LabelInternalNodes=","OutFile=","Width=","OutFileXML="])
except getopt.GetoptError:
print 'Unknown option, call using: ./PlotTree.py -i <InputCAMIFile> -t <Title> -l <LabelLeavesFlag> -n <LabelInternalNodesFlag> -o <OutFile.png> -x <Outfile.xml> -w <Width>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print './PlotTree.py -i <InputCAMIFile> -t <Title> -l <LabelLeavesFlag> -n <LabelInternalNodesFlag> -o <OutFile> -x <OutFile.xml> -w <Width>'
sys.exit(2)
elif opt in ("-i", "--InputFile"):
input_file = arg
elif opt in ("-t", "--Title"):
title = arg
elif opt in ("-l", "--LabelLeaves"):
label_leaves = True
elif opt in ("-n","--LabelInternalNodes"):
label_internal_nodes = True
elif opt in ("-o", "--OutFile"):
out_file = arg
elif opt in ("-w", "--Width"):
width = int(arg)
elif opt in ("-x", "--OutFileXML"):
out_file_xml = arg
schema_names = COLOR_SCHEMES.keys()
#Read the common kmer profile
ckm_tax_paths = []
ckm_name_to_perc = dict()
fid = open(input_file,'r')
file = fid.readlines()
fid.close()
#Put placeholders in for missing names like: "||" -> "|NA1|"
file_noblank = list()
i=0
for line in file:
while "||" in line:
line = line.replace("||","|NONAME|",1)
i = i+1
file_noblank.append(line)
#Get the names and weights
for line in file_noblank:
if line[0]!='#' and line[0]!='@' and line[0]!='\n': #Don't parse comments or blank lines
temp = line.split()[3] #Get the names
ckm_tax_paths.append(temp)
ckm_name_to_perc[temp.split("|")[-1]] = line.split()[-1] #Get the weights
#Create the tree
t=Tree()
names_to_nodes = dict()
for i in range(0,len(ckm_tax_paths)):
split_tax_path = ckm_tax_paths[i].split("|")
if len(split_tax_path)==1: #If len==1, then it's a superkingdom
names_to_nodes[split_tax_path[0]] = t.add_child(name=split_tax_path[0]) #connect directly to tree
else:
if split_tax_path[-2] in names_to_nodes: #If the parent is already in the tree, add to tree
names_to_nodes[split_tax_path[-1]] = names_to_nodes[split_tax_path[-2]].add_child(name=split_tax_path[-1])
else: #Otherwise iterate up until we have something that is in the tree
j=2
while split_tax_path[-j]=="NONAME":
j = j + 1
#This skips over the NONAMES
names_to_nodes[split_tax_path[-1]] = names_to_nodes[split_tax_path[-j]].add_child(name=split_tax_path[-1])
#Show the tree
#print t.get_ascii(show_internal=True)
#scheme = random.sample(schema_names, 1)[0] #'set2' is nice,
scheme = 'set2'
def layout(node):
if node.name in ckm_name_to_perc:
ckm_perc = float(ckm_name_to_perc[node.name])
else:
ckm_perc = 0
F = CircleFace(radius=3.14*math.sqrt(ckm_perc), color="RoyalBlue", style="sphere")
F.border.width = None
F.opacity = 0.6
faces.add_face_to_node(F,node, 0, position="branch-right")
if label_internal_nodes:
faces.add_face_to_node(TextFace(node.name, fsize=7),node, 0, position="branch-top")
ts = TreeStyle()
ts.layout_fn = layout
ts.mode = "r"
ts.show_leaf_name = label_leaves
ts.min_leaf_separation = 50
ts.title.add_face(TextFace(title, fsize=20), column=0)
#Export the tree to a png image
t.render(out_file, w=width, units="mm", tree_style=ts)
#Export the xml file
project = Phyloxml()
phylo = phyloxml.PhyloxmlTree(newick=t.write(format=0, features=[]))
phylo.phyloxml_phylogeny.set_name(title)
project.add_phylogeny(phylo)
project.export(open(out_file_xml,'w'))
# Loads newick tree
phylo = phyloxml.PhyloxmlTree(newick=sys.argv[1])
# Set basic tree info as a phyloxml phylogeny object
phylo.phyloxml_phylogeny.set_name("test_tree")
if len(phylo.children) <= 2:
phylo.phyloxml_phylogeny.set_rooted("true")
else:
phylo.phyloxml_phylogeny.set_rooted("false")
# Add the tree to the phyloxml project
project.add_phylogeny(phylo)
# Export phyloxml document
OUTPUT = StringIO()
project.export(OUTPUT)
# Some ad-hoc changes to the phyloxml formatted document to meet the schema definition
text = OUTPUT.getvalue()
text = text.replace("phy:", "")
text = re.sub('branch_length_attr="[^"]+"', "", text)
header = """
<phyloxml xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.phyloxml.org http://www.phyloxml.org/1.10/phyloxml.xsd"
xmlns="http://www.phyloxml.org">
"""
text = re.sub('<Phyloxml[^>]+>', header, text)
text = text.replace('Phyloxml', 'phyloxml')
# Save result
open(sys.argv[1] + ".phyloxml", "w").write(text)
def main(argv):
input_file = ''
title = 'Title'
label_internal_nodes = False
label_leaves = False
out_file = ''
width = 750
out_file_xml = ''
plot_rectangular = False
common_kmer_data_path = ''
taxonomic_names_on_leaves = False
try:
opts, args = getopt.getopt(argv, "h:i:lnrto:w:x:D:", [
"Help=", "InputCommonKmerXFile=", "LabelLeaves=",
"LabelInternalNodes=", "Rectangular=", "TaxonomicNamesOnLeaves=",
"OutFile=", "Width=", "OutFileXML=", "CommonKmerDataPath="
])
except getopt.GetoptError:
print 'Unknown option, call using: ./PlotNJTree.py -i <InputCommonKmerXFile> -D <CommonKmerDataPath> -l <LabelLeavesFlag> -n <LabelInternalNodesFlag> -r <RectangularPlotFlag> -t <TaxonomicNamesOnLeavesFlag> -o <OutFile.png> -x <Outfile.xml> -w <Width>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print './PlotNJTree.py -i <InputCommonKmerXFile> -D <CommonKmerDataPath> -l <LabelLeavesFlag> -n <LabelInternalNodesFlag> -r <RectangularPlotFlag> -t <TaxonomicNamesOnLeavesFlag> -o <OutFile.png> -x <Outfile.xml> -w <Width>'
sys.exit(2)
elif opt in ("-i", "--InputCommonKmerXFile"):
input_file = arg
elif opt in ("-l", "--LabelLeaves"):
label_leaves = True
elif opt in ("-n", "--LabelInternalNodes"):
label_internal_nodes = True
elif opt in ("-o", "--OutFile"):
out_file = arg
elif opt in ("-w", "--Width"):
width = int(arg)
elif opt in ("-x", "--OutFileXML"):
out_file_xml = arg
elif opt in ("-D", "--CommonKmerDataPath"):
common_kmer_data_path = arg
elif opt in ("-r", "--Rectangular"):
plot_rectangular = True
elif opt in ("-t", "--TaxonomicNamesOnLeaves"):
taxonomic_names_on_leaves = True
#Read in the x vector
fid = open(input_file, 'r')
x = map(lambda y: float(y), fid.readlines())
fid.close()
#Normalize the x vector
#x = map(lambda y: y/sum(x),x)
#Read in the taxonomy
taxonomy = list()
fid = open(os.path.join(common_kmer_data_path, "Taxonomy.txt"), 'r')
for line in fid:
taxonomy.append(
'_'.join(line.split()[0].split("_")[1:])
) #Just take the first line of the taxonomy (erasing the taxID)
fid.close()
#Read in the basis for the ckm matrices
x_file_names = list()
fid = open(os.path.join(common_kmer_data_path, "FileNames.txt"), 'r')
for line in fid:
x_file_names.append(os.path.basename(line.strip()))
fid.close()
#Read in the common kmer matrix
f = h5py.File(
os.path.join(common_kmer_data_path, 'CommonKmerMatrix-30mers.h5'), 'r')
ckm30 = np.array(f['common_kmers'], dtype=np.float64)
f.close()
f = h5py.File(
os.path.join(common_kmer_data_path, 'CommonKmerMatrix-50mers.h5'), 'r')
ckm50 = np.array(f['common_kmers'], dtype=np.float64)
f.close()
ckm30_norm = np.multiply(ckm30, 1 / np.diag(ckm30))
ckm50_norm = np.multiply(ckm50, 1 / np.diag(ckm50))
num_rows = ckm30_norm.shape[0]
num_cols = ckm30_norm.shape[1]
names = x_file_names
matrix = list()
for i in range(num_rows):
matrix.append([
.5 * (1 - .5 * ckm30_norm[i, j] - .5 * ckm30_norm[j, i]) + .5 *
(1 - .5 * ckm50_norm[i, j] - .5 * ckm50_norm[j, i])
for j in range(i + 1)
])
#Construct the tree. Note I could use RapidNJ here, but a few tests have shown that the trees that RapidNJ creates are rubbish.
dm = _DistanceMatrix(names, matrix)
constructor = DistanceTreeConstructor()
tree = constructor.nj(dm)
t = Tree(tree.format('newick'), format=1)
#tree.format('newick')
#Phylo.draw_ascii(tree)
#Now I will put internal nodes in a certain phylogenetic distance between the root and a given node.
#Function to insert a node at a given distance
def insert_node(t, name_to_insert, insert_above, dist_along):
insert_at_node = t.search_nodes(name=insert_above)[0]
parent = (t & insert_above).up
orig_branch_length = t.get_distance(insert_at_node, parent)
if orig_branch_length < dist_along:
raise ValueError(
"error: dist_along larger than orig_branch_length")
removed_node = insert_at_node.detach()
removed_node.dist = orig_branch_length - dist_along
added_node = parent.add_child(name=name_to_insert, dist=dist_along)
added_node.add_child(removed_node)
#Function to insert a node some % along a branch
def insert_hyp_node(t, leaf_name, percent):
total_dist = t.get_distance(t.name, leaf_name)
percent_dist = percent * total_dist
child_node = (t & leaf_name)
ancestor_node = (t & child_node.name).up
while t.get_distance(t.name, ancestor_node) > percent_dist:
child_node = ancestor_node
ancestor_node = (t & child_node.name).up
insert_node(t, leaf_name + "_" + str(percent), child_node.name,
percent_dist - t.get_distance(t.name, ancestor_node))
#Insert hypothetical nodes
hyp_node_names = dict()
cutoffs = [.9, .8, .7, .6, .5, .4, .3, .2, .1]
cutoffs = map(lambda y: y**1.5, cutoffs)
for i in range(len(x_file_names)):
xi = x[i:len(x):len(x_file_names)]
for j in range(1, len(cutoffs) + 1):
if xi[j] > 0:
insert_hyp_node(t, x_file_names[i], cutoffs[j - 1])
hyp_node_names[x_file_names[i] + "_" + str(cutoffs[j - 1])] = [
x_file_names[i], cutoffs[j - 1], j - 1
] #in case there are "_" in the file names
#insert_hyp_node(t, x_file_names[i],.5/t.get_distance(t.name,t&x_file_names[i])*cutoffs[j])
#Now put the bubbles on the nodes
def layout(node):
#print(node)
if node.is_leaf():
if node.name in x_file_names:
#make reconstructed bubble
size = x[x_file_names.index(node.name)]
F = CircleFace(radius=500 * math.sqrt(size),
color="RoyalBlue",
style="sphere")
F.border.width = None
F.opacity = 0.6
faces.add_face_to_node(F, node, 0, position="branch-right")
if taxonomic_names_on_leaves:
nameFace = AttrFace(
"name",
fsize=25,
fgcolor='black',
text_suffix="_" +
taxonomy[x_file_names.index(node.name)])
faces.add_face_to_node(nameFace,
node,
0,
position="branch-right")
else:
nameFace = AttrFace("name", fsize=25, fgcolor='black')
faces.add_face_to_node(nameFace,
node,
0,
position="branch-right")
elif node.name in hyp_node_names: #Otherwise it's a hypothetical node, just use recon x
node_base_name = hyp_node_names[node.name][0]
percent = hyp_node_names[node.name][1]
if node_base_name in x_file_names:
idx = hyp_node_names[node.name][2]
size = x[x_file_names.index(node_base_name) +
(idx + 1) * len(x_file_names)]
F = CircleFace(radius=500 * math.sqrt(size),
color="RoyalBlue",
style="sphere")
F.border.width = None
F.opacity = 0.6
faces.add_face_to_node(F, node, 0, position="branch-right")
#print node
#print size
else:
size = 0
else:
size = 0
#print(size)
ts = TreeStyle()
ts.layout_fn = layout
if plot_rectangular:
ts.mode = "r"
else:
ts.mode = "c"
ts.show_leaf_name = False
ts.min_leaf_separation = 50
#Export the tree to a png image
t.render(out_file, w=width, units="mm", tree_style=ts)
#Export the xml file
project = Phyloxml()
phylo = phyloxml.PhyloxmlTree(newick=t.write(format=0, features=[]))
phylo.phyloxml_phylogeny.set_name(title)
project.add_phylogeny(phylo)
project.export(open(out_file_xml, 'w'))
