def get_mysql_table(db_name, table_name):
import numpy as np
server, db = manipulate_biosqldb.load_db(db_name)
all_taxons_id = manipulate_biosqldb.get_taxon_id_list(server, db_name)
sql_taxons = "id, "
for i in range(0, len(all_taxons_id) - 1):
sql_taxons += ' `%s`,' % all_taxons_id[i]
sql_taxons += ' `%s`' % all_taxons_id[-1]
sql = "select %s from comparative_tables.%s_%s" % (sql_taxons, table_name,
db_name)
mat = np.array(server.adaptor.execute_and_fetchall(sql, ))
f = open("%s_matrix.tab" % table_name, "w")
taxonid2genome = manipulate_biosqldb.taxon_id2genome_description(
server, db_name, True)
taxons_ids = [taxonid2genome[int(i)] for i in all_taxons_id]
f.write('"id"\t"' + '"\t"'.join(taxons_ids) + '"\n')
for row in mat:
row = [str(i) for i in row]
f.write("\t".join(row) + "\n")
def locus_list2identity_in_other_genomes(locus_list, biodb):
server, db = manipulate_biosqldb.load_db(biodb)
locus_tag2seqfeature_id = manipulate_biosqldb.locus_tag2seqfeature_id_dict(
server, biodb)
taxon_id2description = manipulate_biosqldb.taxon_id2genome_description(
server, biodb)
import re
for i in taxon_id2description.keys():
taxon_id2description[i] = re.sub(" subsp\. aureus", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(", complete genome\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(", complete sequence\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub("strain ", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub("str\. ", "", taxon_id2description[i])
taxon_id2description[i] = re.sub(" complete genome sequence\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(" complete genome\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(" chromosome", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub("Staphylococcus aureus ", "",
taxon_id2description[i])
header = 'orthogroup\t'
dico = locus_tag2identity_best_hit_all_genomes(biodb, 'wcw_1594',
'group_417')
for i in dico.keys():
header += taxon_id2description[i] + '\t'
final_out = header + '\n'
for locus in locus_list:
#print "locus", i
seqfeature_id = locus_tag2seqfeature_id[locus]
orthogroup = manipulate_biosqldb.seqfeature_id2orthogroup(
server, seqfeature_id, biodb)
#print "ortho", orthogroup
dico = locus_tag2identity_best_hit_all_genomes(biodb, locus,
orthogroup)
#print "dico done..."
out = '%s\t' % orthogroup
for i in dico.keys():
identity = dico[i]
out += '%s\t' % identity
final_out += out + '\n'
return final_out
def identity_closest_homolog(db_name):
from chlamdb.biosqldb import manipulate_biosqldb
from chlamdb.biosqldb import biosql_own_sql_tables
import sys
server, db = manipulate_biosqldb.load_db(db_name)
sql1 = 'select locus_tag, seqfeature_id from custom_tables.locus2seqfeature_id_%s' % db_name
locus2seqfeature_id = manipulate_biosqldb.to_dict(
server.adaptor.execute_and_fetchall(sql1, ))
sql2 = "CREATE TABLE comparative_tables.identity_closest_homolog2_%s(taxon_1 INT NOT NULL," \
" taxon_2 INT NOT NULL," \
" locus_1 INT NOT NULL," \
" locus_2 INT NOT NULL," \
" identity FLOAT, index locus_1(locus_1)," \
" index locus_2(locus_2), index taxon_1(taxon_1), index taxon_2(taxon_2))" % (db_name)
server.adaptor.execute(sql2)
#identitydico = biosql_own_sql_tables.calculate_average_protein_identity_new_tables(db_name)
taxon2description = manipulate_biosqldb.taxon_id2genome_description(
server, biodatabase_name=db_name)
all_taxons = taxon2description.keys()
for i, taxon_1 in enumerate(all_taxons):
locus2identity = biosql_own_sql_tables.circos_locus2taxon_highest_identity(
db_name, taxon_1)
for taxon_2 in all_taxons:
if taxon_1 == taxon_2:
continue
for locus in locus2identity:
try:
#print taxon_1, taxon_2, locus, locus2identity[locus][long(taxon_2)][1], locus2identity[locus][long(taxon_2)][0]
#sys.stdout.write("%s\t%s\n" % (taxon_1, taxon_2))
sql = 'insert into comparative_tables.identity_closest_homolog2_%s(taxon_1, taxon_2, locus_1, locus_2, identity) ' \
' VALUES ("%s", "%s", "%s", "%s", %s)' % (db_name,
taxon_1,
taxon_2,
locus2seqfeature_id[locus],
locus2seqfeature_id[locus2identity[locus][int(taxon_2)][1]],
locus2identity[locus][int(taxon_2)][0])
server.adaptor.execute(sql)
except KeyError:
# no homologs
continue
server.adaptor.commit()
def locus_list2presence_absence_all_genomes(locus_list, biodb_name):
server, db = manipulate_biosqldb.load_db(biodb_name)
locus_tag2seqfeature_id = manipulate_biosqldb.locus_tag2seqfeature_id_dict(
server, biodb_name)
taxon_id2description = manipulate_biosqldb.taxon_id2genome_description(
server, biodb_name)
import re
for i in taxon_id2description.keys():
taxon_id2description[i] = re.sub(" subsp\. aureus", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(", complete genome\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(", complete sequence\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub("strain ", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub("str\. ", "", taxon_id2description[i])
taxon_id2description[i] = re.sub(" complete genome sequence\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(" complete genome\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(" chromosome", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub("Staphylococcus aureus ", "",
taxon_id2description[i])
header = 'orthogroup\t'
genomes = manipulate_biosqldb.get_genome_taxons_list(server, biodb_name)
for i in genomes:
header += taxon_id2description[i] + '\t'
final_out = header + '\n'
for i in locus_list:
#print "locus", i
seqfeature_id = locus_tag2seqfeature_id[i]
orthogroup = manipulate_biosqldb.seqfeature_id2orthogroup(
server, seqfeature_id, biodb_name)
#print "ortho", orthogroup
dico = heatmap_presence_absence(biodb_name, orthogroup)
#print "dico done..."
#print dico
out = '%s\t' % orthogroup
for i in genomes:
out += '%s\t' % dico[i]
final_out += out + '\n'
return final_out
def biodb2randomized_matrix(bio_db_name):
server, db = manipulate_biosqldb.load_db(bio_db_name)
matrix = np.array(
manipulate_biosqldb.get_orthology_table(server, bio_db_name))
taxon_id2description = manipulate_biosqldb.taxon_id2genome_description(
server, bio_db_name)
#print taxon_id2description
#group_names = matrix[:,0]
taxons_ids = manipulate_biosqldb.get_taxon_id_list(server, bio_db_name)
print 'Number of taxons:', len(taxons_ids)
taxons_ids = [taxon_id2description[str(i)] for i in taxons_ids]
import re
for i, accession in enumerate(taxons_ids):
#print i, accession
description = taxons_ids[i]
description = re.sub(", complete genome\.", "", description)
description = re.sub(", complete genome", "", description)
description = re.sub(", complete sequence\.", "", description)
description = re.sub("strain ", "", description)
description = re.sub("str\. ", "", description)
description = re.sub(" complete genome sequence\.", "", description)
description = re.sub(" complete genome\.", "", description)
description = re.sub(" chromosome", "", description)
description = re.sub(" DNA", "S.", description)
description = re.sub("Merged record from ", "", description)
description = re.sub(", wgs", "", description)
description = re.sub("Candidatus ", "", description)
description = re.sub(".contig.0_1, whole genome shotgun sequence.", "",
description)
description = re.sub("Protochlamydia", "P.", description)
description = re.sub("Chlamydia", "C.", description)
description = re.sub("Chlamydophila", "E.", description)
description = re.sub("Estrella", "E.", description)
description = re.sub("Rhodopirellula", "R.", description)
description = re.sub("Methylacidiphilum", "M.", description)
description = re.sub(" phage", "", description)
description = re.sub("Parachlamydia", "P.", description)
description = re.sub("Neochlamydia", "Neo.", description)
description = re.sub("Simkania", "S.", description)
description = re.sub("Waddlia", "W.", description)
description = re.sub("Pirellula", "P.", description)
description = re.sub("Rhabdochlamydiaceae sp.", "Rhabdo", description)
taxons_ids[i] = description
M = matrix.astype(float) # [:, 1:]
M = heatmap.randomize_table(M)
return (M, taxons_ids)
def convert_tree_taxon2genome(biodb_name,
input_tree,
output_tree,
sqlite=False):
server, db = manipulate_biosqldb.load_db(biodb_name, sqlite=sqlite)
print biodb_name
taxon_id2genome_description = manipulate_biosqldb.taxon_id2genome_description(
server, biodb_name)
print taxon_id2genome_description
#locus2genome = manipulate_biosqldb.locus_tag2genome_name(server, biodb_name)
import re
for i in taxon_id2genome_description.keys():
print i
taxon_id2genome_description[i] = re.sub(" subsp\. aureus", "",
taxon_id2genome_description[i])
taxon_id2genome_description[i] = re.sub(", complete genome\.", "",
taxon_id2genome_description[i])
taxon_id2genome_description[i] = re.sub(", complete sequence\.", "",
taxon_id2genome_description[i])
taxon_id2genome_description[i] = re.sub("strain ", "",
taxon_id2genome_description[i])
taxon_id2genome_description[i] = re.sub("str\. ", "",
taxon_id2genome_description[i])
taxon_id2genome_description[i] = re.sub(" complete genome sequence\.",
"",
taxon_id2genome_description[i])
taxon_id2genome_description[i] = re.sub(" complete genome\.", "",
taxon_id2genome_description[i])
taxon_id2genome_description[i] = re.sub(" chromosome", "",
taxon_id2genome_description[i])
taxon_id2genome_description[i] = re.sub("Staphylococcus", "S.",
taxon_id2genome_description[i])
taxon_id2genome_description[i] = re.sub(" DNA", "S.",
taxon_id2genome_description[i])
#print taxon_id2genome_description[i]
print taxon_id2genome_description
new_tree = parse_newick_tree.convert_terminal_node_names(
input_tree, taxon_id2genome_description)
#print new_tree[0]
print "writing converted tree..."
print output_tree
Phylo.write(new_tree, output_tree, 'newick')
def write_ortho_matrix(bio_db_name):
server, db = manipulate_biosqldb.load_db(bio_db_name)
matrix = np.array(
manipulate_biosqldb.get_orthology_table(server, bio_db_name))
taxon_id2description = manipulate_biosqldb.taxon_id2genome_description(
server, bio_db_name)
group_names = matrix[:, 0]
taxons_ids = manipulate_biosqldb.get_taxon_id_list(server, bio_db_name)
import re
for i in taxon_id2description.keys():
taxon_id2description[i] = re.sub(" subsp\. aureus", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(", complete genome\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(", complete sequence\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub("strain ", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub("str\. ", "", taxon_id2description[i])
taxon_id2description[i] = re.sub(" complete genome sequence\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(" complete genome\.", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(" chromosome", "",
taxon_id2description[i])
taxon_id2description[i] = re.sub(" DNA", "", taxon_id2description[i])
taxons_ids = [taxon_id2description[str(i)] for i in taxons_ids]
#print taxon_id2description
f = open("ortho_matrix.tab", "w")
f.write("orthogroup\t" + "\t".join(taxons_ids) + "\n")
for row in matrix:
f.write("\t".join(row) + "\n")
f.close()
def shared_orthogroups_average_identity(db_name):
from chlamdb.biosqldb import manipulate_biosqldb
from chlamdb.biosqldb import biosql_own_sql_tables
import sys
import numpy
server, db = manipulate_biosqldb.load_db(db_name)
sql = "CREATE TABLE comparative_tables.shared_og_av_id_%s(taxon_1 INT NOT NULL," \
" taxon_2 INT NOT NULL," \
" average_identity FLOAT," \
" median_identity FLOAT," \
" n_pairs INT)" % (db_name)
server.adaptor.execute(sql)
taxon2description = manipulate_biosqldb.taxon_id2genome_description(
server, biodatabase_name=db_name)
all_taxons = list(taxon2description.keys())
for i, taxon_1 in enumerate(all_taxons):
for taxon_2 in all_taxons[i + 1:]:
data_sql = 'select identity from comparative_tables.identity_closest_homolog2_%s where taxon_1=%s and taxon_2=%s' % (
db_name, taxon_1, taxon_2)
data = list([
i[0] for i in server.adaptor.execute_and_fetchall(data_sql, )
])
print(data)
sql = 'insert into comparative_tables.shared_og_av_id_%s(taxon_1, taxon_2, average_identity,' \
' median_identity, n_pairs) values (%s, %s, %s, %s, %s)' % (db_name,
taxon_1,
taxon_2,
numpy.average(data),
numpy.median(data),
len(data))
print(sql)
server.adaptor.execute_and_fetchall(sql, )
server.adaptor.commit()
def plot_tree_stacked_barplot(
tree_file,
taxon2value_list_barplot=False,
header_list=False, # header stackedbarplots
taxon2set2value_heatmap=False,
taxon2label=False,
header_list2=False, # header counts columns
biodb=False,
column_scale=True,
general_max=False,
header_list3=False,
set2taxon2value_list_simple_barplot=False,
set2taxon2value_list_simple_barplot_counts=True,
rotate=False,
taxon2description=False):
'''
taxon2value_list_barplot list of lists:
[[bar1_part1, bar1_part2,...],[bar2_part1, bar2_part2]]
valeures de chaque liste transformes en pourcentages
:param tree_file:
:param taxon2value_list:
:param biodb:
:param exclude_outgroup:
:param bw_scale:
:return:
'''
if biodb:
from chlamdb.biosqldb import manipulate_biosqldb
server, db = manipulate_biosqldb.load_db(biodb)
taxon2description = manipulate_biosqldb.taxon_id2genome_description(
server, biodb, filter_names=True)
t1 = Tree(tree_file)
# Calculate the midpoint node
R = t1.get_midpoint_outgroup()
# and set it as tree outgroup
t1.set_outgroup(R)
colors2 = [
"red", "#FFFF00", "#58FA58", "#819FF7", "#F781F3", "#2E2E2E",
"#F7F8E0", 'black'
]
colors = [
"#7fc97f", "#386cb0", "#fdc086", "#ffffb3", "#fdb462", "#f0027f",
"#F7F8E0", 'black'
] # fdc086ff 386cb0ff f0027fff
tss = TreeStyle()
tss.draw_guiding_lines = True
tss.guiding_lines_color = "gray"
tss.show_leaf_name = False
if column_scale and header_list2:
import matplotlib.cm as cm
from matplotlib.colors import rgb2hex
import matplotlib as mpl
column2scale = {}
col_n = 0
for column in header_list2:
values = taxon2set2value_heatmap[column].values()
#print values
if min(values) == max(values):
min_val = 0
max_val = 1.5 * max(values)
else:
min_val = min(values)
max_val = max(values)
#print 'min-max', min_val, max_val
norm = mpl.colors.Normalize(vmin=min_val, vmax=max_val) # *1.1
if col_n < 4:
cmap = cm.OrRd #
else:
cmap = cm.YlGnBu #PuBu#OrRd
m = cm.ScalarMappable(norm=norm, cmap=cmap)
column2scale[column] = [m, float(max_val)] # *0.7
col_n += 1
for i, lf in enumerate(t1.iter_leaves()):
#if taxon2description[lf.name] == 'Pirellula staleyi DSM 6068':
# lf.name = 'Pirellula staleyi DSM 6068'
# continue
if i == 0:
if taxon2label:
n = TextFace(' ')
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.hz_align = 2
n.vt_align = 2
n.rotation = 270
n.inner_background.color = "white"
n.opacity = 1.
tss.aligned_header.add_face(n, 0)
col_add = 1
else:
col_add = 1
if header_list:
for col, header in enumerate(header_list):
n = TextFace('%s' % (header))
n.margin_top = 0
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.rotation = 270
n.hz_align = 2
n.vt_align = 2
n.inner_background.color = "white"
n.opacity = 1.
tss.aligned_header.add_face(n, col + col_add)
col_add += col + 1
if header_list3:
#print 'header_list 3!'
col_tmp = 0
for header in header_list3:
n = TextFace('%s' % (header))
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.rotation = 270
n.hz_align = 2
n.vt_align = 2
n.inner_background.color = "white"
n.opacity = 1.
if set2taxon2value_list_simple_barplot_counts:
if col_tmp == 0:
col_tmp += 1
tss.aligned_header.add_face(n, col_tmp + 1 + col_add)
n = TextFace(' ')
tss.aligned_header.add_face(n, col_tmp + col_add)
col_tmp += 2
else:
tss.aligned_header.add_face(n, col_tmp + col_add)
col_tmp += 1
if set2taxon2value_list_simple_barplot_counts:
col_add += col_tmp
else:
col_add += col_tmp
if header_list2:
for col, header in enumerate(header_list2):
n = TextFace('%s' % (header))
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.rotation = 270
n.hz_align = 2
n.vt_align = 2
n.inner_background.color = "white"
n.opacity = 1.
tss.aligned_header.add_face(n, col + col_add)
col_add += col + 1
if taxon2label:
try:
n = TextFace('%s' % taxon2label[lf.name])
except:
try:
n = TextFace('%s' % taxon2label[int(lf.name)])
except:
n = TextFace('-')
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
if rotate:
n.rotation = 270
lf.add_face(n, 1, position="aligned")
col_add = 2
else:
col_add = 2
if taxon2value_list_barplot:
try:
val_list_of_lists = taxon2value_list_barplot[lf.name]
except:
val_list_of_lists = taxon2value_list_barplot[int(lf.name)]
#col_count = 0
for col, value_list in enumerate(val_list_of_lists):
total = float(sum(value_list))
percentages = [(i / total) * 100 for i in value_list]
if col % 3 == 0:
col_list = colors2
else:
col_list = colors
b = StackedBarFace(percentages,
width=150,
height=18,
colors=col_list[0:len(percentages)])
b.rotation = 0
b.inner_border.color = "white"
b.inner_border.width = 0
b.margin_right = 5
b.margin_left = 5
if rotate:
b.rotation = 270
lf.add_face(b, col + col_add, position="aligned")
#col_count+=1
col_add += col + 1
if set2taxon2value_list_simple_barplot:
col_list = [
'#fc8d59', '#91bfdb', '#99d594', '#c51b7d', '#f1a340',
'#999999'
]
color_i = 0
col = 0
for one_set in header_list3:
if color_i > 5:
color_i = 0
color = col_list[color_i]
color_i += 1
# values for all taxons
values_lists = [
float(i) for i in
set2taxon2value_list_simple_barplot[one_set].values()
]
#print values_lists
#print one_set
value = set2taxon2value_list_simple_barplot[one_set][lf.name]
if set2taxon2value_list_simple_barplot_counts:
if isinstance(value, float):
a = TextFace(" %s " % str(round(value, 2)))
else:
a = TextFace(" %s " % str(value))
a.margin_top = 1
a.margin_right = 2
a.margin_left = 5
a.margin_bottom = 1
if rotate:
a.rotation = 270
lf.add_face(a, col + col_add, position="aligned")
#print 'value and max', value, max(values_lists)
fraction_biggest = (float(value) / max(values_lists)) * 100
fraction_rest = 100 - fraction_biggest
#print 'fractions', fraction_biggest, fraction_rest
b = StackedBarFace([fraction_biggest, fraction_rest],
width=100,
height=15,
colors=[color, 'white'])
b.rotation = 0
b.inner_border.color = "grey"
b.inner_border.width = 0
b.margin_right = 15
b.margin_left = 0
if rotate:
b.rotation = 270
if set2taxon2value_list_simple_barplot_counts:
if col == 0:
col += 1
lf.add_face(b, col + 1 + col_add, position="aligned")
col += 2
else:
lf.add_face(b, col + col_add, position="aligned")
col += 1
if set2taxon2value_list_simple_barplot_counts:
col_add += col
else:
col_add += col
if taxon2set2value_heatmap:
i = 0
#if not taxon2label:
# col_add-=1
for col2, head in enumerate(header_list2):
col_name = header_list2[i]
try:
value = taxon2set2value_heatmap[col_name][str(lf.name)]
except:
try:
value = taxon2set2value_heatmap[col_name][round(
float(lf.name), 2)]
except:
value = 0
if header_list2[i] == 'duplicates':
print('dupli', lf.name, value)
#print 'val----------------', value
if int(value) > 0:
if int(value) >= 10 and int(value) < 100:
n = TextFace('%4i' % value)
elif int(value) >= 100:
n = TextFace('%3i' % value)
else:
n = TextFace('%5i' % value)
n.margin_top = 1
n.margin_right = 2
n.margin_left = 5
n.margin_bottom = 1
n.hz_align = 1
n.vt_align = 1
if rotate:
n.rotation = 270
n.inner_background.color = rgb2hex(
column2scale[col_name][0].to_rgba(
float(value))) #"orange"
#print 'xaxaxaxaxa', value,
if float(value) > column2scale[col_name][1]:
n.fgcolor = 'white'
n.opacity = 1.
n.hz_align = 1
n.vt_align = 1
lf.add_face(n, col2 + col_add, position="aligned")
i += 1
else:
n = TextFace('')
n.margin_top = 1
n.margin_right = 1
n.margin_left = 5
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
if rotate:
n.rotation = 270
lf.add_face(n, col2 + col_add, position="aligned")
i += 1
#lf.name = taxon2description[lf.name]
n = TextFace(taxon2description[lf.name],
fgcolor="black",
fsize=12,
fstyle='italic')
lf.add_face(n, 0)
for n in t1.traverse():
nstyle = NodeStyle()
if n.support < 1:
nstyle["fgcolor"] = "black"
nstyle["size"] = 6
n.set_style(nstyle)
else:
nstyle["fgcolor"] = "red"
nstyle["size"] = 0
n.set_style(nstyle)
return t1, tss
def plot_heat_tree(tree_file,
biodb="chlamydia_04_16",
exclude_outgroup=False,
bw_scale=True):
from chlamdb.biosqldb import manipulate_biosqldb
import matplotlib.cm as cm
from matplotlib.colors import rgb2hex
import matplotlib as mpl
server, db = manipulate_biosqldb.load_db(biodb)
sql_biodatabase_id = 'select biodatabase_id from biodatabase where name="%s"' % biodb
db_id = server.adaptor.execute_and_fetchall(sql_biodatabase_id, )[0][0]
if type(tree_file) == str:
t1 = Tree(tree_file)
try:
R = t1.get_midpoint_outgroup()
#print 'root', R
# and set it as tree outgroup
t1.set_outgroup(R)
except:
pass
elif isinstance(tree_file, Tree):
t1 = tree_file
else:
IOError('Unkown tree format')
tss = TreeStyle()
tss.draw_guiding_lines = True
tss.guiding_lines_color = "gray"
tss.show_leaf_name = False
#print "tree", t1
sql1 = 'select taxon_id, description from bioentry where biodatabase_id=%s and description not like "%%%%plasmid%%%%"' % db_id
sql2 = 'select t2.taxon_id, t1.GC from genomes_info_%s as t1 inner join bioentry as t2 ' \
' on t1.accession=t2.accession where t2.biodatabase_id=%s and t1.description not like "%%%%plasmid%%%%";' % (biodb, db_id)
sql3 = 'select t2.taxon_id, t1.genome_size from genomes_info_%s as t1 ' \
' inner join bioentry as t2 on t1.accession=t2.accession ' \
' where t2.biodatabase_id=%s and t1.description not like "%%%%plasmid%%%%";' % (biodb, db_id)
sql4 = 'select t2.taxon_id,percent_non_coding from genomes_info_%s as t1 ' \
' inner join bioentry as t2 on t1.accession=t2.accession ' \
' where t2.biodatabase_id=%s and t1.description not like "%%%%plasmid%%%%";' % (biodb, db_id)
sql_checkm_completeness = 'select taxon_id, completeness from custom_tables.checkm_%s;' % biodb
sql_checkm_contamination = 'select taxon_id,contamination from custom_tables.checkm_%s;' % biodb
try:
taxon_id2completeness = manipulate_biosqldb.to_dict(
server.adaptor.execute_and_fetchall(sql_checkm_completeness))
taxon_id2contamination = manipulate_biosqldb.to_dict(
server.adaptor.execute_and_fetchall(sql_checkm_contamination))
except:
taxon_id2completeness = False
#taxon2description = manipulate_biosqldb.to_dict(server.adaptor.execute_and_fetchall(sql1,))
taxon2description = manipulate_biosqldb.taxon_id2genome_description(
server, biodb, filter_names=True)
taxon2gc = manipulate_biosqldb.to_dict(
server.adaptor.execute_and_fetchall(sql2, ))
taxon2genome_size = manipulate_biosqldb.to_dict(
server.adaptor.execute_and_fetchall(sql3, ))
taxon2coding_density = manipulate_biosqldb.to_dict(
server.adaptor.execute_and_fetchall(sql4, ))
my_taxons = [lf.name for lf in t1.iter_leaves()]
# Calculate the midpoint node
if exclude_outgroup:
excluded = str(list(t1.iter_leaves())[0].name)
my_taxons.pop(my_taxons.index(excluded))
genome_sizes = [float(taxon2genome_size[i]) for i in my_taxons]
gc_list = [float(taxon2gc[i]) for i in my_taxons]
fraction_list = [float(taxon2coding_density[i]) for i in my_taxons]
value = 1
max_genome_size = max(genome_sizes) #3424182#
max_gc = max(gc_list) #48.23
cmap = cm.YlGnBu #YlOrRd#OrRd
norm = mpl.colors.Normalize(vmin=min(genome_sizes) - 100000,
vmax=max(genome_sizes))
m1 = cm.ScalarMappable(norm=norm, cmap=cmap)
norm = mpl.colors.Normalize(vmin=min(gc_list), vmax=max(gc_list))
m2 = cm.ScalarMappable(norm=norm, cmap=cmap)
norm = mpl.colors.Normalize(vmin=min(fraction_list),
vmax=max(fraction_list))
m3 = cm.ScalarMappable(norm=norm, cmap=cmap)
for i, lf in enumerate(t1.iter_leaves()):
#if taxon2description[lf.name] == 'Pirellula staleyi DSM 6068':
# lf.name = 'Pirellula staleyi DSM 6068'
# continue
if i == 0:
n = TextFace('Size (Mbp)')
n.rotation = -25
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
#lf.add_face(n, 3, position="aligned")
tss.aligned_header.add_face(n, 3)
n = TextFace('GC (%)')
n.rotation = -25
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
#lf.add_face(n, 5, position="aligned")
tss.aligned_header.add_face(n, 5)
n = TextFace('')
#lf.add_face(n, 2, position="aligned")
tss.aligned_header.add_face(n, 2)
#lf.add_face(n, 4, position="aligned")
tss.aligned_header.add_face(n, 4)
n = TextFace('Non coding (%)')
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
n.rotation = -25
#lf.add_face(n, 7, position="aligned")
tss.aligned_header.add_face(n, 7)
n = TextFace('')
#lf.add_face(n, 6, position="aligned")
tss.aligned_header.add_face(n, 6)
if taxon_id2completeness:
n = TextFace('Completeness (%)')
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
n.rotation = -25
#lf.add_face(n, 7, position="aligned")
tss.aligned_header.add_face(n, 9)
n = TextFace('')
#lf.add_face(n, 6, position="aligned")
tss.aligned_header.add_face(n, 8)
n = TextFace('Contamination (%)')
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
n.rotation = -25
#lf.add_face(n, 7, position="aligned")
tss.aligned_header.add_face(n, 11)
n = TextFace('')
#lf.add_face(n, 6, position="aligned")
tss.aligned_header.add_face(n, 10)
value += 1
#print '------ %s' % lf.name
if exclude_outgroup and i == 0:
lf.name = taxon2description[lf.name]
#print '#######################'
continue
n = TextFace(
' %s ' %
str(round(taxon2genome_size[lf.name] / float(1000000), 2)))
n.margin_top = 1
n.margin_right = 1
n.margin_left = 0
n.margin_bottom = 1
n.fsize = 7
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, 2, position="aligned")
#if max_genome_size > 3424182:
# max_genome_size = 3424182
fraction_biggest = (float(taxon2genome_size[lf.name]) /
max_genome_size) * 100
fraction_rest = 100 - fraction_biggest
if taxon2description[lf.name] == 'Rhabdochlamydia helveticae T3358':
col = '#fc8d59'
else:
if not bw_scale:
col = rgb2hex(m1.to_rgba(float(
taxon2genome_size[lf.name]))) # 'grey'
else:
col = '#fc8d59'
b = StackedBarFace([fraction_biggest, fraction_rest],
width=100,
height=9,
colors=[col, 'white'])
b.rotation = 0
b.inner_border.color = "black"
b.inner_border.width = 0
b.margin_right = 15
b.margin_left = 0
lf.add_face(b, 3, position="aligned")
fraction_biggest = (float(taxon2gc[lf.name]) / max_gc) * 100
fraction_rest = 100 - fraction_biggest
if taxon2description[lf.name] == 'Rhabdochlamydia helveticae T3358':
col = '#91bfdb'
else:
if not bw_scale:
col = rgb2hex(m2.to_rgba(float(taxon2gc[lf.name])))
else:
col = '#91bfdb'
b = StackedBarFace([fraction_biggest, fraction_rest],
width=100,
height=9,
colors=[col, 'white'])
b.rotation = 0
b.inner_border.color = "black"
b.inner_border.width = 0
b.margin_left = 0
b.margin_right = 15
lf.add_face(b, 5, position="aligned")
n = TextFace(' %s ' % str(round(float(taxon2gc[lf.name]), 2)))
n.margin_top = 1
n.margin_right = 0
n.margin_left = 0
n.margin_bottom = 1
n.fsize = 7
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, 4, position="aligned")
if taxon2description[lf.name] == 'Rhabdochlamydia helveticae T3358':
col = '#99d594'
else:
if not bw_scale:
col = rgb2hex(m3.to_rgba(float(taxon2coding_density[lf.name])))
else:
col = '#99d594'
n = TextFace(' %s ' % str(float(taxon2coding_density[lf.name])))
n.margin_top = 1
n.margin_right = 0
n.margin_left = 0
n.margin_right = 0
n.margin_bottom = 1
n.fsize = 7
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, 6, position="aligned")
fraction = (float(taxon2coding_density[lf.name]) /
max(taxon2coding_density.values())) * 100
fraction_rest = ((max(taxon2coding_density.values()) -
taxon2coding_density[lf.name]) /
float(max(taxon2coding_density.values()))) * 100
#print 'fraction, rest', fraction, fraction_rest
b = StackedBarFace(
[fraction, fraction_rest],
width=100,
height=9,
colors=[col, 'white'
]) # 1-round(float(taxon2coding_density[lf.name]), 2)
b.rotation = 0
b.margin_right = 1
b.inner_border.color = "black"
b.inner_border.width = 0
b.margin_left = 5
lf.add_face(b, 7, position="aligned")
if taxon_id2completeness:
n = TextFace(' %s ' % str(float(taxon_id2completeness[lf.name])))
n.margin_top = 1
n.margin_right = 0
n.margin_left = 0
n.margin_right = 0
n.margin_bottom = 1
n.fsize = 7
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, 8, position="aligned")
fraction = float(taxon_id2completeness[lf.name])
fraction_rest = 100 - fraction
#print 'fraction, rest', fraction, fraction_rest
b = StackedBarFace(
[fraction, fraction_rest],
width=100,
height=9,
colors=["#d7191c", 'white'
]) # 1-round(float(taxon2coding_density[lf.name]), 2)
b.rotation = 0
b.margin_right = 1
b.inner_border.color = "black"
b.inner_border.width = 0
b.margin_left = 5
lf.add_face(b, 9, position="aligned")
n = TextFace(' %s ' % str(float(taxon_id2contamination[lf.name])))
n.margin_top = 1
n.margin_right = 0
n.margin_left = 0
n.margin_right = 0
n.margin_bottom = 1
n.fsize = 7
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, 10, position="aligned")
fraction = float(taxon_id2contamination[lf.name])
fraction_rest = 100 - fraction
#print 'fraction, rest', fraction, fraction_rest
b = StackedBarFace(
[fraction, fraction_rest],
width=100,
height=9,
colors=["black", 'white'
]) # 1-round(float(taxon2coding_density[lf.name]), 2)
b.rotation = 0
b.margin_right = 1
b.inner_border.color = "black"
b.inner_border.width = 0
b.margin_left = 5
lf.add_face(b, 11, position="aligned")
#lf.name = taxon2description[lf.name]
n = TextFace(taxon2description[lf.name],
fgcolor="black",
fsize=9,
fstyle='italic')
n.margin_right = 30
lf.add_face(n, 0)
for n in t1.traverse():
nstyle = NodeStyle()
if n.support < 1:
nstyle["fgcolor"] = "black"
nstyle["size"] = 6
n.set_style(nstyle)
else:
nstyle["fgcolor"] = "red"
nstyle["size"] = 0
n.set_style(nstyle)
return t1, tss
def plot_tree_text_metadata(tree_file, header2taxon2text, ordered_header_list,
biodb):
from chlamdb.biosqldb import manipulate_biosqldb
server, db = manipulate_biosqldb.load_db(biodb)
t1 = Tree(tree_file)
taxon2description = manipulate_biosqldb.taxon_id2genome_description(
server, biodb, filter_names=True)
# Calculate the midpoint node
R = t1.get_midpoint_outgroup()
# and set it as tree outgroup
t1.set_outgroup(R)
tss = TreeStyle()
tss.draw_guiding_lines = True
tss.guiding_lines_color = "gray"
tss.show_leaf_name = False
for i, leaf in enumerate(t1.iter_leaves()):
# first leaf, add headers
if i == 0:
for column, header in enumerate(ordered_header_list):
n = TextFace('%s' % (header))
n.margin_top = 0
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.rotation = 270
n.hz_align = 2
n.vt_align = 2
n.inner_background.color = "white"
n.opacity = 1.
tss.aligned_header.add_face(n, column)
for column, header in enumerate(ordered_header_list):
text = header2taxon2text[header][int(leaf.name)]
n = TextFace('%s' % text)
n.margin_top = 1
n.margin_right = 1
n.margin_left = 5
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
#n.rotation = 270
leaf.add_face(n, column + 1, position="aligned")
# rename leaf (taxon_id => description)
n = TextFace(taxon2description[leaf.name],
fgcolor="black",
fsize=12,
fstyle='italic')
leaf.add_face(n, 0)
for n in t1.traverse():
# rename leaf
nstyle = NodeStyle()
if n.support < 1:
nstyle["fgcolor"] = "black"
nstyle["size"] = 6
n.set_style(nstyle)
else:
nstyle["fgcolor"] = "red"
nstyle["size"] = 0
n.set_style(nstyle)
return t1, tss
def plot_tree_barplot(tree_file,
taxon2value_list_barplot,
header_list,
taxon2set2value_heatmap=False,
header_list2=False,
presence_only=True,
biodb="chlamydia_04_16",
column_scale=True,
general_max=False,
barplot2percentage=False):
'''
display one or more barplot
:param tree_file:
:param taxon2value_list:
:param biodb:
:param exclude_outgroup:
:param bw_scale:
:param barplot2percentage: list of bool to indicates if the number are percentages and the range should be set to 0-100
:return:
'''
from chlamdb.biosqldb import manipulate_biosqldb
import matplotlib.cm as cm
from matplotlib.colors import rgb2hex
import matplotlib as mpl
server, db = manipulate_biosqldb.load_db(biodb)
taxon2description = manipulate_biosqldb.taxon_id2genome_description(
server, biodb, filter_names=True)
#print isinstance(tree_file, Tree)
#print type(tree_file)
if isinstance(tree_file, Tree):
t1 = tree_file
else:
t1 = Tree(tree_file)
# Calculate the midpoint node
R = t1.get_midpoint_outgroup()
# and set it as tree outgroup
t1.set_outgroup(R)
tss = TreeStyle()
value = 1
tss.draw_guiding_lines = True
tss.guiding_lines_color = "gray"
tss.show_leaf_name = False
if column_scale and header_list2:
import matplotlib.cm as cm
from matplotlib.colors import rgb2hex
import matplotlib as mpl
column2scale = {}
for column in header_list2:
values = taxon2set2value_heatmap[column].values()
norm = mpl.colors.Normalize(vmin=min(values), vmax=max(values))
cmap = cm.OrRd
m = cm.ScalarMappable(norm=norm, cmap=cmap)
column2scale[column] = m
cmap = cm.YlGnBu #YlOrRd#OrRd
values_lists = taxon2value_list_barplot.values()
scale_list = []
max_value_list = []
for n, header in enumerate(header_list):
#print 'scale', n, header
data = [float(i[n]) for i in values_lists]
if barplot2percentage is False:
max_value = max(data) #3424182#
min_value = min(data) #48.23
else:
if barplot2percentage[n] is True:
max_value = 100
min_value = 0
else:
max_value = max(data) #3424182#
min_value = min(data) #48.23
norm = mpl.colors.Normalize(vmin=min_value, vmax=max_value)
m1 = cm.ScalarMappable(norm=norm, cmap=cmap)
scale_list.append(m1)
if not general_max:
max_value_list.append(float(max_value))
else:
max_value_list.append(general_max)
for i, lf in enumerate(t1.iter_leaves()):
#if taxon2description[lf.name] == 'Pirellula staleyi DSM 6068':
# lf.name = 'Pirellula staleyi DSM 6068'
# continue
if i == 0:
col_add = 0
for col, header in enumerate(header_list):
#lf.add_face(n, column, position="aligned")
n = TextFace(' ')
n.margin_top = 1
n.margin_right = 2
n.margin_left = 2
n.margin_bottom = 1
n.rotation = 90
n.inner_background.color = "white"
n.opacity = 1.
n.hz_align = 2
n.vt_align = 2
tss.aligned_header.add_face(n, col_add)
n = TextFace('%s' % header)
n.margin_top = 1
n.margin_right = 2
n.margin_left = 2
n.margin_bottom = 80
n.rotation = 270
n.inner_background.color = "white"
n.opacity = 1.
n.hz_align = 2
n.vt_align = 2
tss.aligned_header.add_face(n, col_add + 1)
col_add += 2
if header_list2:
for col, header in enumerate(header_list2):
n = TextFace('%s' % header)
n.margin_top = 1
n.margin_right = 20
n.margin_left = 2
n.margin_bottom = 1
n.rotation = 270
n.hz_align = 2
n.vt_align = 2
n.inner_background.color = "white"
n.opacity = 1.
tss.aligned_header.add_face(n, col + col_add)
try:
val_list = taxon2value_list_barplot[lf.name]
except:
try:
val_list = taxon2value_list_barplot[int(lf.name)]
except:
val_list = [0]
col_add = 0
for col, value in enumerate(val_list):
# show value itself
try:
n = TextFace(' %s ' % str(value))
except:
n = TextFace(' %s ' % str(value))
n.margin_top = 1
n.margin_right = 10
n.margin_left = 2
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, col_add, position="aligned")
# show bar
color = rgb2hex(scale_list[col].to_rgba(float(value)))
try:
percentage = (value / max_value_list[col]) * 100
#percentage = value
except:
percentage = 0
maximum_bar = (
(max_value_list[col] - value) / max_value_list[col]) * 100
#maximum_bar = 100-percentage
b = StackedBarFace([percentage, maximum_bar],
width=100,
height=10,
colors=[color, "white"])
b.rotation = 0
b.inner_border.color = "grey"
b.inner_border.width = 0
b.margin_right = 15
b.margin_left = 0
lf.add_face(b, col_add + 1, position="aligned")
col_add += 2
if taxon2set2value_heatmap:
shift = col + col_add + 1
i = 0
for col, col_name in enumerate(header_list2):
try:
value = taxon2set2value_heatmap[col_name][lf.name]
except:
try:
value = taxon2set2value_heatmap[col_name][int(lf.name)]
except:
value = 0
if int(value) > 0:
if int(value) > 9:
n = TextFace(' %i ' % int(value))
else:
n = TextFace(' %i ' % int(value))
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.fgcolor = "white"
n.inner_background.color = rgb2hex(
column2scale[col_name].to_rgba(
float(value))) #"orange"
n.opacity = 1.
lf.add_face(n, col + col_add, position="aligned")
i += 1
else:
n = TextFace(' ') #% str(value))
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, col + col_add, position="aligned")
#lf.name = taxon2description[lf.name]
try:
n = TextFace(taxon2description[lf.name],
fgcolor="black",
fsize=12,
fstyle='italic')
except:
n = TextFace(lf.name, fgcolor="black", fsize=12, fstyle='italic')
lf.add_face(n, 0)
for n in t1.traverse():
nstyle = NodeStyle()
if n.support < 1:
nstyle["fgcolor"] = "black"
nstyle["size"] = 6
n.set_style(nstyle)
else:
nstyle["fgcolor"] = "red"
nstyle["size"] = 0
n.set_style(nstyle)
#print t1
return t1, tss
def plot_heatmap_tree_locus(biodb,
tree_file,
taxid2count,
taxid2identity=False,
taxid2locus=False,
reference_taxon=False,
n_paralogs_barplot=False):
'''
plot tree and associated heatmap with count of homolgs
optional:
- add identity of closest homolog
- add locus tag of closest homolog
'''
from chlamdb.biosqldb import manipulate_biosqldb
server, db = manipulate_biosqldb.load_db(biodb)
taxid2organism = manipulate_biosqldb.taxon_id2genome_description(
server, biodb, True)
t1 = Tree(tree_file)
ts = TreeStyle()
ts.draw_guiding_lines = True
ts.guiding_lines_color = "gray"
# Calculate the midpoint node
R = t1.get_midpoint_outgroup()
# and set it as tree outgroup
t1.set_outgroup(R)
leaf_number = 0
for lf in t1.iter_leaves():
if str(lf.name) not in taxid2count:
taxid2count[str(lf.name)] = 0
max_count = max([taxid2count[str(lf.name)] for lf in t1.iter_leaves()])
for i, lf in enumerate(t1.iter_leaves()):
# top leaf, add header
if i == 0:
n = TextFace('Number of homologs')
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
n.rotation = -25
#lf.add_face(n, 7, position="aligned")
ts.aligned_header.add_face(n, 1)
if taxid2identity:
n = TextFace('Protein identity')
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
n.rotation = -25
#lf.add_face(n, 7, position="aligned")
ts.aligned_header.add_face(n, 2)
if taxid2locus:
n = TextFace('Locus tag')
n.margin_top = 1
n.margin_right = 1
n.margin_left = 20
n.margin_bottom = 1
n.inner_background.color = "white"
n.opacity = 1.
n.rotation = -25
#lf.add_face(n, 7, position="aligned")
ts.aligned_header.add_face(n, 3)
leaf_number += 1
lf.branch_vertical_margin = 0
data = [taxid2count[str(lf.name)]]
# possibility to add one or more columns
for col, value in enumerate(data):
col_index = col
if value > 0:
n = TextFace(' %s ' % str(value))
n.margin_top = 2
n.margin_right = 2
if col == 0:
n.margin_left = 20
else:
n.margin_left = 2
n.margin_bottom = 2
n.inner_background.color = "white" # #81BEF7
n.opacity = 1.
lf.add_face(n, col, position="aligned")
else:
n = TextFace(' %s ' % str(value))
n.margin_top = 2
n.margin_right = 2
if col == 0:
n.margin_left = 20
else:
n.margin_left = 2
n.margin_bottom = 2
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, col, position="aligned")
# optionally indicate number of paralogs as a barplot
if n_paralogs_barplot:
col_index += 1
percent = (float(value) / max_count) * 100
n = StackedBarFace([percent, 100 - percent],
width=150,
height=18,
colors=['#6699ff', 'white'],
line_color='white')
n.rotation = 0
n.inner_border.color = "white"
n.inner_border.width = 0
n.margin_right = 15
n.margin_left = 0
lf.add_face(n, col + 1, position="aligned")
# optionally add additionnal column with identity
if taxid2identity:
import matplotlib.cm as cm
from matplotlib.colors import rgb2hex
import matplotlib as mpl
norm = mpl.colors.Normalize(vmin=0, vmax=100)
cmap = cm.OrRd
m = cm.ScalarMappable(norm=norm, cmap=cmap)
try:
if round(taxid2identity[str(lf.name)], 2) != 100:
value = "%.2f" % round(taxid2identity[str(lf.name)], 2)
else:
value = "%.1f" % round(taxid2identity[str(lf.name)], 2)
except:
value = '-'
if str(lf.name) == str(reference_taxon):
value = ' '
n = TextFace(' %s ' % value)
n.margin_top = 2
n.margin_right = 2
n.margin_left = 20
n.margin_bottom = 2
if not value.isspace() and value is not '-':
n.inner_background.color = rgb2hex(m.to_rgba(float(value)))
if float(value) > 82:
n.fgcolor = 'white'
n.opacity = 1.
if str(lf.name) == str(reference_taxon):
n.inner_background.color = '#800000'
lf.add_face(n, col_index + 1, position="aligned")
# optionaly add column with locus name
if taxid2locus:
try:
value = str(taxid2locus[str(lf.name)])
except:
value = '-'
n = TextFace(' %s ' % value)
n.margin_top = 2
n.margin_right = 2
n.margin_left = 2
n.margin_bottom = 2
if str(lf.name) != str(reference_taxon):
n.inner_background.color = "white"
else:
n.fgcolor = '#ff0000'
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, col_index + 2, position="aligned")
lf.name = taxid2organism[str(lf.name)]
return t1, leaf_number, ts
def plot_heat_tree(biodb, taxid2n, tree_file):
'''
Plot heatmap next to a tree. The order of the heatmap **MUST** be the same,
as order of the leafs on the tree. The tree must be in the Newick format. If
*output_file* is specified, then heat-tree will be rendered as a PNG,
otherwise interactive browser will pop-up with your heat-tree.
Parameters
----------
heatmap_file: str
Path to the heatmap file. The first row must have '#Names' as first
element of the header.
e.g. #Names, A, B, C, D
row1, 2, 4, 0, 4
row2, 4, 6, 2, -1
tree_file: str
Path to the tree file in Newick format. The leaf node labels should
be the same as as row names in the heatmap file. E.g. row1, row2.
output_file: str, optional
If specified the heat-tree will be rendered in that file as a PNG image,
otherwise interactive browser will pop-up. **N.B.** program will wait
for you to exit the browser before continuing.
'''
from chlamdb.biosqldb import manipulate_biosqldb
server, db = manipulate_biosqldb.load_db(biodb)
taxid2organism = manipulate_biosqldb.taxon_id2genome_description(
server, biodb, True)
t1 = Tree(tree_file)
# Calculate the midpoint node
R = t1.get_midpoint_outgroup()
# and set it as tree outgroup
t1.set_outgroup(R)
leaf_number = 0
for lf in t1.iter_leaves():
leaf_number += 1
lf.branch_vertical_margin = 0
try:
data = [taxid2n[str(lf.name)]]
except:
data = [0]
#print 'taxon', int(lf.name)
lf.name = taxid2organism[int(lf.name)]
for col, value in enumerate(data):
if value > 0:
n = TextFace(' %s ' % str(value))
n.margin_top = 2
n.margin_right = 2
n.margin_left = 2
n.margin_bottom = 2
n.inner_background.color = "#81BEF7"
n.opacity = 1.
lf.add_face(n, col, position="aligned")
else:
n = TextFace(' %s ' % str(value))
n.margin_top = 2
n.margin_right = 2
n.margin_left = 2
n.margin_bottom = 2
n.inner_background.color = "white"
n.opacity = 1.
lf.add_face(n, col, position="aligned")
return t1, leaf_number
