def calculate_matrix_svg(snplst,
pop,
request,
genome_build,
r2_d="r2",
collapseTranscript=True):
# Set data directories using config.yml
with open('config.yml', 'r') as yml_file:
config = yaml.load(yml_file)
env = config['env']
api_mongo_addr = config['api']['api_mongo_addr']
population_samples_dir = config['data']['population_samples_dir']
data_dir = config['data']['data_dir']
tmp_dir = config['data']['tmp_dir']
genotypes_dir = config['data']['genotypes_dir']
aws_info = config['aws']
mongo_username = config['database']['mongo_user_readonly']
mongo_password = config['database']['mongo_password']
mongo_port = config['database']['mongo_port']
export_s3_keys = retrieveAWSCredentials()
# Ensure tmp directory exists
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
# Open SNP list file
snps_raw = open(snplst).readlines()
# Remove duplicate RS numbers
snps = []
for snp_raw in snps_raw:
snp = snp_raw.strip().split()
if snp not in snps:
snps.append(snp)
# Select desired ancestral populations
pops = pop.split("+")
pop_dirs = []
for pop_i in pops:
if pop_i in [
"ALL", "AFR", "AMR", "EAS", "EUR", "SAS", "ACB", "ASW", "BEB",
"CDX", "CEU", "CHB", "CHS", "CLM", "ESN", "FIN", "GBR", "GIH",
"GWD", "IBS", "ITU", "JPT", "KHV", "LWK", "MSL", "MXL", "PEL",
"PJL", "PUR", "STU", "TSI", "YRI"
]:
pop_dirs.append(data_dir + population_samples_dir + pop_i + ".txt")
get_pops = "cat " + " ".join(pop_dirs)
pop_list = [
x.decode('utf-8') for x in subprocess.Popen(
get_pops, shell=True, stdout=subprocess.PIPE).stdout.readlines()
]
ids = [i.strip() for i in pop_list]
pop_ids = list(set(ids))
# Connect to Mongo snp database
if env == 'local':
mongo_host = api_mongo_addr
else:
mongo_host = 'localhost'
client = MongoClient(
'mongodb://' + mongo_username + ':' + mongo_password + '@' +
mongo_host + '/admin', mongo_port)
db = client["LDLink"]
def get_coords(db, rsid):
rsid = rsid.strip("rs")
query_results = db.dbsnp.find_one({"id": rsid})
query_results_sanitized = json.loads(json_util.dumps(query_results))
return query_results_sanitized
# Query genomic coordinates
def get_rsnum(db, coord):
temp_coord = coord.strip("chr").split(":")
chro = temp_coord[0]
pos = temp_coord[1]
query_results = db.dbsnp.find({
"chromosome":
chro.upper() if chro == 'x' or chro == 'y' else str(chro),
genome_build_vars[genome_build]['position']:
str(pos)
})
query_results_sanitized = json.loads(json_util.dumps(query_results))
return query_results_sanitized
# Replace input genomic coordinates with variant ids (rsids)
def replace_coords_rsid(db, snp_lst):
new_snp_lst = []
for snp_raw_i in snp_lst:
if snp_raw_i[0][0:2] == "rs":
new_snp_lst.append(snp_raw_i)
else:
snp_info_lst = get_rsnum(db, snp_raw_i[0])
print("snp_info_lst")
print(snp_info_lst)
if snp_info_lst != None:
if len(snp_info_lst) > 1:
var_id = "rs" + snp_info_lst[0]['id']
ref_variants = []
for snp_info in snp_info_lst:
if snp_info['id'] == snp_info['ref_id']:
ref_variants.append(snp_info['id'])
if len(ref_variants) > 1:
var_id = "rs" + ref_variants[0]
elif len(ref_variants) == 0 and len(snp_info_lst) > 1:
var_id = "rs" + snp_info_lst[0]['id']
else:
var_id = "rs" + ref_variants[0]
new_snp_lst.append([var_id])
elif len(snp_info_lst) == 1:
var_id = "rs" + snp_info_lst[0]['id']
new_snp_lst.append([var_id])
else:
new_snp_lst.append(snp_raw_i)
else:
new_snp_lst.append(snp_raw_i)
return new_snp_lst
snps = replace_coords_rsid(db, snps)
# Find RS numbers in snp database
rs_nums = []
snp_pos = []
snp_coords = []
tabix_coords = ""
for snp_i in snps:
if len(snp_i) > 0:
if len(snp_i[0]) > 2:
if (snp_i[0][0:2] == "rs"
or snp_i[0][0:3] == "chr") and snp_i[0][-1].isdigit():
snp_coord = get_coords(db, snp_i[0])
if snp_coord != None and snp_coord[genome_build_vars[
genome_build]['position']] != "NA":
# check if variant is on chrY for genome build = GRCh38
if not (snp_coord['chromosome'] == "Y" and
(genome_build == "grch38"
or genome_build == "grch38_high_coverage")):
rs_nums.append(snp_i[0])
snp_pos.append(snp_coord[
genome_build_vars[genome_build]['position']])
temp = [
snp_i[0], snp_coord['chromosome'],
snp_coord[genome_build_vars[genome_build]
['position']]
]
snp_coords.append(temp)
# Check max distance between SNPs
distance_bp = []
for i in range(len(snp_coords)):
distance_bp.append(int(snp_coords[i][2]))
# Sort coordinates and make tabix formatted coordinates
snp_pos_int = [int(i) for i in snp_pos]
snp_pos_int.sort()
snp_coord_str = [
genome_build_vars[genome_build]['1000G_chr_prefix'] +
snp_coords[0][1] + ":" + str(i) + "-" + str(i) for i in snp_pos_int
]
tabix_coords = " " + " ".join(snp_coord_str)
# Extract 1000 Genomes phased genotypes
vcf_filePath = "%s/%s%s/%s" % (
config['aws']['data_subfolder'], genotypes_dir,
genome_build_vars[genome_build]['1000G_dir'],
genome_build_vars[genome_build]['1000G_file'] % (snp_coords[0][1]))
vcf_query_snp_file = "s3://%s/%s" % (config['aws']['bucket'], vcf_filePath)
checkS3File(aws_info, config['aws']['bucket'], vcf_filePath)
# Define function to correct indel alleles
def set_alleles(a1, a2):
if len(a1) == 1 and len(a2) == 1:
a1_n = a1
a2_n = a2
elif len(a1) == 1 and len(a2) > 1:
a1_n = "-"
a2_n = a2[1:]
elif len(a1) > 1 and len(a2) == 1:
a1_n = a1[1:]
a2_n = "-"
elif len(a1) > 1 and len(a2) > 1:
a1_n = a1[1:]
a2_n = a2[1:]
return (a1_n, a2_n)
# Import SNP VCF files
tabix_snps = export_s3_keys + " cd {2}; tabix -fhD {0}{1} | grep -v -e END".format(
vcf_query_snp_file, tabix_coords, data_dir + genotypes_dir +
genome_build_vars[genome_build]['1000G_dir'])
vcf = [
x.decode('utf-8') for x in subprocess.Popen(
tabix_snps, shell=True, stdout=subprocess.PIPE).stdout.readlines()
]
h = 0
while vcf[h][0:2] == "##":
h += 1
head = vcf[h].strip().split()
# Extract haplotypes
index = []
for i in range(9, len(head)):
if head[i] in pop_ids:
index.append(i)
hap1 = [[]]
for i in range(len(index) - 1):
hap1.append([])
hap2 = [[]]
for i in range(len(index) - 1):
hap2.append([])
rsnum_lst = []
allele_lst = []
pos_lst = []
for g in range(h + 1, len(vcf)):
geno = vcf[g].strip().split()
geno[0] = geno[0].lstrip('chr')
if geno[1] not in snp_pos:
continue
if snp_pos.count(geno[1]) == 1:
rs_query = rs_nums[snp_pos.index(geno[1])]
else:
pos_index = []
for p in range(len(snp_pos)):
if snp_pos[p] == geno[1]:
pos_index.append(p)
for p in pos_index:
if rs_nums[p] not in rsnum_lst:
rs_query = rs_nums[p]
break
if rs_query in rsnum_lst:
continue
rs_1000g = geno[2]
if rs_query == rs_1000g:
rsnum = rs_1000g
else:
count = -2
found = "false"
while count <= 2 and count + g < len(vcf):
geno_next = vcf[g + count].strip().split()
geno_next[0] = geno_next[0].lstrip('chr')
if len(geno_next) >= 3 and rs_query == geno_next[2]:
found = "true"
break
count += 1
if found == "false":
indx = [i[0] for i in snps].index(rs_query)
# snps[indx][0] = geno[2]
# rsnum = geno[2]
snps[indx][0] = rs_query
rsnum = rs_query
else:
continue
if "," not in geno[3] and "," not in geno[4]:
a1, a2 = set_alleles(geno[3], geno[4])
for i in range(len(index)):
if geno[index[i]] == "0|0":
hap1[i].append(a1)
hap2[i].append(a1)
elif geno[index[i]] == "0|1":
hap1[i].append(a1)
hap2[i].append(a2)
elif geno[index[i]] == "1|0":
hap1[i].append(a2)
hap2[i].append(a1)
elif geno[index[i]] == "1|1":
hap1[i].append(a2)
hap2[i].append(a2)
elif geno[index[i]] == "0":
hap1[i].append(a1)
hap2[i].append(".")
elif geno[index[i]] == "1":
hap1[i].append(a2)
hap2[i].append(".")
else:
hap1[i].append(".")
hap2[i].append(".")
rsnum_lst.append(rsnum)
position = "chr" + geno[0] + ":" + geno[1] + "-" + geno[1]
pos_lst.append(position)
alleles = a1 + "/" + a2
allele_lst.append(alleles)
# Calculate Pairwise LD Statistics
all_haps = hap1 + hap2
ld_matrix = [[[None for v in range(2)] for i in range(len(all_haps[0]))]
for j in range(len(all_haps[0]))]
for i in range(len(all_haps[0])):
for j in range(i, len(all_haps[0])):
hap = {}
for k in range(len(all_haps)):
# Extract haplotypes
hap_k = all_haps[k][i] + all_haps[k][j]
if hap_k in hap:
hap[hap_k] += 1
else:
hap[hap_k] = 1
# Remove Missing Haplotypes
keys = list(hap.keys())
for key in keys:
if "." in key:
hap.pop(key, None)
# Check all haplotypes are present
if len(hap) != 4:
snp_i_a = allele_lst[i].split("/")
snp_j_a = allele_lst[j].split("/")
haps = [
snp_i_a[0] + snp_j_a[0], snp_i_a[0] + snp_j_a[1],
snp_i_a[1] + snp_j_a[0], snp_i_a[1] + snp_j_a[1]
]
for h in haps:
if h not in hap:
hap[h] = 0
# Perform LD calculations
A = hap[sorted(hap)[0]]
B = hap[sorted(hap)[1]]
C = hap[sorted(hap)[2]]
D = hap[sorted(hap)[3]]
tmax = max(A, B, C, D)
delta = float(A * D - B * C)
Ms = float((A + C) * (B + D) * (A + B) * (C + D))
if Ms != 0:
# D prime
if delta < 0:
D_prime = round(
abs(delta / min((A + C) * (A + B), (B + D) * (C + D))),
3)
else:
D_prime = round(
abs(delta / min((A + C) * (C + D), (A + B) * (B + D))),
3)
# R2
r2 = round((delta**2) / Ms, 3)
# Find Correlated Alleles
if str(r2) != "NA" and float(r2) > 0.1:
Ac = hap[sorted(hap)[0]]
Bc = hap[sorted(hap)[1]]
Cc = hap[sorted(hap)[2]]
Dc = hap[sorted(hap)[3]]
if ((Ac * Dc) / max((Bc * Cc), 0.01) > 1):
match = sorted(hap)[0][0] + "=" + sorted(
hap)[0][1] + "," + sorted(
hap)[3][0] + "=" + sorted(hap)[3][1]
else:
match = sorted(hap)[1][0] + "=" + sorted(
hap)[1][1] + "," + sorted(
hap)[2][0] + "=" + sorted(hap)[2][1]
else:
match = " = , = "
else:
D_prime = "NA"
r2 = "NA"
match = " = , = "
snp1 = rsnum_lst[i]
snp2 = rsnum_lst[j]
pos1 = pos_lst[i].split("-")[0]
pos2 = pos_lst[j].split("-")[0]
allele1 = allele_lst[i]
allele2 = allele_lst[j]
corr = match.split(",")[0].split("=")[1] + "=" + match.split(
",")[0].split("=")[0] + "," + match.split(",")[1].split(
"=")[1] + "=" + match.split(",")[1].split("=")[0]
corr_f = match
ld_matrix[i][j] = [
snp1, snp2, allele1, allele2, corr, pos1, pos2, D_prime, r2
]
ld_matrix[j][i] = [
snp2, snp1, allele2, allele1, corr_f, pos2, pos1, D_prime, r2
]
# Generate Plot Variables
out = [j for i in ld_matrix for j in i]
xnames = []
ynames = []
xA = []
yA = []
corA = []
xpos = []
ypos = []
D = []
R = []
box_color = []
box_trans = []
if r2_d not in ["r2", "d"]:
r2_d = "r2"
for i in range(len(out)):
snp1, snp2, allele1, allele2, corr, pos1, pos2, D_prime, r2 = out[i]
xnames.append(snp1)
ynames.append(snp2)
xA.append(allele1)
yA.append(allele2)
corA.append(corr)
xpos.append(pos1)
ypos.append(pos2)
sqrti = math.floor(math.sqrt(len(out)))
if sqrti == 0:
D.append(str(round(float(D_prime), 4)))
R.append(str(round(float(r2), 4)))
box_color.append("red")
box_trans.append(r2)
elif i % sqrti < i // sqrti and r2 != "NA":
D.append(str(round(float(D_prime), 4)))
R.append(str(round(float(r2), 4)))
box_color.append("blue")
box_trans.append(abs(D_prime))
elif i % sqrti > i // sqrti and D_prime != "NA":
D.append(str(round(float(D_prime), 4)))
R.append(str(round(float(r2), 4)))
box_color.append("red")
box_trans.append(r2)
elif i % sqrti == i // sqrti and D_prime != "NA":
D.append(str(round(float(D_prime), 4)))
R.append(str(round(float(r2), 4)))
box_color.append("purple")
box_trans.append(r2)
else:
D.append("NA")
R.append("NA")
box_color.append("gray")
box_trans.append(0.1)
# Import plotting modules
from collections import OrderedDict
from bokeh.embed import components, file_html
from bokeh.layouts import gridplot
from bokeh.models import HoverTool, LinearAxis, Range1d
from bokeh.plotting import ColumnDataSource, curdoc, figure, output_file, reset_output, save
from bokeh.resources import CDN
from bokeh.io import export_svgs
import svgutils.compose as sg
from math import pi
reset_output()
# Aggregate Plotting Data
x = []
y = []
w = []
h = []
coord_snps_plot = []
snp_id_plot = []
alleles_snp_plot = []
for i in range(0, len(xpos), int(len(xpos)**0.5)):
x.append(int(xpos[i].split(":")[1]) / 1000000.0)
y.append(0.5)
w.append(0.00003)
h.append(1.06)
coord_snps_plot.append(xpos[i])
snp_id_plot.append(xnames[i])
alleles_snp_plot.append(xA[i])
buffer = (x[-1] - x[0]) * 0.025
xr = Range1d(start=x[0] - buffer, end=x[-1] + buffer)
yr = Range1d(start=-0.03, end=1.03)
y2_ll = [-0.03] * len(x)
y2_ul = [1.03] * len(x)
yr_pos = Range1d(start=(x[-1] + buffer) * -1, end=(x[0] - buffer) * -1)
yr0 = Range1d(start=0, end=1)
yr2 = Range1d(start=0, end=3.8)
yr3 = Range1d(start=0, end=1)
spacing = (x[-1] - x[0] + buffer + buffer) / (len(x) * 1.0)
x2 = []
y0 = []
y1 = []
y2 = []
y3 = []
y4 = []
for i in range(len(x)):
x2.append(x[0] - buffer + spacing * (i + 0.5))
y0.append(0)
y1.append(0.20)
y2.append(0.80)
y3.append(1)
y4.append(1.15)
xname_pos = []
for i in x2:
for j in range(len(x2)):
xname_pos.append(i)
data = {
'xname': xnames,
'xname_pos': xname_pos,
'yname': ynames,
'xA': xA,
'yA': yA,
'xpos': xpos,
'ypos': ypos,
'R2': R,
'Dp': D,
'corA': corA,
'box_color': box_color,
'box_trans': box_trans
}
source = ColumnDataSource(data)
threshold = 70
if len(snps) < threshold:
matrix_plot = figure(
outline_line_color="white",
min_border_top=0,
min_border_bottom=2,
min_border_left=100,
min_border_right=5,
x_range=xr,
y_range=list(reversed(rsnum_lst)),
h_symmetry=False,
v_symmetry=False,
border_fill_color='white',
x_axis_type=None,
logo=None,
tools="hover,undo,redo,reset,pan,box_zoom,previewsave",
title=" ",
plot_width=800,
plot_height=700)
else:
matrix_plot = figure(
outline_line_color="white",
min_border_top=0,
min_border_bottom=2,
min_border_left=100,
min_border_right=5,
x_range=xr,
y_range=list(reversed(rsnum_lst)),
h_symmetry=False,
v_symmetry=False,
border_fill_color='white',
x_axis_type=None,
y_axis_type=None,
logo=None,
tools="hover,undo,redo,reset,pan,box_zoom,previewsave",
title=" ",
plot_width=800,
plot_height=700)
matrix_plot.rect(x='xname_pos',
y='yname',
width=0.95 * spacing,
height=0.95,
source=source,
color="box_color",
alpha="box_trans",
line_color=None)
matrix_plot.grid.grid_line_color = None
matrix_plot.axis.axis_line_color = None
matrix_plot.axis.major_tick_line_color = None
if len(snps) < threshold:
matrix_plot.axis.major_label_text_font_size = "8pt"
matrix_plot.xaxis.major_label_orientation = "vertical"
matrix_plot.axis.major_label_text_font_style = "normal"
matrix_plot.xaxis.major_label_standoff = 0
sup_2 = "\u00B2"
hover = matrix_plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Variant 1", " " + "@yname (@yA)"),
("Variant 2", " " + "@xname (@xA)"),
("D\'", " " + "@Dp"),
("R" + sup_2, " " + "@R2"),
("Correlated Alleles", " " + "@corA"),
])
# Connecting and Rug Plots
# Connector Plot
if len(snps) < threshold:
connector = figure(outline_line_color="white",
y_axis_type=None,
x_axis_type=None,
x_range=xr,
y_range=yr2,
border_fill_color='white',
title="",
min_border_left=100,
min_border_right=5,
min_border_top=0,
min_border_bottom=0,
h_symmetry=False,
v_symmetry=False,
plot_width=800,
plot_height=90,
tools="xpan,tap")
connector.segment(x, y0, x, y1, color="black")
connector.segment(x, y1, x2, y2, color="black")
connector.segment(x2, y2, x2, y3, color="black")
connector.text(x2,
y4,
text=snp_id_plot,
alpha=1,
angle=pi / 2,
text_font_size="8pt",
text_baseline="middle",
text_align="left")
else:
connector = figure(outline_line_color="white",
y_axis_type=None,
x_axis_type=None,
x_range=xr,
y_range=yr3,
border_fill_color='white',
title="",
min_border_left=100,
min_border_right=5,
min_border_top=0,
min_border_bottom=0,
h_symmetry=False,
v_symmetry=False,
plot_width=800,
plot_height=30,
tools="xpan,tap")
connector.segment(x, y0, x, y1, color="black")
connector.segment(x, y1, x2, y2, color="black")
connector.segment(x2, y2, x2, y3, color="black")
connector.yaxis.major_label_text_color = None
connector.yaxis.minor_tick_line_alpha = 0 # Option does not work
connector.yaxis.axis_label = " "
connector.grid.grid_line_color = None
connector.axis.axis_line_color = None
connector.axis.major_tick_line_color = None
connector.axis.minor_tick_line_color = None
connector.toolbar_location = None
data_rug = {
'x': x,
'y': y,
'w': w,
'h': h,
'coord_snps_plot': coord_snps_plot,
'snp_id_plot': snp_id_plot,
'alleles_snp_plot': alleles_snp_plot
}
source_rug = ColumnDataSource(data_rug)
# Rug Plot
rug = figure(x_range=xr,
y_range=yr,
y_axis_type=None,
title="",
min_border_top=1,
min_border_bottom=0,
min_border_left=100,
min_border_right=5,
h_symmetry=False,
v_symmetry=False,
plot_width=800,
plot_height=50,
tools="hover,xpan,tap")
rug.rect(x='x',
y='y',
width='w',
height='h',
fill_color='red',
dilate=True,
line_color=None,
fill_alpha=0.6,
source=source_rug)
hover = rug.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("SNP", "@snp_id_plot (@alleles_snp_plot)"),
("Coord", "@coord_snps_plot"),
])
rug.toolbar_location = None
if collapseTranscript == "false":
# Gene Plot (All Transcripts)
genes_file = tmp_dir + "genes_" + request + ".json"
genes_raw = open(genes_file).readlines()
genes_plot_start = []
genes_plot_end = []
genes_plot_y = []
genes_plot_name = []
exons_plot_x = []
exons_plot_y = []
exons_plot_w = []
exons_plot_h = []
exons_plot_name = []
exons_plot_id = []
exons_plot_exon = []
message = ["Too many genes to plot."]
lines = [0]
gap = 80000
tall = 0.75
if genes_raw != None and len(genes_raw) > 0:
for gene_raw_obj in genes_raw:
gene_obj = json.loads(gene_raw_obj)
bin = gene_obj["bin"]
name_id = gene_obj["name"]
chrom = gene_obj["chrom"]
strand = gene_obj["strand"]
txStart = gene_obj["txStart"]
txEnd = gene_obj["txEnd"]
cdsStart = gene_obj["cdsStart"]
cdsEnd = gene_obj["cdsEnd"]
exonCount = gene_obj["exonCount"]
exonStarts = gene_obj["exonStarts"]
exonEnds = gene_obj["exonEnds"]
score = gene_obj["score"]
name2 = gene_obj["name2"]
cdsStartStat = gene_obj["cdsStartStat"]
cdsEndStat = gene_obj["cdsEndStat"]
exonFrames = gene_obj["exonFrames"]
name = name2
id = name_id
e_start = exonStarts.split(",")
e_end = exonEnds.split(",")
# Determine Y Coordinate
i = 0
y_coord = None
while y_coord == None:
if i > len(lines) - 1:
y_coord = i + 1
lines.append(int(txEnd))
elif int(txStart) > (gap + lines[i]):
y_coord = i + 1
lines[i] = int(txEnd)
else:
i += 1
genes_plot_start.append(int(txStart) / 1000000.0)
genes_plot_end.append(int(txEnd) / 1000000.0)
genes_plot_y.append(y_coord)
genes_plot_name.append(name + " ")
for i in range(len(e_start) - 1):
if strand == "+":
exon = i + 1
else:
exon = len(e_start) - 1 - i
width = (int(e_end[i]) - int(e_start[i])) / 1000000.0
x_coord = int(e_start[i]) / 1000000.0 + (width / 2)
exons_plot_x.append(x_coord)
exons_plot_y.append(y_coord)
exons_plot_w.append(width)
exons_plot_h.append(tall)
exons_plot_name.append(name)
exons_plot_id.append(id)
exons_plot_exon.append(exon)
n_rows = len(lines)
genes_plot_yn = [n_rows - w + 0.5 for w in genes_plot_y]
exons_plot_yn = [n_rows - w + 0.5 for w in exons_plot_y]
yr2 = Range1d(start=0, end=n_rows)
data_gene_plot = {
'exons_plot_x': exons_plot_x,
'exons_plot_yn': exons_plot_yn,
'exons_plot_w': exons_plot_w,
'exons_plot_h': exons_plot_h,
'exons_plot_name': exons_plot_name,
'exons_plot_id': exons_plot_id,
'exons_plot_exon': exons_plot_exon,
'coord_snps_plot': coord_snps_plot,
'snp_id_plot': snp_id_plot,
'alleles_snp_plot': alleles_snp_plot
}
source_gene_plot = ColumnDataSource(data_gene_plot)
max_genes = 40
# if len(lines) < 3 or len(genes_raw) > max_genes:
if len(lines) < 3:
plot_h_pix = 250
else:
plot_h_pix = 250 + (len(lines) - 2) * 50
gene_plot = figure(
min_border_top=2,
min_border_bottom=0,
min_border_left=100,
min_border_right=5,
x_range=xr,
y_range=yr2,
border_fill_color='white',
title="",
h_symmetry=False,
v_symmetry=False,
logo=None,
plot_width=800,
plot_height=plot_h_pix,
tools=
"hover,xpan,box_zoom,wheel_zoom,tap,undo,redo,reset,previewsave")
# if len(genes_raw) <= max_genes:
gene_plot.segment(genes_plot_start,
genes_plot_yn,
genes_plot_end,
genes_plot_yn,
color="black",
alpha=1,
line_width=2)
gene_plot.rect(x='exons_plot_x',
y='exons_plot_yn',
width='exons_plot_w',
height='exons_plot_h',
source=source_gene_plot,
fill_color='grey',
line_color="grey")
gene_plot.text(genes_plot_start,
genes_plot_yn,
text=genes_plot_name,
alpha=1,
text_font_size="7pt",
text_font_style="bold",
text_baseline="middle",
text_align="right",
angle=0)
hover = gene_plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Gene", "@exons_plot_name"),
("ID", "@exons_plot_id"),
("Exon", "@exons_plot_exon"),
])
# else:
# x_coord_text = x[0] + (x[-1] - x[0]) / 2.0
# gene_plot.text(x_coord_text, n_rows / 2.0, text=message, alpha=1,
# text_font_size="12pt", text_font_style="bold", text_baseline="middle", text_align="center", angle=0)
gene_plot.xaxis.axis_label = "Chromosome " + \
snp_coords[1][1] + " Coordinate (Mb)(" + genome_build_vars[genome_build]['title'] + ")"
gene_plot.yaxis.axis_label = "Genes (All Transcripts)"
gene_plot.ygrid.grid_line_color = None
gene_plot.yaxis.axis_line_color = None
gene_plot.yaxis.minor_tick_line_color = None
gene_plot.yaxis.major_tick_line_color = None
gene_plot.yaxis.major_label_text_color = None
gene_plot.toolbar_location = "below"
# Gene Plot (Collapsed)
else:
genes_c_file = tmp_dir + "genes_c_" + request + ".json"
genes_c_raw = open(genes_c_file).readlines()
genes_c_plot_start = []
genes_c_plot_end = []
genes_c_plot_y = []
genes_c_plot_name = []
exons_c_plot_x = []
exons_c_plot_y = []
exons_c_plot_w = []
exons_c_plot_h = []
exons_c_plot_name = []
exons_c_plot_id = []
message_c = ["Too many genes to plot."]
lines_c = [0]
gap = 80000
tall = 0.75
if genes_c_raw != None and len(genes_c_raw) > 0:
for gene_c_raw_obj in genes_c_raw:
gene_c_obj = json.loads(gene_c_raw_obj)
chrom = gene_c_obj["chrom"]
txStart = gene_c_obj["txStart"]
txEnd = gene_c_obj["txEnd"]
exonStarts = gene_c_obj["exonStarts"]
exonEnds = gene_c_obj["exonEnds"]
name2 = gene_c_obj["name2"]
transcripts = gene_c_obj["transcripts"]
name = name2
e_start = exonStarts.split(",")
e_end = exonEnds.split(",")
e_transcripts = transcripts.split(",")
# Determine Y Coordinate
i = 0
y_coord = None
while y_coord == None:
if i > len(lines_c) - 1:
y_coord = i + 1
lines_c.append(int(txEnd))
elif int(txStart) > (gap + lines_c[i]):
y_coord = i + 1
lines_c[i] = int(txEnd)
else:
i += 1
genes_c_plot_start.append(int(txStart) / 1000000.0)
genes_c_plot_end.append(int(txEnd) / 1000000.0)
genes_c_plot_y.append(y_coord)
genes_c_plot_name.append(name + " ")
# for i in range(len(e_start)):
for i in range(len(e_start) - 1):
width = (int(e_end[i]) - int(e_start[i])) / 1000000.0
x_coord = int(e_start[i]) / 1000000.0 + (width / 2)
exons_c_plot_x.append(x_coord)
exons_c_plot_y.append(y_coord)
exons_c_plot_w.append(width)
exons_c_plot_h.append(tall)
exons_c_plot_name.append(name)
exons_c_plot_id.append(e_transcripts[i].replace("-", ","))
n_rows_c = len(lines_c)
genes_c_plot_yn = [n_rows_c - x + 0.5 for x in genes_c_plot_y]
exons_c_plot_yn = [n_rows_c - x + 0.5 for x in exons_c_plot_y]
yr2_c = Range1d(start=0, end=n_rows_c)
data_gene_c_plot = {
'exons_c_plot_x': exons_c_plot_x,
'exons_c_plot_yn': exons_c_plot_yn,
'exons_c_plot_w': exons_c_plot_w,
'exons_c_plot_h': exons_c_plot_h,
'exons_c_plot_name': exons_c_plot_name,
'exons_c_plot_id': exons_c_plot_id
}
source_gene_c_plot = ColumnDataSource(data_gene_c_plot)
max_genes_c = 40
# if len(lines_c) < 3 or len(genes_c_raw) > max_genes_c:
if len(lines_c) < 3:
plot_h_pix = 250
else:
plot_h_pix = 250 + (len(lines_c) - 2) * 50
gene_plot = figure(
min_border_top=2,
min_border_bottom=0,
min_border_left=100,
min_border_right=5,
x_range=xr,
y_range=yr2_c,
border_fill_color='white',
title="",
h_symmetry=False,
v_symmetry=False,
logo=None,
plot_width=900,
plot_height=plot_h_pix,
tools=
"hover,xpan,box_zoom,wheel_zoom,tap,undo,redo,reset,previewsave")
# if len(genes_c_raw) <= max_genes_c:
gene_plot.segment(genes_c_plot_start,
genes_c_plot_yn,
genes_c_plot_end,
genes_c_plot_yn,
color="black",
alpha=1,
line_width=2)
gene_plot.rect(x='exons_c_plot_x',
y='exons_c_plot_yn',
width='exons_c_plot_w',
height='exons_c_plot_h',
source=source_gene_c_plot,
fill_color="grey",
line_color="grey")
gene_plot.text(genes_c_plot_start,
genes_c_plot_yn,
text=genes_c_plot_name,
alpha=1,
text_font_size="7pt",
text_font_style="bold",
text_baseline="middle",
text_align="right",
angle=0)
hover = gene_plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Gene", "@exons_c_plot_name"),
("Transcript IDs", "@exons_c_plot_id"),
])
# else:
# x_coord_text = coord1/1000000.0 + (coord2/1000000.0 - coord1/1000000.0) / 2.0
# gene_c_plot.text(x_coord_text, n_rows_c / 2.0, text=message_c, alpha=1,
# text_font_size="12pt", text_font_style="bold", text_baseline="middle", text_align="center", angle=0)
gene_plot.xaxis.axis_label = "Chromosome " + snp_coords[1][
1] + " Coordinate (Mb)(" + genome_build_vars[genome_build][
'title'] + ")"
gene_plot.yaxis.axis_label = "Genes (Transcripts Collapsed)"
gene_plot.ygrid.grid_line_color = None
gene_plot.yaxis.axis_line_color = None
gene_plot.yaxis.minor_tick_line_color = None
gene_plot.yaxis.major_tick_line_color = None
gene_plot.yaxis.major_label_text_color = None
gene_plot.toolbar_location = "below"
# Change output backend to SVG temporarily for headless export
# Will be changed back to canvas in LDlink.js
matrix_plot.output_backend = "svg"
connector.output_backend = "svg"
rug.output_backend = "svg"
gene_plot.output_backend = "svg"
export_svgs(matrix_plot,
filename=tmp_dir + "matrix_plot_1_" + request + ".svg")
export_svgs(connector,
filename=tmp_dir + "connector_1_" + request + ".svg")
export_svgs(rug, filename=tmp_dir + "rug_1_" + request + ".svg")
export_svgs(gene_plot,
filename=tmp_dir + "gene_plot_1_" + request + ".svg")
# 1 pixel = 0.0264583333 cm
svg_height = str(25.00 + (0.0264583333 * plot_h_pix)) + "cm"
svg_height_scaled = str(110.00 + (0.1322916665 * plot_h_pix)) + "cm"
# Concatenate svgs
sg.Figure(
"21.59cm", svg_height,
sg.SVG(tmp_dir + "matrix_plot_1_" + request + ".svg"),
sg.SVG(tmp_dir + "connector_1_" + request + ".svg").scale(.97).move(
0, 700),
sg.SVG(tmp_dir + "rug_1_" + request + ".svg").scale(.97).move(0, 790),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").scale(.97).move(
0, 840)).save(tmp_dir + "matrix_plot_" + request + ".svg")
sg.Figure(
"107.95cm", svg_height_scaled,
sg.SVG(tmp_dir + "matrix_plot_1_" + request + ".svg").scale(5),
sg.SVG(tmp_dir + "connector_1_" + request + ".svg").scale(4.85).move(
0, 3500),
sg.SVG(tmp_dir + "rug_1_" + request + ".svg").scale(4.85).move(
0, 3930),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").scale(4.85).move(
0, 4160)).save(tmp_dir + "matrix_plot_scaled_" + request + ".svg")
# Export to PDF
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "matrix_plot_" +
request + ".svg " + tmp_dir + "matrix_plot_" + request +
".pdf",
shell=True)
# Export to PNG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir +
"matrix_plot_scaled_" + request + ".svg " + tmp_dir +
"matrix_plot_" + request + ".png",
shell=True)
# Export to JPEG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir +
"matrix_plot_scaled_" + request + ".svg " + tmp_dir +
"matrix_plot_" + request + ".jpeg",
shell=True)
# Remove individual SVG files after they are combined
subprocess.call("rm " + tmp_dir + "matrix_plot_1_" + request + ".svg",
shell=True)
subprocess.call("rm " + tmp_dir + "gene_plot_1_" + request + ".svg",
shell=True)
subprocess.call("rm " + tmp_dir + "rug_1_" + request + ".svg", shell=True)
subprocess.call("rm " + tmp_dir + "connector_1_" + request + ".svg",
shell=True)
# Remove scaled SVG file after it is converted to png and jpeg
subprocess.call("rm " + tmp_dir + "matrix_plot_scaled_" + request + ".svg",
shell=True)
# Remove temporary file(s)
subprocess.call("rm " + tmp_dir + "genes_*" + request + "*.json",
shell=True)
reset_output()
return None
def calculate_matrix_svg(snplst, pop, request, r2_d="r2"):
# Set data directories using config.yml
with open('config.yml', 'r') as f:
config = yaml.load(f)
gene_dir=config['data']['gene_dir']
snp_dir=config['data']['snp_dir']
pop_dir=config['data']['pop_dir']
vcf_dir=config['data']['vcf_dir']
tmp_dir = "./tmp/"
# Ensure tmp directory exists
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
# Open SNP list file
snps_raw = open(snplst).readlines()
# Remove duplicate RS numbers
snps = []
for snp_raw in snps_raw:
snp = snp_raw.strip().split()
if snp not in snps:
snps.append(snp)
# Select desired ancestral populations
pops = pop.split("+")
pop_dirs = []
for pop_i in pops:
if pop_i in ["ALL", "AFR", "AMR", "EAS", "EUR", "SAS", "ACB", "ASW", "BEB", "CDX", "CEU", "CHB", "CHS", "CLM", "ESN", "FIN", "GBR", "GIH", "GWD", "IBS", "ITU", "JPT", "KHV", "LWK", "MSL", "MXL", "PEL", "PJL", "PUR", "STU", "TSI", "YRI"]:
pop_dirs.append(pop_dir + pop_i + ".txt")
get_pops = "cat " + " ".join(pop_dirs)
proc = subprocess.Popen(get_pops, shell=True, stdout=subprocess.PIPE)
pop_list = proc.stdout.readlines()
ids = [i.strip() for i in pop_list]
pop_ids = list(set(ids))
# Connect to snp database
conn = sqlite3.connect(snp_dir)
conn.text_factory = str
cur = conn.cursor()
def get_coords(rs):
id = rs.strip("rs")
t = (id,)
cur.execute("SELECT * FROM tbl_" + id[-1] + " WHERE id=?", t)
return cur.fetchone()
# Find RS numbers in snp database
rs_nums = []
snp_pos = []
snp_coords = []
tabix_coords = ""
for snp_i in snps:
if len(snp_i) > 0:
if len(snp_i[0]) > 2:
if snp_i[0][0:2] == "rs" and snp_i[0][-1].isdigit():
snp_coord = get_coords(snp_i[0])
if snp_coord != None:
rs_nums.append(snp_i[0])
snp_pos.append(snp_coord[2])
temp = [snp_i[0], snp_coord[1], snp_coord[2]]
snp_coords.append(temp)
# Close snp connection
cur.close()
conn.close()
# Check max distance between SNPs
distance_bp = []
for i in range(len(snp_coords)):
distance_bp.append(int(snp_coords[i][2]))
# Sort coordinates and make tabix formatted coordinates
snp_pos_int = [int(i) for i in snp_pos]
snp_pos_int.sort()
snp_coord_str = [snp_coords[0][1] + ":" +
str(i) + "-" + str(i) for i in snp_pos_int]
tabix_coords = " " + " ".join(snp_coord_str)
# Extract 1000 Genomes phased genotypes
vcf_file = vcf_dir + \
snp_coords[0][
1] + ".phase3_shapeit2_mvncall_integrated_v5.20130502.genotypes.vcf.gz"
tabix_snps = "tabix -h {0}{1} | grep -v -e END".format(
vcf_file, tabix_coords)
proc = subprocess.Popen(tabix_snps, shell=True, stdout=subprocess.PIPE)
# Define function to correct indel alleles
def set_alleles(a1, a2):
if len(a1) == 1 and len(a2) == 1:
a1_n = a1
a2_n = a2
elif len(a1) == 1 and len(a2) > 1:
a1_n = "-"
a2_n = a2[1:]
elif len(a1) > 1 and len(a2) == 1:
a1_n = a1[1:]
a2_n = "-"
elif len(a1) > 1 and len(a2) > 1:
a1_n = a1[1:]
a2_n = a2[1:]
return(a1_n, a2_n)
# Import SNP VCF files
vcf = proc.stdout.readlines()
h = 0
while vcf[h][0:2] == "##":
h += 1
head = vcf[h].strip().split()
# Extract haplotypes
index = []
for i in range(9, len(head)):
if head[i] in pop_ids:
index.append(i)
hap1 = [[]]
for i in range(len(index) - 1):
hap1.append([])
hap2 = [[]]
for i in range(len(index) - 1):
hap2.append([])
rsnum_lst = []
allele_lst = []
pos_lst = []
for g in range(h + 1, len(vcf)):
geno = vcf[g].strip().split()
if geno[1] not in snp_pos:
continue
if snp_pos.count(geno[1]) == 1:
rs_query = rs_nums[snp_pos.index(geno[1])]
else:
pos_index = []
for p in range(len(snp_pos)):
if snp_pos[p] == geno[1]:
pos_index.append(p)
for p in pos_index:
if rs_nums[p] not in rsnum_lst:
rs_query = rs_nums[p]
break
if rs_query in rsnum_lst:
continue
rs_1000g = geno[2]
if rs_query == rs_1000g:
rsnum = rs_1000g
else:
count = -2
found = "false"
while count <= 2 and count + g < len(vcf):
geno_next = vcf[g + count].strip().split()
if rs_query == geno_next[2]:
found = "true"
break
count += 1
if found == "false":
indx = [i[0] for i in snps].index(rs_query)
# snps[indx][0] = geno[2]
# rsnum = geno[2]
snps[indx][0]=rs_query
rsnum=rs_query
else:
continue
if "," not in geno[3] and "," not in geno[4]:
a1, a2 = set_alleles(geno[3], geno[4])
for i in range(len(index)):
if geno[index[i]] == "0|0":
hap1[i].append(a1)
hap2[i].append(a1)
elif geno[index[i]] == "0|1":
hap1[i].append(a1)
hap2[i].append(a2)
elif geno[index[i]] == "1|0":
hap1[i].append(a2)
hap2[i].append(a1)
elif geno[index[i]] == "1|1":
hap1[i].append(a2)
hap2[i].append(a2)
elif geno[index[i]] == "0":
hap1[i].append(a1)
hap2[i].append(".")
elif geno[index[i]] == "1":
hap1[i].append(a2)
hap2[i].append(".")
else:
hap1[i].append(".")
hap2[i].append(".")
rsnum_lst.append(rsnum)
position = "chr" + geno[0] + ":" + geno[1] + "-" + geno[1]
pos_lst.append(position)
alleles = a1 + "/" + a2
allele_lst.append(alleles)
# Calculate Pairwise LD Statistics
all_haps = hap1 + hap2
ld_matrix = [[[None for v in range(2)] for i in range(
len(all_haps[0]))] for j in range(len(all_haps[0]))]
for i in range(len(all_haps[0])):
for j in range(i, len(all_haps[0])):
hap = {}
for k in range(len(all_haps)):
# Extract haplotypes
hap_k = all_haps[k][i] + all_haps[k][j]
if hap_k in hap:
hap[hap_k] += 1
else:
hap[hap_k] = 1
# Remove Missing Haplotypes
keys = hap.keys()
for key in keys:
if "." in key:
hap.pop(key, None)
# Check all haplotypes are present
if len(hap) != 4:
snp_i_a = allele_lst[i].split("/")
snp_j_a = allele_lst[j].split("/")
haps = [snp_i_a[0] + snp_j_a[0], snp_i_a[0] + snp_j_a[1],
snp_i_a[1] + snp_j_a[0], snp_i_a[1] + snp_j_a[1]]
for h in haps:
if h not in hap:
hap[h] = 0
# Perform LD calculations
A = hap[sorted(hap)[0]]
B = hap[sorted(hap)[1]]
C = hap[sorted(hap)[2]]
D = hap[sorted(hap)[3]]
tmax = max(A, B, C, D)
delta = float(A * D - B * C)
Ms = float((A + C) * (B + D) * (A + B) * (C + D))
if Ms != 0:
# D prime
if delta < 0:
D_prime = round(
abs(delta / min((A + C) * (A + B), (B + D) * (C + D))), 3)
else:
D_prime = round(
abs(delta / min((A + C) * (C + D), (A + B) * (B + D))), 3)
# R2
r2 = round((delta**2) / Ms, 3)
# Find Correlated Alleles
if r2 > 0.1:
N = A + B + C + D
# Expected Cell Counts
eA = (A + B) * (A + C) / N
eB = (B + A) * (B + D) / N
eC = (C + A) * (C + D) / N
eD = (D + C) * (D + B) / N
# Calculate Deltas
dA = (A - eA)**2
dB = (B - eB)**2
dC = (C - eC)**2
dD = (D - eD)**2
dmax = max(dA, dB, dC, dD)
if dA == dB == dC == dD:
if tmax == dA or tmax == dD:
match = sorted(hap)[0][
0] + "=" + sorted(hap)[0][1] + "," + sorted(hap)[2][0] + "=" + sorted(hap)[1][1]
else:
match = sorted(hap)[0][
0] + "=" + sorted(hap)[1][1] + "," + sorted(hap)[2][0] + "=" + sorted(hap)[0][1]
elif dmax == dA or dmax == dD:
match = sorted(hap)[0][
0] + "=" + sorted(hap)[0][1] + "," + sorted(hap)[2][0] + "=" + sorted(hap)[1][1]
else:
match = sorted(hap)[0][
0] + "=" + sorted(hap)[1][1] + "," + sorted(hap)[2][0] + "=" + sorted(hap)[0][1]
else:
match = " = , = "
else:
D_prime = "NA"
r2 = "NA"
match = " = , = "
snp1 = rsnum_lst[i]
snp2 = rsnum_lst[j]
pos1 = pos_lst[i].split("-")[0]
pos2 = pos_lst[j].split("-")[0]
allele1 = allele_lst[i]
allele2 = allele_lst[j]
corr = match.split(",")[0].split("=")[1] + "=" + match.split(",")[0].split("=")[
0] + "," + match.split(",")[1].split("=")[1] + "=" + match.split(",")[1].split("=")[0]
corr_f = match
ld_matrix[i][j] = [snp1, snp2, allele1,
allele2, corr, pos1, pos2, D_prime, r2]
ld_matrix[j][i] = [snp2, snp1, allele2,
allele1, corr_f, pos2, pos1, D_prime, r2]
# Generate Plot Variables
out = [j for i in ld_matrix for j in i]
xnames = []
ynames = []
xA = []
yA = []
corA = []
xpos = []
ypos = []
D = []
R = []
box_color = []
box_trans = []
if r2_d not in ["r2", "d"]:
r2_d = "r2"
for i in range(len(out)):
snp1, snp2, allele1, allele2, corr, pos1, pos2, D_prime, r2 = out[i]
xnames.append(snp1)
ynames.append(snp2)
xA.append(allele1)
yA.append(allele2)
corA.append(corr)
xpos.append(pos1)
ypos.append(pos2)
if r2_d == "r2" and r2 != "NA":
D.append(str(round(float(D_prime), 4)))
R.append(str(round(float(r2), 4)))
box_color.append("red")
box_trans.append(r2)
elif r2_d == "d" and D_prime != "NA":
D.append(str(round(float(D_prime), 4)))
R.append(str(round(float(r2), 4)))
box_color.append("red")
box_trans.append(abs(D_prime))
else:
D.append("NA")
R.append("NA")
box_color.append("blue")
box_trans.append(0.1)
# Import plotting modules
from collections import OrderedDict
from bokeh.embed import components, file_html
from bokeh.layouts import gridplot
from bokeh.models import HoverTool, LinearAxis, Range1d
from bokeh.plotting import ColumnDataSource, curdoc, figure, output_file, reset_output, save
from bokeh.resources import CDN
from bokeh.io import export_svgs
import svgutils.compose as sg
from math import pi
reset_output()
# Aggregate Plotting Data
x = []
y = []
w = []
h = []
coord_snps_plot = []
snp_id_plot = []
alleles_snp_plot = []
for i in range(0, len(xpos), int(len(xpos)**0.5)):
x.append(int(xpos[i].split(":")[1]) / 1000000.0)
y.append(0.5)
w.append(0.00003)
h.append(1.06)
coord_snps_plot.append(xpos[i])
snp_id_plot.append(xnames[i])
alleles_snp_plot.append(xA[i])
buffer = (x[-1] - x[0]) * 0.025
xr = Range1d(start=x[0] - buffer, end=x[-1] + buffer)
yr = Range1d(start=-0.03, end=1.03)
y2_ll = [-0.03] * len(x)
y2_ul = [1.03] * len(x)
yr_pos = Range1d(start=(x[-1] + buffer) * -1, end=(x[0] - buffer) * -1)
yr0 = Range1d(start=0, end=1)
yr2 = Range1d(start=0, end=3.8)
yr3 = Range1d(start=0, end=1)
spacing = (x[-1] - x[0] + buffer + buffer) / (len(x) * 1.0)
x2 = []
y0 = []
y1 = []
y2 = []
y3 = []
y4 = []
for i in range(len(x)):
x2.append(x[0] - buffer + spacing * (i + 0.5))
y0.append(0)
y1.append(0.20)
y2.append(0.80)
y3.append(1)
y4.append(1.15)
xname_pos = []
for i in x2:
for j in range(len(x2)):
xname_pos.append(i)
data = {
'xname': xnames,
'xname_pos': xname_pos,
'yname': ynames,
'xA': xA,
'yA': yA,
'xpos': xpos,
'ypos': ypos,
'R2': R,
'Dp': D,
'corA': corA,
'box_color': box_color,
'box_trans': box_trans
}
source = ColumnDataSource(data)
threshold = 70
if len(snps) < threshold:
matrix_plot = figure(outline_line_color="white", min_border_top=0, min_border_bottom=2, min_border_left=100, min_border_right=5,
x_range=xr, y_range=list(reversed(rsnum_lst)),
h_symmetry=False, v_symmetry=False, border_fill_color='white', x_axis_type=None, logo=None,
tools="hover,undo,redo,reset,pan,box_zoom,previewsave", title=" ", plot_width=800, plot_height=700)
else:
matrix_plot = figure(outline_line_color="white", min_border_top=0, min_border_bottom=2, min_border_left=100, min_border_right=5,
x_range=xr, y_range=list(reversed(rsnum_lst)),
h_symmetry=False, v_symmetry=False, border_fill_color='white', x_axis_type=None, y_axis_type=None, logo=None,
tools="hover,undo,redo,reset,pan,box_zoom,previewsave", title=" ", plot_width=800, plot_height=700)
matrix_plot.rect(x='xname_pos', y='yname', width=0.95 * spacing, height=0.95, source=source,
color="box_color", alpha="box_trans", line_color=None)
matrix_plot.grid.grid_line_color = None
matrix_plot.axis.axis_line_color = None
matrix_plot.axis.major_tick_line_color = None
if len(snps) < threshold:
matrix_plot.axis.major_label_text_font_size = "8pt"
matrix_plot.xaxis.major_label_orientation = "vertical"
matrix_plot.axis.major_label_text_font_style = "normal"
matrix_plot.xaxis.major_label_standoff = 0
sup_2 = u"\u00B2"
hover = matrix_plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Variant 1", " " + "@yname (@yA)"),
("Variant 2", " " + "@xname (@xA)"),
("D\'", " " + "@Dp"),
("R" + sup_2, " " + "@R2"),
("Correlated Alleles", " " + "@corA"),
])
# Connecting and Rug Plots
# Connector Plot
if len(snps) < threshold:
connector = figure(outline_line_color="white", y_axis_type=None, x_axis_type=None,
x_range=xr, y_range=yr2, border_fill_color='white',
title="", min_border_left=100, min_border_right=5, min_border_top=0, min_border_bottom=0, h_symmetry=False, v_symmetry=False,
plot_width=800, plot_height=90, tools="xpan,tap")
connector.segment(x, y0, x, y1, color="black")
connector.segment(x, y1, x2, y2, color="black")
connector.segment(x2, y2, x2, y3, color="black")
connector.text(x2, y4, text=snp_id_plot, alpha=1, angle=pi / 2,
text_font_size="8pt", text_baseline="middle", text_align="left")
else:
connector = figure(outline_line_color="white", y_axis_type=None, x_axis_type=None,
x_range=xr, y_range=yr3, border_fill_color='white',
title="", min_border_left=100, min_border_right=5, min_border_top=0, min_border_bottom=0, h_symmetry=False, v_symmetry=False,
plot_width=800, plot_height=30, tools="xpan,tap")
connector.segment(x, y0, x, y1, color="black")
connector.segment(x, y1, x2, y2, color="black")
connector.segment(x2, y2, x2, y3, color="black")
connector.yaxis.major_label_text_color = None
connector.yaxis.minor_tick_line_alpha = 0 # Option does not work
connector.yaxis.axis_label = " "
connector.grid.grid_line_color = None
connector.axis.axis_line_color = None
connector.axis.major_tick_line_color = None
connector.axis.minor_tick_line_color = None
connector.toolbar_location = None
data_rug = {
'x': x,
'y': y,
'w': w,
'h': h,
'coord_snps_plot': coord_snps_plot,
'snp_id_plot': snp_id_plot,
'alleles_snp_plot': alleles_snp_plot
}
source_rug = ColumnDataSource(data_rug)
# Rug Plot
rug = figure(x_range=xr, y_range=yr, y_axis_type=None,
title="", min_border_top=1, min_border_bottom=0, min_border_left=100, min_border_right=5, h_symmetry=False, v_symmetry=False,
plot_width=800, plot_height=50, tools="hover,xpan,tap")
rug.rect(x='x', y='y', width='w', height='h', fill_color='red', dilate=True, line_color=None, fill_alpha=0.6, source=source_rug)
hover = rug.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("SNP", "@snp_id_plot (@alleles_snp_plot)"),
("Coord", "@coord_snps_plot"),
])
rug.toolbar_location = None
# Gene Plot
tabix_gene = "tabix -fh {0} {1}:{2}-{3} > {4}".format(gene_dir, snp_coords[1][1], int(
(x[0] - buffer) * 1000000), int((x[-1] + buffer) * 1000000), tmp_dir + "genes_" + request + ".txt")
subprocess.call(tabix_gene, shell=True)
filename = tmp_dir + "genes_" + request + ".txt"
genes_raw = open(filename).readlines()
genes_plot_start = []
genes_plot_end = []
genes_plot_y = []
genes_plot_name = []
exons_plot_x = []
exons_plot_y = []
exons_plot_w = []
exons_plot_h = []
exons_plot_name = []
exons_plot_id = []
exons_plot_exon = []
message = ["Too many genes to plot."]
lines = [0]
gap = 80000
tall = 0.75
if genes_raw != None:
for i in range(len(genes_raw)):
bin, name_id, chrom, strand, txStart, txEnd, cdsStart, cdsEnd, exonCount, exonStarts, exonEnds, score, name2, cdsStartStat, cdsEndStat, exonFrames = genes_raw[
i].strip().split()
name = name2
id = name_id
e_start = exonStarts.split(",")
e_end = exonEnds.split(",")
# Determine Y Coordinate
i = 0
y_coord = None
while y_coord == None:
if i > len(lines) - 1:
y_coord = i + 1
lines.append(int(txEnd))
elif int(txStart) > (gap + lines[i]):
y_coord = i + 1
lines[i] = int(txEnd)
else:
i += 1
genes_plot_start.append(int(txStart) / 1000000.0)
genes_plot_end.append(int(txEnd) / 1000000.0)
genes_plot_y.append(y_coord)
genes_plot_name.append(name + " ")
for i in range(len(e_start) - 1):
if strand == "+":
exon = i + 1
else:
exon = len(e_start) - 1 - i
width = (int(e_end[i]) - int(e_start[i])) / 1000000.0
x_coord = int(e_start[i]) / 1000000.0 + (width / 2)
exons_plot_x.append(x_coord)
exons_plot_y.append(y_coord)
exons_plot_w.append(width)
exons_plot_h.append(tall)
exons_plot_name.append(name)
exons_plot_id.append(id)
exons_plot_exon.append(exon)
n_rows = len(lines)
genes_plot_yn = [n_rows - w + 0.5 for w in genes_plot_y]
exons_plot_yn = [n_rows - w + 0.5 for w in exons_plot_y]
yr2 = Range1d(start=0, end=n_rows)
data_gene_plot = {
'exons_plot_x': exons_plot_x,
'exons_plot_yn': exons_plot_yn,
'exons_plot_w': exons_plot_w,
'exons_plot_h': exons_plot_h,
'exons_plot_name': exons_plot_name,
'exons_plot_id': exons_plot_id,
'exons_plot_exon': exons_plot_exon,
'coord_snps_plot': coord_snps_plot,
'snp_id_plot': snp_id_plot,
'alleles_snp_plot': alleles_snp_plot
}
source_gene_plot = ColumnDataSource(data_gene_plot)
max_genes = 40
if len(lines) < 3 or len(genes_raw) > max_genes:
plot_h_pix = 150
else:
plot_h_pix = 150 + (len(lines) - 2) * 50
gene_plot = figure(min_border_top=2, min_border_bottom=0, min_border_left=100, min_border_right=5,
x_range=xr, y_range=yr2, border_fill_color='white',
title="", h_symmetry=False, v_symmetry=False, logo=None,
plot_width=800, plot_height=plot_h_pix, tools="hover,xpan,box_zoom,wheel_zoom,tap,undo,redo,reset,previewsave")
if len(genes_raw) <= max_genes:
gene_plot.segment(genes_plot_start, genes_plot_yn, genes_plot_end,
genes_plot_yn, color="black", alpha=1, line_width=2)
gene_plot.rect(x='exons_plot_x', y='exons_plot_yn', width='exons_plot_w', height='exons_plot_h',
source=source_gene_plot, fill_color='grey', line_color="grey")
gene_plot.text(genes_plot_start, genes_plot_yn, text=genes_plot_name, alpha=1, text_font_size="7pt",
text_font_style="bold", text_baseline="middle", text_align="right", angle=0)
hover = gene_plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Gene", "@exons_plot_name"),
("ID", "@exons_plot_id"),
("Exon", "@exons_plot_exon"),
])
else:
x_coord_text = x[0] + (x[-1] - x[0]) / 2.0
gene_plot.text(x_coord_text, n_rows / 2.0, text=message, alpha=1,
text_font_size="12pt", text_font_style="bold", text_baseline="middle", text_align="center", angle=0)
gene_plot.xaxis.axis_label = "Chromosome " + \
snp_coords[1][1] + " Coordinate (Mb)(GRCh37)"
gene_plot.yaxis.axis_label = "Genes"
gene_plot.ygrid.grid_line_color = None
gene_plot.yaxis.axis_line_color = None
gene_plot.yaxis.minor_tick_line_color = None
gene_plot.yaxis.major_tick_line_color = None
gene_plot.yaxis.major_label_text_color = None
gene_plot.toolbar_location = "below"
# Change output backend to SVG temporarily for headless export
# Will be changed back to canvas in LDlink.js
matrix_plot.output_backend = "svg"
rug.output_backend = "svg"
gene_plot.output_backend = "svg"
export_svgs(matrix_plot, filename=tmp_dir + "matrix_plot_1_" + request + ".svg")
export_svgs(gene_plot, filename=tmp_dir + "gene_plot_1_" + request + ".svg")
# Concatenate svgs
sg.Figure("21.59cm", "27.94cm",
sg.SVG(tmp_dir + "matrix_plot_1_" + request + ".svg"),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").move(0, 720)
).save(tmp_dir + "matrix_plot_" + request + ".svg")
sg.Figure("107.95cm", "139.70cm",
sg.SVG(tmp_dir + "matrix_plot_1_" + request + ".svg").scale(5),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").scale(5).move(0, 3600)
).save(tmp_dir + "matrix_plot_scaled_" + request + ".svg")
# Export to PDF
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "matrix_plot_" + request + ".svg " + tmp_dir + "matrix_plot_" + request + ".pdf", shell=True)
# Export to PNG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "matrix_plot_scaled_" + request + ".svg " + tmp_dir + "matrix_plot_" + request + ".png", shell=True)
# Export to JPEG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "matrix_plot_scaled_" + request + ".svg " + tmp_dir + "matrix_plot_" + request + ".jpeg", shell=True)
# Remove individual SVG files after they are combined
subprocess.call("rm " + tmp_dir + "matrix_plot_1_" + request + ".svg", shell=True)
subprocess.call("rm " + tmp_dir + "gene_plot_1_" + request + ".svg", shell=True)
# Remove scaled SVG file after it is converted to png and jpeg
subprocess.call("rm " + tmp_dir + "matrix_plot_scaled_" + request + ".svg", shell=True)
reset_output()
return None
def calculate_assoc_svg(file, region, pop, request, myargs, myargsName, myargsOrigin):
# Set data directories using config.yml
with open('config.yml', 'r') as f:
config = yaml.load(f)
gene_dir2 = config['data']['gene_dir2']
vcf_dir = config['data']['vcf_dir']
tmp_dir = "./tmp/"
# Ensure tmp directory exists
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
chrs=["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","X","Y"]
# Define parameters for --variant option
if region=="variant":
if myargsOrigin=="None":
return None
if myargsOrigin!="None":
# Find coordinates (GRCh37/hg19) for SNP RS number
if myargsOrigin[0:2]=="rs":
snp=myargsOrigin
# Connect to Mongo snp database
client = MongoClient('mongodb://'+username+':'+password+'@localhost/admin', port)
db = client["LDLink"]
def get_coords_var(db, rsid):
rsid = rsid.strip("rs")
query_results = db.dbsnp151.find_one({"id": rsid})
query_results_sanitized = json.loads(json_util.dumps(query_results))
return query_results_sanitized
# Find RS number in snp database
var_coord=get_coords_var(db, snp)
if var_coord==None:
return None
elif myargsOrigin.split(":")[0].strip("chr") in chrs and len(myargsOrigin.split(":"))==2:
snp=myargsOrigin
var_coord=[None,myargsOrigin.split(":")[0].strip("chr"),myargsOrigin.split(":")[1]]
else:
return None
chromosome = var_coord['chromosome']
org_coord = var_coord['position']
# Open Association Data
header_list=[]
header_list.append(myargs['chr'])
header_list.append(myargs['bp'])
header_list.append(myargs['pval'])
# Load input file
with open(file) as fp:
header = fp.readline().strip().split()
first = fp.readline().strip().split()
if len(header)!=len(first):
return None
# Check header
for item in header_list:
if item not in header:
return None
len_head=len(header)
chr_index=header.index(myargs['chr'])
pos_index=header.index(myargs['bp'])
p_index=header.index(myargs['pval'])
# Define window of interest around query SNP
if myargs['window']==None:
if region=="variant":
window=500000
elif region=="gene":
window=100000
else:
window=0
else:
window=myargs['window']
if region=="variant":
coord1=int(org_coord)-window
if coord1<0:
coord1=0
coord2=int(org_coord)+window
elif region=="gene":
if myargsName=="None":
return None
# Connect to gene database
conn=sqlite3.connect(gene_dir2)
conn.text_factory=str
cur=conn.cursor()
def get_coords_gene(gene_raw):
gene=gene_raw.upper()
t=(gene,)
cur.execute("SELECT * FROM genes WHERE name=?", t)
return cur.fetchone()
# Find RS number in snp database
gene_coord=get_coords_gene(myargsName)
# Close snp connection
cur.close()
conn.close()
if gene_coord==None:
return None
# Define search coordinates
coord1=int(gene_coord[2])-window
if coord1<0:
coord1=0
coord2=int(gene_coord[3])+window
# Run with --origin option
if myargsOrigin!="None":
if gene_coord[1]!=chromosome:
return None
if coord1>int(org_coord) or int(org_coord)>coord2:
return None
else:
chromosome=gene_coord[1]
elif region=="region":
if myargs['start']==None:
return None
if myargs['end']==None:
return None
# Parse out chr and positions for --region option
if len(myargs['start'].split(":"))!=2:
return None
if len(myargs['end'].split(":"))!=2:
return None
chr_s=myargs['start'].strip("chr").split(":")[0]
coord_s=myargs['start'].split(":")[1]
chr_e=myargs['end'].strip("chr").split(":")[0]
coord_e=myargs['end'].split(":")[1]
if chr_s not in chrs:
return None
if chr_e not in chrs:
return None
if chr_s!=chr_e:
return None
if coord_s>=coord_e:
return None
coord1=int(coord_s)-window
if coord1<0:
coord1=0
coord2=int(coord_e)+window
# Run with --origin option
if myargsOrigin!="None":
if chr_s!=chromosome:
return None
if coord1>int(org_coord) or int(org_coord)>coord2:
return None
else:
chromosome=chr_s
# Generate coordinate list and P-value dictionary
max_window=3000000
if coord2-coord1>max_window:
return None
assoc_coords=[]
a_pos=[]
assoc_dict={}
assoc_list=[]
with open(file) as fp:
for line in fp:
col=line.strip().split()
if len(col)==len_head:
if col[chr_index].strip("chr")==chromosome:
try:
int(col[pos_index])
except ValueError:
continue
else:
if coord1<=int(col[pos_index])<=coord2:
try:
float(col[p_index])
except ValueError:
continue
else:
coord_i=col[chr_index].strip("chr")+":"+col[pos_index]+"-"+col[pos_index]
assoc_coords.append(coord_i)
a_pos.append(col[pos_index])
assoc_dict[coord_i]=[col[p_index]]
assoc_list.append([coord_i,float(col[p_index])])
# Coordinate list checks
if len(assoc_coords)==0:
return None
# Get population ids from population output file from LDassoc.py
pop_list=open(tmp_dir+"pops_"+request+".txt").readlines()
ids=[]
for i in range(len(pop_list)):
ids.append(pop_list[i].strip())
pop_ids=list(set(ids))
# Define LD origin coordinate
try:
org_coord
except NameError:
for var_p in sorted(assoc_list, key=operator.itemgetter(1)):
snp="chr"+var_p[0].split("-")[0]
# Extract lowest P SNP phased genotypes
vcf_file=vcf_dir+chromosome+".phase3_shapeit2_mvncall_integrated_v5.20130502.genotypes.vcf.gz"
tabix_snp_h="tabix -H {0} | grep CHROM".format(vcf_file)
proc_h=subprocess.Popen(tabix_snp_h, shell=True, stdout=subprocess.PIPE)
head=[x.decode('utf-8') for x in proc_h.stdout.readlines()][0].strip().split()
# Check lowest P SNP is in the 1000G population and not monoallelic from LDassoc.py output file
vcf=open(tmp_dir+"snp_no_dups_"+request+".vcf").readlines()
if len(vcf)==0:
continue
elif len(vcf)>1:
geno=vcf[0].strip().split()
else:
geno=vcf[0].strip().split()
if "," in geno[3] or "," in geno[4]:
continue
index=[]
for i in range(9,len(head)):
if head[i] in pop_ids:
index.append(i)
genotypes={"0":0, "1":0}
for i in index:
sub_geno=geno[i].split("|")
for j in sub_geno:
if j in genotypes:
genotypes[j]+=1
else:
genotypes[j]=1
if genotypes["0"]==0 or genotypes["1"]==0:
continue
org_coord=var_p[0].split("-")[1]
break
else:
if chromosome+":"+org_coord+"-"+org_coord not in assoc_coords:
return None
# Extract query SNP phased genotypes
vcf_file=vcf_dir+chromosome+".phase3_shapeit2_mvncall_integrated_v5.20130502.genotypes.vcf.gz"
tabix_snp_h="tabix -H {0} | grep CHROM".format(vcf_file)
proc_h=subprocess.Popen(tabix_snp_h, shell=True, stdout=subprocess.PIPE)
head=[x.decode('utf-8') for x in proc_h.stdout.readlines()][0].strip().split()
tabix_snp="tabix {0} {1}:{2}-{2} | grep -v -e END > {3}".format(vcf_file, chromosome, org_coord, tmp_dir+"snp_no_dups_"+request+".vcf")
subprocess.call(tabix_snp, shell=True)
# Check query SNP is in the 1000G population, has the correct RS number, and not monoallelic
vcf=open(tmp_dir+"snp_no_dups_"+request+".vcf").readlines()
if len(vcf)==0:
subprocess.call("rm "+tmp_dir+"pops_"+request+".txt", shell=True)
subprocess.call("rm "+tmp_dir+"*"+request+"*.vcf", shell=True)
return None
elif len(vcf)>1:
geno=[]
for i in range(len(vcf)):
if vcf[i].strip().split()[2]==snp:
geno=vcf[i].strip().split()
if geno==[]:
subprocess.call("rm "+tmp_dir+"pops_"+request+".txt", shell=True)
subprocess.call("rm "+tmp_dir+"*"+request+"*.vcf", shell=True)
return None
else:
geno=vcf[0].strip().split()
if geno[2]!=snp and snp[0:2]=="rs":
snp=geno[2]
if "," in geno[3] or "," in geno[4]:
subprocess.call("rm "+tmp_dir+"pops_"+request+".txt", shell=True)
subprocess.call("rm "+tmp_dir+"*"+request+"*.vcf", shell=True)
return None
index=[]
for i in range(9,len(head)):
if head[i] in pop_ids:
index.append(i)
genotypes={"0":0, "1":0}
for i in index:
sub_geno=geno[i].split("|")
for j in sub_geno:
if j in genotypes:
genotypes[j]+=1
else:
genotypes[j]=1
if genotypes["0"]==0 or genotypes["1"]==0:
subprocess.call("rm "+tmp_dir+"pops_"+request+".txt", shell=True)
subprocess.call("rm "+tmp_dir+"*"+request+"*.vcf", shell=True)
return None
# Calculate proxy LD statistics in parallel
if len(assoc_coords)<60:
threads=1
else:
threads=4
block=len(assoc_coords)/threads
commands=[]
for i in range(threads):
if i==min(range(threads)) and i==max(range(threads)):
command="python LDassoc_sub.py "+snp+" "+chromosome+" "+"_".join(assoc_coords)+" "+request+" "+str(i)
elif i==min(range(threads)):
command="python LDassoc_sub.py "+snp+" "+chromosome+" "+"_".join(assoc_coords[:block])+" "+request+" "+str(i)
elif i==max(range(threads)):
command="python LDassoc_sub.py "+snp+" "+chromosome+" "+"_".join(assoc_coords[(block*i)+1:])+" "+request+" "+str(i)
else:
command="python LDassoc_sub.py "+snp+" "+chromosome+" "+"_".join(assoc_coords[(block*i)+1:block*(i+1)])+" "+request+" "+str(i)
commands.append(command)
processes=[subprocess.Popen(command, shell=True, stdout=subprocess.PIPE) for command in commands]
# collect output in parallel
def get_output(process):
return process.communicate()[0].splitlines()
pool = Pool(len(processes))
out_raw=pool.map(get_output, processes)
pool.close()
pool.join()
# Aggregate output
out_prox=[]
for i in range(len(out_raw)):
for j in range(len(out_raw[i])):
col=out_raw[i][j].decode('utf-8').strip().split("\t")
col[6]=int(col[6])
col[7]=float(col[7])
col[8]=float(col[8])
col.append(abs(int(col[6])))
pos_i_j=col[5].split(":")[1]
coord_i_j=chromosome+":"+pos_i_j+"-"+pos_i_j
if coord_i_j in assoc_dict:
col.append(float(assoc_dict[coord_i_j][0]))
out_prox.append(col)
out_dist_sort=sorted(out_prox, key=operator.itemgetter(14))
out_p_sort=sorted(out_dist_sort, key=operator.itemgetter(15), reverse=False)
# Organize scatter plot data
q_rs=[]
q_allele=[]
q_coord=[]
q_maf=[]
p_rs=[]
p_allele=[]
p_coord=[]
p_pos=[]
p_maf=[]
dist=[]
d_prime=[]
d_prime_round=[]
r2=[]
r2_round=[]
corr_alleles=[]
regdb=[]
funct=[]
color=[]
alpha=[]
size=[]
p_val=[]
neg_log_p=[]
for i in range(len(out_p_sort)):
q_rs_i,q_allele_i,q_coord_i,p_rs_i,p_allele_i,p_coord_i,dist_i,d_prime_i,r2_i,corr_alleles_i,regdb_i,q_maf_i,p_maf_i,funct_i,dist_abs,p_val_i=out_p_sort[i]
q_rs.append(q_rs_i)
q_allele.append(q_allele_i)
q_coord.append(float(q_coord_i.split(":")[1])/1000000)
q_maf.append(str(round(float(q_maf_i),4)))
if p_rs_i==".":
p_rs_i=p_coord_i
p_rs.append(p_rs_i)
p_allele.append(p_allele_i)
p_coord.append(float(p_coord_i.split(":")[1])/1000000)
p_pos.append(p_coord_i.split(":")[1])
p_maf.append(str(round(float(p_maf_i),4)))
dist.append(str(round(dist_i/1000000.0,4)))
d_prime.append(float(d_prime_i))
d_prime_round.append(str(round(float(d_prime_i),4)))
r2.append(float(r2_i))
r2_round.append(str(round(float(r2_i),4)))
corr_alleles.append(corr_alleles_i)
# P-value
p_val.append(p_val_i)
neg_log_p.append(-log10(p_val_i))
# Correct Missing Annotations
if regdb_i==".":
regdb_i=""
regdb.append(regdb_i)
if funct_i==".":
funct_i=""
if funct_i=="NA":
funct_i="none"
funct.append(funct_i)
# Set Color
reds=["#FFCCCC","#FFCACA","#FFC8C8","#FFC6C6","#FFC4C4","#FFC2C2","#FFC0C0","#FFBEBE","#FFBCBC","#FFBABA","#FFB8B8","#FFB6B6","#FFB4B4","#FFB1B1","#FFAFAF","#FFADAD","#FFABAB","#FFA9A9","#FFA7A7","#FFA5A5","#FFA3A3","#FFA1A1","#FF9F9F","#FF9D9D","#FF9B9B","#FF9999","#FF9797","#FF9595","#FF9393","#FF9191","#FF8F8F","#FF8D8D","#FF8B8B","#FF8989","#FF8787","#FF8585","#FF8383","#FF8181","#FF7E7E","#FF7C7C","#FF7A7A","#FF7878","#FF7676","#FF7474","#FF7272","#FF7070","#FF6E6E","#FF6C6C","#FF6A6A","#FF6868","#FF6666","#FF6464","#FF6262","#FF6060","#FF5E5E","#FF5C5C","#FF5A5A","#FF5858","#FF5656","#FF5454","#FF5252","#FF5050","#FF4E4E","#FF4B4B","#FF4949","#FF4747","#FF4545","#FF4343","#FF4141","#FF3F3F","#FF3D3D","#FF3B3B","#FF3939","#FF3737","#FF3535","#FF3333","#FF3131","#FF2F2F","#FF2D2D","#FF2B2B","#FF2929","#FF2727","#FF2525","#FF2323","#FF2121","#FF1F1F","#FF1D1D","#FF1B1B","#FF1818","#FF1616","#FF1414","#FF1212","#FF1010","#FF0E0E","#FF0C0C","#FF0A0A","#FF0808","#FF0606","#FF0404","#FF0202","#FF0000"]
if q_coord_i==p_coord_i:
color_i="#0000FF"
alpha_i=0.7
else:
if myargs['dprime']==True:
color_i=reds[int(d_prime_i*100.0)]
alpha_i=0.7
elif myargs['dprime']==False:
color_i=reds[int(r2_i*100.0)]
alpha_i=0.7
color.append(color_i)
alpha.append(alpha_i)
# Set Size
size_i=9+float(p_maf_i)*14.0
size.append(size_i)
# Pull out SNPs from association file not found in 1000G
p_plot_pos=[]
p_plot_pval=[]
p_plot_pos2=[]
p_plot_pval2=[]
p_plot_dist=[]
index_var_pos=float(q_coord_i.split(":")[1])/1000000
for input_pos in a_pos:
if input_pos not in p_pos:
p_plot_pos.append(float(input_pos)/1000000)
p_plot_pval.append(-log10(float(assoc_dict[chromosome+":"+input_pos+"-"+input_pos][0])))
p_plot_pos2.append("chr"+chromosome+":"+input_pos)
p_plot_pval2.append(float(assoc_dict[chromosome+":"+input_pos+"-"+input_pos][0]))
p_plot_dist.append(str(round(float(input_pos)/1000000-index_var_pos,4)))
# Begin Bokeh Plotting
from collections import OrderedDict
from bokeh.embed import components,file_html
from bokeh.layouts import gridplot
from bokeh.models import HoverTool,LinearAxis,Range1d
from bokeh.plotting import ColumnDataSource,curdoc,figure,output_file,reset_output,save
from bokeh.resources import CDN
from bokeh.io import export_svgs
import svgutils.compose as sg
reset_output()
data_p = {'p_plot_posX': p_plot_pos, 'p_plot_pvalY': p_plot_pval, 'p_plot_pos2': p_plot_pos2, 'p_plot_pval2': p_plot_pval2, 'p_plot_dist': p_plot_dist}
source_p = ColumnDataSource(data_p)
# Assoc Plot
x=p_coord
y=neg_log_p
data = {'x': x, 'y': y, 'qrs': q_rs, 'q_alle': q_allele, 'q_maf': q_maf, 'prs': p_rs, 'p_alle': p_allele, 'p_maf': p_maf, 'dist': dist, 'r': r2_round, 'd': d_prime_round, 'alleles': corr_alleles, 'regdb': regdb, 'funct': funct, 'p_val': p_val, 'size': size, 'color': color, 'alpha': alpha}
source = ColumnDataSource(data)
whitespace=0.01
xr=Range1d(start=coord1/1000000.0-whitespace, end=coord2/1000000.0+whitespace)
yr=Range1d(start=-0.03, end=max(y)*1.03)
sup_2="\u00B2"
assoc_plot=figure(
title="P-values and Regional LD for "+snp+" in "+pop,
min_border_top=2, min_border_bottom=2, min_border_left=60, min_border_right=60, h_symmetry=False, v_symmetry=False,
plot_width=900,
plot_height=600,
x_range=xr, y_range=yr,
tools="tap,pan,box_zoom,wheel_zoom,box_select,undo,redo,reset,previewsave", logo=None,
toolbar_location="above")
assoc_plot.title.align="center"
# Add recombination rate from LDassoc.py output file
filename=tmp_dir+"recomb_"+request+".txt"
recomb_raw=open(filename).readlines()
recomb_x=[]
recomb_y=[]
for i in range(len(recomb_raw)):
chr,pos,rate=recomb_raw[i].strip().split()
recomb_x.append(int(pos)/1000000.0)
recomb_y.append(float(rate)/100*max(y))
assoc_plot.line(recomb_x, recomb_y, line_width=1, color="black", alpha=0.5)
# Add genome-wide significance
a = [coord1/1000000.0-whitespace,coord2/1000000.0+whitespace]
b = [-log10(0.00000005),-log10(0.00000005)]
assoc_plot.line(a, b, color="blue", alpha=0.5)
assoc_points_not1000G=assoc_plot.circle(x='p_plot_posX', y='p_plot_pvalY', size=9+float("0.25")*14.0, source=source_p, line_color="gray", fill_color="white")
assoc_points=assoc_plot.circle(x='x', y='y', size='size', color='color', alpha='alpha', source=source)
assoc_plot.add_tools(HoverTool(renderers=[assoc_points_not1000G], tooltips=OrderedDict([("Variant", "@p_plot_pos2"), ("P-value", "@p_plot_pval2"), ("Distance (Mb)", "@p_plot_dist")])))
hover=HoverTool(renderers=[assoc_points])
hover.tooltips=OrderedDict([
("Variant", "@prs @p_alle"),
("P-value", "@p_val"),
("Distance (Mb)", "@dist"),
("MAF", "@p_maf"),
("R"+sup_2+" ("+q_rs[0]+")", "@r"),
("D\' ("+q_rs[0]+")", "@d"),
("Correlated Alleles", "@alleles"),
("RegulomeDB", "@regdb"),
("Functional Class", "@funct"),
])
assoc_plot.add_tools(hover)
# Annotate RebulomeDB scores
if myargs['annotate']==True:
assoc_plot.text(x, y, text=regdb, alpha=1, text_font_size="7pt", text_baseline="middle", text_align="center", angle=0)
assoc_plot.yaxis.axis_label="-log10 P-value"
assoc_plot.extra_y_ranges = {"y2_axis": Range1d(start=-3, end=103)}
assoc_plot.add_layout(LinearAxis(y_range_name="y2_axis", axis_label="Combined Recombination Rate (cM/Mb)"), "right") ## Need to confirm units
# Rug Plot
y2_ll=[-0.03]*len(x)
y2_ul=[1.03]*len(x)
yr_rug=Range1d(start=-0.03, end=1.03)
data_rug = {'x': x, 'y': y, 'y2_ll': y2_ll, 'y2_ul': y2_ul,'qrs': q_rs, 'q_alle': q_allele, 'q_maf': q_maf, 'prs': p_rs, 'p_alle': p_allele, 'p_maf': p_maf, 'dist': dist, 'r': r2_round, 'd': d_prime_round, 'alleles': corr_alleles, 'regdb': regdb, 'funct': funct, 'p_val': p_val, 'size': size, 'color': color, 'alpha': alpha}
source_rug = ColumnDataSource(data_rug)
rug=figure(
x_range=xr, y_range=yr_rug, border_fill_color='white', y_axis_type=None,
title="", min_border_top=2, min_border_bottom=2, min_border_left=60, min_border_right=60, h_symmetry=False, v_symmetry=False,
plot_width=900, plot_height=50, tools="xpan,tap,wheel_zoom", logo=None)
rug.segment(x0='x', y0='y2_ll', x1='x', y1='y2_ul', source=source_rug, color='color', alpha='alpha', line_width=1)
rug.toolbar_location=None
# Gene Plot (All Transcripts)
if myargs['transcript']==True:
# Get genes from LDassoc.py output file
filename=tmp_dir+"genes_"+request+".txt"
genes_raw=open(filename).readlines()
genes_plot_start=[]
genes_plot_end=[]
genes_plot_y=[]
genes_plot_name=[]
exons_plot_x=[]
exons_plot_y=[]
exons_plot_w=[]
exons_plot_h=[]
exons_plot_name=[]
exons_plot_id=[]
exons_plot_exon=[]
message = ["Too many genes to plot."]
lines=[0]
gap=80000
tall=0.75
if genes_raw!=None:
for i in range(len(genes_raw)):
bin,name_id,chrom,strand,txStart,txEnd,cdsStart,cdsEnd,exonCount,exonStarts,exonEnds,score,name2,cdsStartStat,cdsEndStat,exonFrames=genes_raw[i].strip().split()
name=name2
id=name_id
e_start=exonStarts.split(",")
e_end=exonEnds.split(",")
# Determine Y Coordinate
i=0
y_coord=None
while y_coord==None:
if i>len(lines)-1:
y_coord=i+1
lines.append(int(txEnd))
elif int(txStart)>(gap+lines[i]):
y_coord=i+1
lines[i]=int(txEnd)
else:
i+=1
genes_plot_start.append(int(txStart)/1000000.0)
genes_plot_end.append(int(txEnd)/1000000.0)
genes_plot_y.append(y_coord)
genes_plot_name.append(name+" ")
for i in range(len(e_start)-1):
if strand=="+":
exon=i+1
else:
exon=len(e_start)-1-i
width=(int(e_end[i])-int(e_start[i]))/1000000.0
x_coord=int(e_start[i])/1000000.0+(width/2)
exons_plot_x.append(x_coord)
exons_plot_y.append(y_coord)
exons_plot_w.append(width)
exons_plot_h.append(tall)
exons_plot_name.append(name)
exons_plot_id.append(id)
exons_plot_exon.append(exon)
n_rows=len(lines)
genes_plot_yn=[n_rows-x+0.5 for x in genes_plot_y]
exons_plot_yn=[n_rows-x+0.5 for x in exons_plot_y]
yr2=Range1d(start=0, end=n_rows)
data_gene_plot = {'exons_plot_x': exons_plot_x, 'exons_plot_yn': exons_plot_yn, 'exons_plot_w': exons_plot_w, 'exons_plot_h': exons_plot_h,'exons_plot_name': exons_plot_name, 'exons_plot_id': exons_plot_id, 'exons_plot_exon': exons_plot_exon}
source_gene_plot=ColumnDataSource(data_gene_plot)
max_genes = 40
# if len(lines) < 3 or len(genes_raw) > max_genes:
if len(lines) < 3:
plot_h_pix = 150
else:
plot_h_pix = 150 + (len(lines) - 2) * 50
gene_plot = figure(min_border_top=2, min_border_bottom=0, min_border_left=100, min_border_right=5,
x_range=xr, y_range=yr2, border_fill_color='white',
title="", h_symmetry=False, v_symmetry=False, logo=None,
plot_width=900, plot_height=plot_h_pix, tools="hover,xpan,box_zoom,wheel_zoom,tap,undo,redo,reset,previewsave")
# if len(genes_raw) <= max_genes:
gene_plot.segment(genes_plot_start, genes_plot_yn, genes_plot_end,
genes_plot_yn, color="black", alpha=1, line_width=2)
gene_plot.rect(x='exons_plot_x', y='exons_plot_yn', width='exons_plot_w', height='exons_plot_h',
source=source_gene_plot, fill_color="grey", line_color="grey")
gene_plot.text(genes_plot_start, genes_plot_yn, text=genes_plot_name, alpha=1, text_font_size="7pt",
text_font_style="bold", text_baseline="middle", text_align="right", angle=0)
hover = gene_plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Gene", "@exons_plot_name"),
("Transcript ID", "@exons_plot_id"),
("Exon", "@exons_plot_exon"),
])
# else:
# x_coord_text = coord1/1000000.0 + (coord2/1000000.0 - coord1/1000000.0) / 2.0
# gene_plot.text(x_coord_text, n_rows / 2.0, text=message, alpha=1,
# text_font_size="12pt", text_font_style="bold", text_baseline="middle", text_align="center", angle=0)
gene_plot.xaxis.axis_label = "Chromosome " + chromosome + " Coordinate (Mb)(GRCh37)"
gene_plot.yaxis.axis_label = "Genes (All Transcripts)"
gene_plot.ygrid.grid_line_color = None
gene_plot.yaxis.axis_line_color = None
gene_plot.yaxis.minor_tick_line_color = None
gene_plot.yaxis.major_tick_line_color = None
gene_plot.yaxis.major_label_text_color = None
gene_plot.toolbar_location = "below"
# Change output backend to SVG temporarily for headless export
assoc_plot.output_backend = "svg"
rug.output_backend = "svg"
gene_plot.output_backend = "svg"
export_svgs(assoc_plot, filename=tmp_dir + "assoc_plot_1_" + request + ".svg")
export_svgs(gene_plot, filename=tmp_dir + "gene_plot_1_" + request + ".svg")
# 1 pixel = 0.0264583333 cm
svg_height = str(20.00 + (0.0264583333 * plot_h_pix)) + "cm"
svg_height_scaled = str(100.00 + (0.1322916665 * plot_h_pix)) + "cm"
# Concatenate svgs
sg.Figure("24.59cm", svg_height,
sg.SVG(tmp_dir + "assoc_plot_1_" + request + ".svg"),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").move(-40, 630)
).save(tmp_dir + "assoc_plot_" + request + ".svg")
sg.Figure("122.95cm", svg_height_scaled,
sg.SVG(tmp_dir + "assoc_plot_1_" + request + ".svg").scale(5),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").scale(5).move(-200, 3150)
).save(tmp_dir + "assoc_plot_scaled_" + request + ".svg")
# Export to PDF
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "assoc_plot_" + request + ".svg " + tmp_dir + "assoc_plot_" + request + ".pdf", shell=True)
# Export to PNG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "assoc_plot_scaled_" + request + ".svg " + tmp_dir + "assoc_plot_" + request + ".png", shell=True)
# Export to JPEG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "assoc_plot_scaled_" + request + ".svg " + tmp_dir + "assoc_plot_" + request + ".jpeg", shell=True)
# Remove individual SVG files after they are combined
subprocess.call("rm " + tmp_dir + "assoc_plot_1_" + request + ".svg", shell=True)
subprocess.call("rm " + tmp_dir + "gene_plot_1_" + request + ".svg", shell=True)
# Remove scaled SVG file after it is converted to png and jpeg
subprocess.call("rm " + tmp_dir + "assoc_plot_scaled_" + request + ".svg", shell=True)
# Gene Plot (Collapsed)
else:
# Get genes from LDassoc.py output file
filename_c=tmp_dir+"genes_c_"+request+".txt"
genes_c_raw=open(filename_c).readlines()
genes_c_plot_start=[]
genes_c_plot_end=[]
genes_c_plot_y=[]
genes_c_plot_name=[]
exons_c_plot_x=[]
exons_c_plot_y=[]
exons_c_plot_w=[]
exons_c_plot_h=[]
exons_c_plot_name=[]
exons_c_plot_id=[]
message_c = ["Too many genes to plot."]
lines_c=[0]
gap=80000
tall=0.75
if genes_c_raw!=None:
for i in range(len(genes_c_raw)):
chrom,txStart,txEnd,name,exonStarts,exonEnds,transcripts=genes_c_raw[i].strip().split()
e_start=exonStarts.split(",")
e_end=exonEnds.split(",")
e_transcripts=transcripts.split(",")
# Determine Y Coordinate
i=0
y_coord=None
while y_coord==None:
if i>len(lines_c)-1:
y_coord=i+1
lines_c.append(int(txEnd))
elif int(txStart)>(gap+lines_c[i]):
y_coord=i+1
lines_c[i]=int(txEnd)
else:
i+=1
genes_c_plot_start.append(int(txStart)/1000000.0)
genes_c_plot_end.append(int(txEnd)/1000000.0)
genes_c_plot_y.append(y_coord)
genes_c_plot_name.append(name+" ")
for i in range(len(e_start)):
width=(int(e_end[i])-int(e_start[i]))/1000000.0
x_coord=int(e_start[i])/1000000.0+(width/2)
exons_c_plot_x.append(x_coord)
exons_c_plot_y.append(y_coord)
exons_c_plot_w.append(width)
exons_c_plot_h.append(tall)
exons_c_plot_name.append(name)
exons_c_plot_id.append(e_transcripts[i].replace("-",","))
n_rows_c=len(lines_c)
genes_c_plot_yn=[n_rows_c-x+0.5 for x in genes_c_plot_y]
exons_c_plot_yn=[n_rows_c-x+0.5 for x in exons_c_plot_y]
yr2_c=Range1d(start=0, end=n_rows_c)
data_gene_c_plot = {'exons_c_plot_x': exons_c_plot_x, 'exons_c_plot_yn': exons_c_plot_yn, 'exons_c_plot_w': exons_c_plot_w, 'exons_c_plot_h': exons_c_plot_h, 'exons_c_plot_name': exons_c_plot_name, 'exons_c_plot_id': exons_c_plot_id}
source_gene_c_plot=ColumnDataSource(data_gene_c_plot)
max_genes_c = 40
# if len(lines_c) < 3 or len(genes_c_raw) > max_genes_c:
if len(lines_c) < 3:
plot_c_h_pix = 150
else:
plot_c_h_pix = 150 + (len(lines_c) - 2) * 50
gene_c_plot = figure(min_border_top=2, min_border_bottom=0, min_border_left=100, min_border_right=5,
x_range=xr, y_range=yr2_c, border_fill_color='white',
title="", h_symmetry=False, v_symmetry=False, logo=None,
plot_width=900, plot_height=plot_c_h_pix, tools="hover,xpan,box_zoom,wheel_zoom,tap,undo,redo,reset,previewsave")
# if len(genes_c_raw) <= max_genes_c:
gene_c_plot.segment(genes_c_plot_start, genes_c_plot_yn, genes_c_plot_end,
genes_c_plot_yn, color="black", alpha=1, line_width=2)
gene_c_plot.rect(x='exons_c_plot_x', y='exons_c_plot_yn', width='exons_c_plot_w', height='exons_c_plot_h',
source=source_gene_c_plot, fill_color="grey", line_color="grey")
gene_c_plot.text(genes_c_plot_start, genes_c_plot_yn, text=genes_c_plot_name, alpha=1, text_font_size="7pt",
text_font_style="bold", text_baseline="middle", text_align="right", angle=0)
hover = gene_c_plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Gene", "@exons_c_plot_name"),
("Transcript IDs", "@exons_c_plot_id"),
])
# else:
# x_coord_text = coord1/1000000.0 + (coord2/1000000.0 - coord1/1000000.0) / 2.0
# gene_c_plot.text(x_coord_text, n_rows_c / 2.0, text=message_c, alpha=1,
# text_font_size="12pt", text_font_style="bold", text_baseline="middle", text_align="center", angle=0)
gene_c_plot.xaxis.axis_label = "Chromosome " + chromosome + " Coordinate (Mb)(GRCh37)"
gene_c_plot.yaxis.axis_label = "Genes (Transcripts Collapsed)"
gene_c_plot.ygrid.grid_line_color = None
gene_c_plot.yaxis.axis_line_color = None
gene_c_plot.yaxis.minor_tick_line_color = None
gene_c_plot.yaxis.major_tick_line_color = None
gene_c_plot.yaxis.major_label_text_color = None
gene_c_plot.toolbar_location = "below"
# Change output backend to SVG temporarily for headless export
assoc_plot.output_backend = "svg"
rug.output_backend = "svg"
gene_c_plot.output_backend = "svg"
export_svgs(assoc_plot, filename=tmp_dir + "assoc_plot_1_" + request + ".svg")
export_svgs(gene_c_plot, filename=tmp_dir + "gene_plot_1_" + request + ".svg")
# 1 pixel = 0.0264583333 cm
svg_height = str(20.00 + (0.0264583333 * plot_c_h_pix)) + "cm"
svg_height_scaled = str(100.00 + (0.1322916665 * plot_c_h_pix)) + "cm"
# Concatenate svgs
sg.Figure("24.59cm", svg_height,
sg.SVG(tmp_dir + "assoc_plot_1_" + request + ".svg"),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").move(-40, 630)
).save(tmp_dir + "assoc_plot_" + request + ".svg")
sg.Figure("122.95cm", svg_height_scaled,
sg.SVG(tmp_dir + "assoc_plot_1_" + request + ".svg").scale(5),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").scale(5).move(-200, 3150)
).save(tmp_dir + "assoc_plot_scaled_" + request + ".svg")
# Export to PDF
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "assoc_plot_" + request + ".svg " + tmp_dir + "assoc_plot_" + request + ".pdf", shell=True)
# Export to PNG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "assoc_plot_scaled_" + request + ".svg " + tmp_dir + "assoc_plot_" + request + ".png", shell=True)
# Export to JPEG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "assoc_plot_scaled_" + request + ".svg " + tmp_dir + "assoc_plot_" + request + ".jpeg", shell=True)
# Remove individual SVG files after they are combined
subprocess.call("rm " + tmp_dir + "assoc_plot_1_" + request + ".svg", shell=True)
subprocess.call("rm " + tmp_dir + "gene_plot_1_" + request + ".svg", shell=True)
# Remove scaled SVG file after it is converted to png and jpeg
subprocess.call("rm " + tmp_dir + "assoc_plot_scaled_" + request + ".svg", shell=True)
reset_output()
# Remove temporary files
subprocess.call("rm "+tmp_dir+"pops_"+request+".txt", shell=True)
subprocess.call("rm "+tmp_dir+"*"+request+"*.vcf", shell=True)
subprocess.call("rm "+tmp_dir+"genes_*"+request+"*.txt", shell=True)
subprocess.call("rm "+tmp_dir+"recomb_"+request+".txt", shell=True)
subprocess.call("rm "+tmp_dir+"assoc_args"+request+".json", shell=True)
print("Bokeh high quality image export complete!")
# Return plot output
return None
def calculate_proxy_svg(snp, pop, request, r2_d="r2"):
# Set data directories using config.yml
with open('config.yml', 'r') as f:
config = yaml.load(f)
vcf_dir = config['data']['vcf_dir']
tmp_dir = "./tmp/"
# Ensure tmp directory exists
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
if request is False:
request = str(time.strftime("%I%M%S"))
# Create JSON output
# Find coordinates (GRCh37/hg19) for SNP RS number
# Connect to Mongo snp database
client = MongoClient(
'mongodb://' + username + ':' + password + '@localhost/admin', port)
db = client["LDLink"]
def get_coords(db, rsid):
rsid = rsid.strip("rs")
query_results = db.dbsnp151.find_one({"id": rsid})
query_results_sanitized = json.loads(json_util.dumps(query_results))
return query_results_sanitized
# Query genomic coordinates
def get_rsnum(db, coord):
temp_coord = coord.strip("chr").split(":")
chro = temp_coord[0]
pos = temp_coord[1]
query_results = db.dbsnp151.find({
"chromosome":
chro.upper() if chro == 'x' or chro == 'y' else chro,
"position":
pos
})
query_results_sanitized = json.loads(json_util.dumps(query_results))
return query_results_sanitized
# Replace input genomic coordinates with variant ids (rsids)
def replace_coord_rsid(db, snp):
if snp[0:2] == "rs":
return snp
else:
snp_info_lst = get_rsnum(db, snp)
print "snp_info_lst"
print snp_info_lst
if snp_info_lst != None:
if len(snp_info_lst) > 1:
var_id = "rs" + snp_info_lst[0]['id']
ref_variants = []
for snp_info in snp_info_lst:
if snp_info['id'] == snp_info['ref_id']:
ref_variants.append(snp_info['id'])
if len(ref_variants) > 1:
var_id = "rs" + ref_variants[0]
elif len(ref_variants) == 0 and len(snp_info_lst) > 1:
var_id = "rs" + snp_info_lst[0]['id']
else:
var_id = "rs" + ref_variants[0]
return var_id
elif len(snp_info_lst) == 1:
var_id = "rs" + snp_info_lst[0]['id']
return var_id
else:
return snp
else:
return snp
return snp
snp = replace_coord_rsid(db, snp)
# Find RS number in snp database
snp_coord = get_coords(db, snp)
# Get population ids from LDproxy.py tmp output files
pop_list = open(tmp_dir + "pops_" + request + ".txt").readlines()
ids = []
for i in range(len(pop_list)):
ids.append(pop_list[i].strip())
pop_ids = list(set(ids))
# Extract query SNP phased genotypes
vcf_file = vcf_dir + \
snp_coord['chromosome'] + ".phase3_shapeit2_mvncall_integrated_v5.20130502.genotypes.vcf.gz"
tabix_snp_h = "tabix -H {0} | grep CHROM".format(vcf_file)
proc_h = subprocess.Popen(tabix_snp_h, shell=True, stdout=subprocess.PIPE)
head = proc_h.stdout.readlines()[0].strip().split()
tabix_snp = "tabix {0} {1}:{2}-{2} | grep -v -e END > {3}".format(
vcf_file, snp_coord['chromosome'], snp_coord['position'],
tmp_dir + "snp_no_dups_" + request + ".vcf")
subprocess.call(tabix_snp, shell=True)
# Check SNP is in the 1000G population, has the correct RS number, and not
# monoallelic
vcf = open(tmp_dir + "snp_no_dups_" + request + ".vcf").readlines()
if len(vcf) == 0:
subprocess.call("rm " + tmp_dir + "pops_" + request + ".txt",
shell=True)
subprocess.call("rm " + tmp_dir + "*" + request + "*.vcf", shell=True)
return None
elif len(vcf) > 1:
geno = []
for i in range(len(vcf)):
if vcf[i].strip().split()[2] == snp:
geno = vcf[i].strip().split()
if geno == []:
subprocess.call("rm " + tmp_dir + "pops_" + request + ".txt",
shell=True)
subprocess.call("rm " + tmp_dir + "*" + request + "*.vcf",
shell=True)
return None
else:
geno = vcf[0].strip().split()
if geno[2] != snp:
snp = geno[2]
if "," in geno[3] or "," in geno[4]:
subprocess.call("rm " + tmp_dir + "pops_" + request + ".txt",
shell=True)
subprocess.call("rm " + tmp_dir + "*" + request + "*.vcf", shell=True)
return None
index = []
for i in range(9, len(head)):
if head[i] in pop_ids:
index.append(i)
genotypes = {"0": 0, "1": 0}
for i in index:
sub_geno = geno[i].split("|")
for j in sub_geno:
if j in genotypes:
genotypes[j] += 1
else:
genotypes[j] = 1
if genotypes["0"] == 0 or genotypes["1"] == 0:
subprocess.call("rm " + tmp_dir + "pops_" + request + ".txt",
shell=True)
subprocess.call("rm " + tmp_dir + "*" + request + "*.vcf", shell=True)
return None
# Define window of interest around query SNP
window = 500000
coord1 = int(snp_coord['position']) - window
if coord1 < 0:
coord1 = 0
coord2 = int(snp_coord['position']) + window
# Calculate proxy LD statistics in parallel
threads = 4
block = (2 * window) / 4
commands = []
for i in range(threads):
if i == min(range(threads)) and i == max(range(threads)):
command = "python LDproxy_sub.py " + "True " + snp + " " + \
snp_coord['chromosome'] + " " + str(coord1) + " " + \
str(coord2) + " " + request + " " + str(i)
elif i == min(range(threads)):
command = "python LDproxy_sub.py " + "True " + snp + " " + \
snp_coord['chromosome'] + " " + str(coord1) + " " + \
str(coord1 + block) + " " + request + " " + str(i)
elif i == max(range(threads)):
command = "python LDproxy_sub.py " + "True " + snp + " " + snp_coord[
'chromosome'] + " " + str(
coord1 + (block * i) +
1) + " " + str(coord2) + " " + request + " " + str(i)
else:
command = "python LDproxy_sub.py " + "True " + snp + " " + snp_coord[
'chromosome'] + " " + str(
coord1 + (block * i) +
1) + " " + str(coord1 +
(block *
(i + 1))) + " " + request + " " + str(i)
commands.append(command)
processes = [
subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)
for command in commands
]
# collect output in parallel
def get_output(process):
return process.communicate()[0].splitlines()
if not hasattr(threading.current_thread(), "_children"):
threading.current_thread()._children = weakref.WeakKeyDictionary()
pool = Pool(len(processes))
out_raw = pool.map(get_output, processes)
pool.close()
pool.join()
# Aggregate output
out_prox = []
for i in range(len(out_raw)):
for j in range(len(out_raw[i])):
col = out_raw[i][j].strip().split("\t")
col[6] = int(col[6])
col[7] = float(col[7])
col[8] = float(col[8])
col.append(abs(int(col[6])))
out_prox.append(col)
# Sort output
if r2_d not in ["r2", "d"]:
r2_d = "r2"
out_dist_sort = sorted(out_prox, key=operator.itemgetter(14))
if r2_d == "r2":
out_ld_sort = sorted(out_dist_sort,
key=operator.itemgetter(8),
reverse=True)
else:
out_ld_sort = sorted(out_dist_sort,
key=operator.itemgetter(7),
reverse=True)
# Organize scatter plot data
q_rs = []
q_allele = []
q_coord = []
q_maf = []
p_rs = []
p_allele = []
p_coord = []
p_maf = []
dist = []
d_prime = []
d_prime_round = []
r2 = []
r2_round = []
corr_alleles = []
regdb = []
funct = []
color = []
size = []
for i in range(len(out_ld_sort)):
q_rs_i, q_allele_i, q_coord_i, p_rs_i, p_allele_i, p_coord_i, dist_i, d_prime_i, r2_i, corr_alleles_i, regdb_i, q_maf_i, p_maf_i, funct_i, dist_abs = out_ld_sort[
i]
if float(r2_i) > 0.01:
q_rs.append(q_rs_i)
q_allele.append(q_allele_i)
q_coord.append(float(q_coord_i.split(":")[1]) / 1000000)
q_maf.append(str(round(float(q_maf_i), 4)))
if p_rs_i == ".":
p_rs_i = p_coord_i
p_rs.append(p_rs_i)
p_allele.append(p_allele_i)
p_coord.append(float(p_coord_i.split(":")[1]) / 1000000)
p_maf.append(str(round(float(p_maf_i), 4)))
dist.append(str(round(dist_i / 1000000.0, 4)))
d_prime.append(float(d_prime_i))
d_prime_round.append(str(round(float(d_prime_i), 4)))
r2.append(float(r2_i))
r2_round.append(str(round(float(r2_i), 4)))
corr_alleles.append(corr_alleles_i)
# Correct Missing Annotations
if regdb_i == ".":
regdb_i = ""
regdb.append(regdb_i)
if funct_i == ".":
funct_i = ""
if funct_i == "NA":
funct_i = "none"
funct.append(funct_i)
# Set Color
if i == 0:
color_i = "blue"
elif funct_i != "none" and funct_i != "":
color_i = "red"
else:
color_i = "orange"
color.append(color_i)
# Set Size
size_i = 9 + float(p_maf_i) * 14.0
size.append(size_i)
# Begin Bokeh Plotting
from collections import OrderedDict
from bokeh.embed import components, file_html
from bokeh.layouts import gridplot
from bokeh.models import HoverTool, LinearAxis, Range1d
from bokeh.plotting import ColumnDataSource, curdoc, figure, output_file, reset_output, save
from bokeh.resources import CDN
from bokeh.io import export_svgs
import svgutils.compose as sg
reset_output()
# Proxy Plot
x = p_coord
if r2_d == "r2":
y = r2
else:
y = d_prime
whitespace = 0.01
xr = Range1d(start=coord1 / 1000000.0 - whitespace,
end=coord2 / 1000000.0 + whitespace)
yr = Range1d(start=-0.03, end=1.03)
sup_2 = u"\u00B2"
proxy_plot = figure(
title="Proxies for " + snp + " in " + pop,
min_border_top=2,
min_border_bottom=2,
min_border_left=60,
min_border_right=60,
h_symmetry=False,
v_symmetry=False,
plot_width=900,
plot_height=600,
x_range=xr,
y_range=yr,
tools="hover,tap,pan,box_zoom,box_select,undo,redo,reset,previewsave",
logo=None,
toolbar_location="above")
proxy_plot.title.align = "center"
# Get recomb from LDproxy.py tmp output files
filename = tmp_dir + "recomb_" + request + ".txt"
recomb_raw = open(filename).readlines()
recomb_x = []
recomb_y = []
for i in range(len(recomb_raw)):
chr, pos, rate = recomb_raw[i].strip().split()
recomb_x.append(int(pos) / 1000000.0)
recomb_y.append(float(rate) / 100.0)
data = {
'x': x,
'y': y,
'qrs': q_rs,
'q_alle': q_allele,
'q_maf': q_maf,
'prs': p_rs,
'p_alle': p_allele,
'p_maf': p_maf,
'dist': dist,
'r': r2_round,
'd': d_prime_round,
'alleles': corr_alleles,
'regdb': regdb,
'funct': funct,
'size': size,
'color': color
}
source = ColumnDataSource(data)
proxy_plot.line(recomb_x, recomb_y, line_width=1, color="black", alpha=0.5)
proxy_plot.circle(x='x',
y='y',
size='size',
color='color',
alpha=0.5,
source=source)
hover = proxy_plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Query Variant", "@qrs @q_alle"),
("Proxy Variant", "@prs @p_alle"),
("Distance (Mb)", "@dist"),
("MAF (Query,Proxy)", "@q_maf,@p_maf"),
("R" + sup_2, "@r"),
("D\'", "@d"),
("Correlated Alleles", "@alleles"),
("RegulomeDB", "@regdb"),
("Functional Class", "@funct"),
])
proxy_plot.text(x,
y,
text=regdb,
alpha=1,
text_font_size="7pt",
text_baseline="middle",
text_align="center",
angle=0)
if r2_d == "r2":
proxy_plot.yaxis.axis_label = "R" + sup_2
else:
proxy_plot.yaxis.axis_label = "D\'"
proxy_plot.extra_y_ranges = {"y2_axis": Range1d(start=-3, end=103)}
proxy_plot.add_layout(
LinearAxis(y_range_name="y2_axis",
axis_label="Combined Recombination Rate (cM/Mb)"), "right")
# Rug Plot
y2_ll = [-0.03] * len(x)
y2_ul = [1.03] * len(x)
yr_rug = Range1d(start=-0.03, end=1.03)
data_rug = {
'x': x,
'y': y,
'y2_ll': y2_ll,
'y2_ul': y2_ul,
'qrs': q_rs,
'q_alle': q_allele,
'q_maf': q_maf,
'prs': p_rs,
'p_alle': p_allele,
'p_maf': p_maf,
'dist': dist,
'r': r2_round,
'd': d_prime_round,
'alleles': corr_alleles,
'regdb': regdb,
'funct': funct,
'size': size,
'color': color
}
source_rug = ColumnDataSource(data_rug)
rug = figure(x_range=xr,
y_range=yr_rug,
border_fill_color='white',
y_axis_type=None,
title="",
min_border_top=2,
min_border_bottom=2,
min_border_left=60,
min_border_right=60,
h_symmetry=False,
v_symmetry=False,
plot_width=900,
plot_height=50,
tools="xpan,tap",
logo=None)
rug.segment(x0='x',
y0='y2_ll',
x1='x',
y1='y2_ul',
source=source_rug,
color='color',
alpha=0.5,
line_width=1)
rug.toolbar_location = None
# Gene Plot
# Get genes from LDproxy.py tmp output files
filename = tmp_dir + "genes_" + request + ".txt"
genes_raw = open(filename).readlines()
genes_plot_start = []
genes_plot_end = []
genes_plot_y = []
genes_plot_name = []
exons_plot_x = []
exons_plot_y = []
exons_plot_w = []
exons_plot_h = []
exons_plot_name = []
exons_plot_id = []
exons_plot_exon = []
lines = [0]
gap = 80000
tall = 0.75
if genes_raw != None:
for i in range(len(genes_raw)):
bin, name_id, chrom, strand, txStart, txEnd, cdsStart, cdsEnd, exonCount, exonStarts, exonEnds, score, name2, cdsStartStat, cdsEndStat, exonFrames = genes_raw[
i].strip().split()
name = name2
id = name_id
e_start = exonStarts.split(",")
e_end = exonEnds.split(",")
# Determine Y Coordinate
i = 0
y_coord = None
while y_coord == None:
if i > len(lines) - 1:
y_coord = i + 1
lines.append(int(txEnd))
elif int(txStart) > (gap + lines[i]):
y_coord = i + 1
lines[i] = int(txEnd)
else:
i += 1
genes_plot_start.append(int(txStart) / 1000000.0)
genes_plot_end.append(int(txEnd) / 1000000.0)
genes_plot_y.append(y_coord)
genes_plot_name.append(name + " ")
for i in range(len(e_start) - 1):
if strand == "+":
exon = i + 1
else:
exon = len(e_start) - 1 - i
width = (int(e_end[i]) - int(e_start[i])) / 1000000.0
x_coord = int(e_start[i]) / 1000000.0 + (width / 2)
exons_plot_x.append(x_coord)
exons_plot_y.append(y_coord)
exons_plot_w.append(width)
exons_plot_h.append(tall)
exons_plot_name.append(name)
exons_plot_id.append(id)
exons_plot_exon.append(exon)
n_rows = len(lines)
genes_plot_yn = [n_rows - x + 0.5 for x in genes_plot_y]
exons_plot_yn = [n_rows - x + 0.5 for x in exons_plot_y]
yr2 = Range1d(start=0, end=n_rows)
data_gene_plot = {
'exons_plot_x': exons_plot_x,
'exons_plot_yn': exons_plot_yn,
'exons_plot_w': exons_plot_w,
'exons_plot_h': exons_plot_h,
'exons_plot_name': exons_plot_name,
'exons_plot_id': exons_plot_id,
'exons_plot_exon': exons_plot_exon
}
source_gene_plot = ColumnDataSource(data_gene_plot)
if len(lines) < 3:
plot_h_pix = 150
else:
plot_h_pix = 150 + (len(lines) - 2) * 50
gene_plot = figure(
x_range=xr,
y_range=yr2,
border_fill_color='white',
title="",
min_border_top=2,
min_border_bottom=2,
min_border_left=60,
min_border_right=60,
h_symmetry=False,
v_symmetry=False,
plot_width=900,
plot_height=plot_h_pix,
tools="hover,tap,xpan,box_zoom,undo,redo,reset,previewsave",
logo=None)
gene_plot.segment(genes_plot_start,
genes_plot_yn,
genes_plot_end,
genes_plot_yn,
color="black",
alpha=1,
line_width=2)
gene_plot.rect(x='exons_plot_x',
y='exons_plot_yn',
width='exons_plot_w',
height='exons_plot_h',
source=source_gene_plot,
fill_color="grey",
line_color="grey")
gene_plot.xaxis.axis_label = "Chromosome " + \
snp_coord['chromosome'] + " Coordinate (Mb)(GRCh37)"
gene_plot.yaxis.axis_label = "Genes"
gene_plot.ygrid.grid_line_color = None
gene_plot.yaxis.axis_line_color = None
gene_plot.yaxis.minor_tick_line_color = None
gene_plot.yaxis.major_tick_line_color = None
gene_plot.yaxis.major_label_text_color = None
hover = gene_plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("Gene", "@exons_plot_name"),
("ID", "@exons_plot_id"),
("Exon", "@exons_plot_exon"),
])
gene_plot.text(genes_plot_start,
genes_plot_yn,
text=genes_plot_name,
alpha=1,
text_font_size="7pt",
text_font_style="bold",
text_baseline="middle",
text_align="right",
angle=0)
gene_plot.toolbar_location = "below"
# Change output backend to SVG temporarily for headless export
# Will be changed back to canvas in LDlink.js
proxy_plot.output_backend = "svg"
rug.output_backend = "svg"
gene_plot.output_backend = "svg"
export_svgs(proxy_plot,
filename=tmp_dir + "proxy_plot_1_" + request + ".svg")
export_svgs(gene_plot,
filename=tmp_dir + "gene_plot_1_" + request + ".svg")
# 1 pixel = 0.0264583333 cm
svg_height = str(20.00 + (0.0264583333 * plot_h_pix)) + "cm"
svg_height_scaled = str(100.00 + (0.1322916665 * plot_h_pix)) + "cm"
# Concatenate svgs
sg.Figure("24.59cm", svg_height,
sg.SVG(tmp_dir + "proxy_plot_1_" + request + ".svg"),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").move(
0, 630)).save(tmp_dir + "proxy_plot_" + request + ".svg")
sg.Figure(
"122.95cm", svg_height_scaled,
sg.SVG(tmp_dir + "proxy_plot_1_" + request + ".svg").scale(5),
sg.SVG(tmp_dir + "gene_plot_1_" + request + ".svg").scale(5).move(
0, 3150)).save(tmp_dir + "proxy_plot_scaled_" + request + ".svg")
# Export to PDF
subprocess.call("phantomjs ./rasterize.js " + tmp_dir + "proxy_plot_" +
request + ".svg " + tmp_dir + "proxy_plot_" + request +
".pdf",
shell=True)
# Export to PNG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir +
"proxy_plot_scaled_" + request + ".svg " + tmp_dir +
"proxy_plot_" + request + ".png",
shell=True)
# Export to JPEG
subprocess.call("phantomjs ./rasterize.js " + tmp_dir +
"proxy_plot_scaled_" + request + ".svg " + tmp_dir +
"proxy_plot_" + request + ".jpeg",
shell=True)
# Remove individual SVG files after they are combined
subprocess.call("rm " + tmp_dir + "proxy_plot_1_" + request + ".svg",
shell=True)
subprocess.call("rm " + tmp_dir + "gene_plot_1_" + request + ".svg",
shell=True)
# Remove scaled SVG file after it is converted to png and jpeg
subprocess.call("rm " + tmp_dir + "proxy_plot_scaled_" + request + ".svg",
shell=True)
reset_output()
# Remove temporary files
subprocess.call("rm " + tmp_dir + "pops_" + request + ".txt", shell=True)
subprocess.call("rm " + tmp_dir + "*" + request + "*.vcf", shell=True)
subprocess.call("rm " + tmp_dir + "genes_" + request + ".txt", shell=True)
subprocess.call("rm " + tmp_dir + "recomb_" + request + ".txt", shell=True)
# Return plot output
return None
def fsapt_analyze(lig_dir, mode, ene_type):
lig_name = os.path.basename(os.path.abspath(lig_dir))
matrix_dfs = []
outfiles = glob('%s/FSAPT*out' % lig_dir)
for of in outfiles:
df = _get_ene_matrix(of, ene_type)
if not df is None:
matrix_dfs.append(df)
all_df = pd.concat(matrix_dfs, axis=1)
mean_df = all_df.stack().groupby(level=[0, 1]).mean().unstack()
std_df = all_df.stack().groupby(level=[0, 1]).std().unstack()
if mode in ['prolig', 'proliglig']:
old_columns = mean_df.columns[:]
new_labels = []
numbering = []
for old_label in old_columns:
if old_label == 'Total':
new_labels.append('Total')
numbering.append(100000)
else:
labels = old_label.split('-')
if len(labels) == 2:
new_labels.append(''.join(labels))
numbering.append(float(labels[-1]))
elif len(labels) == 3:
new_labels.append('-'.join(labels[1:]))
numbering.append(0.5 *
(float(labels[-1]) + float(labels[-2])))
new_columns = [nl for _, nl in sorted(zip(numbering, new_labels))]
old_columns = [ol for _, ol in sorted(zip(numbering, old_columns))]
new_mean_df = pd.DataFrame()
new_std_df = pd.DataFrame()
for nc, oc in zip(new_columns, old_columns):
new_mean_df[nc] = mean_df[oc]
new_std_df[nc] = std_df[oc]
mean_df = new_mean_df
std_df = new_std_df
mean_anno = mean_df.applymap(lambda x: '%+.2f\n' % x)
std_anno = std_df.applymap(lambda x: r'+/-%.2f' % x)
all_anno = mean_anno + std_anno
matrix_svg = '%s/ene_matrix_%s.svg' % (lig_dir, ene_type)
plot_matrix(mean_df, all_anno, matrix_svg, mode, ene_type)
mean_df.to_csv('%s/ene_mean_%s_%s_%s.csv' %
(lig_dir, lig_name, mode, ene_type))
std_df.to_csv('%s/ene_std_%s_%s_%s.csv' %
(lig_dir, lig_name, mode, ene_type))
# Plot the ligand
dpi = 96
width = len(mean_df.columns) + 2
height = 4
ligmol = cs._RdkitMolBase.from_file('MD/%s/cmp_sybyl.mol2' % lig_name)
ligmol._init_atominfo(reset=False)
ligmol.charged_mol2file = 'MD/%s/cmp_sybyl.mol2' % lig_name
ligmol.get_noh_mol()
AllChem.Compute2DCoords(ligmol.noh_mol, canonOrient=True, bondLength=1.5)
drawer = rdMolDraw2D.MolDraw2DSVG(width * dpi, height * dpi)
opts = drawer.drawOptions()
opts.additionalAtomLabelPadding = 0.1
frag_dict, _ = fragment_mol(ligmol, 'L1')
for noha in ligmol.noh_mol.GetAtoms():
noh_idx = noha.GetIdx()
h_idx = ligmol.noh_to_h_atom_mapping[noh_idx]
frag_label = str(frag_dict[h_idx]['resid'])
if not 'L1-%02d' % int(frag_label) in mean_df.index:
continue
if noha.GetAtomicNum() == 6:
opts.atomLabels[noh_idx] = '%02d' % int(frag_label)
else:
elem = ligmol.GetAtomWithIdx(h_idx).GetProp(
'_TriposAtomType').split('.')[0]
opts.atomLabels[noh_idx] = '%s/%02d' % (elem, int(frag_label))
drawer.DrawMolecule(ligmol.noh_mol)
drawer.FinishDrawing()
svg = drawer.GetDrawingText().replace('svg:', '')
struct_svg = '%s/lig_frag_%s.svg' % (lig_dir, ene_type)
with open(struct_svg, 'w') as fh:
fh.writelines(svg)
# Consolidate the panels
if mode == 'prolig':
mat_title = 'Protein-Ligand %s Interaction' % ene_type.capitalize()
else:
mat_title = 'Ligand-Ligand %s Interaction' % ene_type.capitalize()
mat_title = sc.Panel(sc.Text(mat_title, size=24)).move(20, 20)
mat_panel = sc.Panel(sc.SVG(matrix_svg).scale(1.4)).move(0, 20)
struct_title = sc.Panel(sc.Text('Ligand %s' % lig_name,
size=24)).move(20,
dpi * len(mean_df) + 20)
struct_panel = sc.Panel(sc.SVG(struct_svg)).move(0,
dpi * len(mean_df) + 20)
final_figure = sc.Figure(dpi * width,
dpi * (len(mean_df) + height) + 40, mat_panel,
mat_title, struct_panel, struct_title)
final_name = '%s/%s_%s_%s' % (lig_dir, lig_name, mode, ene_type)
final_figure.save('%s.svg' % final_name)
os.system('convert -density 100 %s.svg %s.pdf' % (final_name, final_name))
os.system('rm -f %s %s' % (matrix_svg, struct_svg))
# Write pdb for pymol
inpdb = '%s/frame0/fsapt.pdb' % lig_dir
outpdb = '%s_pymol.pdb' % final_name
write_pymol_pdb(inpdb, outpdb, mean_df)
import svgutils.compose as cg
from tqdm import tqdm
for c in tqdm([1,2,4,9,18]):
wh = str(16*c/12)+"cm"
cg.Figure(wh,wh,*[cg.SVG('img/cpu.svg').scale(3) for __ in range(c*c)]).tile(c,c).save("img/cpugrids/cpu1-"+str(c)+".svg")
def put_list_of_figs_to_svg_fig(
FIGS,
fig_name="fig.svg",
initial_guess=True,
visualize=False,
export_as_png=False,
Props=None,
figsize=None,
fontsize=9,
SCALING_FACTOR=1.34, # needed to get the right cm size ...
with_top_left_letter=False,
transparent=True):
""" take a list of figures and make a multi panel plot"""
label = list(string.ascii_uppercase)[:len(FIGS)]
SIZE = []
for fig in FIGS:
if type(fig) == str:
SIZE.append([1., 1.])
else:
SIZE.append(fig.get_size_inches())
width = np.max([s[0] for s in SIZE])
height = np.max([s[1] for s in SIZE])
if Props is None:
LABELS, XCOORD, YCOORD = [], [], []
# saving as svg
for i in range(len(FIGS)):
LABELS.append(label[i])
XCOORD.append((i % 3) * width * 100)
YCOORD.append(int(i / 3) * height * 100)
XCOORD_LABELS,\
YCOORD_LABELS = XCOORD, YCOORD
else:
XCOORD, YCOORD = Props['XCOORD'],\
Props['YCOORD'],
if 'LABELS' in Props:
LABELS = Props['LABELS']
else:
LABELS = ['' for x in XCOORD]
if 'XCOORD_LABELS' in Props:
XCOORD_LABELS,\
YCOORD_LABELS = Props['XCOORD_LABELS'],\
Props['YCOORD_LABELS']
else:
XCOORD_LABELS,\
YCOORD_LABELS = XCOORD, YCOORD
LOCATIONS = []
for i in range(len(FIGS)):
if type(FIGS[i]) is str:
LOCATIONS.append(FIGS[i])
else:
LOCATIONS.append(os.path.join(gettempdir(), str(i) + '.svg'))
FIGS[i].savefig(LOCATIONS[-1],
format='svg',
transparent=transparent)
PANELS = []
for i in range(len(FIGS)):
PANELS.append(sg.Panel(\
sg.SVG(LOCATIONS[i]).move(XCOORD[i],YCOORD[i])))
for i in range(len(LABELS)):
PANELS.append(sg.Panel(\
sg.Text(LABELS[i], 15, 10,
size=fontsize, weight='bold').move(\
XCOORD_LABELS[i],YCOORD_LABELS[i]))\
)
sg.Figure("21cm", "29.7cm", *PANELS).scale(SCALING_FACTOR).save(fig_name)
# if figsize is None:
# sg.Figure("21cm", "29.7cm", *PANELS).save(fig_name)
# else:
# sg.Figure(str(inch2cm(figsize[0]*A0_format['width'])[0])+"cm",\
# str(inch2cm(figsize[1]*A0_format['height'])[0])+"cm",\
# *PANELS).scale(SCALING_FACTOR).save(fig_name)
if visualize:
os.system('open ' + fig_name) # works well with 'Gapplin' on OS-X