def panel4x4(fn0, fn1, fn2, fn3, fn_out):
single_size = plot_config.single_figure_size
sc.Figure(
str(single_size[0] * 150) + "px",
str(single_size[1] * 150) + "px",
sc.Panel(sc.SVG(fn0).scale(1.0).move(0, 0)),
sc.Text("A", 5, 25, size=plot_config.fontsize_xhuge, weight='bold'),
sc.Panel(sc.SVG(fn1).scale(1.0).move(single_size[0] * 150 / 2., 0)),
sc.Text("B",
single_size[0] * 150 / 2 + 5.,
25,
size=plot_config.fontsize_xhuge,
weight='bold'),
sc.Panel(sc.SVG(fn2).scale(1.0).move(0, single_size[1] * 150 / 2.)),
sc.Text("C",
5,
single_size[1] * 150 / 2. + 25,
size=plot_config.fontsize_xhuge,
weight='bold'),
sc.Panel(
sc.SVG(fn3).scale(1.0).move(single_size[0] * 150 / 2.,
single_size[1] * 150 / 2.)),
sc.Text("D",
single_size[0] * 150 / 2. + 5,
single_size[1] * 150 / 2. + 25,
size=plot_config.fontsize_xhuge,
weight='bold'),
).save(fn_out)
def plot_analysis(self, display=True):
figdir = set_figdir(verbose=False)
figure_name = f"{figdir}/{timestamp()}-analysis.svg"
fig_handles = [
self.plot_singular_vals(savefig=True, verbose=False,
display=False)[-1],
self.plot_eigs(savefig=True, verbose=False, display=False)[-1],
self.plot_power_spectrum(savefig=True,
verbose=False,
display=False)[-1]
]
svgs = []
for fig in fig_handles:
svgs.append(sc.SVG(fig + '.svg', fix_mpl=True))
os.remove(f"{fig}.svg")
sc.Figure(
sum(svg.width for svg in svgs), max([svg.height for svg in svgs]),
sc.Panel(svgs[0], sc.Text("(a)", 6, 16, size=11)).move(0, 0),
sc.Panel(svgs[1], sc.Text("(b)", 6, 16,
size=11)).move(svgs[0].width, 0),
sc.Panel(svgs[2],
sc.Text("(c)", 6, 16,
size=11)).move(svgs[0].width + svgs[1].width,
0)).save(figure_name)
if display:
IPython.display.display(IPython.display.SVG(figure_name))
def plot(self, gr1=None, gr2=None):
"""
Parameters
----------
gr1 : {str, GenomeRange}
First genome range
gr2 : {str, GenomeRange}, optional
Second genome range
"""
frame2grange = self.frame_granges(gr1, gr2)
gr1, gr2 = self.current_range
sub_frames = self.properties['sub_frames']
frame_svgs = self.plot_frames(frame2grange)
center_svg = self.plot_center(gr1, gr2)
center_offsets = self.__get_center_offsets(sub_frames)
center_svg.move(*self.cm2px(center_offsets))
self.__transform_sub_svgs(frame_svgs, sub_frames, center_offsets)
figsize = self.cm2px(self.__get_figsize(sub_frames))
fig = sc.Figure(f"{figsize[0]}px", f"{figsize[1]}px",
sc.Panel(center_svg),
*[sc.Panel(svg) for svg in frame_svgs.values()])
return fig
def layout(codes, filename):
"""export pdf A4 pages filled with these barcodes, so that they
can be printed as stickers
filename will prefix temporary files produced and removed during
the process, it shouldn't have any extension
TODO: I guess it's possible achieving this without creating so
many intermediate files. Try to get rid of them ;)
"""
# A4
sheet_size = WH(210., 297.) * XY.mm
sticker_size = EAN13Data.full_size
# so how many codes fit on one sheet?
n_stickers = sheet_size / sticker_size
n_stickers = np.floor(n_stickers).astype(int)
stickers_per_sheet = n_stickers.w * n_stickers.h
# so how many sheets do we need?
n_sheets = ceil(len(codes) / stickers_per_sheet)
sheets = [] # store produced .pdf sheets filenames here
# iterate until they are all consumed
stickers = iter(codes)
for n in range(n_sheets):
panels = [] # according to sc logic
try:
for i in range(n_stickers.w):
for j in range(n_stickers.h):
# export this code as svg temp file
code = next(stickers)
tpfile = code.id + '.svg'
code.draw(tpfile)
panels.append(
sc.Panel(
sc.SVG(tpfile).scale(1.).move(
i * sticker_size.w, j * sticker_size.h)))
# cleanup
os.remove(tpfile)
except StopIteration:
pass # no more stickers to print
sheetname = filename + ('-' + str(n + 1) if n_sheets > 1 else '')
ssvg = sheetname + '.svg'
spdf = sheetname + '.pdf'
sc.Figure(sheet_size.w, sheet_size.h, *panels).save(ssvg)
# convert to .pdf
renderPDF.drawToFile(svg2rlg(ssvg), spdf)
# remove .svg file
os.remove(ssvg)
# remember this other temp file
sheets.append(spdf)
# bring all sheets together into pdf pages
if n_sheets > 1:
final = PdfFileWriter()
for sheet in sheets:
append_pdf(PdfFileReader(sheet), final)
final.write(open(filename + '.pdf', 'wb'))
# so we can now supress them
while sheets:
os.remove(sheets.pop())
def test_embedded_svg():
svg = sc.SVG("examples/files/svg_logo.svg")
fig = sc.Figure("5cm", "5cm", svg)
poly = fig.root.find(".//{}polygon".format(SVG))
ok_(poly.get("id") == "V")
ok_(svg.height is None)
ok_(svg.width is None)
def panel2x2(fn0, fn1, fn_out, single_size=plot_config.single_figure_size):
sc.Figure(
str(single_size[0] * 150) + "px",
str(single_size[1] * 80) + "px",
sc.Panel(sc.SVG(fn0).scale(1.0).move(0, 15)),
sc.Text("A",
4,
12,
size=plot_config.fontsize_large,
weight='bold',
font='serif'),
sc.Panel(sc.SVG(fn1).scale(1.0).move(single_size[0] * 150 / 2., 15)),
sc.Text("B",
single_size[0] * 150 / 2 + 4.,
12,
size=plot_config.fontsize_large,
weight='bold',
font='serif')).save(fn_out)
def compose_svg(svg_board, svg_plot, svg_combined):
"""
Create a combined SVG in which the board image is put in the background of
the axes area of the plot image.
:param svg_board: filename of existing board image
:type svg_board: str
:param svg_plot: filename of existing plot image
:type svg_plot: str
:param svg_combined: filename of combined image, to be written
:type svg_combined: str
"""
scale = TOP_RIGHT_MARGIN - LEFT_BOTTOM_MARGIN
xdel = LEFT_BOTTOM_MARGIN * SIZE
ydel = (1.0 - TOP_RIGHT_MARGIN) * SIZE
compose.Figure(
SIZE, SIZE,
compose.Panel(compose.SVG(svg_board).scale(scale).move(xdel, ydel)),
compose.Panel(compose.SVG(svg_plot))).save(svg_combined)
def make_multipanel_fig(FIGS, CAP_SIZE=14,\
fig_name="fig.svg",\
transparent=True, correc_factor=70., DPI=100.):
""" take a list of figures and make a multi panel plot"""
label = list(string.ascii_uppercase)[:len(FIGS)]
SIZE = []
for fig in FIGS:
SIZE.append(fig.get_size_inches())
width = np.max([s[0] for s in SIZE])
height = np.max([s[1] for s in SIZE])
LABELS, XCOORD, YCOORD, SCALE = [], [], [], []
for i in range(len(FIGS)):
ff = 'f.svg'
FIGS[i].savefig('/tmp/' + str(i) + '.svg',
format='svg',
transparent=transparent)
if translate_to_bitmap_if_too_big(FIGS[i], '/tmp/' + str(i) + '.svg'):
SCALE.append(.7)
else:
SCALE.append(1.)
LABELS.append(label[i])
XCOORD.append((i % 3) * width * correc_factor)
YCOORD.append(int(i / 3) * height * correc_factor)
PANELS = []
for i in range(len(FIGS)):
PANELS.append(sg.Panel(\
sg.SVG('/tmp/'+str(i)+'.svg').move(XCOORD[i],YCOORD[i]).scale(SCALE[i]),\
sg.Text(LABELS[i], 25, 20, size=22, weight='bold').move(\
XCOORD[i]-15,YCOORD[i]))\
)
sg.Figure(str((min(len(FIGS)%3,3))*inch_to_cm(width))+"cm",\
str(inch_to_cm(height)*(int(len(FIGS)/3.01)+1))+"cm",\
*PANELS).save(fig_name)
def draw_plot(data, output):
"""Draws piecharts from given data on lab012 background
Args:
data - list of lists of three points with experiment results
output - name of generated svg file
"""
positions = [[1.6, 1], [1.6, 3], [1.6, 5], [5, 1], [5, 3], [5, 5],
[8.7, 1], [8.7, 3], [8.7, 5], [12, 1], [12, 3], [12, 5]]
# prepare background
fig, ax = plt.subplots()
ax.imshow([[[0, 0, 0, 0]], [[0, 0, 0, 0]]], extent=[-1, 13.25, -1, 7.3])
ax.patch.set_alpha(0.0)
ax.axis('off')
# robot blob
draw_single_pie([1.3, 5.7],
0.3, [1, 1, 1],
ax, ('k', 'k', 'k'),
plot_labels=False)
ax.text(1.6, 5.9, "Mikrofon")
# draw points
for i, d in enumerate(zip(positions, data)):
draw_single_pie(d[0], 0.5, d[1], ax, ('blue', 'orange', 'green'),
str(i + 1))
# save
fig.tight_layout()
fig.savefig("out.svg", dpi=200, transparent=True)
# merge with proper background
sc.Figure("247.59521mm", "129.31232mm",
sc.Panel(sc.SVG("./assets/012-base.svg").scale(0.352)),
sc.Panel(sc.SVG("out.svg").scale(0.6).move(-25,
-40))).save(output)
os.remove("./out.svg")
def multipanel_figure(graph_env,
FIGS,
X = None, Y = None, Labels=None,
LABELS = None, X_LABELS = None, Y_LABELS = None,
width=85.,# mm
height=None, # mm
grid=False,
autoposition=False,
SCALING_FACTOR = 1.34, fontsize=None, fontweight='bold',
export_to_png=False, bg='white',
fig_name='fig.svg'):
"""
"""
# building the figure matrix if not explicited
if type(FIGS) is mpl.figure.Figure:
FIGS = [[FIGS]]
elif type(FIGS) is list:
if (len(FIGS)>0) and (type(FIGS[0]) is mpl.figure.Figure):
FIGS = [FIGS]
elif (len(FIGS)>0) and (type(FIGS[0]) is str):
FIGS = [FIGS]
# else should be list of list
if autoposition:
X, Y = [], []
y = [0]
for i, lfig in enumerate(FIGS):
Y.append([np.max(y) for fig in lfig])
x = []
for fig in lfig:
if type(fig) is not str:
x.append(72.*fig.get_size_inches()[0])
y.append(72.*fig.get_size_inches()[1])
else:
x.append(120)
y.append(80)
X.append([0]+list(np.cumsum(x)))
y = [dy+Y[-1][0] for dy in y]
Y.append([np.max(y)])
print('X = ', X)
print('Y = ', Y)
if X is None:
X = [[0 for fig in lfig] for lfig in FIGS]
if Y is None:
Y = [[0 for fig in lfig] for lfig in FIGS]
if LABELS is None:
LABELS = [['' for fig in lfig] for lfig in FIGS]
if X_LABELS is None:
X_LABELS = X
if Y_LABELS is None:
Y_LABELS = Y
if height is None:
try:
height = np.max([50, Y[-1][-1]])*0.27 # TO BE SET UP
except IndexError:
height = 50
# size
if width=='single-column':
width = 85.
elif width=='one-and-a-half-column':
width = 114.
elif width=='one-column-and-a-half':
width = 114.
elif width=='double-column':
width = 174.
if fontsize is None:
fontsize = graph_env.fontsize+1
LOCATIONS, PANELS = [], []
for i, lfig in enumerate(FIGS):
LOCATIONS.append([])
for j, fig in enumerate(lfig):
if type(FIGS[i][j]) is str:
LOCATIONS[i].append(FIGS[i][j])
# 1.26625 -- NEW SCALING FACTOR
else:
LOCATIONS[i].append(os.path.join(gettempdir(), '%i_%i.svg' % (i,j)))
FIGS[i][j].savefig(LOCATIONS[i][j], format='svg',
transparent=graph_env.transparency)
PANELS.append(sg.Panel(sg.SVG(LOCATIONS[i][j]).move(X[i][j], Y[i][j])))
for i, labels in enumerate(LABELS):
for j, label in enumerate(labels):
if label!='':
PANELS.append(sg.Panel(sg.Text(label, 3, 10,
size=fontsize, weight=fontweight).move(\
X_LABELS[i][j],Y_LABELS[i][j])))
if grid:
sg.Figure("%.1fcm" % (width/10.), "%.1fcm" % (height/10.),
*PANELS, sg.Grid(40,40)).scale(SCALING_FACTOR).save(fig_name.replace('.png', '.svg'))
else:
sg.Figure("%.1fcm" % (width/10.), "%.1fcm" % (height/10.),
*PANELS).scale(SCALING_FACTOR).save(fig_name.replace('.png', '.svg'))
if fig_name.endswith('.png'):
export_as_png(fig_name.replace('.png', '.svg'), dpi=300, background=bg)
os.remove(fig_name.replace('.png', '.svg'))
print('[ok] removed %s' % fig_name.replace('.png', '.svg'))
elif export_to_png:
export_as_png(fig_name, dpi=300, background=bg)
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
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 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_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"):
start_time = time.time()
# Set data directories using config.yml
with open('config.yml', 'r') as f:
config = yaml.load(f)
gene_dir = config['data']['gene_dir']
recomb_dir = config['data']['recomb_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)
if request is False:
request = str(time.strftime("%I%M%S"))
# Create JSON output
# Find coordinates (GRCh37/hg19) for SNP RS number
# 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 number in snp database
snp_coord = get_coords(snp)
# Close snp connection
cur.close()
conn.close()
# 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) + " > " + \
tmp_dir + "pops_" + request + ".txt"
subprocess.call(get_pops, shell=True)
# Get population ids
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[
1] + ".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[1], snp_coord[2],
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[2]) - window
if coord1 < 0:
coord1 = 0
coord2 = int(snp_coord[2]) + 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 " + snp + " " + \
snp_coord[1] + " " + str(coord1) + " " + \
str(coord2) + " " + request + " " + str(i)
elif i == min(range(threads)):
command = "python LDproxy_sub.py " + snp + " " + \
snp_coord[1] + " " + str(coord1) + " " + \
str(coord1 + block) + " " + request + " " + str(i)
elif i == max(range(threads)):
command = "python LDproxy_sub.py " + snp + " " + snp_coord[
1] + " " + str(coord1 + (block * i) + 1) + " " + str(
coord2) + " " + request + " " + str(i)
else:
command = "python LDproxy_sub.py " + snp + " " + snp_coord[
1] + " " + 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"
tabix_recomb = "tabix -fh {0} {1}:{2}-{3} > {4}".format(
recomb_dir, snp_coord[1], coord1 - whitespace, coord2 + whitespace,
tmp_dir + "recomb_" + request + ".txt")
subprocess.call(tabix_recomb, shell=True)
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
tabix_gene = "tabix -fh {0} {1}:{2}-{3} > {4}".format(
gene_dir, snp_coord[1], coord1, coord2,
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 = []
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[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
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")
# Concatenate svgs
sg.Figure("24.59cm", "27.94cm",
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", "139.70cm",
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 composite(
fig_spec: FigureSpec,
memoize_panels: bool = False,
recompute_panels: bool = True,
delete_png: bool = True,
) -> None:
"""
Function that composites a figure from a FigureSpec.
Parameters
----------
fig_spec : FigureSpec
memoize_panels : bool
recompute_panels : bool
delete_png : bool
See the pubfig.compositor decorator for a description of the parameters.
Returns
-------
None
"""
import tempfile
svg_path = fig_spec.output_file
if isinstance(svg_path, str):
svg_path = Path(svg_path)
assert not svg_path.is_dir(), "The output file name you provided is a directory"
if svg_path.suffix != ".svg":
svg_path = svg_path.with_suffix(".svg")
svg_path = svg_path.expanduser()
if not svg_path.parent.exists():
svg_path.parent.mkdir(parents=True, exist_ok=True)
if memoize_panels:
panels_path = svg_path.parent / ".panels"
if not panels_path.exists():
panels_path.mkdir()
else:
panels_path = Path(tempfile.gettempdir())
panels = []
if fig_spec.plot_grid_every > 0:
panels.append(_generate_grid(fig_spec.figure_size, fig_spec.plot_grid_every, font_size=8))
auto_label = fig_spec.auto_label_options
label_generator = auto_label.label_generator(auto_label.first_char.text)
for name in fig_spec.panels._fields:
panel = getattr(fig_spec.panels, name)
assert isinstance(panel, Panel)
panel_elements = []
assert isinstance(panel.fig, (plt.Figure, VectorImage, RasterImage))
content_offset = _location_to_str(
panel.location.units or fig_spec.figure_size.units, panel.content_offset
)
if isinstance(panel, PanelFig):
svg = _get_panel_content(panels_path, panel, name, memoize_panels, recompute_panels)
panel_elements.append(svg.move(*content_offset))
elif isinstance(panel.fig, VectorImage):
scale = panel.scale or panel.fig.scale
print(f"Scaling vector image {panel.fig.file.absolute()} by {scale:.3f}")
panel_elements.append(panel.fig.svg.scale(scale).move(*content_offset))
elif isinstance(panel.fig, RasterImage):
img_size = panel.fig.img_size
scale = panel.scale or 1.
img = sc.Image(
img_size.units.to_px(img_size.width),
img_size.units.to_px(img_size.height),
f"{panel.fig.file}",
)
panel_elements.append(img.scale(scale).move(*content_offset))
else:
raise TypeError(f"Unknown type of panel content {type(panel.fig)} for panel {name}")
if panel.text is not None:
panel_text = [
sc.Text(
t.text,
*_location_to_str(fig_spec.figure_size.units, Location(t.x, t.y, panel.location.units)),
**t.kwargs
) for t in panel.text]
for t, pt in zip(panel_text, panel.text):
# Need separate loop because rotate doesn't return the Text Element
t.move(*content_offset).rotate(pt.angle)
panel_elements += panel_text
if panel.auto_label:
label = sc.Text(
next(label_generator),
*_location_to_str(
fig_spec.figure_size.units,
Location(auto_label.first_char.x, auto_label.first_char.y)
),
**auto_label.first_char.kwargs
)
panel_elements.append(label)
location = _location_to_str(fig_spec.figure_size.units, panel.location)
panels.append(sc.Panel(*panel_elements).move(*location))
fs = fig_spec.figure_size
sc.Figure(
f"{fs.units.to_px(fs.width):.2f}px",
f"{fs.units.to_px(fs.height):.2f}px",
*panels
).save(svg_path)
if fig_spec.generate_image != ImageType.none:
""" Taken from this shell script:
#!/bin/sh
# Convert all arguments (assumed SVG) to a TIFF acceptable to PLOS
# Requires Inkscape and ImageMagick 6.8 (doesn't work with 6.6.9)
for i in $@; do
BN=$(basename $i .svg)
inkscape --without-gui --export-png="$BN.png" --export-dpi 400 $i
convert -compress LZW -alpha remove $BN.png $BN.tiff
mogrify -alpha off $BN.tiff
rm $BN.png
done
"""
basename = f"{svg_path}"[:-4]
image_name = f"{basename}.png"
_run(f"inkscape --without-gui --export-png='{image_name}' --export-dpi {fig_spec.image_dpi} {svg_path}")
if fig_spec.generate_image == ImageType.tiff:
tiff_name = f"{basename}.tiff"
_run(f"convert -compress LZW -alpha remove {image_name} {tiff_name}")
_run(f"mogrify -alpha off {tiff_name}")
if delete_png:
_run(f"rm {image_name}")
image_name = tiff_name
_run(f"eog {image_name}")
def main(self):
settings = self.settings
SMILESSTRING = settings['SMILESSTRING']
resulting_plots = []
pRList = []
mol_svg, d2d, dm = self.draw_smiles()
replace_index = []
for scope_plot in self.plots:
# for each scope plot, make a vals list containing empty first items for the wedge with alpha=0
if type(scope_plot) != dict:
continue
sizes = [
360 - scope_plot['coverangle_wedges']
] + [scope_plot['coverangle_wedges'] / scope_plot['no_wedges']
] * scope_plot['no_wedges']
label_inner_circle, label_outer_circle = [
''
] + [''] * scope_plot['no_wedges'], [
''
] + [''] * scope_plot['no_wedges']
if (len(scope_plot['value_inner_circle']) !=
scope_plot['no_wedges']
or len(scope_plot['value_outer_circle']) !=
scope_plot['no_wedges']):
print('not equal')
value_inner_circle, value_outer_circle = scope_plot[
'value_inner_circle'], scope_plot['value_outer_circle']
rounding_boundary = scope_plot['rounding_boundary']
value_groups = scope_plot['value_groups']
for i in range(scope_plot['no_wedges']):
if scope_plot['rounding']:
if value_inner_circle[i] >= rounding_boundary:
label_inner_circle[i + 1] = ">" + str(
value_inner_circle[i])
else:
label_inner_circle[i + 1] = str(value_inner_circle[i])
if value_outer_circle[i] >= rounding_boundary:
label_outer_circle[i + 1] = ">" + str(
value_outer_circle[i])
else:
label_outer_circle[i + 1] = str(value_outer_circle[i])
else:
label_inner_circle[i + 1] = str(value_inner_circle[i])
label_outer_circle[i + 1] = str(value_outer_circle[i])
j = 0
for i, item in enumerate(value_groups):
if item[0] == '~':
replace_index.append(('~' + str(j), item[1:]))
value_groups[i] = '~' + str(j)
j = j + 1
vals = [
sizes, # size of the wedges, the first wedge is transparent and will not be shown
[0] +
value_inner_circle, # colormap values for the inner circle, maximum value determines intensity, first is for the transparent wedge and should stay 0
[0] +
value_outer_circle, # colormap values for the outer circle, maximum value determines intensity, first is for the transparent wedge and should stay 0
label_inner_circle, #labels for the inner circle
label_outer_circle, #labels for the outer circle
[""] + value_groups, #groups
]
resulting_plots.append(
self.plot_figure_and_colorbar(scope_plot, vals))
# get the atom id from the settings and save its position
rIdx = scope_plot['attach_atom_id']
pRList.append(
d2d.GetDrawCoords(
Geometry.Point2D(dm.GetConformer().GetAtomPosition(rIdx))))
# take colorbar from first plot #ToDo extension to multiple colorbars
colorbar = compose.Panel(
strSVG(resulting_plots[0][1]).scale(0.8).move(-350, 400))
panels = [compose.Panel(strSVG('<svg></svg>'))] * len(resulting_plots)
for i, plot in enumerate(resulting_plots):
panels[i] = strSVG(resulting_plots[i][0]).move(
-369, -358).scale(1).move(pRList[i].x, pRList[i].y)
#panels[i]=strSVG(resulting_plots[i][0]).move(-369*1,-358*1).scale(0.4).move(pRList[i].x,pRList[i].y)
compose.Figure(
"600",
"600", #720 default`
compose.Panel(strSVG(mol_svg).scale(1).move(0, 0)),
colorbar,
*panels
#).move(350,350).scale(self.settings['scalefactor']).save("substrate_scope.svg")
).move(350, 100).scale(
self.settings['scalefactor']).save("substrate_scope.svg")
new_svg = SVG('substrate_scope.svg')._data
for item in replace_index:
new_svg = self.replace_label_with_smiles(svg_file=new_svg,
smiles=item[1],
search_index=item[0])
if settings['use_bold_font']:
new_svg.replace('font-weight:normal', 'font-weight:bold')
f = open("substrate_scope_replaced.svg", "w")
f.write(new_svg)
f.close()
print('File written to:',
os.getcwd() + '/substrate_scope_replaced.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
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)
def calculate_proxy_svg(snp,
pop,
request,
genome_build,
r2_d="r2",
window=500000,
collapseTranscript=True):
# Set data directories using config.yml
with open('config.yml', 'r') as yml_file:
config = yaml.load(yml_file)
env = config['env']
connect_external = config['database']['connect_external']
api_mongo_addr = config['database']['api_mongo_addr']
data_dir = config['data']['data_dir']
tmp_dir = config['data']['tmp_dir']
genotypes_dir = config['data']['genotypes_dir']
mongo_username = config['database']['mongo_user_readonly']
mongo_password = config['database']['mongo_password']
mongo_port = config['database']['mongo_port']
aws_info = config['aws']
num_subprocesses = config['performance']['num_subprocesses']
export_s3_keys = retrieveAWSCredentials()
# 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) or (GRCh38/hg38) for SNP RS number
# Connect to Mongo snp database
if env == 'local' or connect_external:
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_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_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_coord['chromosome']))
vcf_query_snp_file = "s3://%s/%s" % (config['aws']['bucket'], vcf_filePath)
checkS3File(aws_info, config['aws']['bucket'], vcf_filePath)
tabix_snp_h = export_s3_keys + " cd {1}; tabix -HD {0} | grep CHROM".format(
vcf_query_snp_file, data_dir + genotypes_dir +
genome_build_vars[genome_build]['1000G_dir'])
head = [
x.decode('utf-8')
for x in subprocess.Popen(tabix_snp_h,
shell=True,
stdout=subprocess.PIPE).stdout.readlines()
][0].strip().split()
tabix_snp = export_s3_keys + " cd {4}; tabix -D {0} {1}:{2}-{2} | grep -v -e END > {3}".format(
vcf_query_snp_file, genome_build_vars[genome_build]['1000G_chr_prefix']
+ snp_coord['chromosome'],
snp_coord[genome_build_vars[genome_build]['position']],
tmp_dir + "snp_no_dups_" + request + ".vcf", data_dir + genotypes_dir +
genome_build_vars[genome_build]['1000G_dir'])
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()
geno[0] = geno[0].lstrip('chr')
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()
geno[0] = geno[0].lstrip('chr')
if geno[2] != snp and snp[0:2] == "rs" and "rs" in geno[2]:
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[genome_build_vars[genome_build]['position']]) - window
if coord1 < 0:
coord1 = 0
coord2 = int(
snp_coord[genome_build_vars[genome_build]['position']]) + window
# Calculate proxy LD statistics in parallel
# threads = 4
# block = (2 * window) // 4
# block = (2 * window) // num_subprocesses
windowChunkRanges = chunkWindow(
int(snp_coord[genome_build_vars[genome_build]['position']]), window,
num_subprocesses)
commands = []
# for i in range(num_subprocesses):
# if i == min(range(num_subprocesses)) and i == max(range(num_subprocesses)):
# command = "python3 LDproxy_sub.py " + "True " + snp + " " + \
# snp_coord['chromosome'] + " " + str(coord1) + " " + \
# str(coord2) + " " + request + " " + str(i)
# elif i == min(range(num_subprocesses)):
# command = "python3 LDproxy_sub.py " + "True " + snp + " " + \
# snp_coord['chromosome'] + " " + str(coord1) + " " + \
# str(coord1 + block) + " " + request + " " + str(i)
# elif i == max(range(num_subprocesses)):
# command = "python3 LDproxy_sub.py " + "True " + snp + " " + snp_coord['chromosome'] + " " + str(
# coord1 + (block * i) + 1) + " " + str(coord2) + " " + request + " " + str(i)
# else:
# command = "python3 LDproxy_sub.py " + "True " + snp + " " + snp_coord['chromosome'] + " " + str(coord1 + (
# block * i) + 1) + " " + str(coord1 + (block * (i + 1))) + " " + request + " " + str(i)
# commands.append(command)
for subprocess_id in range(num_subprocesses):
getWindowVariantsArgs = " ".join([
"True",
str(snp),
str(snp_coord['chromosome']),
str(windowChunkRanges[subprocess_id][0]),
str(windowChunkRanges[subprocess_id][1]),
str(request), genome_build,
str(subprocess_id)
])
commands.append("python3 LDproxy_sub.py " + getWindowVariantsArgs)
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].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])))
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 = "\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"
# Add recombination rate from LDproxy.py output file
recomb_file = tmp_dir + "recomb_" + request + ".json"
recomb_raw = open(recomb_file).readlines()
recomb_x = []
recomb_y = []
for recomb_raw_obj in recomb_raw:
recomb_obj = json.loads(recomb_raw_obj)
recomb_x.append(
int(recomb_obj[genome_build_vars[genome_build]['position']]) /
1000000.0)
recomb_y.append(float(recomb_obj['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
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 = []
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 - 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 = 250
else:
plot_h_pix = 250 + (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)(" + 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
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"
# 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_coord[
'chromosome'] + " 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
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 + "*.json",
shell=True)
subprocess.call("rm " + tmp_dir + "recomb_" + request + ".txt", shell=True)
# Return plot output
return None
def add_title(self, fig, title, y_pos=-0.2, w=None):
"""
`fig`: a plotly/svgutils Figure object, or the path of a png file.
`title`: figure title.
`w`: output image width in px.
"""
if isinstance(fig, str):
if not os.path.exists(fig):
print('File %s does exist!'%(fig))
return
if not os.path.splitext(os.path.abspath(fig))[-1]=='.png':
print('Only png image supported.')
return
im = Image.open(fig)
if w:
_w = w
else:
_w = self.output_width
_scalar = _w * 1.0 / im.width
svg_fig = sc.Figure(_w, ceil(_scalar * im.height),
sc.Image(
im.width,
im.height,
fig
).scale(_scalar)
)
fig = svg_fig
assert isinstance(fig, go.Figure) or isinstance(fig, sc.Figure)
# if input a plotly Figure object, convert it into a svg file first
title_txt = '%s%s '%(self.prefix, self.current_num) + title
if isinstance(fig, go.Figure):
# compute image size
if w:
_w = w
else:
_w = self.output_width
if not fig.layout.width is None:
_scalar = _w * 1.0 / fig.layout.width
else:
# default width is 700px
_scalar = _w * 1.0 / 700
if not fig.layout.height is None:
_h = fig.layout.height * _scalar
else:
# default height is 450px
_h = 450.0 * _scalar
title_annotation = go.layout.Annotation(
xref = 'paper',
yref = 'paper',
x = 0.5,
y = y_pos,
xanchor = 'center',
yanchor = 'top',
text = title_txt,
font = dict(
family = self.font_family,
size = self.font_size,
color = "#000000",
),
showarrow = False,
)
fig.update_layout(
width = _w,
height = _h,
annotations = list(fig.layout['annotations']) + [title_annotation],
)
# increasing counter number
self.current_num += 1
return fig
else:
# compute image size
if w:
_w = w
else:
_w = self.output_width
_scalar = _w * 1.0 / fig.width.value
_h = fig.height.value * _scalar + 25
if y_pos==-0.2:
text_y = _h - 5
else:
assert y_pos<=1 and y_pos>=0
text_y = int(_h*(1-y_pos))
# if w < default output width, add margin
if _w < self.output_width:
_outw = self.output_width
_move_x = int((_outw - _w) / 2)
else:
_outw = _w
_move_x = int((_outw - _w) / 2)
# add title
new_figure = sc.Figure(_outw, _h,
fig.scale(_scalar).move(_move_x, 0),
sc.Text(title_txt,
_outw / 2,
text_y,
anchor='middle',
size=self.font_size,
font=self.font_family,
)
)
# increasing counter number
self.current_num += 1
return new_figure
