def plot_figure_S6_panels():
"""
Plot panels used in Figure S6.
"""
###
# Reproduction of manuscript Figure 3D varying the number of SNPs
# interrogated on the chromosome of interest.
###
wgs_file = "wgs_method_10000affected.csv"
snp_dirs = [
ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.0005_snps1000",
ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.0005",
ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.0005_snps6000",
ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.0005_snps10000"
]
outfiles = [
"Fig_S6_01_snp_1000snps.png", "Fig_S6_02_snp_3348snps.png",
"Fig_S6_03_snp_6000snps.png", "Fig_S6_04_snp_10000snps.png"
]
for snp_dir, outfile in zip(snp_dirs, outfiles):
ylab = False
if snp_dir == snp_dirs[0]:
ylab = True
comp_sensitivity_plot(ANALYSES_DIR + wgs_file,
snp_dir,
FIGURES_DIR + outfile,
ylab=ylab)
def plot_figure_S1_panels():
"""
Plot panels used in Figure S1.
"""
###
# A Panels - plot effect of depth on bin counts across simulated chromosome
# 21
###
depths = [5e6, 11.5e6, 16e6,
25e6] #Note that nbdisp = 1 collapses to Poisson
outfiles = [
"Fig_S1A_wgs_d5M.png", "Fig_S1A_wgs_d11.5M.png",
"Fig_S1A_wgs_d16M.png", "Fig_S1A_wgs_d25M.png"
]
for depth, outfile in zip(depths, outfiles):
if depth == 5e6:
plot_wgs_chr21_dip_tri(depth=depth,
outfile=outfile,
ylab=True,
var_y=True)
else:
plot_wgs_chr21_dip_tri(depth=depth, outfile=outfile, var_y=True)
###
# B Panels - Reproduction of manuscript Figure 3D under different
# sequencing depths for the WGS method.
###
wgs_files = [
"wgs_method_10000affected_d5M.csv",
"wgs_method_10000affected_d11.5M.csv", "wgs_method_10000affected.csv",
"wgs_method_10000affected_d25M.csv"
]
outfiles = [
"Fig_S1B_wgs_depth_5M.png", "Fig_S1B_wgs_depth_11.5M.png",
"Fig_S1B_wgs_depth_16M.png", "Fig_S1B_wgs_depth_25M.png"
]
snp_dir = ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.0005"
for wgs_file, outfile in zip(wgs_files, outfiles):
ylab = False
if wgs_file == "wgs_method_10000affected_d5M.csv":
ylab = True
comp_sensitivity_plot(ANALYSES_DIR + wgs_file,
snp_dir,
FIGURES_DIR + outfile,
ylab=ylab)
def plot_figure_S2_panels():
"""
Plot panels used in Figure S2.
"""
###
# A Panels - plot of dispersion of bin counts across simulated chromosome
# 21
###
nbdisps = [0.1, 0.333, 0.666,
1] #Note that nbdisp = 1 collapses to Poisson
outfiles = [
"Fig_S2A_wgs_neg_binom_0.1.png", "Fig_S2A_wgs_neg_binom_0.333.png",
"Fig_S2A_wgs_neg_binom_0.666.png", "Fig_S2A_wgs_neg_binom_Poisson.png"
]
for nbdisp, outfile in zip(nbdisps, outfiles):
if nbdisp == 0.1:
plot_wgs_chr21_dip_tri(nbdisp=nbdisp, outfile=outfile, ylab=True)
else:
plot_wgs_chr21_dip_tri(nbdisp=nbdisp, outfile=outfile)
###
# B Panels - Reproduction of manuscript Figure 3D under different values of
# nbdisp for the WGS method.
###
wgs_files = [
"wgs_method_10000affected_nb0.1.csv",
"wgs_method_10000affected_nb0.333.csv",
"wgs_method_10000affected_nb0.666.csv", "wgs_method_10000affected.csv"
]
outfiles = [
"Fig_S2B_wgs_neg_binom_0.1.png", "Fig_S2B_wgs_neg_binom_0.333.png",
"Fig_S2B_wgs_neg_binom_0.666.png", "Fig_S2B_wgs_neg_binom_Poisson.png"
]
snp_dir = ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.0005"
for wgs_file, outfile in zip(wgs_files, outfiles):
ylab = False
if wgs_file == "wgs_method_10000affected_nb0.1.csv":
ylab = True
comp_sensitivity_plot(ANALYSES_DIR + wgs_file,
snp_dir,
FIGURES_DIR + outfile,
ylab=ylab)
def plot_figure_S5_panels():
"""
Plot panels used in Figure S5.
"""
###
# A Panels - Allele fractions and corresponding distributions for samples
# simulated under the SNP method with varying values of nbdisp
###
ff = 0.1 # Fetal fraction
num_snps = 3348 # Number of SNPs per chromosome
mean_counts = 859 # Mean counts per SNP
bbdisp = 1000 # Beta binomial dispersion parameter
panels = {
"01_disomy_nbdisp0.0005": [(1, 1, None), 0.0005],
"02_disomy_nbdisp0.005": [(1, 1, None), 0.005],
"03_disomy_nbdisp0.05": [(1, 1, None), 0.05],
"04_disomy_nbdisp1": [(1, 1, None), 1],
"05_maternal_m1_nbdisp0.0005": [(2, 1, 1), 0.0005],
"06_maternal_m1_nbdisp0.005": [(2, 1, 1), 0.005],
"07_maternal_m1_nbdisp0.05": [(2, 1, 1), 0.05],
"08_maternal_m1_nbdisp1": [(2, 1, 1), 1],
}
for key, val in panels.iteritems():
ylab = False
if (key == "01_disomy_nbdisp0.0005"
or key == "05_maternal_m1_nbdisp0.0005"):
ylab = True
df_points = simulate_snp_sample(ff=ff,
H=val[0],
num_snps=num_snps,
mean_counts=mean_counts,
bbdisp=bbdisp,
nbdisp=val[1])
df_dist = simulate_snp_sample(ff=ff,
H=val[0],
num_snps=100000,
mean_counts=mean_counts,
bbdisp=bbdisp,
nbdisp=val[1])
generate_snp_method_fig1_plot(points=df_points,
distros=df_dist,
outfile="Fig_S5A_{}.png".format(key),
bbdisp=bbdisp,
nbdisp=val[1],
ylab=ylab)
###
# B Panels - Reproduction of manuscript Figure 3D under different values of
# nbdisp under the SNP method.
###
wgs_file = "wgs_method_10000affected.csv"
snp_dirs = [
ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.0005",
ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.005",
ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.05",
ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp1"
]
outfiles = [
"Fig_S5B_01_snp_nbdisp0.0005.png", "Fig_S5B_02_snp_nbdisp0.005.png",
"Fig_S5B_03_snp_nbdisp0.05.png", "Fig_S5B_04_snp_nbdisp1.png"
]
for snp_dir, outfile in zip(snp_dirs, outfiles):
ylab = False
if snp_dir == snp_dirs[0]:
ylab = True
comp_sensitivity_plot(ANALYSES_DIR + wgs_file,
snp_dir,
FIGURES_DIR + outfile,
ylab=ylab)
def plot_figure_S4_panels():
"""
Plot panels used in Figure S4.
"""
###
# A Panels - Allele fractions and corresponding distributions for samples
# simulated under the SNP method with varying values of bbdisp
###
ff = 0.1 # Fetal fraction
num_snps = 3348 # Number of SNPs per chromosome
mean_counts = 859 # Mean counts per SNP
nbdisp = 0.0005 # Negative binomial dispersion parameter
panels = {
"01_disomy_bbdisp100": [(1, 1, None), 100],
"02_disomy_bbdisp1000": [(1, 1, None), 1000],
"03_disomy_bbdisp10000": [(1, 1, None), 10000],
"04_disomy_binomial": [(1, 1, None), 'binomial'],
"05_maternal_m1_bbdisp100": [(2, 1, 1), 100],
"06_maternal_m1_bbdisp1000": [(2, 1, 1), 1000],
"07_maternal_m1_bbdisp10000": [(2, 1, 1), 10000],
"08_maternal_m1_binomial": [(2, 1, 1), 'binomial'],
}
for key, val in panels.iteritems():
ylab = False
if (key == "01_disomy_bbdisp100" or key == "05_maternal_m1_bbdisp100"):
ylab = True
df_points = simulate_snp_sample(ff=ff,
H=val[0],
num_snps=num_snps,
mean_counts=mean_counts,
bbdisp=val[1],
nbdisp=nbdisp)
df_dist = simulate_snp_sample(ff=ff,
H=val[0],
num_snps=100000,
mean_counts=mean_counts,
bbdisp=val[1],
nbdisp=nbdisp)
#For distribution plotting purposes, setting bbdisp to extremely high
#value is indistiguishable from the binomial.
generate_snp_method_fig1_plot(points=df_points,
distros=df_dist,
outfile="Fig_S4A_{}.png".format(key),
bbdisp=val[1],
nbdisp=nbdisp,
ylab=ylab)
###
# B Panels - Reproduction of manuscript Figure 3D under different values of
# bbdisp under the SNP method.
###
wgs_file = "wgs_method_10000affected.csv"
snp_dirs = [
ANALYSES_DIR + "snp_method_bbdisp100_nbdisp0.0005",
ANALYSES_DIR + "snp_method_bbdisp1000_nbdisp0.0005",
ANALYSES_DIR + "snp_method_bbdisp10000_nbdisp0.0005",
ANALYSES_DIR + "snp_method_binomial_nbdisp0.0005"
]
outfiles = [
"Fig_S4B_01_snp_bbdisp100.png", "Fig_S4B_02_snp_bbdisp1000.png",
"Fig_S4B_03_snp_bbdisp10000.png", "Fig_S4B_04_snp_binomial.png"
]
for snp_dir, outfile in zip(snp_dirs, outfiles):
ylab = False
if snp_dir == snp_dirs[0]:
ylab = True
comp_sensitivity_plot(ANALYSES_DIR + wgs_file,
snp_dir,
FIGURES_DIR + outfile,
ylab=ylab)