def plot_length_dist(fig_path, lengths, g_a, g_b, _max, _mean, _n50, isPb=False, b_width = 1000):
x = np.linspace(0, gamma.ppf(0.99, g_a, 0, g_b))
est_dist = gamma(g_a, 0, g_b)
plt.hist(lengths, histtype='step', bins=np.arange(min(lengths),_max + b_width, b_width), color=rgb(214,39,40), alpha=0.7, density=True)
plt.grid(True)
plt.xlabel('Read length')
plt.ylabel('Probability density')
plt.axvline(x=_mean, linestyle='dashed', linewidth=2, color=rgb(214,39,40), alpha=0.8)
plt.axvline(x=_n50, linewidth=2, color=rgb(214,39,40), alpha=0.8)
plt.xlim(0, gamma.ppf(0.99, g_a, 0, g_b))
ymin, ymax = plt.gca().get_ylim()
xmin, xmax = plt.gca().get_xlim()
if not isPb:
plt.text(xmax*0.6, ymax*0.72, r'$\alpha=%.3f,\ \beta=%.3f$' % (g_a, g_b) )
plt.text(xmax*0.6, ymax*0.77, r'Gamma dist params:' )
plt.plot(x, est_dist.pdf(x), color=rgb(214,39,40) )
plt.text(xmax*0.6, ymax*0.85, r'sample mean: %.3f' % (_mean,) )
plt.text(xmax*0.6, ymax*0.9, r'N50: %.3f' % (_n50,) )
plt.text(_mean, ymax*0.85, r'Mean', color=rgb(214,39,40))
plt.text(_n50, ymax*0.9, r'N50', color=rgb(214,39,40))
plt.axis('tight')
plt.xlim(0, gamma.ppf(0.99, g_a, 0, g_b))
plt.savefig(fig_path, bbox_inches="tight", transparent=True)
#plt.show()
plt.close()
def run_platformqc(data_path, output_path, *, suffix=None, b_width=1000):
if not suffix:
suffix = ""
else:
suffix = "_" + suffix
log_path = os.path.join(output_path, "log",
"log_rs2_platformqc" + suffix + ".txt")
fig_path = os.path.join(output_path, "fig",
"fig_rs2_platformqc_length" + suffix + ".png")
fig_path2 = os.path.join(output_path, "fig",
"fig_rs2_platformqc_score" + suffix + ".png")
json_path = os.path.join(output_path, "QC_vals_rs" + suffix + ".json")
# json
tobe_json = {}
# output_path will be made too.
if not os.path.isdir(os.path.join(output_path, "log")):
os.makedirs(os.path.join(output_path, "log"), exist_ok=True)
if not os.path.isdir(os.path.join(output_path, "fig")):
os.makedirs(os.path.join(output_path, "fig"), exist_ok=True)
### logging conf ###
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
fh = logging.FileHandler(log_path, 'w')
sh = logging.StreamHandler()
formatter = logging.Formatter(
'%(module)s:%(asctime)s:%(lineno)d:%(levelname)s:%(message)s')
fh.setFormatter(formatter)
sh.setFormatter(formatter)
logger.addHandler(sh)
logger.addHandler(fh)
#####################
logger.info("Started RS-II platform QC for %s" % data_path)
xml_file = get_sts_xml_path(data_path, logger)
if not xml_file:
logger.warning("sts.xml is missing. Productivity won't be shown")
[p0, p1, p2] = [None] * 3
else:
[p0, p1, p2] = parse_sts_xml(
xml_file,
ns="http://pacificbiosciences.com/PipelineStats/PipeStats.xsd")
logger.info("Parsed sts.xml")
csv_path = get_sts_csv_path(data_path, logger)
if not csv_path:
logger.ERROR("Platform QC failed due to missing csv files")
return 1
df = load_sts_csv(csv_path)
logger.info("Stat file was loaded.")
vals = df['HQRegionEnd'].values[df['ReadScore'] > 0.1] - df[
'HQRegionStart'].values[df['ReadScore'] > 0.1]
(a, b) = lq_gamma.estimate_gamma_dist_scipy(vals, logger)
logger.info("Fitting by Gamma dist finished.")
_max = np.array(vals).max()
_mean = np.array(vals).mean()
_n50 = get_N50(vals)
_n90 = get_NXX(vals, 90)
throughput = np.sum(vals)
### HQ fraction over numbases
fracs = vals / df['NumBases'].values[df['ReadScore'] > 0.1]
#plt.hist(vals, histtype='bar', bins=np.arange(0.0, 1.0 + 0.02, 0.02), color='blue')
#plt.show()
tobe_json["Productivity"] = {"P0": p0, "P1": p1, "P2": p2}
tobe_json["Throughput"] = int(throughput)
tobe_json["Longest_read"] = int(_max)
tobe_json["Num_of_reads"] = len(vals)
tobe_json["polread_gamma_params"] = [float(a), float(b)]
tobe_json["Mean_polread_length"] = float(_mean)
tobe_json["N50_polread_length"] = float(_n50)
tobe_json["Mean_HQ_fraction"] = float(np.mean(fracs))
with open(json_path, "w") as f:
logger.info("Quality measurements were written into a JSON file: %s" %
json_path)
json.dump(tobe_json, f, indent=4)
x = np.linspace(0, gamma.ppf(0.99, a, 0, b))
est_dist = gamma(a, 0, b)
plt.plot(x, est_dist.pdf(x), c=rgb(214, 39, 40))
plt.grid(True)
plt.hist(vals,
histtype='step',
bins=np.arange(min(vals), _max + b_width, b_width),
color=rgb(214, 39, 40),
alpha=0.7,
density=True)
plt.xlabel('Read length')
plt.ylabel('Probability density')
if _mean >= 10000: # pol read mean is expected >= 10k and <= 15k, but omit the <= 15k condition.
plt.axvline(x=_mean,
linestyle='dashed',
linewidth=2,
color=rgb(44, 160, 44),
alpha=0.8)
else:
plt.axvline(x=_mean,
linestyle='dashed',
linewidth=2,
color=rgb(188, 189, 34),
alpha=0.8)
if _n50 >= 20000: # recent brochure says 20kb, but some old announcement says 14kb. let's see
plt.axvline(x=_n50, linewidth=2, color=rgb(44, 160, 44), alpha=0.8)
else:
plt.axvline(x=_n50, linewidth=2, color=rgb(188, 189, 34), alpha=0.8)
vals = df['NumBases'].values[df['ReadScore'] > 0.1]
plt.hist(vals,
histtype='step',
bins=np.arange(min(vals),
max(vals) + b_width, b_width),
color=rgb(31, 119, 180),
alpha=0.7,
density=True)
ymin, ymax = plt.gca().get_ylim()
xmin, xmax = plt.gca().get_xlim()
plt.text(xmax * 0.6, ymax * 0.72, r'$\alpha=%.3f,\ \beta=%.3f$' % (a, b))
plt.text(xmax * 0.6, ymax * 0.77, r'Gamma dist params:')
plt.text(xmax * 0.6, ymax * 0.85, r'sample mean: %.3f' % (_mean, ))
plt.text(xmax * 0.6, ymax * 0.9, r'N50: %.3f' % (_n50, ))
plt.text(xmax * 0.6, ymax * 0.95, r'N90: %.3f' % (_n90, ))
plt.text(_mean, ymax * 0.85, r'Mean')
plt.text(_n50, ymax * 0.9, r'N50')
plt.savefig(fig_path, bbox_inches="tight")
plt.close()
#plt.show()
### read score after size binning.
subplot = gen_boxplot_length_vs_score(df, b_width)
xmin, xmax = plt.gca().get_xlim()
plt.title("Read scores over different length reads")
plt.xticks(np.arange(xmax + 1),
[int(i) for i in np.arange(xmax + 1) * b_width])
plt.suptitle("")
plt.savefig(fig_path2, bbox_inches="tight")
#plt.show()
plt.close()
logger.info("Figs were generated.")
logger.info("Finished all processes.")
def run_platformqc(data_path, output_path, *, suffix=None, b_width=1000):
if not suffix:
suffix = ""
else:
suffix = "_" + suffix
log_path = os.path.join(output_path, "log",
"log_sequel_platformqc" + suffix + ".txt")
fig_path = os.path.join(output_path, "fig",
"fig_sequel_platformqc_length" + suffix + ".png")
fig_path_bar = os.path.join(
output_path, "fig", "fig_sequel_platformqc_adapter" + suffix + ".png")
json_path = os.path.join(output_path, "QC_vals_sequel" + suffix + ".json")
# json
tobe_json = {}
# output_path will be made too.
if not os.path.isdir(os.path.join(output_path, "log")):
os.makedirs(os.path.join(output_path, "log"), exist_ok=True)
if not os.path.isdir(os.path.join(output_path, "fig")):
os.makedirs(os.path.join(output_path, "fig"), exist_ok=True)
### logging conf ###
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
fh = logging.FileHandler(log_path, 'w')
sh = logging.StreamHandler()
formatter = logging.Formatter(
'%(module)s:%(asctime)s:%(lineno)d:%(levelname)s:%(message)s')
fh.setFormatter(formatter)
sh.setFormatter(formatter)
logger.addHandler(sh)
logger.addHandler(fh)
#####################
logger.info("Started sequel platform QC for %s" % data_path)
# sequel
xml_file = get_sts_xml_path(data_path, logger)
if not xml_file:
logger.warning("sts.xml is missing. Productivity won't be shown")
[p0, p1, p2] = [None] * 3
else:
[p0, p1, p2] = parse_sts_xml(
xml_file,
ns="http://pacificbiosciences.com/PacBioBaseDataModel.xsd")
logger.info("Parsed sts.xml")
[subr_bam_p, scrap_bam_p] = get_bam_path(data_path, logger)
if subr_bam_p and scrap_bam_p:
scrap_bam = pysam.AlignmentFile(scrap_bam_p, 'rb', check_sq=False)
subr_bam = pysam.AlignmentFile(subr_bam_p, 'rb', check_sq=False)
else:
logger.ERROR("Platform QC failed due to missing bam files")
return 1
bam_reads = {}
snr = [[], [], [], []]
hr_fraction = []
tot_lengths = []
hr_lengths = []
ad_num_stat = {}
control_throughput = 0
if get_readtype(scrap_bam.header) == 'SCRAP':
logger.info("Started to load scraps.bam...")
control_throughput = set_scrap(bam_reads, scrap_bam, snr)
else:
logger.ERROR("the given scrap file has incorrect header.")
logger.info("Scrap reads were loaded.")
if get_readtype(subr_bam.header) == 'SUBREAD':
logger.info("Started to load subreads.bam...")
set_subreads(bam_reads, subr_bam, snr)
else:
logger.ERROR("the given subread file has incorrect header.")
logger.info("Subreads were loaded.")
for k, v in bam_reads.items():
#print(k)
l = construct_polread(v)
#print(l)
if l[4]:
hr_fraction.append(l[2] / l[3])
tot_lengths.append(l[3])
hr_lengths.append(l[2])
if l[5] in ad_num_stat:
ad_num_stat[l[5]] += 1
else:
ad_num_stat[l[5]] = 1
max_adnum = max(ad_num_stat.keys())
min_adnum = min(ad_num_stat.keys())
left = []
height = []
for i in range(min_adnum, max_adnum + 1):
left.append(i)
if i in ad_num_stat:
height.append(ad_num_stat[i])
else:
height.append(0)
plt.bar(left, height)
plt.savefig(fig_path_bar, bbox_inches="tight")
plt.close()
logger.info("Plotted bar plot for adpter occurence")
(a, b) = lq_gamma.estimate_gamma_dist_scipy(hr_lengths)
logger.info("Fitting by Gamma dist finished.")
_max = np.array(hr_lengths).max()
_mean = np.array(hr_lengths).mean()
_n50 = get_N50(hr_lengths)
_n90 = get_NXX(hr_lengths, 90)
throughput = np.sum(hr_lengths)
longest = np.max(hr_lengths)
fracs = np.mean(hr_fraction)
tobe_json["Productivity"] = {"P0": p0, "P1": p1, "P2": p2}
tobe_json["Throughput"] = int(throughput)
tobe_json["Throughput(Control)"] = int(control_throughput)
tobe_json["Longest_read"] = int(_max)
tobe_json["Num_of_reads"] = len(hr_lengths)
tobe_json["polread_gamma_params"] = [float(a), float(b)]
tobe_json["Mean_polread_length"] = float(_mean)
tobe_json["N50_polread_length"] = float(_n50)
tobe_json["Mean_HQ_fraction"] = float(np.mean(fracs))
tobe_json["Adapter_observation"] = ad_num_stat
with open(json_path, "w") as f:
logger.info("Quality measurements were written into a JSON file: %s" %
json_path)
json.dump(tobe_json, f, indent=4)
x = np.linspace(0, gamma.ppf(0.99, a, 0, b))
est_dist = gamma(a, 0, b)
plt.plot(x, est_dist.pdf(x), c=rgb(214, 39, 40))
plt.grid(True)
plt.hist(hr_lengths,
histtype='step',
bins=np.arange(min(hr_lengths), _max + b_width, b_width),
color=rgb(214, 39, 40),
alpha=0.7,
normed=True)
plt.xlabel('Read length')
plt.ylabel('Probability density')
if _mean >= 10000: # pol read mean is expected >= 10k and <= 15k, but omit the <= 15k condition.
plt.axvline(x=_mean,
linestyle='dashed',
linewidth=2,
color=rgb(44, 160, 44),
alpha=0.8)
else:
plt.axvline(x=_mean,
linestyle='dashed',
linewidth=2,
color=rgb(188, 189, 34),
alpha=0.8)
if _n50 >= 20000:
plt.axvline(x=_n50, linewidth=2, color=rgb(44, 160, 44), alpha=0.8)
else:
plt.axvline(x=_n50, linewidth=2, color=rgb(188, 189, 34), alpha=0.8)
plt.hist(tot_lengths,
histtype='step',
bins=np.arange(min(tot_lengths),
max(tot_lengths) + b_width, b_width),
color=rgb(31, 119, 180),
alpha=0.7,
normed=True)
ymin, ymax = plt.gca().get_ylim()
xmin, xmax = plt.gca().get_xlim()
plt.text(xmax * 0.6, ymax * 0.72, r'$\alpha=%.3f,\ \beta=%.3f$' % (a, b))
plt.text(xmax * 0.6, ymax * 0.77, r'Gamma dist params:')
plt.text(xmax * 0.6, ymax * 0.85, r'sample mean: %.3f' % (_mean, ))
plt.text(xmax * 0.6, ymax * 0.9, r'N50: %.3f' % (_n50, ))
plt.text(xmax * 0.6, ymax * 0.95, r'N90: %.3f' % (_n90, ))
plt.text(_mean, ymax * 0.85, r'Mean')
plt.text(_n50, ymax * 0.9, r'N50')
plt.savefig(fig_path, bbox_inches="tight")
plt.close()
#plt.show()
logger.info("Figs were generated.")
logger.info("Finished all processes.")