def align_upper_pm(peaks, ladder, anchor_pairs, anchor_z):
# this is another attempt to perform ladder - size standard alignment one peak by one
anchor_pairs = sorted(anchor_pairs)
anchor_rtimes, anchor_bpsizes = zip(*anchor_pairs)
anchor_rtimes = list(anchor_rtimes)
anchor_bpsizes = list(anchor_bpsizes)
remaining_sizes = [x for x in ladder['sizes'] if x > anchor_bpsizes[-1]]
current_sizes = anchor_bpsizes
order = ladder['order']
z = estimate_z(anchor_rtimes, anchor_bpsizes, order).z
f = ZFunc(peaks, current_sizes, anchor_pairs, estimate=True)
pairs, rss = f.get_pairs(z)
while True:
if not remaining_sizes:
return pairs, z, rss, f
current_sizes.append(remaining_sizes.pop(0))
f.set_sizes(current_sizes)
score, next_z = minimize_score(f, z, order)
pairs, rss = f.get_pairs(z)
if rss < 100:
z = next_z
if is_verbosity(5):
plot(f.rtimes, f.sizes, z, pairs)
def align_lower_pm(peaks, ladder, anchor_pairs, anchor_z):
# this is another attempt to perform ladder - size standard alignment one peak by one
anchor_pairs = sorted(anchor_pairs)
anchor_rtimes, anchor_bpsizes = zip(*anchor_pairs)
anchor_rtimes = list(anchor_rtimes)
anchor_bpsizes = list(anchor_bpsizes)
remaining_sizes = [x for x in ladder['sizes'] if x < anchor_bpsizes[0]]
current_sizes = anchor_bpsizes
z = estimate_z(anchor_rtimes, anchor_bpsizes, 3).z
f = ZFunc(peaks, current_sizes, anchor_pairs, estimate=True)
pairs, rss = f.get_pairs(z)
while True:
if not remaining_sizes:
return pairs, z, rss, f
current_sizes.insert(0, remaining_sizes.pop(-1))
f.set_sizes(current_sizes)
score, z = minimize_score(f, z, 3)
pairs, rss = f.get_pairs(z)
if is_verbosity(5):
plot(f.rtimes, f.sizes, z, pairs)
def align_upper_pm(peaks, ladder, anchor_pairs, anchor_z):
# this is another attempt to perform ladder - size standard alignment one peak by one
anchor_pairs = sorted(anchor_pairs)
anchor_rtimes, anchor_bpsizes = zip( *anchor_pairs )
anchor_rtimes = list(anchor_rtimes)
anchor_bpsizes = list(anchor_bpsizes)
remaining_sizes = [x for x in ladder['sizes'] if x > anchor_bpsizes[-1]]
current_sizes = anchor_bpsizes
order = ladder['order']
zres = estimate_z(anchor_rtimes, anchor_bpsizes, order)
z,rss = zres.z, zres.rss
f = ZFunc(peaks, current_sizes, anchor_pairs)
while remaining_sizes:
current_sizes.append( remaining_sizes.pop(0) )
if ( remaining_sizes and
(remaining_sizes[-1] - current_sizes[-1]) < 100 and
(remaining_sizes[0] - current_sizes[-1]) < 11 ):
current_sizes.append( remaining_sizes.pop(0) )
f.set_sizes(current_sizes)
score, next_z = minimize_score(f, z, order)
next_pairs, next_rss = f.get_pairs(z)
if (next_rss - rss) < 70:
z = next_z
rss = next_rss
pairs = next_pairs
if is_verbosity(5):
plot(f.rtimes, f.sizes, z, pairs )
# finalize the alignment with stringent criteria
dp_result = align_dp(f.rtimes, f.sizes, f.similarity, z, rss)
if dp_result.rss - rss > 50:
return pairs, z, rss, f
dp_pairs = [(x[1], x[0]) for x in dp_result.sized_peaks]
if is_verbosity(5):
plot(f.rtimes, f.sizes, dp_result.z, dp_pairs)
return dp_pairs, dp_result.z, dp_result.rss, f
def align_upper_pm(peaks, ladder, anchor_pairs, anchor_z):
# this is another attempt to perform ladder - size standard alignment one peak by one
anchor_pairs = sorted(anchor_pairs)
anchor_rtimes, anchor_bpsizes = zip( *anchor_pairs )
anchor_rtimes = list(anchor_rtimes)
anchor_bpsizes = list(anchor_bpsizes)
remaining_sizes = [x for x in ladder['sizes'] if x > anchor_bpsizes[-1]]
current_sizes = anchor_bpsizes
order = ladder['order']
zres = estimate_z(anchor_rtimes, anchor_bpsizes, order)
z,rss = zres.z, zres.rss
f = ZFunc(peaks, current_sizes, anchor_pairs)
while remaining_sizes:
current_sizes.append( remaining_sizes.pop(0) )
if ( remaining_sizes and
(remaining_sizes[-1] - current_sizes[-1]) < 100 and
(remaining_sizes[0] - current_sizes[-1]) < 11 ):
current_sizes.append( remaining_sizes.pop(0) )
f.set_sizes(current_sizes)
score, next_z = minimize_score(f, z, order)
next_pairs, next_rss = f.get_pairs(z)
if (next_rss - rss) < 70:
z = next_z
rss = next_rss
pairs = next_pairs
if is_verbosity(5):
plot(f.rtimes, f.sizes, z, pairs )
# finalize the alignment with stringent criteria
dp_result = align_dp(f.rtimes, f.sizes, f.similarity, z, rss)
if dp_result.rss - rss > 50:
return pairs, z, rss, f
dp_pairs = [(x[1], x[0]) for x in dp_result.sized_peaks]
if is_verbosity(5):
plot(f.rtimes, f.sizes, dp_result.z, dp_pairs)
return dp_pairs, dp_result.z, dp_result.rss, f
def align_lower_pm(peaks, ladder, anchor_pairs, anchor_z):
# this is another attempt to perform ladder - size standard alignment one peak by one
anchor_pairs = sorted(anchor_pairs)
anchor_rtimes, anchor_bpsizes = zip( *anchor_pairs )
anchor_rtimes = list(anchor_rtimes)
anchor_bpsizes = list(anchor_bpsizes)
remaining_sizes = [x for x in ladder['sizes'] if x < anchor_bpsizes[0]]
current_sizes = anchor_bpsizes
zscore = estimate_z(anchor_rtimes, anchor_bpsizes, 3)
z = zscore.z
rss = zscore.rss
f = ZFunc(peaks, current_sizes, anchor_pairs)
while True:
if not remaining_sizes:
return pairs, z, rss, f
current_sizes.insert(0, remaining_sizes.pop(-1))
f.set_sizes(current_sizes)
score, next_z = minimize_score(f, z, 3)
next_pairs, next_rss = f.get_pairs(next_z)
# if delta rss (current rss - prev rss) is above certain threshold,
# then assume the latest peak standar is not appropriate, and
# use previous z and rss
if (next_rss - rss) > 20:
current_sizes.pop(0)
else:
z = next_z
rss = next_rss
pairs = next_pairs
if is_verbosity(5):
plot(f.rtimes, f.sizes, z, pairs )
def align_lower_pm(peaks, ladder, anchor_pairs, anchor_z):
# this is another attempt to perform ladder - size standard alignment one peak by one
anchor_pairs = sorted(anchor_pairs)
anchor_rtimes, anchor_bpsizes = zip(*anchor_pairs)
anchor_rtimes = list(anchor_rtimes)
anchor_bpsizes = list(anchor_bpsizes)
remaining_sizes = [x for x in ladder['sizes'] if x < anchor_bpsizes[0]]
current_sizes = anchor_bpsizes
zscore = estimate_z(anchor_rtimes, anchor_bpsizes, 3)
z = zscore.z
rss = zscore.rss
f = ZFunc(peaks, current_sizes, anchor_pairs)
while True:
if not remaining_sizes:
return pairs, z, rss, f
current_sizes.insert(0, remaining_sizes.pop(-1))
f.set_sizes(current_sizes)
score, next_z = minimize_score(f, z, 3)
next_pairs, next_rss = f.get_pairs(next_z)
# if delta rss (current rss - prev rss) is above certain threshold,
# then assume the latest peak standar is not appropriate, and
# use previous z and rss
if (next_rss - rss) > 20:
current_sizes.pop(0)
else:
z = next_z
rss = next_rss
pairs = next_pairs
if is_verbosity(5):
plot(f.rtimes, f.sizes, z, pairs)