def writePredictedRepReadsToFile(p1, rep_reads, fout):
counts = getProfileCounts(p1)
idx = 0
for cnt, indel, _, _ in counts:
if cnt < 0.5: break
fout.write(u'%d\t%s\t%s\n' % (idx, rep_reads[indel], indel))
idx += 1
def writeMCISummary(fout, id, p1, stats1, oligo_det, more_indels=False):
if not more_indels: mcis = [getHighestIndel(p1)]
else: mcis = [x[1] for x in getProfileCounts(p1) if x[1] != '-']
for mci in mcis:
mci_reads = p1[mci]
total_reads = stats1[0] - stats1[2]
itype, isize, details, muts = tokFullIndel(mci)
pam_loc, pam_dir, seq = oligo_det
mh_seq, altered_seq = '', ''
if itype == 'D' and ('I' not in details or details['I'] == 0):
if details['C'] > 0:
left_c_seq = getSequence(oligo_det, details['L'] + 1,
details['L'] + details['C'])
right_c_seq = getSequence(oligo_det,
details['R'] - details['C'],
details['R'] - 1)
if left_c_seq == right_c_seq:
mh_seq = left_c_seq
altered_seq = getSequence(oligo_det, details['L'] + 1,
details['R'] -
1) #Note includes MH seq at both ends
str_args = (id, mci, details['L'], details['R'], details['C'], itype,
isize, mci_reads, total_reads, mh_seq, altered_seq)
fout.write(u'%s\t%s\t%d\t%d\t%d\t%s\t%d\t%d\t%d\t%s\t%s\n' % str_args)
def computePredictedProfile(data, theta, feature_columns):
data['expThetaX'] = np.exp(
data.apply(calcThetaX, axis=1, args=(theta, feature_columns)))
sum_exp = data['expThetaX'].sum()
profile = {
x: expthetax * 100 / sum_exp
for (x, expthetax) in zip(data['Indel'], data['expThetaX'])
}
counts = getProfileCounts(profile)
return profile, counts
def writePredictedProfileToSummary(p1, fout):
counts = getProfileCounts(p1)
for cnt, indel, _, _ in counts:
if cnt < 0.5: break
fout.write(u'%s\t-\t%d\n' % (indel, np.round(cnt)))
def plotProfiles(profiles,
rep_reads,
pam_idxs,
reverses,
labels,
title='',
max_lines=10):
if len(profiles) == 0: raise Exception('Empty list of profiles')
colors = [
FORECAST_GREEN, 'C0', 'C2', 'C2', 'C1', 'C1', 'C3', 'C3', 'C4', 'C4',
'C5', 'C5', 'C6'
]
PL.rcParams['svg.fonttype'] = 'none'
ocounts = [getProfileCounts(p1) for p1 in profiles]
counts = [{
indel: (cnt, indel, perc1a, perc1b)
for (cnt, indel, perc1a, perc1b) in x
} for x in ocounts]
#Count total non-null reads for each sample (to report in labels)
nonnull_reads = [
sum([x[indel][0] for indel in x if indel != '-']) for x in counts
]
labels = [
'%s(%d Reads)' % (tit, nn) for (tit, nn) in zip(labels, nonnull_reads)
]
#Fetch the indels to display as union of top N indels across profiles
num_top = 20
top_indels = [[y[1] for y in x[:num_top]] for x in ocounts]
union_top_indels = set()
for x in top_indels:
union_top_indels = union_top_indels.union(set(x))
for indel in union_top_indels:
for count in counts:
if indel not in count:
count[indel] = (0, indel, 0.0, 0.0)
union_top_indels = [x for x in union_top_indels]
indel_toks = [tokFullIndel(indel) for indel in union_top_indels]
max_insert = max([0] + [toks[1] for toks in indel_toks if toks[0] == 'I'])
#Order indels by decreasing average percentage across profiles
top_av_percs = [(np.mean([x[indel][-1] for x in counts]), indel)
for indel in union_top_indels]
top_av_percs.sort(reverse=True)
max_indels = max_lines / len(profiles)
#Figure out Trims
null_reads = [
x['-'] if '-' in x else [x[y[1]] for y in ocnt if y[1] in x][0]
for x, ocnt in zip(rep_reads, ocounts)
]
null_reads = [
Bio.Seq.reverse_complement(x) if rev else x
for x, rev in zip(null_reads, reverses)
]
pam_idxs = [
len(x) - pam if rev else pam
for x, pam, rev in zip(null_reads, pam_idxs, reverses)
]
min_null, pam_idx = min([(len(null), pidx)
for (null, pidx) in zip(null_reads, pam_idxs)])
Ls = [x - pam_idx for x in pam_idxs]
Rs = [L + min_null - len(null) for (L, null) in zip(Ls, null_reads)]
#Plot
scale_factor = 10.0 / max([x[1][3] for x in ocounts])
fig = PL.figure(figsize=(9, 5 * len(labels)))
fig.patch.set_visible(False)
ax = PL.gca()
ax.axis('off')
N = min(len(union_top_indels), max_indels)
line_height = 0.8
min_xloc, max_xloc = MIN_X, MAX_X
PL.ylim((0, (N + 1.0) * line_height))
bar_ypos, bar_len = [[] for x in profiles], [[] for x in profiles]
for i, (av_perc, indel) in enumerate(top_av_percs):
if i > max_indels: break
for repr, cnts, rev, L1, R1, j in zip(rep_reads, counts, reverses, Ls,
Rs, range(len(Rs))):
(cnt1, indel1, perc1a, perc1b) = cnts[indel]
if indel in repr:
if R1 == 0: R1 = len(repr[indel])
seq = Bio.Seq.reverse_complement(
repr[indel])[L1:R1] if rev else repr[indel][L1:R1]
padded_seq, red_idxs, green_idxs = padReadForIndel(
seq, indel, pam_idx)
min_xloc, max_xloc = plotSeqLetterwise(
padded_seq,
(N - i + (j + 0.3) * 1.0 / len(profiles)) * line_height,
pam_idx,
red_idxs=red_idxs,
green_idxs=green_idxs)
if indel != '-':
bar_ypos[j].append(
(N - i + (j + 0.4) * 1.0 / len(profiles)) * line_height)
bar_len[j].append(perc1b * scale_factor)
hist_loc = max_xloc + 10
for bar1_ypos, bar1_len, label1, clr in zip(bar_ypos, bar_len, labels,
colors):
PL.barh(bar1_ypos,
bar1_len,
height=0.8 * line_height / len(profiles),
left=hist_loc,
label=label1,
color=clr)
for (ypos, blen) in zip(bar1_ypos, bar1_len):
PL.text(hist_loc + blen + 1,
ypos - 0.5 / len(profiles) * line_height,
'%.1f%%' % (blen / scale_factor))
xlims = (min_xloc - 10, MAX_X + 20 + (min_xloc - MIN_X))
PL.xlim(xlims)
for i, (av_perc, indel) in enumerate(top_av_percs):
if i > max_indels: break
if indel == '-':
PL.text(xlims[0], (N - i + 0.4) * line_height,
'Target:',
fontweight='bold')
else:
PL.text(xlims[0], (N - i + 0.4) * line_height,
indel.split('_')[0],
fontweight='bold')
PL.plot([min_xloc - 10, max_xloc + 10],
[(N - i) * line_height, (N - i) * line_height], 'lightgrey')
PL.plot([0, 0], [0, (N + 1) * line_height], 'k--')
PL.plot([min_xloc - 10, hist_loc], [N * line_height, N * line_height], 'k')
PL.plot([hist_loc, hist_loc], [0, N * line_height], 'k')
PL.xticks([])
PL.yticks([])
if len(labels) > 1: PL.legend(loc='upper right')
PL.text(hist_loc, (N + 0.5) * line_height, title, fontweight='bold')
PL.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05)
PL.show(block=False)
PL.axis('off')
saveFig('%s_%d' % (title.replace(' ', '_'), len(labels)), bbox=False)
return fig
