def plotDominantBars(all_result_outputs, label=''):
pie_labels = ['I1_Rpt Left Reads - NonAmb','Ambiguous Rpt Reads','I1_Rpt Right Reads - NonAmb','I1_NonRpt Reads']
mci_merged_data = mergeSamples(all_result_outputs, [], data_label='i1IndelData')
mci_merged_data['Equal MCI'] = (mci_merged_data['Most Common Indel']==mci_merged_data['Most Common Indel 2']) & (mci_merged_data['Most Common Indel']==mci_merged_data['Most Common Indel 3'])
mci_merged_data['Is Dominant I1'] = (mci_merged_data['Equal MCI'] & (mci_merged_data['MCI Type'] == 'I1'))
oligo_data = pd.read_csv(getHighDataDir() + '/ST_June_2017/data/self_target_oligos_details_with_pam_details.csv',sep='\t')
remove_under = lambda x: x.replace('_','')
oligo_data['Oligo Id'] = oligo_data['ID'].apply(remove_under)
merged_mci_data = pd.merge(mci_merged_data, oligo_data[['Oligo Id','Guide']], how='inner',on='Oligo Id')
nt_perc_i1, cnt_labels = [], []
nts = 'ATGC'
for nt in nts:
is_nt = lambda guide: (guide[-4] == nt)
nt_data = merged_mci_data.loc[merged_mci_data['Guide'].apply(is_nt)]
nt_perc_i1.append(sum(nt_data['Is Dominant I1'])*100.0/len(nt_data))
cnt_labels.append('%d/%d' % (sum(nt_data['Is Dominant I1']), len(nt_data)))
PL.figure()
PL.bar(range(4), nt_perc_i1, width=0.8)
for i, cnt in enumerate(cnt_labels):
PL.text(i-0.3,nt_perc_i1[i]+5.0,cnt)
PL.xticks(range(4), [x for x in nts])
PL.xlabel('Nucleotide on Left of cut-site')
PL.ylabel('Percent gRNAs with single nucleotide insertion\nas most common indel in all 3 replicates')
PL.show(block=False)
saveFig('I1_bar_3_rep')
def plotMergedPieDataWithAmbig(all_result_outputs, label='', norm='I1 Total'):
pie_labels = [
'I1_Rpt Left Reads - NonAmb', 'Ambiguous Rpt Reads',
'I1_Rpt Right Reads - NonAmb', 'I1_NonRpt Reads'
]
merged_data = mergeSamples(all_result_outputs,
pie_labels + [norm],
data_label='i1IndelData',
merge_on=['Oligo Id'])
labels = [
'Repeated\nleft nucleotide', 'Ambiguous\n(Left = Right)',
'Repeated\nright nucleotide', 'Non-repeated\nnucleotide'
]
pie_data = {
x: (merged_data[x + ' Sum'] * 100.0 /
merged_data[norm + ' Sum']).mean(axis=0)
for x in pie_labels
}
PL.figure(figsize=(3, 3))
PL.pie([pie_data[x] for x in pie_labels],
labels=labels,
autopct='%.1f',
labeldistance=1.05,
startangle=120.0,
counterclock=False)
PL.title('Single nucleotide insertions (I1)')
PL.show(block=False)
saveFig('ambig_pie')
def plotMCIPie(all_result_outputs, label=''):
mci_merged_data = mergeSamples(all_result_outputs,
['MCI Type', 'Most Common Indel'],
data_label='perOligoMCI')
mci_common = mci_merged_data.loc[(mci_merged_data['Most Common Indel'] ==
mci_merged_data['Most Common Indel 2']) &
(mci_merged_data['Most Common Indel'] ==
mci_merged_data['Most Common Indel 3'])]
pie_vals, pie_labels = [], []
for mci_type in ALL_LABELS:
pie_vals.append(len(
mci_common.loc[mci_common['MCI Type'] == mci_type]))
pie_labels.append(mci_type)
pie_vals.append(len(mci_merged_data) - len(mci_common))
pie_labels.append('Inconsistent\nbetween\nreplicates')
PL.figure(figsize=(4, 4))
PL.pie(pie_vals,
labels=pie_labels,
autopct='%.1f',
labeldistance=1.05,
startangle=90.0,
counterclock=False,
colors=COLORS)
PL.title('Most frequent\nmutation per gRNA')
PL.show(block=False)
saveFig('pie_chart_cats_dominant')
def plotSumPie(all_result_outputs, label=''):
mapping = {
'Large D, No MH': 'D>=4,\nno MH',
'Large D, MH': 'D>=4,\nMH',
'Small D, No MH': 'D<4, no MH',
'Small D, MH': 'D<4, MH'
}
merged_data = mergeSamples(all_result_outputs,
['Total reads'] + ALL_LABELS,
data_label='perOligoCounts')
for col in ALL_LABELS:
merged_data[col + ' Perc'] = merged_data[
col + ' Sum'] * 100.0 / merged_data['Total reads Sum']
merged_data.to_csv('data_dump_indel_pie.txt',
sep='\t',
columns=['Oligo Id'] +
[col + ' Perc' for col in ALL_LABELS])
pie_vals = [merged_data[col + ' Perc'].mean() for col in ALL_LABELS]
PL.figure(figsize=(4, 4))
wedge_labels = [mapping[x] if x in mapping else x for x in ALL_LABELS]
PL.pie(pie_vals,
labels=wedge_labels,
autopct='%.1f',
labeldistance=1.05,
startangle=90.0,
counterclock=False,
colors=COLORS)
PL.title('Average distribution\n of mutations\n per gRNA')
PL.show(block=False)
saveFig('pie_chart_cats')
def plotDominantPieDataWithAmbig(all_result_outputs, label=''):
pie_labels = ['I1_Rpt Left Reads - NonAmb','Ambiguous Rpt Reads','I1_Rpt Right Reads - NonAmb','I1_NonRpt Reads']
mci_merged_data = mergeSamples(all_result_outputs, [], data_label='i1IndelData')
mci_merged_data['Equal MCI'] = (mci_merged_data['Most Common Indel']==mci_merged_data['Most Common Indel 2']) & (mci_merged_data['Most Common Indel']==mci_merged_data['Most Common Indel 3'])
mci_common_i1 = mci_merged_data.loc[mci_merged_data['Equal MCI'] & (mci_merged_data['MCI Type'] == 'I1')]
labels = ['Repeated\nleft nucleotide', 'Ambiguous\n(Left = Right)', 'Repeated\nright nucleotide', 'Non-repeated\nnucleotide']
pie_data = []
for label in pie_labels:
is_rpt = lambda row: row['MCI Reads'] == row[label]
pie_data.append(sum(mci_common_i1.apply(is_rpt,axis=1))*100.0/len(mci_common_i1))
PL.figure(figsize=(3,3))
PL.pie(pie_data, labels=labels, autopct='%.1f', labeldistance=1.05, startangle=180.0, counterclock=False)
PL.title('Dominant single nucleotide insertions (I1)\n%d from %d gRNAs' % (len(mci_common_i1), len(mci_merged_data)))
PL.show(block=False)
saveFig('I1_dom_pie_3_rep')
def plotMergedI1Repeats(all_result_outputs, label=''):
merged_data = mergeSamples(all_result_outputs, ['I1_Rpt Left Reads - NonAmb','Total reads'], data_label='i1IndelData', merge_on=['Oligo Id','Repeat Nucleotide Left'])
nt_mean_percs, nts = [], ['A','T','G','C']
for nt in nts:
nt_data = merged_data.loc[merged_data['Repeat Nucleotide Left'] == nt]
nt_mean_percs.append((nt_data['I1_Rpt Left Reads - NonAmb Sum']*100.0/nt_data['Total reads Sum']).mean())
PL.figure(figsize=(3,3))
PL.bar(range(4),nt_mean_percs)
for i in range(4):
PL.text(i-0.25,nt_mean_percs[i]+0.8,'%.1f' % nt_mean_percs[i])
PL.xticks(range(4),nts)
PL.ylim((0,26))
PL.xlabel('PAM distal nucleotide\nadjacent to the cut site')
PL.ylabel('I1 repeated left nucleotide\nas percent of total mutated reads')
PL.show(block=False)
saveFig('i1_rtp_nt')
def plotMicrohomologyMismatches(all_result_outputs, label=''):
mut_hdrs = ['Left Mut', 'Right Mut','Merged Mut1', 'Merged Mut2']
cols_to_sum = [x + ' Indel Reads in Mut' for x in mut_hdrs] + ['Orig Indel Reads in Orig', 'Mut Non-Null Reads', 'Orig Non-Null Reads']
common_cols = ['Oligo ID','Mapped Oligo Id','Num Mismatches','Orig MH','Left Mut-MH','Right Mut-MH','Merged Mut 1 MH','Merged Mut 2 MH','Orig Indel','Left Mut-MH Indel','Right Mut-MH Indel','Merge Mut 1 Indel','Merge Mut 2 Indel']
data = mergeSamples(all_result_outputs, cols_to_sum, merge_on=common_cols)
getLeft = lambda indel: tokFullIndel(indel)[2]['L']
getRight = lambda indel: tokFullIndel(indel)[2]['R']
getMHSize = lambda indel: tokFullIndel(indel)[2]['C']
oligo_data = pd.read_csv(getHighDataDir() + '/ST_June_2017/data/self_target_oligos_details_with_pam_details.csv', sep='\t')
oligo_data['Guide is matched'] = oligo_data.apply(isMatched, axis=1)
reverse_lookup = {x: y == 'REVERSE' for (x,y) in zip(oligo_data['ID'],oligo_data['PAM Direction'])}
is_reverse = lambda x: reverse_lookup[x]
data = pd.merge(data, oligo_data[['ID','Guide is matched']], left_on='Oligo ID', right_on='ID', how='inner')
data['MH Size'] = data['Orig Indel'].apply(getMHSize)
data = data.loc[(data['MH Size'] != 0) & (data['Guide is matched'])]
data['MH Left Loc'] = data['Orig Indel'].apply(getLeft) + data['MH Size']
data['MH Right Loc'] = data['Orig Indel'].apply(getRight) - data['MH Size']
data['Is Reverse'] = data['Oligo ID'].apply(is_reverse)
for hdrL,hdrR in [mut_hdrs[:2], mut_hdrs[2:]]:
data[hdrL + ' Reads'] = data['Is Reverse']*data[hdrR + ' Indel Reads in Mut Sum'] + (1- data['Is Reverse'])*data[hdrL + ' Indel Reads in Mut Sum']
data[hdrR + ' Reads'] = data['Is Reverse']*data[hdrL + ' Indel Reads in Mut Sum'] + (1- data['Is Reverse'])*data[hdrR + ' Indel Reads in Mut Sum']
data[hdrL + ' Reads Ratio'] = data[hdrL + ' Reads']*100.0/data['Mut Non-Null Reads Sum']
data[hdrR + ' Reads Ratio'] = data[hdrR + ' Reads']*100.0/data['Mut Non-Null Reads Sum']
data['Orig Indel Reads Ratio'] = data['Orig Indel Reads in Orig Sum']*100.0/data['Orig Non-Null Reads Sum']
data['All Mut Reads Ratio'] = (data[[x + ' Reads' for x in mut_hdrs]].sum(axis=1))*100.0/data['Mut Non-Null Reads Sum']
data['MH Dist'] = data['MH Right Loc'] - data['MH Left Loc']
data['1st Mismatch'] = data.apply(getMismatch, axis=1)
data['Last Mismatch'] = data.apply(getLastMismatch, axis=1)
data['MH GC Content'] = data.apply(getMhGC, axis=1)
mh_indel_types = [('Orig Indel','Left Mut'), ('Orig Indel','Right Mut'), ('Orig Indel','All Mut'),('Left Mut','Right Mut') ]
label_lookup = {'Orig Indel': 'Perc. mutated reads of corresponding microhomology-\nmediated deletion with no sequence mismatches',
'Left Mut': 'Perc. mutated reads of mismatched microhomology-\nmediated deletion with retained left sequence',
'Right Mut': 'Perc mutated reads of mismatched microhomology-\nmediated deletion with retained right sequence',
'All Mut': 'Perc mutated reads of mismatched microhomology-\nmediated deletion (All)'
}
fig1 = PL.figure(figsize=(4,4))
fig_all = PL.figure(figsize=(10,10))
for i, (mh_typex, mh_typey) in enumerate(mh_indel_types):
figs = [(fig_all, True), (fig1,False)] if i==2 else [(fig_all, True)]
for fig, is_all in figs:
PL.figure(fig.number)
if is_all: PL.subplot(2,2,i+1)
for nm,clr in zip([1,2],['royalblue','orange']):
nm_data = data.loc[data['Num Mismatches'] == nm]
sty, lsty = 'o', '-'
sel_data = nm_data.loc[(nm_data['MH Size'] >= 6) & (nm_data['MH Size'] <= 15)]
PL.plot(sel_data[mh_typex + ' Reads Ratio'], sel_data[mh_typey + ' Reads Ratio'], sty, color=clr, markersize=4, label='No. MH Mismatches=%d' % (nm))
rx, ry, grad = getRegrLine(sel_data[[mh_typex + ' Reads Ratio']], sel_data[[mh_typey + ' Reads Ratio']])
if not is_all: print(grad, nm, mh_typex, mh_typey)
if i != 3: PL.plot(rx, ry, lsty, color=clr, linewidth=2)
PL.xlabel(label_lookup[mh_typex])
PL.ylabel(label_lookup[mh_typey])
PL.xlim((0,80))
PL.ylim((0,80))
PL.plot([0,80],[0,80],'k--')
PL.legend()
PL.show(block=False)
saveFig('mm_mismatch_all')
PL.figure(fig1.number)
saveFig('mm_mismatch_one')
def plotD2(all_result_outputs, label=''):
#Merge replicates
mci_merged_data = mergeSamples(all_result_outputs, [],
data_label='perOligoMCI')
mci_merged_data['Equal MCI'] = (
mci_merged_data['Most Common Indel']
== mci_merged_data['Most Common Indel 2']) & (
mci_merged_data['Most Common Indel']
== mci_merged_data['Most Common Indel 3'])
oligo_data = pd.read_csv(
getHighDataDir() +
'/ST_June_2017/data/self_target_oligos_details_with_pam_details.csv',
sep='\t')
remove_under = lambda x: x.replace('_', '')
oligo_data['Oligo Id'] = oligo_data['ID'].apply(remove_under)
mci_merged_data_guides = pd.merge(mci_merged_data,
oligo_data[['Oligo Id', 'Guide']],
how='inner',
on='Oligo Id')
mci_common = mci_merged_data_guides.loc[mci_merged_data['Equal MCI']]
dmci_data = mci_common.loc[(
mci_common['MCI Type'] == 'D2'
)] #Note: type check discards equally most common indels
pie_vals, pie_labels = [], []
is_left_rpt = lambda row: row['Guide'][-5] == row['Guide'][
-3] and tokFullIndel(row['Most Common Indel'])[2][
'R'] >= 1 and tokFullIndel(row['Most Common Indel'])[2]['L'] <= -3
is_right_rpt = lambda row: row['Guide'][-4] == row['Guide'][
-2] and tokFullIndel(row['Most Common Indel'])[2][
'R'] >= 2 and tokFullIndel(row['Most Common Indel'])[2]['L'] <= -2
is_left_only_rpt = lambda row: is_left_rpt(row) and not is_right_rpt(row)
is_right_only_rpt = lambda row: is_right_rpt(row) and not is_left_rpt(row)
is_both_rpt = lambda row: is_right_rpt(row) and is_left_rpt(row)
lrpt_data = dmci_data.loc[dmci_data.apply(is_left_only_rpt, axis=1)]
pie_labels.append('Y|XY->Y')
pie_vals.append(len(lrpt_data))
rrpt_data = dmci_data.loc[dmci_data.apply(is_right_only_rpt, axis=1)]
pie_labels.append('XY|X->X')
pie_vals.append(len(rrpt_data))
rpt_data = dmci_data.loc[dmci_data.apply(is_both_rpt, axis=1)]
pie_labels.append('XY|XY->XY')
pie_vals.append(len(rpt_data))
is_r0 = lambda row: tokFullIndel(row['Most Common Indel'])[2]['R'] == 0
ro_data = dmci_data.loc[dmci_data.apply(is_r0, axis=1)]
pie_labels.append('Z|XY->Z')
pie_vals.append(len(ro_data))
is_l1 = lambda row: tokFullIndel(row['Most Common Indel'])[2]['L'] == -1
l1_data = dmci_data.loc[dmci_data.apply(is_l1, axis=1)]
pie_labels.append('XY|Z->Z')
pie_vals.append(len(l1_data))
seen_ids = set(rpt_data['Oligo Id']).union(set(ro_data['Oligo Id'])).union(
set(l1_data['Oligo Id'])).union(set(lrpt_data['Oligo Id'])).union(
set(rrpt_data['Oligo Id']))
is_unseen = lambda id: id not in seen_ids
unseen_data = dmci_data.loc[dmci_data['Oligo Id'].apply(is_unseen)]
print(unseen_data)
assert (len(unseen_data) == 0)
#pie_labels.append('Other')
#pie_vals.append(len(unseen_data))
#pie_labels = [x for x in dmci_data['Most Common Indel'].unique()]
#pie_vals = [len(dmci_data.loc[dmci_data['Most Common Indel']==indel]) for indel in pie_labels]
PL.figure(figsize=(4, 4))
PL.pie(pie_vals,
labels=pie_labels,
autopct='%.1f',
labeldistance=1.1,
counterclock=False,
colors=COLORS)
PL.title(
'Size 2 deletions that are\n"most common" for their gRNA in all 3 replicates\n(%d gRNAs from %d total)'
% (len(dmci_data), len(mci_merged_data)))
PL.show(block=False)
saveFig('pie_chart_D2_indel_cats')
PL.figure(figsize=(12, 8))
#XY|XY->XY
PL.subplot(2, 3, 1)
pie_vals, pie_labels = [], []
for mh_str in [y + x for x in 'ATGC' for y in 'ATGC']:
pie_labels.append(mh_str)
is_mh_str = lambda guide: guide[-5:-3] == mh_str
pie_vals.append(len(rpt_data.loc[rpt_data['Guide'].apply(is_mh_str)]))
for dnt, cnt in zip(pie_labels, pie_vals):
print(dnt, cnt * 100 / sum(pie_vals))
PL.pie(pie_vals,
labels=pie_labels,
autopct='%.1f',
labeldistance=1.1,
counterclock=False,
colors=COLORS)
PL.title('XY|XY->XY\n(%d gRNAs)' % len(rpt_data))
PL.show(block=False)
#__|
PL.subplot(2, 3, 2)
pie_vals, pie_labels = [], []
for mh_str in [y + x for x in 'ATGC' for y in 'ATGC']:
pie_labels.append(mh_str)
is_mh_str = lambda guide: guide[-5:-3] == mh_str
pie_vals.append(len(ro_data.loc[ro_data['Guide'].apply(is_mh_str)]))
for dnt, cnt in zip(pie_labels, pie_vals):
print(dnt, cnt * 100 / sum(pie_vals))
PL.pie(pie_vals,
labels=pie_labels,
autopct='%.1f',
labeldistance=1.1,
counterclock=False,
colors=COLORS)
PL.title('XY| -> __|\n(%d gRNAs)' % len(ro_data))
PL.show(block=False)
#|__
PL.subplot(2, 3, 3)
pie_vals, pie_labels = [], []
for mh_str in [y + x for x in 'ATGC' for y in 'ATGC']:
pie_labels.append(mh_str)
is_mh_str = lambda guide: guide[-3:-1] == mh_str
pie_vals.append(len(l1_data.loc[l1_data['Guide'].apply(is_mh_str)]))
for dnt, cnt in zip(pie_labels, pie_vals):
print(dnt, cnt * 100 / sum(pie_vals))
PL.pie(pie_vals,
labels=pie_labels,
autopct='%.1f',
labeldistance=1.1,
counterclock=False,
colors=COLORS)
PL.title('|XY -> |__\n(%d gRNAs)' % len(l1_data))
PL.show(block=False)
#XY|X->X
PL.subplot(2, 3, 4)
pie_vals, pie_labels = [], []
for nt in 'ATGC':
pie_labels.append('%sN|%s -> %s' % (nt, nt, nt))
is_mh_str = lambda guide: guide[-5] == nt
pie_vals.append(len(
lrpt_data.loc[lrpt_data['Guide'].apply(is_mh_str)]))
PL.pie(pie_vals,
labels=pie_labels,
autopct='%.1f',
labeldistance=1.1,
counterclock=False,
colors=COLORS)
PL.title('XY|X->X\n(%d gRNAs)' % len(lrpt_data))
PL.show(block=False)
#X|YX->X
PL.subplot(2, 3, 5)
pie_vals, pie_labels = [], []
for nt in 'ATGC':
pie_labels.append('%s|N%s -> %s' % (nt, nt, nt))
is_mh_str = lambda guide: guide[-4] == nt
pie_vals.append(len(
rrpt_data.loc[rrpt_data['Guide'].apply(is_mh_str)]))
PL.pie(pie_vals,
labels=pie_labels,
autopct='%.1f',
labeldistance=1.1,
counterclock=False,
colors=COLORS)
PL.title('X|YX->X\n(%d gRNAs)' % len(rrpt_data))
PL.show(block=False)
PL.subplots_adjust(left=0.05,
right=0.95,
top=0.9,
bottom=0.1,
hspace=0.3,
wspace=0.3)
saveFig('D2_nts_per_cat')
PL.figure(figsize=(12, 8))
#XY|XY->XY
PL.subplot(2, 3, 1)
bar_heights, bar_labels, d2_dnt_counts, dnt_counts = [], [], [], []
for dnt in [y + x for x in 'ATGC' for y in 'ATGC']:
has_dnt = lambda guide: guide[-5:-3] == dnt and guide[-3:-1] == dnt
dnt_data = mci_merged_data_guides.loc[
mci_merged_data_guides['Guide'].apply(has_dnt)]
dnt_counts.append(
len(
set(rpt_data['Oligo Id']).intersection(
set(dnt_data['Oligo Id']))))
d2_dnt_counts.append(len(dnt_data))
bar_heights.append(dnt_counts[-1] * 100.0 /
d2_dnt_counts[-1] if d2_dnt_counts[-1] > 0 else 0)
bar_labels.append(dnt)
print(
dnt, dnt_counts[-1] * 100.0 /
d2_dnt_counts[-1] if d2_dnt_counts[-1] > 0 else 0)
PL.bar(range(len(bar_labels)), bar_heights, width=0.8)
for i, (hgt, d2cnt,
cnt) in enumerate(zip(bar_heights, d2_dnt_counts, dnt_counts)):
PL.text(i - 0.3, hgt + 15, '%d/%d' % (cnt, d2cnt), rotation='vertical')
PL.xticks(range(len(bar_labels)), bar_labels, rotation='vertical')
PL.ylim((0, 90))
PL.xlabel('XY')
PL.title('XY|XY->XY')
PL.ylabel(
'Percent gRNAs with XY|XY->XY deletion\nas most common indel in all 3 replicates'
)
PL.show(block=False)
#__|
PL.subplot(2, 3, 2)
bar_heights, bar_labels, d2_dnt_counts, dnt_counts = [], [], [], []
for dnt in [y + x for x in 'ATGC' for y in 'ATGC']:
has_dnt = lambda guide: guide[-5:-3] == dnt
dnt_data = mci_merged_data_guides.loc[
mci_merged_data_guides['Guide'].apply(has_dnt)]
dnt_counts.append(
len(
set(ro_data['Oligo Id']).intersection(set(
dnt_data['Oligo Id']))))
d2_dnt_counts.append(len(dnt_data))
bar_heights.append(dnt_counts[-1] * 100.0 /
d2_dnt_counts[-1] if d2_dnt_counts[-1] > 0 else 0)
bar_labels.append(dnt)
print(
dnt, dnt_counts[-1] * 100.0 /
d2_dnt_counts[-1] if d2_dnt_counts[-1] > 0 else 0)
PL.bar(range(len(bar_labels)), bar_heights, width=0.8)
for i, (hgt, d2cnt,
cnt) in enumerate(zip(bar_heights, d2_dnt_counts, dnt_counts)):
PL.text(i - 0.3,
hgt + 1.5,
'%d/%d' % (cnt, d2cnt),
rotation='vertical')
PL.xticks(range(len(bar_labels)), bar_labels, rotation='vertical')
PL.ylim((0, 8))
PL.xlabel('XY')
PL.title('XY| -> __|')
PL.ylabel(
'Percent gRNAs with XY| -> __| deletion\nas most common indel in all 3 replicates'
)
PL.show(block=False)
#|__
PL.subplot(2, 3, 3)
bar_heights, bar_labels, d2_dnt_counts, dnt_counts = [], [], [], []
for dnt in [y + x for x in 'ATGC' for y in 'ATGC']:
has_dnt = lambda guide: guide[-3:-1] == dnt
dnt_data = mci_merged_data_guides.loc[
mci_merged_data_guides['Guide'].apply(has_dnt)]
dnt_counts.append(
len(
set(l1_data['Oligo Id']).intersection(set(
dnt_data['Oligo Id']))))
d2_dnt_counts.append(len(dnt_data))
bar_heights.append(dnt_counts[-1] * 100.0 /
d2_dnt_counts[-1] if d2_dnt_counts[-1] > 0 else 0)
bar_labels.append(dnt)
print(
dnt, dnt_counts[-1] * 100.0 /
d2_dnt_counts[-1] if d2_dnt_counts[-1] > 0 else 0)
PL.bar(range(len(bar_labels)), bar_heights, width=0.8)
for i, (hgt, d2cnt,
cnt) in enumerate(zip(bar_heights, d2_dnt_counts, dnt_counts)):
PL.text(i - 0.3,
hgt + 1.5,
'%d/%d' % (cnt, d2cnt),
rotation='vertical')
PL.xticks(range(len(bar_labels)), bar_labels, rotation='vertical')
PL.ylim((0, 8))
PL.xlabel('XY')
PL.title('|XY -> |__')
PL.ylabel(
'Percent gRNAs with |XY -> |__ deletion\nas most common indel in all 3 replicates'
)
PL.show(block=False)
#XY|X->X
PL.subplot(2, 3, 4)
bar_heights, bar_labels, d2_nt_counts, nt_counts = [], [], [], []
for nt in 'ATGC':
has_nt = lambda guide: guide[-3] == nt and guide[-5] == nt
nt_data = mci_merged_data_guides.loc[
mci_merged_data_guides['Guide'].apply(has_nt)]
nt_counts.append(
len(
set(lrpt_data['Oligo Id']).intersection(
set(nt_data['Oligo Id']))))
d2_nt_counts.append(len(nt_data))
bar_heights.append(nt_counts[-1] * 100.0 /
d2_nt_counts[-1] if d2_nt_counts[-1] > 0 else 0)
bar_labels.append(nt)
PL.bar(range(len(bar_labels)), bar_heights, width=0.8)
for i, (hgt, d2cnt,
cnt) in enumerate(zip(bar_heights, d2_nt_counts, nt_counts)):
PL.text(i - 0.3, hgt + 0.05, '%d/%d' % (cnt, d2cnt))
PL.xticks(range(len(bar_labels)), bar_labels)
PL.ylim((0, 5))
PL.xlabel('X')
PL.title('XY|X->X')
PL.ylabel(
'Percent gRNAs with XY|X->X deletion\nas most common indel in all 3 replicates'
)
PL.show(block=False)
#X|YX->X
PL.subplot(2, 3, 5)
bar_heights, bar_labels, d2_nt_counts, nt_counts = [], [], [], []
for nt in 'ATGC':
has_nt = lambda guide: guide[-4] == nt and guide[-2] == nt
nt_data = mci_merged_data_guides.loc[
mci_merged_data_guides['Guide'].apply(has_nt)]
nt_counts.append(
len(
set(rrpt_data['Oligo Id']).intersection(
set(nt_data['Oligo Id']))))
d2_nt_counts.append(len(nt_data))
bar_heights.append(nt_counts[-1] * 100.0 /
d2_nt_counts[-1] if d2_nt_counts[-1] > 0 else 0)
bar_labels.append(nt)
PL.bar(range(len(bar_labels)), bar_heights, width=0.8)
for i, (hgt, d2cnt,
cnt) in enumerate(zip(bar_heights, d2_nt_counts, nt_counts)):
PL.text(i - 0.3, hgt + 0.05, '%d/%d' % (cnt, d2cnt))
PL.xticks(range(len(bar_labels)), bar_labels)
PL.ylim((0, 5))
PL.xlabel('X')
PL.title('X|YX->X')
PL.ylabel(
'Percent gRNAs with X|YX->X deletion\nas most common indel in all 3 replicates'
)
PL.show(block=False)
PL.subplots_adjust(left=0.05,
right=0.95,
top=0.9,
bottom=0.1,
hspace=0.3,
wspace=0.3)
saveFig('D2_nts_per_cat_bars')
def plotD1(all_result_outputs, label=''):
mci_merged_data = mergeSamples(all_result_outputs, [],
data_label='perOligoMCI')
mci_merged_data['Equal MCI'] = (
mci_merged_data['Most Common Indel']
== mci_merged_data['Most Common Indel 2']) & (
mci_merged_data['Most Common Indel']
== mci_merged_data['Most Common Indel 3'])
mci_common = mci_merged_data.loc[mci_merged_data['Equal MCI']]
pie_vals, pie_labels = [], []
dmci_data = mci_common.loc[(
mci_common['MCI Type'] == 'D1'
)] #Note: type check discards equally most common indels
spans_cutsite = lambda indel: tokFullIndel(indel)[2][
'L'] < -1 and tokFullIndel(indel)[2]['R'] > 0
for nt in 'ATGC':
is_mh = lambda alt_seq: len(alt_seq) >= 2 and alt_seq == (len(alt_seq)
* nt)
num_repeat_nt = len(dmci_data.loc[
dmci_data['Altered Sequence'].apply(is_mh)
& dmci_data['Most Common Indel'].apply(spans_cutsite)])
pie_vals.append(num_repeat_nt * 100.0 / len(dmci_data))
print(num_repeat_nt)
pie_labels.append('Removal of %s\nfrom %s|%s' % (nt, nt, nt))
is_non_repeat = lambda seq: len(seq) < 2 or seq != (seq[0] * len(seq))
num_non_repeat = len(
dmci_data.loc[dmci_data['Altered Sequence'].apply(is_non_repeat)
| ~dmci_data['Most Common Indel'].apply(spans_cutsite)])
pie_vals.append(num_non_repeat * 100.0 / len(dmci_data))
print(num_non_repeat)
pie_labels.append('Removal from non-repeat')
PL.figure(figsize=(4, 4))
PL.pie(pie_vals,
labels=pie_labels,
autopct='%.1f',
labeldistance=1.1,
counterclock=False,
colors=OLD_COLORS)
PL.title(
'Size 1 deletions that are\n"most common" for their gRNA in all 3 replicates\n(%d gRNAs from %d total)'
% (len(dmci_data), len(mci_merged_data)))
PL.show(block=False)
saveFig('pie_chart_D1')
oligo_data = pd.read_csv(
getHighDataDir() +
'/ST_June_2017/data/self_target_oligos_details_with_pam_details.csv',
sep='\t')
remove_under = lambda x: x.replace('_', '')
oligo_data['Oligo Id'] = oligo_data['ID'].apply(remove_under)
merged_mci_data = pd.merge(mci_merged_data,
oligo_data[['Oligo Id', 'Guide']],
how='inner',
on='Oligo Id')
print(len(merged_mci_data))
nt_dbl_perc_d1, cnt_labels = [], []
is_d1 = lambda indel: (indel.split('_')[0] == 'D1')
non_dbl_nt = lambda row: row['Guide'][-4] != row['Guide'][-3]
nts = 'ATGC'
for nt in nts:
double_nt = lambda row: row['Guide'][-4:-2] == (nt + nt)
dbl_data = merged_mci_data.loc[merged_mci_data.apply(double_nt,
axis=1)]
num_dbl_d1 = sum(
dbl_data['Most Common Indel'].apply(is_d1) & dbl_data['Equal MCI']
& (dbl_data['Oligo Id'] != 'Oligo28137')
) #Oligo28137: Corner case where a guide has CT|T and loses the C
nt_dbl_perc_d1.append(num_dbl_d1 * 100.0 / len(dbl_data))
cnt_labels.append('%d/%d' % (num_dbl_d1, len(dbl_data)))
print(len(dbl_data))
non_dbl_data = merged_mci_data.loc[merged_mci_data.apply(non_dbl_nt,
axis=1)]
print(len(non_dbl_data))
num_non_dbl_d1 = sum(non_dbl_data['Most Common Indel'].apply(is_d1)
& non_dbl_data['Equal MCI'])
nt_dbl_perc_d1.append(num_non_dbl_d1 * 100.0 / len(non_dbl_data))
cnt_labels.append('%d/%d' % (num_non_dbl_d1, len(non_dbl_data)))
PL.figure()
PL.bar(range(5), nt_dbl_perc_d1, width=0.8)
for i, cnt in enumerate(cnt_labels):
PL.text(i - 0.3, nt_dbl_perc_d1[i] + 5.0, cnt)
PL.xticks(range(5), ['%s' % x * 2 for x in nts] + ['Other'])
PL.ylim((0, 40))
PL.xlabel('Nucleotides on either side of cut site')
PL.ylabel(
'Percent gRNAs with single nucleotide deletion\nas most common indel in all 3 replicates'
)
PL.show(block=False)
saveFig('D1_bar_3_rep')
