def bed_meta2(bed_ls,
bed_label_ls,
icshape_out_ls,
savefn,
extend=20,
species='human'):
fa_dict = util.read_fa(fa=None, species=species, pureID=1)
fig, ax = plt.subplots(figsize=(16, 8))
for bed, bed_label, icshape_out in zip(bed_ls, bed_label_ls,
icshape_out_ls):
print(bed, bed_label, icshape_out)
region_mean, entry_num, df = single_bed_meta(bed, icshape_out, extend,
savefn, bed_label,
fa_dict)
print(region_mean)
print(df.head())
ax.plot(region_mean,
label="%s(n=%s)" % (bed_label, str(entry_num)),
marker='.')
# plt.axvspan(extend, len(region_mean)-extend-1, color='grey', alpha=0.5)
ax.set_ylim(0, 0.6)
plt.legend()
plt.tight_layout()
plt.savefig(savefn)
plt.close()
def plot_shape_tx_null_pct(out1=None,
out2=None,
out1_label='True',
out2_label='Predict',
savefn=None,
species='human'):
out_dict1 = util.read_icshape_out(out1)
out_dict2 = util.read_icshape_out(out2)
tx_common = set(out_dict1.keys()) & set(out_dict2.keys())
null_pct1_ls = []
null_pct2_ls = []
for tx in tx_common:
null_pct1 = (out_dict1[tx]['reactivity_ls'].count('NULL') +
out_dict1[tx]['reactivity_ls'].count('-1.0') +
out_dict1[tx]['reactivity_ls'].count('-1')) / float(
out_dict1[tx]['length'])
null_pct2 = (out_dict2[tx]['reactivity_ls'].count('NULL') +
out_dict2[tx]['reactivity_ls'].count('-1.0') +
out_dict1[tx]['reactivity_ls'].count('-1')) / float(
out_dict2[tx]['length'])
null_pct1_ls.append(null_pct1)
null_pct2_ls.append(null_pct2)
print('{}: n={}'.format(out1, len(out_dict1)))
print('{}: n={}'.format(out2, len(out_dict2)))
print('common tx: n={}'.format(len(tx_common)))
fa_dict = util.read_fa(fa=None, species=species, pureID=1)
stat1 = util.shape_dict_stat(out_dict1,
fa_dict,
None,
RNA_type=None,
trim5Len=5,
trim3Len=30)
stat2 = util.shape_dict_stat(out_dict2,
fa_dict,
None,
RNA_type=None,
trim5Len=5,
trim3Len=30)
print(pd.DataFrame.from_dict(stat1, orient='index'),
pd.DataFrame.from_dict(stat2, orient='index'))
df = pd.DataFrame.from_dict({
out1_label: null_pct1_ls,
out2_label: null_pct2_ls
})
print(df.head())
fig, ax = plt.subplots(figsize=(6, 6))
sns.scatterplot(x=out1_label, y=out2_label, data=df, ax=ax, s=10)
plt.xlabel('{} (null_pct: {:.2f})'.format(out1_label,
stat1['total_bases(NULL_pct)']))
plt.ylabel('{} (null_pct: {:.2f})'.format(out2_label,
stat2['total_bases(NULL_pct)']))
plt.tight_layout()
plt.savefig(savefn)
plt.close()
return stat1, stat2
def search(species='human', savefn=None):
fa_dict = util.read_fa(fa=None, species=species, pureID=1)
with open(savefn, 'w') as SAVEFN:
p = re.compile(r'[AG][AG]AC[ATC]')
n = 0
for i, j in fa_dict.items():
for m in p.finditer(j[0:]):
n += 1
# SAVEFN.write('\t'.join([i, str(m.span()[0]), str(m.span()[1]), str(n)+'_'+m.group(), '0', '+'])+'\n')
SAVEFN.write('\t'.join([
i,
str(m.span()[0] + 2),
str(m.span()[0] + 3),
str(n) + '_' + m.group(), '0', '+'
]) + '\n')
def generate_mask_region_validate(shape_out,
tx,
species,
mask_start,
mask_end,
fragment_start,
fragment_end,
savefn_dir,
plot_gradient=1):
out_dict = util.read_icshape_out(out=shape_out, pureID=1)
fa_dict = util.read_fa(fa=None, species=species, pureID=1)
shape_true_ls = []
shape_mask_ls = []
for i in range(fragment_start, fragment_end):
r = out_dict[tx]['reactivity_ls'][i]
if i >= mask_start and i < mask_end:
r_mask = -1
else:
r_mask = r
shape_true_ls.append(r)
shape_mask_ls.append(r_mask)
shape_true_ls = ['-1' if i == 'NULL' else i for i in shape_true_ls]
shape_true_ls = map(str, shape_true_ls)
shape_mask_ls = ['-1' if i == 'NULL' else i for i in shape_mask_ls]
shape_mask_ls = map(str, shape_mask_ls)
seq = fa_dict[tx][fragment_start:fragment_end]
savefn = '{}/{}.F{}-{}.M{}-{}.txt'.format(savefn_dir, tx, fragment_start,
fragment_end, mask_start,
mask_end)
with open(savefn, 'w') as SAVEFN:
SAVEFN.write('\t'.join(
map(str, [
tx, '1869', fragment_start, fragment_end, '.', '.', seq,
','.join(shape_mask_ls), ','.join(shape_true_ls)
])) + '\n')
if plot_gradient:
plot_savefn = savefn.replace('.txt', '.gradient.pdf')
subprocess.call([
"cd /home/gongjing/project/shape_imputation/ShapeImputation/scripts; python gradcam_SHAPEimpute.py --filename_validation {} --plot_savefn {}"
.format(savefn, plot_savefn)
],
shell=True)
return savefn
def generate_windows(out=None,
window_len_ls=None,
sliding_ls=None,
species=None,
all_valid_reactivity=0,
null_pct_max=0.9,
split_train_validate=0,
generate_random_null_and_ratio=0):
if out is None:
out = '/home/gongjing/project/shape_imputation/data/DMSseq_fibroblast_vivo/3.shape/shape.c200T2M0m0.out'
if window_len_ls is None: window_len_ls = [50, 100]
if species is None: species = 'human'
fa_dict = util.read_fa(species=species)
save_dir = out + '.windowsHasNull'
util.check_dir_or_make(save_dir)
for window_len in window_len_ls:
for sliding in range(window_len, window_len + 1, 10):
savefn = save_dir + '/' + 'windowLen%s.sliding%s.txt' % (
window_len, sliding)
# util.shape_fragmentation(out=out, savefn=savefn, window_len=window_len, sliding=sliding, all_valid_reactivity=1) # no null
shape_fragmentation(out=out,
fa_dict=fa_dict,
savefn=savefn,
window_len=window_len,
sliding=sliding,
all_valid_reactivity=all_valid_reactivity,
null_pct_max=null_pct_max) # has null
if split_train_validate:
np.random.seed(1234)
csv_train, csv_validate = util.fragment_split(fragment=savefn,
train_frac=0.7,
cols=8)
if generate_random_null_and_ratio:
data_random_null_filterNULL(
csv_train,
null_pct=generate_random_null_and_ratio,
col=9,
savefn=None,
seed=1234)
data_random_null_filterNULL(
csv_validate,
null_pct=generate_random_null_and_ratio,
col=9,
savefn=None,
seed=1234)
def generate_dbn_react(shape_out=None,
species=None,
dots=None,
savefn_prefix=None,
min_len=0):
dbn = savefn_prefix + '.dbn'
react = savefn_prefix + '.react'
shape_dict = util.read_icshape_out(shape_out, pureID=0)
fa_dict = util.read_fa(fa=None, species=species, pureID=0)
dot_dict = util.read_dots(dot=dots)
DBN = open(dbn, 'w')
REACT = open(react, 'w')
for i in shape_dict:
if i in fa_dict:
seq = fa_dict[i][0:]
else:
continue
if i in dot_dict:
dot = dot_dict[i]['dotbracket']
else:
continue
print(i, seq, dot, shape_dict[i]['reactivity_ls'])
if len(seq) < min_len: continue
if len(set([len(seq),
len(dot),
len(shape_dict[i]['reactivity_ls'])])) != 1:
continue
DBN.write('>' + i + '\n')
DBN.write(seq + '\n')
DBN.write(dot + '\n')
DBN.write('\n')
REACT.write('>' + i + '\n')
for n, v in enumerate(shape_dict[i]['reactivity_ls']):
REACT.write(str(n + 1) + '\t' + v.replace('NULL', 'NA') + '\n')
REACT.write('\n')
DBN.close()
REACT.close()
def extract_start_codon_shape(species, shape, savefn, max_null_pct=0.4, extend=50):
trans_dict = util.loadTransGtfBed2(species=species)
out_dict = util.read_icshape_out(out=shape, pureID=1)
fa_dict = util.read_fa(fa=None, species=species, pureID=1)
# start codon
savefn1 = savefn.replace('.shape', '.start_codon.ok.shape')
savefn2 = savefn.replace('.shape', '.start_codon.null.shape')
SAVEFN1 = open(savefn1, 'w')
SAVEFN2 = open(savefn2, 'w')
tx_with_shape = []
for i,j in out_dict.items():
if i not in trans_dict: continue
if int(trans_dict[i]['utr_5_end']) < extend: continue
if int(trans_dict[i]['cds_end']) - int(trans_dict[i]['cds_start']) < extend: continue
tx_with_shape.append(i)
# 0-based
start = int(trans_dict[i]['utr_5_end']) - 49 - 1
end = int(trans_dict[i]['cds_start']) + 49
shape = out_dict[i]['reactivity_ls'][start:end]
seq = fa_dict[i][start:end]
null_pct = shape.count('NULL') / len(shape)
shape_str = ','.join(shape).replace('NULL', '-1')
if null_pct == 1: continue
if null_pct >= max_null_pct:
state = 'null'
SAVEFN2.write('\t'.join(map(str, [i, len(fa_dict[i][0:]), start, end, '.', '.', seq, shape_str, shape_str]))+'\n')
# SAVEFN2.write('\t'.join(map(str, [i, start, end]))+'\n')
else:
state = 'ok'
SAVEFN1.write('\t'.join(map(str, [i, len(fa_dict[i][0:]), start, end, '.', '.', seq, shape_str, shape_str]))+'\n')
# SAVEFN1.write('\t'.join(map(str, [i, start, end]))+'\n')
SAVEFN1.close()
SAVEFN2.close()
print('tx_with_shape: {}'.format(len(tx_with_shape)))
savefn1_sort,_ = util.sort_two_shape(shape1=savefn1, value_col1=7, shape2=savefn1)
savefn2_sort,_ = util.sort_two_shape(shape1=savefn2, value_col1=7, shape2=savefn2)
df1 = util.plot_heatmap(fn=savefn1_sort, savefn=savefn1_sort+'.heatmap.pdf', value_col=7, fig_size_x=10, fig_size_y=20, cmap='summer', facecolor='black')
df2 = util.plot_heatmap(fn=savefn2_sort, savefn=savefn2_sort+'.heatmap.pdf', value_col=7, fig_size_x=10, fig_size_y=20, cmap='summer', facecolor='black')
df1_mean = list(df1.mean())
df2_mean = list(df2.mean())
fig,ax=plt.subplots(figsize=(16,8))
ax.plot(df2_mean, label="%s(n=%s)"%('null',df2.shape[0]), marker='.')
ax.plot(df1_mean, label="%s(n=%s)"%('ok',df1.shape[0]), marker='.')
ax.set_ylim(0,0.6)
plt.legend()
plt.tight_layout()
plt.savefig(savefn1_sort+'.meta.pdf')
plt.close()
# stop codon
savefn1 = savefn.replace('.shape', '.stop_codon.ok.shape')
savefn2 = savefn.replace('.shape', '.stop_codon.null.shape')
SAVEFN1 = open(savefn1, 'w')
SAVEFN2 = open(savefn2, 'w')
tx_with_shape = []
for i,j in out_dict.items():
if i not in trans_dict: continue
if int(trans_dict[i]['utr_3_end']) - int(trans_dict[i]['utr_3_start']) < extend: continue
if int(trans_dict[i]['cds_end']) - int(trans_dict[i]['cds_start']) < extend: continue
tx_with_shape.append(i)
# 0-based
start = int(trans_dict[i]['cds_end']) - 49 - 1
end = int(trans_dict[i]['utr_3_start']) + 49
shape = out_dict[i]['reactivity_ls'][start:end]
null_pct = shape.count('NULL') / len(shape)
seq = fa_dict[i][start:end]
shape_str = ','.join(shape).replace('NULL', '-1')
if null_pct == 1: continue
if null_pct >= max_null_pct:
state = 'null'
SAVEFN2.write('\t'.join(map(str, [i, len(fa_dict[i][0:]), start, end, '.', '.', seq, shape_str, shape_str]))+'\n')
# SAVEFN2.write('\t'.join(map(str, [i, start, end]))+'\n')
else:
state = 'ok'
SAVEFN1.write('\t'.join(map(str, [i, len(fa_dict[i][0:]), start, end, '.', '.', seq, shape_str, shape_str]))+'\n')
# SAVEFN1.write('\t'.join(map(str, [i, start, end]))+'\n')
SAVEFN1.close()
SAVEFN2.close()
print('tx_with_shape: {}'.format(len(tx_with_shape)))
savefn1_sort,_ = util.sort_two_shape(shape1=savefn1, value_col1=7, shape2=savefn1)
savefn2_sort,_ = util.sort_two_shape(shape1=savefn2, value_col1=7, shape2=savefn2)
df1 = util.plot_heatmap(fn=savefn1_sort, savefn=savefn1_sort+'.heatmap.pdf', value_col=7, fig_size_x=10, fig_size_y=20, cmap='summer', facecolor='black')
df2 = util.plot_heatmap(fn=savefn2_sort, savefn=savefn2_sort+'.heatmap.pdf', value_col=7, fig_size_x=10, fig_size_y=20, cmap='summer', facecolor='black')
df1_mean = list(df1.mean())
df2_mean = list(df2.mean())
fig,ax=plt.subplots(figsize=(16,8))
ax.plot(df2_mean, label="%s(n=%s)"%('null',df2.shape[0]), marker='.')
ax.plot(df1_mean, label="%s(n=%s)"%('ok',df1.shape[0]), marker='.')
ax.set_ylim(0,0.6)
plt.legend()
plt.tight_layout()
plt.savefig(savefn1_sort+'.meta.pdf')
plt.close()
def complete_shape_out(icshape=None,
species='human',
predict_label=None,
predict_model=None,
pct=0.5,
window_len=100,
sliding=50,
output_dir=None,
gpu_id=1):
if not os.path.isdir(output_dir): os.mkdir(output_dir)
fa_dict = util.read_fa(fa=None, species=species, pureID=1)
icshape_fragment_all = output_dir + '/' + 'allfragment.txt'
icshape_fragment_all2 = icshape_fragment_all + '2'
util.shape_fragmentation(out=icshape,
fa_dict=fa_dict,
savefn=icshape_fragment_all,
window_len=window_len,
sliding=sliding,
all_valid_reactivity=0,
null_pct_max=2)
cmd = '''awk '{print $0"\\t"$NF}' ''' + " {} > {}; sed -i 's/NULL/-1/g' {}".format(
icshape_fragment_all, icshape_fragment_all2, icshape_fragment_all2)
# print(cmd)
subprocess.call([cmd], shell=True)
icshape_fragment_pct = output_dir + '/' + 'allfragment.{}.txt'.format(pct)
util.shape_fragmentation(out=icshape,
fa_dict=fa_dict,
savefn=icshape_fragment_pct,
window_len=window_len,
sliding=sliding,
all_valid_reactivity=0,
null_pct_max=pct)
icshape_fragment_pct2 = icshape_fragment_pct + '2'
cmd = '''awk '{print $0"\\t"$NF}' ''' + " {} > {}; sed -i 's/NULL/-1/g' {}".format(
icshape_fragment_pct, icshape_fragment_pct2, icshape_fragment_pct2)
# print(cmd)
subprocess.call([cmd], shell=True)
predict = output_dir + '/' + 'predict.txt'
cmd_predict = 'bash predict_new_icshape.sh {} {} {} {}'.format(
gpu_id, icshape_fragment_pct2, predict, predict_model)
subprocess.call([cmd_predict], shell=True)
# predict = '/home/gongjing/project/shape_imputation/exper/{}/prediction.{}.txt'.format(predict_model, predict_label)
predict_shape_out = predict.replace('.txt', '.out')
util.predict_to_shape(validation=icshape_fragment_pct2,
predict=predict,
shape_out=predict_shape_out)
# 从总的fragment减去小于pct的，得到剩余的不用预测的大于pct的fragment
icshape_fragment_exceed_pct2 = output_dir + '/' + 'allfragment.exceed{}.txt2'.format(
pct)
cmd = ''' awk 'NR==FNR{a[$1$3$4];next} !($1$3$4 in a){print $0}' ''' + '''{} {} > {}'''.format(
icshape_fragment_pct2, icshape_fragment_all2,
icshape_fragment_exceed_pct2)
subprocess.call([cmd], shell=True)
# 对大于pct的fragment生成预测值文件：直接使用原来的值（即最后一列即可）
icshape_fragment_exceed_pct2_predict = icshape_fragment_exceed_pct2 + '.predict' # not truly predict but generate a pseudo file
cmd = ''' awk '{print $NF}' ''' + ''' {} > {} '''.format(
icshape_fragment_exceed_pct2, icshape_fragment_exceed_pct2_predict)
subprocess.call([cmd], shell=True)
# 重新合并小于pct和大于pct的fragment文件(相当于validation文件)
icshape_fragment_pct_plus_exceed_pct2 = output_dir + '/' + 'allfragment.{}+exceed{}.txt2'.format(
pct, pct)
cmd = ''' cat {} {} > {}'''.format(icshape_fragment_pct2,
icshape_fragment_exceed_pct2,
icshape_fragment_pct_plus_exceed_pct2)
subprocess.call([cmd], shell=True)
# 合并预测的文件
icshape_fragment_pct_plus_exceed_predict = output_dir + '/' + 'allfragment.{}+exceed{}.txt2.predict'.format(
pct, pct)
cmd = ''' cat {} {} > {} '''.format(
predict, icshape_fragment_exceed_pct2_predict,
icshape_fragment_pct_plus_exceed_predict)
subprocess.call([cmd], shell=True)
# 根据重新合并的validation，预测文件，生成.out文件
icshape_fragment_pct_plus_exceed_predict_shapeout = icshape_fragment_pct_plus_exceed_predict + '.out'
util.predict_to_shape(
validation=icshape_fragment_pct_plus_exceed_pct2,
predict=icshape_fragment_pct_plus_exceed_predict,
shape_out=icshape_fragment_pct_plus_exceed_predict_shapeout)
# 画真实和预测的null pct scatter
savefn = icshape_fragment_pct_plus_exceed_predict_shapeout + '.scatter.pdf'
stat1, stat2 = plot_two_shape_common_tx_pct.plot_shape_tx_null_pct(
out1=icshape,
out2=icshape_fragment_pct_plus_exceed_predict_shapeout,
out1_label='True',
out2_label='Predict',
savefn=savefn,
species=species)
return icshape_fragment_pct_plus_exceed_predict_shapeout, stat1, stat2
def get_stat(coverage_ls=None, RT_ls=None, prefix=None):
if coverage_ls is None:
coverage_ls = [0, 50, 100, 150, 200, 250]
if RT_ls is None:
RT_ls = [0, 1, 2, 3]
if prefix is None:
prefix = '/Share2/home/zhangqf5/gongjing/RNA-structure-profile-imputation/data/hek_wc_vivo/3.shape'
fa_dict = util.read_fa(species='human')
trans_dict = util.loadTransGtfBed2(species='human')
stat_dict = nested_dict(3, int)
for coverage in coverage_ls:
for RT in RT_ls:
shape_out = '%s/shape.c%sT%sM0m0.out' % (prefix, coverage, RT)
out_dict = util.read_icshape_out(shape_out)
out_dict_stat = util.shape_dict_stat(shape_dict=out_dict,
fa_dict=fa_dict,
trans_dict=trans_dict,
RNA_type='all')
print(out_dict_stat)
stat_dict['tx_count'][coverage][RT] = len(out_dict)
stat_dict['total_bases'][coverage][RT] = out_dict_stat[
'total_bases']
stat_dict['total_bases(NULL_pct)'][coverage][RT] = out_dict_stat[
'total_bases(NULL_pct)']
stat_dict['A(NULL_pct)'][coverage][RT] = out_dict_stat[
'A(NULL_pct)']
stat_dict['T(NULL_pct)'][coverage][RT] = out_dict_stat[
'T(NULL_pct)']
stat_dict['C(NULL_pct)'][coverage][RT] = out_dict_stat[
'C(NULL_pct)']
stat_dict['G(NULL_pct)'][coverage][RT] = out_dict_stat[
'G(NULL_pct)']
print(pd.DataFrame.from_dict(stat_dict['tx_count'], orient='index'))
print(pd.DataFrame.from_dict(stat_dict['total_bases'], orient='index'))
print(
pd.DataFrame.from_dict(stat_dict['total_bases(NULL_pct)'],
orient='index'))
# fig,ax=plt.subplots()
# sns.heatmap(tx_count_df,annot=True,fmt='d',linewidths=0.5)
# ax.set_yticklabels(ax.yaxis.get_majorticklabels(), rotation=0)
# ax.set_xlabel('Cutoff (average RT stop count)')
# ax.set_ylabel('Cutoff (base density)')
# savefn=prefix+'/shape.tx_num.pdf'
# plt.tight_layout()
# plt.savefig(savefn)
# plt.close()
util.heatmap(pd.DataFrame.from_dict(stat_dict['tx_count'], orient='index'),
xlabel='Cutoff (average RT stop count)',
ylabel='Cutoff (base density)',
savefn=prefix + '/shape.tx_num.pdf',
fmt='d')
util.heatmap(pd.DataFrame.from_dict(stat_dict['total_bases'],
orient='index'),
xlabel='Cutoff (average RT stop count)',
ylabel='Cutoff (base density)',
savefn=prefix + '/shape.total_bases.pdf',
fmt='.2g')
util.heatmap(pd.DataFrame.from_dict(stat_dict['total_bases(NULL_pct)'],
orient='index'),
xlabel='Cutoff (average RT stop count)',
ylabel='Cutoff (base density)',
savefn=prefix + '/shape.total_null_pct.pdf',
fmt='.2g')
util.heatmap(pd.DataFrame.from_dict(stat_dict['A(NULL_pct)'],
orient='index'),
xlabel='Cutoff (average RT stop count)',
ylabel='Cutoff (base density)',
savefn=prefix + '/shape.A_null_pct.pdf',
fmt='.2g')
util.heatmap(pd.DataFrame.from_dict(stat_dict['T(NULL_pct)'],
orient='index'),
xlabel='Cutoff (average RT stop count)',
ylabel='Cutoff (base density)',
savefn=prefix + '/shape.T_null_pct.pdf',
fmt='.2g')
util.heatmap(pd.DataFrame.from_dict(stat_dict['C(NULL_pct)'],
orient='index'),
xlabel='Cutoff (average RT stop count)',
ylabel='Cutoff (base density)',
savefn=prefix + '/shape.C_null_pct.pdf',
fmt='.2g')
util.heatmap(pd.DataFrame.from_dict(stat_dict['G(NULL_pct)'],
orient='index'),
xlabel='Cutoff (average RT stop count)',
ylabel='Cutoff (base density)',
savefn=prefix + '/shape.G_null_pct.pdf',
fmt='.2g')
