def main():
try:
description = [
'~~~CRISPRessoCompare~~~',
'-Comparison of two CRISPResso analyses-'
]
compare_header = r'''
___________________________
| __ __ __ __ __ |
|/ / \|\/||__) /\ |__)|_ |
|\__\__/| || /--\| \ |__ |
|___________________________|
'''
compare_header = CRISPRessoShared.get_crispresso_header(
description, compare_header)
print(compare_header)
parser = argparse.ArgumentParser(
description='CRISPRessoCompare Parameters',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'crispresso_output_folder_1',
type=str,
help='First output folder with CRISPResso analysis')
parser.add_argument(
'crispresso_output_folder_2',
type=str,
help='Second output folder with CRISPResso analysis')
#OPTIONALS
parser.add_argument('-n', '--name', help='Output name', default='')
parser.add_argument('-n1',
'--sample_1_name',
help='Sample 1 name',
default='Sample_1')
parser.add_argument('-n2',
'--sample_2_name',
help='Sample 2 name',
default='Sample_2')
parser.add_argument('-o', '--output_folder', help='', default='')
parser.add_argument(
'--min_frequency_alleles_around_cut_to_plot',
type=float,
help=
'Minimum %% reads required to report an allele in the alleles table plot.',
default=0.2)
parser.add_argument(
'--max_rows_alleles_around_cut_to_plot',
type=int,
help='Maximum number of rows to report in the alleles table plot. ',
default=50)
parser.add_argument(
'--save_also_png',
help='Save also .png images additionally to .pdf files',
action='store_true')
parser.add_argument('--debug',
help='Show debug messages',
action='store_true')
args = parser.parse_args()
debug_flag = args.debug
#check that the CRISPResso output is present and fill amplicon_info
quantification_file_1, amplicon_names_1, amplicon_info_1 = CRISPRessoShared.check_output_folder(
args.crispresso_output_folder_1)
quantification_file_2, amplicon_names_2, amplicon_info_2 = CRISPRessoShared.check_output_folder(
args.crispresso_output_folder_2)
get_name_from_folder = lambda x: os.path.basename(os.path.abspath(
x)).replace('CRISPResso_on_', '')
if not args.name:
database_id = '%s_VS_%s' % (
get_name_from_folder(args.crispresso_output_folder_1),
get_name_from_folder(args.crispresso_output_folder_2))
else:
database_id = args.name
OUTPUT_DIRECTORY = 'CRISPRessoCompare_on_%s' % database_id
if args.output_folder:
OUTPUT_DIRECTORY = os.path.join(
os.path.abspath(args.output_folder), OUTPUT_DIRECTORY)
_jp = lambda filename: os.path.join(
OUTPUT_DIRECTORY, filename
) #handy function to put a file in the output directory
log_filename = _jp('CRISPRessoCompare_RUNNING_LOG.txt')
try:
info('Creating Folder %s' % OUTPUT_DIRECTORY)
os.makedirs(OUTPUT_DIRECTORY)
info('Done!')
except:
warn('Folder %s already exists.' % OUTPUT_DIRECTORY)
log_filename = _jp('CRISPRessoCompare_RUNNING_LOG.txt')
logging.getLogger().addHandler(logging.FileHandler(log_filename))
with open(log_filename, 'w+') as outfile:
outfile.write(
'[Command used]:\nCRISPRessoCompare %s\n\n[Execution log]:\n' %
' '.join(sys.argv))
#LOAD DATA
amplicon_names_in_both = [
amplicon_name for amplicon_name in amplicon_names_1
if amplicon_name in amplicon_names_2
]
n_refs = len(amplicon_names_in_both)
def get_plot_title_with_ref_name(plotTitle, refName):
if n_refs > 1:
return (plotTitle + ": " + refName)
return plotTitle
for amplicon_name in amplicon_names_in_both:
profile_1 = parse_profile(
amplicon_info_1[amplicon_name]['quantification_file'])
profile_2 = parse_profile(
amplicon_info_2[amplicon_name]['quantification_file'])
try:
assert np.all(profile_1[:, 0] == profile_2[:, 0])
except:
raise DifferentAmpliconLengthException(
'Different amplicon lengths for the two amplicons.')
len_amplicon = profile_1.shape[0]
effect_vector_any_1 = profile_1[:, 1]
effect_vector_any_2 = profile_2[:, 1]
cut_points, sgRNA_intervals = load_cut_points_sgRNA_intervals(
args.crispresso_output_folder_1, amplicon_name)
#Quantification comparison barchart
fig = plt.figure(figsize=(30, 15))
n_groups = 2
N_TOTAL_1 = float(amplicon_info_1[amplicon_name]['Total'])
N_UNMODIFIED_1 = float(
amplicon_info_1[amplicon_name]['Unmodified'])
N_MODIFIED_1 = float(amplicon_info_1[amplicon_name]['Modified'])
N_TOTAL_2 = float(amplicon_info_2[amplicon_name]['Total'])
N_UNMODIFIED_2 = float(
amplicon_info_2[amplicon_name]['Unmodified'])
N_MODIFIED_2 = float(amplicon_info_2[amplicon_name]['Modified'])
means_sample_1 = np.array([N_UNMODIFIED_1, N_MODIFIED_1
]) / N_TOTAL_1 * 100
means_sample_2 = np.array([N_UNMODIFIED_2, N_MODIFIED_2
]) / N_TOTAL_2 * 100
ax1 = fig.add_subplot(1, 2, 1)
index = np.arange(n_groups)
bar_width = 0.35
opacity = 0.4
error_config = {'ecolor': '0.3'}
rects1 = ax1.bar(index,
means_sample_1,
bar_width,
alpha=opacity,
color=(0, 0, 1, 0.4),
label=args.sample_1_name)
rects2 = ax1.bar(index + bar_width,
means_sample_2,
bar_width,
alpha=opacity,
color=(1, 0, 0, 0.4),
label=args.sample_2_name)
plt.ylabel('% Sequences')
plt.title(
get_plot_title_with_ref_name(
'%s VS %s' % (args.sample_1_name, args.sample_2_name),
amplicon_name))
plt.xticks(index + bar_width / 2.0, ('Unmodified', 'Modified'))
plt.legend()
# plt.xlim(index[0]-0.2,(index + bar_width)[-1]+bar_width+0.2)
plt.tight_layout()
ax2 = fig.add_subplot(1, 2, 2)
ax2.bar(index,
means_sample_1 - means_sample_2,
bar_width + 0.35,
alpha=opacity,
color=(0, 1, 1, 0.4),
label='')
plt.ylabel('% Sequences Difference')
plt.title(
get_plot_title_with_ref_name(
'%s - %s' % (args.sample_1_name, args.sample_2_name),
amplicon_name))
plt.xticks(index, ['Unmodified', 'Modified'])
# plt.xlim(index[0]-bar_width/2, (index+bar_width)[-1]+2*bar_width)
plt.tight_layout()
plt.savefig(_jp('1.' + amplicon_name +
'.Comparison_Efficiency.pdf'),
bbox_inches='tight')
if args.save_also_png:
plt.savefig(_jp('1.' + amplicon_name +
'.Comparison_Efficiency.png'),
bbox_inches='tight')
#profile comparion
fig = plt.figure(figsize=(20, 10))
ax1 = fig.add_subplot(1, 2, 1)
plt.title(
get_plot_title_with_ref_name('Mutation position distribution',
amplicon_name))
y_max = max(effect_vector_any_1.max(),
effect_vector_any_2.max()) * 1.2
plt.plot(effect_vector_any_1,
color=(0, 0, 1, 0.3),
lw=4,
label='%s combined mutations' % args.sample_1_name)
# plt.hold(True)
plt.plot(effect_vector_any_2,
color=(1, 0, 0, 0.3),
lw=4,
label='%s combined mutations' % args.sample_2_name)
if cut_points:
for idx, cut_point in enumerate(cut_points):
if idx == 0:
plt.plot([cut_point, cut_point], [0, y_max],
'--k',
lw=2,
label='Predicted cleavage position')
else:
plt.plot([cut_point, cut_point], [0, y_max],
'--k',
lw=2,
label='_nolegend_')
for idx, sgRNA_int in enumerate(sgRNA_intervals):
if idx == 0:
plt.plot([sgRNA_int[0], sgRNA_int[1]], [0, 0],
lw=10,
c=(0, 0, 0, 0.15),
label='sgRNA')
else:
plt.plot([sgRNA_int[0], sgRNA_int[1]], [0, 0],
lw=10,
c=(0, 0, 0, 0.15),
label='_nolegend_')
lgd = plt.legend(loc='center',
bbox_to_anchor=(0.5, -0.3),
ncol=1,
fancybox=True,
shadow=False)
plt.xticks(
np.arange(0, len_amplicon,
max(3, (len_amplicon / 6) -
(len_amplicon / 6) % 5)).astype(int))
plt.xlabel('Reference amplicon position (bp)')
plt.ylabel('Sequences %')
plt.ylim(0, max(1, y_max))
plt.xlim(xmax=len_amplicon - 1)
ax2 = fig.add_subplot(1, 2, 2)
effect_vector_any_diff = effect_vector_any_1 - effect_vector_any_2
y_max = effect_vector_any_diff.max() * 1.2
y_min = effect_vector_any_diff.min() * 1.2
plt.title(
get_plot_title_with_ref_name(
'%s - %s' % (args.sample_1_name, args.sample_2_name),
amplicon_name))
plt.plot(effect_vector_any_diff,
color=(0, 1, 0, 0.4),
lw=3,
label='Difference')
if cut_points:
for idx, cut_point in enumerate(cut_points):
if idx == 0:
plt.plot(
[cut_point, cut_point],
[min(-1, y_min), max(1, y_max)],
'--k',
lw=2,
label='Predicted cleavage position')
else:
plt.plot(
[cut_point, cut_point],
[min(-1, y_min), max(1, y_max)],
'--k',
lw=2,
label='_nolegend_')
for idx, sgRNA_int in enumerate(sgRNA_intervals):
if idx == 0:
plt.plot(
[sgRNA_int[0], sgRNA_int[1]],
[min(-1, y_min), min(-1, y_min)],
lw=10,
c=(0, 0, 0, 0.15),
label='sgRNA')
else:
plt.plot(
[sgRNA_int[0], sgRNA_int[1]],
[min(-1, y_min), min(-1, y_min)],
lw=10,
c=(0, 0, 0, 0.15),
label='_nolegend_')
lgd2 = plt.legend(loc='center',
bbox_to_anchor=(0.5, -0.2),
ncol=1,
fancybox=True,
shadow=False)
plt.xticks(
np.arange(0, len_amplicon,
max(3, (len_amplicon / 6) -
(len_amplicon / 6) % 5)).astype(int))
plt.xlabel('Reference amplicon position (bp)')
plt.ylabel('Sequences Difference %')
plt.xlim(xmax=len_amplicon - 1)
plt.ylim(min(-1, y_min), max(1, y_max))
plt.savefig(_jp(
'2.' + amplicon_name +
'.Comparison_Combined_Insertion_Deletion_Substitution_Locations.pdf'
),
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
if args.save_also_png:
plt.savefig(_jp(
'2.' + amplicon_name +
'.Comparison_Insertion_Deletion_Substitution_Locations.png'
),
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
mod_file_1 = amplicon_info_1[amplicon_name][
'modification_count_file']
amp_seq_1, mod_freqs_1 = CRISPRessoShared.parse_count_file(
mod_file_1)
mod_file_2 = amplicon_info_2[amplicon_name][
'modification_count_file']
amp_seq_2, mod_freqs_2 = CRISPRessoShared.parse_count_file(
mod_file_2)
consensus_sequence = amp_seq_1
if amp_seq_2 != consensus_sequence:
raise DifferentAmpliconLengthException(
'Different amplicon lengths for the two amplicons.')
for mod in [
'Insertions', 'Deletions', 'Substitutions',
'All_modifications'
]:
mod_counts_1 = np.array(mod_freqs_1[mod], dtype=float)
tot_counts_1 = np.array(mod_freqs_1['Total'], dtype=float)
unmod_counts_1 = tot_counts_1 - mod_counts_1
mod_counts_2 = np.array(mod_freqs_2[mod], dtype=float)
tot_counts_2 = np.array(mod_freqs_2['Total'], dtype=float)
unmod_counts_2 = tot_counts_2 - mod_counts_2
fisher_results = [
stats.fisher_exact([[z[0], z[1]], [z[2], z[3]]])
if max(z) > 0 else [nan, 1.0]
for z in zip(mod_counts_1, unmod_counts_1, mod_counts_2,
unmod_counts_2)
]
oddsratios, pvalues = [a for a, b in fisher_results
], [b for a, b in fisher_results]
mod_df = []
row = [args.sample_1_name + '_' + mod]
row.extend(mod_counts_1)
mod_df.append(row)
row = [args.sample_1_name + '_total']
row.extend(tot_counts_1)
mod_df.append(row)
row = [args.sample_2_name + '_' + mod]
row.extend(mod_counts_2)
mod_df.append(row)
row = [args.sample_2_name + '_total']
row.extend(tot_counts_2)
mod_df.append(row)
row = ['odds_ratios']
row.extend(oddsratios)
mod_df.append(row)
row = ['pvalues']
row.extend(pvalues)
mod_df.append(row)
colnames = ['Reference']
colnames.extend(list(consensus_sequence))
mod_df = pd.DataFrame(mod_df, columns=colnames)
# mod_df = pd.concat([mod_df.iloc[:,0:2], mod_df.iloc[:,2:].apply(pd.to_numeric)],axis=1)
#write to file
mod_df.to_csv(_jp(amplicon_name + '.' + mod +
'_quantification.txt'),
sep='\t',
index=None)
#plot
fig = plt.figure(figsize=(20, 10))
ax1 = fig.add_subplot(2, 1, 1)
diff = np.divide(mod_counts_1, tot_counts_1) - np.divide(
mod_counts_2, tot_counts_2)
diff_plot = ax1.plot(diff,
color=(0, 1, 0, 0.4),
lw=3,
label='Difference')
ax1.set_title(
get_plot_title_with_ref_name(
'%s: %s - %s' %
(mod, args.sample_1_name, args.sample_2_name),
amplicon_name))
ax1.set_xticks(
np.arange(
0, len_amplicon,
max(3, (len_amplicon / 6) -
(len_amplicon / 6) % 5)).astype(int))
ax1.set_ylabel('Sequences Difference %')
ax1.set_xlim(xmin=0, xmax=len_amplicon - 1)
pvalues = np.array(pvalues)
min_nonzero = np.min(pvalues[np.nonzero(pvalues)])
pvalues[pvalues == 0] = min_nonzero
#ax2 = ax1.twinx()
ax2 = fig.add_subplot(2, 1, 2)
pval_plot = ax2.plot(-1 * np.log10(pvalues),
color=(1, 0, 0, 0.4),
lw=2,
label='-log10 P-value')
ax2.set_ylabel('-log10 P-value')
ax2.set_xlim(xmin=0, xmax=len_amplicon - 1)
ax2.set_xticks(
np.arange(
0, len_amplicon,
max(3, (len_amplicon / 6) -
(len_amplicon / 6) % 5)).astype(int))
ax2.set_xlabel('Reference amplicon position (bp)')
#bonferroni correction
corrected_p = -1 * np.log10(
0.01 / float(len(consensus_sequence)))
cutoff_plot = ax2.plot([0, len(consensus_sequence)],
[corrected_p, corrected_p],
color='k',
dashes=(5, 10),
label='Bonferronni corrected cutoff')
plots = diff_plot + pval_plot + cutoff_plot
diff_y_min, diff_y_max = ax1.get_ylim()
p_y_min, p_y_max = ax2.get_ylim()
if cut_points:
for idx, cut_point in enumerate(cut_points):
if idx == 0:
plot_cleavage = ax1.plot(
[cut_point, cut_point],
[diff_y_min, diff_y_max],
'--k',
lw=2,
label='Predicted cleavage position')
ax2.plot([cut_point, cut_point],
[p_y_min, p_y_max],
'--k',
lw=2,
label='Predicted cleavage position')
plots = plots + plot_cleavage
else:
ax1.plot([cut_point, cut_point],
[diff_y_min, diff_y_max],
'--k',
lw=2,
label='_nolegend_')
ax2.plot([cut_point, cut_point],
[diff_y_min, diff_y_max],
'--k',
lw=2,
label='_nolegend_')
for idx, sgRNA_int in enumerate(sgRNA_intervals):
if idx == 0:
p2 = ax1.plot([sgRNA_int[0], sgRNA_int[1]],
[diff_y_min, diff_y_min],
lw=10,
c=(0, 0, 0, 0.15),
label='sgRNA')
ax2.plot([sgRNA_int[0], sgRNA_int[1]],
[p_y_min, p_y_min],
lw=10,
c=(0, 0, 0, 0.15),
label='sgRNA')
plots = plots + p2
else:
ax1.plot([sgRNA_int[0], sgRNA_int[1]],
[diff_y_min, diff_y_min],
lw=10,
c=(0, 0, 0, 0.15),
label='_nolegend_')
ax2.plot([sgRNA_int[0], sgRNA_int[1]],
[p_y_min, p_y_min],
lw=10,
c=(0, 0, 0, 0.15),
label='_nolegend_')
labs = [p.get_label() for p in plots]
lgd = plt.legend(plots,
labs,
loc='upper center',
bbox_to_anchor=(0.5, -0.2),
ncol=1,
fancybox=True,
shadow=False)
plt.savefig(_jp('2.' + amplicon_name + '.' + mod +
'.quantification.pdf'),
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
if args.save_also_png:
plt.savefig(_jp('2.' + amplicon_name + '.' + mod +
'.quantification.png'),
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
#create merged heatmaps for each cut site
allele_files_1 = amplicon_info_1[amplicon_name]['allele_files']
allele_files_2 = amplicon_info_2[amplicon_name]['allele_files']
for allele_file_1 in allele_files_1:
allele_file_1_name = os.path.split(allele_file_1)[
1] #get file part of path
for allele_file_2 in allele_files_2:
allele_file_2_name = os.path.split(allele_file_2)[
1] #get file part of path
#if files are the same (same amplicon, cut site, guide), run comparison
if allele_file_1_name == allele_file_2_name:
df1 = pd.read_csv(allele_file_1, sep="\t")
df2 = pd.read_csv(allele_file_2, sep="\t")
#find unmodified reference for comparison (if it exists)
ref_seq_around_cut = ""
if len(df1.loc[df1['Reference_Sequence'].str.contains(
'-') == False]) > 0:
ref_seq_around_cut = df1.loc[
df1['Reference_Sequence'].str.contains('-') ==
False]['Reference_Sequence'].iloc[0]
#otherwise figure out which sgRNA was used for this comparison
elif len(df2.loc[df2['Reference_Sequence'].str.
contains('-') == False]) > 0:
ref_seq_around_cut = df2.loc[
df2['Reference_Sequence'].str.contains('-') ==
False]['Reference_Sequence'].iloc[0]
else:
seq_len = df2[df2['Unedited'] ==
True]['Reference_Sequence'].iloc[0]
for sgRNA_interval, cut_point in zip(
sgRNA_intervals, cut_points):
sgRNA_seq = consensus_sequence[
sgRNA_interval[0]:sgRNA_interval[1]]
if sgRNA_seq in allele_file_1_name:
this_sgRNA_seq = sgRNA_seq
this_cut_point = cut_point
ref_seq_around_cut = consensus_sequence[max(
0, this_cut_point -
args.offset_around_cut_to_plot +
1):min(
len(reference_seq), cut_point +
args.offset_around_cut_to_plot +
1)]
break
merged = pd.merge(df1,
df2,
on=[
'Aligned_Sequence',
'Reference_Sequence', 'Unedited',
'n_deleted', 'n_inserted',
'n_mutated'
],
suffixes=('_' + args.sample_1_name,
'_' + args.sample_2_name),
how='outer')
quant_cols = [
'#Reads_' + args.sample_1_name,
'%Reads_' + args.sample_1_name,
'#Reads_' + args.sample_2_name,
'%Reads_' + args.sample_2_name
]
merged[quant_cols] = merged[quant_cols].fillna(0)
lfc_error = 0.1
merged['each_LFC'] = np.log2(
((merged['%Reads_' + args.sample_1_name] +
lfc_error) /
(merged['%Reads_' + args.sample_2_name] +
lfc_error)).astype(float)).replace(
[np.inf, np.NaN], 0)
merged = merged.reset_index().set_index(
'Aligned_Sequence')
output_root = allele_file_1_name.replace(".txt", "")
merged.to_csv(_jp(output_root + ".txt"),
sep="\t",
index=None)
CRISPRessoPlot.plot_alleles_table_compare(
ref_seq_around_cut,
merged.sort_values(['each_LFC'], ascending=True),
args.sample_1_name,
args.sample_2_name,
_jp('3.' + output_root + "_top"),
MIN_FREQUENCY=args.
min_frequency_alleles_around_cut_to_plot,
MAX_N_ROWS=args.
max_rows_alleles_around_cut_to_plot,
SAVE_ALSO_PNG=args.save_also_png)
CRISPRessoPlot.plot_alleles_table_compare(
ref_seq_around_cut,
merged.sort_values(['each_LFC'], ascending=False),
args.sample_1_name,
args.sample_2_name,
_jp('3.' + output_root + "_bottom"),
MIN_FREQUENCY=args.
min_frequency_alleles_around_cut_to_plot,
MAX_N_ROWS=args.
max_rows_alleles_around_cut_to_plot,
SAVE_ALSO_PNG=args.save_also_png)
info('All Done!')
print(CRISPRessoShared.get_crispresso_footer())
sys.exit(0)
except Exception as e:
debug_flag = False
if 'args' in vars() and 'debug' in args:
debug_flag = args.debug
if debug_flag:
traceback.print_exc(file=sys.stdout)
error('\n\nERROR: %s' % e)
sys.exit(-1)
def main():
try:
description = [
'~~~CRISPRessoCompare~~~',
'-Comparison of two CRISPResso analyses-'
]
compare_header = r'''
___________________________
| __ __ __ __ __ |
|/ / \|\/||__) /\ |__)|_ |
|\__\__/| || /--\| \ |__ |
|___________________________|
'''
compare_header = CRISPRessoShared.get_crispresso_header(
description, compare_header)
print(compare_header)
parser = argparse.ArgumentParser(
description='CRISPRessoCompare Parameters',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'crispresso_output_folder_1',
type=str,
help='First output folder with CRISPResso analysis')
parser.add_argument(
'crispresso_output_folder_2',
type=str,
help='Second output folder with CRISPResso analysis')
#OPTIONALS
parser.add_argument('-n', '--name', help='Output name', default='')
parser.add_argument('-n1', '--sample_1_name', help='Sample 1 name')
parser.add_argument('-n2', '--sample_2_name', help='Sample 2 name')
parser.add_argument('-o', '--output_folder', help='', default='')
parser.add_argument(
'--min_frequency_alleles_around_cut_to_plot',
type=float,
help=
'Minimum %% reads required to report an allele in the alleles table plot.',
default=0.2)
parser.add_argument(
'--max_rows_alleles_around_cut_to_plot',
type=int,
help='Maximum number of rows to report in the alleles table plot. ',
default=50)
parser.add_argument('--suppress_report',
help='Suppress output report',
action='store_true')
parser.add_argument(
'--place_report_in_output_folder',
help=
'If true, report will be written inside the CRISPResso output folder. By default, the report will be written one directory up from the report output.',
action='store_true')
parser.add_argument('--debug',
help='Show debug messages',
action='store_true')
args = parser.parse_args()
debug_flag = args.debug
#check that the CRISPResso output is present and fill amplicon_info
quantification_file_1, amplicon_names_1, amplicon_info_1 = CRISPRessoShared.check_output_folder(
args.crispresso_output_folder_1)
quantification_file_2, amplicon_names_2, amplicon_info_2 = CRISPRessoShared.check_output_folder(
args.crispresso_output_folder_2)
run_info_1_file = os.path.join(args.crispresso_output_folder_1,
'CRISPResso2_info.pickle')
if os.path.isfile(run_info_1_file) is False:
raise CRISPRessoShared.OutputFolderIncompleteException(
'The folder %s is not a valid CRISPResso2 output folder. Cannot find run data at %s'
% (args.crispresso_output_folder_1, run_info_1_file))
run_info_1 = cp.load(open(run_info_1_file, 'rb'))
run_info_2_file = os.path.join(args.crispresso_output_folder_2,
'CRISPResso2_info.pickle')
if os.path.isfile(run_info_2_file) is False:
raise CRISPRessoShared.OutputFolderIncompleteException(
'The folder %s is not a valid CRISPResso2 output folder. Cannot find run data at %s'
% (args.crispresso_output_folder_2, run_info_2_file))
run_info_2 = cp.load(open(run_info_2_file, 'rb'))
sample_1_name = args.sample_1_name
if args.sample_1_name is None:
sample_1_name = "Sample 1"
if 'name' in run_info_1 and run_info_1['name'] != '':
sample_1_name = run_info_1['name']
sample_2_name = args.sample_2_name
if args.sample_2_name is None:
sample_2_name = "Sample 2"
if 'name' in run_info_2 and run_info_2['name'] != '':
sample_2_name = run_info_2['name']
get_name_from_folder = lambda x: os.path.basename(os.path.abspath(
x)).replace('CRISPResso_on_', '')
if not args.name:
database_id = '%s_VS_%s' % (
get_name_from_folder(args.crispresso_output_folder_1),
get_name_from_folder(args.crispresso_output_folder_2))
else:
database_id = args.name
OUTPUT_DIRECTORY = 'CRISPRessoCompare_on_%s' % database_id
if args.output_folder:
OUTPUT_DIRECTORY = os.path.join(
os.path.abspath(args.output_folder), OUTPUT_DIRECTORY)
_jp = lambda filename: os.path.join(
OUTPUT_DIRECTORY, filename
) #handy function to put a file in the output directory
log_filename = _jp('CRISPRessoCompare_RUNNING_LOG.txt')
try:
info('Creating Folder %s' % OUTPUT_DIRECTORY)
os.makedirs(OUTPUT_DIRECTORY)
info('Done!')
except:
warn('Folder %s already exists.' % OUTPUT_DIRECTORY)
log_filename = _jp('CRISPRessoCompare_RUNNING_LOG.txt')
logging.getLogger().addHandler(logging.FileHandler(log_filename))
with open(log_filename, 'w+') as outfile:
outfile.write(
'[Command used]:\nCRISPRessoCompare %s\n\n[Execution log]:\n' %
' '.join(sys.argv))
crispresso2Compare_info_file = os.path.join(
OUTPUT_DIRECTORY, 'CRISPResso2Compare_info.pickle')
crispresso2_info = {
} #keep track of all information for this run to be pickled and saved at the end of the run
crispresso2_info['version'] = CRISPRessoShared.__version__
crispresso2_info['args'] = deepcopy(args)
crispresso2_info['log_filename'] = os.path.basename(log_filename)
crispresso2_info['summary_plot_names'] = []
crispresso2_info['summary_plot_titles'] = {}
crispresso2_info['summary_plot_labels'] = {}
crispresso2_info['summary_plot_datas'] = {}
save_png = True
if args.suppress_report:
save_png = False
#LOAD DATA
amplicon_names_in_both = [
amplicon_name for amplicon_name in amplicon_names_1
if amplicon_name in amplicon_names_2
]
n_refs = len(amplicon_names_in_both)
def get_plot_title_with_ref_name(plotTitle, refName):
if n_refs > 1:
return (plotTitle + ": " + refName)
return plotTitle
for amplicon_name in amplicon_names_in_both:
profile_1 = parse_profile(
amplicon_info_1[amplicon_name]['quantification_file'])
profile_2 = parse_profile(
amplicon_info_2[amplicon_name]['quantification_file'])
amplicon_plot_name = amplicon_name + "."
if len(amplicon_names_in_both
) == 1 and amplicon_name == "Reference":
amplicon_plot_name = ""
try:
assert np.all(profile_1[:, 0] == profile_2[:, 0])
except:
raise DifferentAmpliconLengthException(
'Different amplicon lengths for the two amplicons.')
len_amplicon = profile_1.shape[0]
effect_vector_any_1 = profile_1[:, 1]
effect_vector_any_2 = profile_2[:, 1]
cut_points = run_info_1['refs'][amplicon_name]['sgRNA_cut_points']
sgRNA_intervals = run_info_1['refs'][amplicon_name][
'sgRNA_intervals']
#Quantification comparison barchart
fig = plt.figure(figsize=(30, 15))
n_groups = 2
N_TOTAL_1 = float(amplicon_info_1[amplicon_name]['Reads_aligned'])
N_UNMODIFIED_1 = float(
amplicon_info_1[amplicon_name]['Unmodified'])
N_MODIFIED_1 = float(amplicon_info_1[amplicon_name]['Modified'])
N_TOTAL_2 = float(amplicon_info_2[amplicon_name]['Reads_aligned'])
N_UNMODIFIED_2 = float(
amplicon_info_2[amplicon_name]['Unmodified'])
N_MODIFIED_2 = float(amplicon_info_2[amplicon_name]['Modified'])
means_sample_1 = np.array([N_UNMODIFIED_1, N_MODIFIED_1
]) / N_TOTAL_1 * 100
means_sample_2 = np.array([N_UNMODIFIED_2, N_MODIFIED_2
]) / N_TOTAL_2 * 100
ax1 = fig.add_subplot(1, 2, 1)
index = np.arange(n_groups)
bar_width = 0.35
opacity = 0.4
error_config = {'ecolor': '0.3'}
rects1 = ax1.bar(index,
means_sample_1,
bar_width,
alpha=opacity,
color=(0, 0, 1, 0.4),
label=sample_1_name)
rects2 = ax1.bar(index + bar_width,
means_sample_2,
bar_width,
alpha=opacity,
color=(1, 0, 0, 0.4),
label=sample_2_name)
plt.ylabel('% Sequences')
plt.title(
get_plot_title_with_ref_name(
'%s VS %s' % (sample_1_name, sample_2_name),
amplicon_name))
plt.xticks(index + bar_width / 2.0, ('Unmodified', 'Modified'))
plt.legend()
# plt.xlim(index[0]-0.2,(index + bar_width)[-1]+bar_width+0.2)
plt.tight_layout()
ax2 = fig.add_subplot(1, 2, 2)
ax2.bar(index,
means_sample_1 - means_sample_2,
bar_width + 0.35,
alpha=opacity,
color=(0, 1, 1, 0.4),
label='')
plt.ylabel('% Sequences Difference')
plt.title(
get_plot_title_with_ref_name(
'%s - %s' % (sample_1_name, sample_2_name), amplicon_name))
plt.xticks(index, ['Unmodified', 'Modified'])
# plt.xlim(index[0]-bar_width/2, (index+bar_width)[-1]+2*bar_width)
plt.tight_layout()
plot_name = '1.' + amplicon_plot_name + 'Editing_comparison'
plt.savefig(_jp(plot_name) + '.pdf', bbox_inches='tight')
if save_png:
plt.savefig(_jp(plot_name) + '.png', bbox_inches='tight')
crispresso2_info['summary_plot_names'].append(plot_name)
crispresso2_info['summary_plot_titles'][
plot_name] = 'Editing efficiency comparison'
crispresso2_info['summary_plot_labels'][
plot_name] = 'Figure 1: Comparison for amplicon ' + amplicon_name + '; Left: Percentage of modified and unmodified reads in each sample; Right: relative percentage of modified and unmodified reads'
output_1 = os.path.join(args.crispresso_output_folder_1,
run_info_1['report_filename'])
output_2 = os.path.join(args.crispresso_output_folder_1,
run_info_2['report_filename'])
crispresso2_info['summary_plot_datas'][plot_name] = []
if os.path.isfile(output_1):
crispresso2_info['summary_plot_datas'][plot_name].append(
(sample_1_name + ' output',
os.path.relpath(output_1, OUTPUT_DIRECTORY)))
if os.path.isfile(output_2):
crispresso2_info['summary_plot_datas'][plot_name].append(
(sample_2_name + ' output',
os.path.relpath(output_2, OUTPUT_DIRECTORY)))
mod_file_1 = amplicon_info_1[amplicon_name][
'modification_count_file']
amp_seq_1, mod_freqs_1 = CRISPRessoShared.parse_count_file(
mod_file_1)
mod_file_2 = amplicon_info_2[amplicon_name][
'modification_count_file']
amp_seq_2, mod_freqs_2 = CRISPRessoShared.parse_count_file(
mod_file_2)
consensus_sequence = amp_seq_1
if amp_seq_2 != consensus_sequence:
raise DifferentAmpliconLengthException(
'Different amplicon lengths for the two amplicons.')
for mod in [
'Insertions', 'Deletions', 'Substitutions',
'All_modifications'
]:
mod_name = mod
if mod == "All_modifications":
mod_name = "Combined modifications (insertions, deletions and substitutions)"
mod_counts_1 = np.array(mod_freqs_1[mod], dtype=float)
tot_counts_1 = np.array(mod_freqs_1['Total'], dtype=float)
unmod_counts_1 = tot_counts_1 - mod_counts_1
mod_counts_2 = np.array(mod_freqs_2[mod], dtype=float)
tot_counts_2 = np.array(mod_freqs_2['Total'], dtype=float)
unmod_counts_2 = tot_counts_2 - mod_counts_2
fisher_results = [
stats.fisher_exact([[z[0], z[1]], [z[2], z[3]]])
if max(z) > 0 else [nan, 1.0]
for z in zip(mod_counts_1, unmod_counts_1, mod_counts_2,
unmod_counts_2)
]
oddsratios, pvalues = [a for a, b in fisher_results
], [b for a, b in fisher_results]
mod_df = []
row = [sample_1_name + '_' + mod]
row.extend(mod_counts_1)
mod_df.append(row)
row = [sample_1_name + '_total']
row.extend(tot_counts_1)
mod_df.append(row)
row = [sample_2_name + '_' + mod]
row.extend(mod_counts_2)
mod_df.append(row)
row = [sample_2_name + '_total']
row.extend(tot_counts_2)
mod_df.append(row)
row = ['odds_ratios']
row.extend(oddsratios)
mod_df.append(row)
row = ['pvalues']
row.extend(pvalues)
mod_df.append(row)
colnames = ['Reference']
colnames.extend(list(consensus_sequence))
mod_df = pd.DataFrame(mod_df, columns=colnames)
# mod_df = pd.concat([mod_df.iloc[:,0:2], mod_df.iloc[:,2:].apply(pd.to_numeric)],axis=1)
#write to file
mod_filename = _jp(amplicon_plot_name + mod +
"_quantification.txt")
mod_df.to_csv(mod_filename, sep='\t', index=None)
#plot
fig = plt.figure(figsize=(20, 10))
ax1 = fig.add_subplot(2, 1, 1)
diff = np.divide(mod_counts_1, tot_counts_1) - np.divide(
mod_counts_2, tot_counts_2)
diff_plot = ax1.plot(diff,
color=(0, 1, 0, 0.4),
lw=3,
label='Difference')
ax1.set_title(
get_plot_title_with_ref_name(
'%s: %s - %s' % (mod, sample_1_name, sample_2_name),
amplicon_name))
ax1.set_xticks(
np.arange(
0, len_amplicon,
max(3, (len_amplicon / 6) -
(len_amplicon / 6) % 5)).astype(int))
ax1.set_ylabel('Sequences Difference %')
ax1.set_xlim(xmin=0, xmax=len_amplicon - 1)
pvalues = np.array(pvalues)
min_nonzero = np.min(pvalues[np.nonzero(pvalues)])
pvalues[pvalues == 0] = min_nonzero
#ax2 = ax1.twinx()
ax2 = fig.add_subplot(2, 1, 2)
pval_plot = ax2.plot(-1 * np.log10(pvalues),
color=(1, 0, 0, 0.4),
lw=2,
label='-log10 P-value')
ax2.set_ylabel('-log10 P-value')
ax2.set_xlim(xmin=0, xmax=len_amplicon - 1)
ax2.set_xticks(
np.arange(
0, len_amplicon,
max(3, (len_amplicon / 6) -
(len_amplicon / 6) % 5)).astype(int))
ax2.set_xlabel('Reference amplicon position (bp)')
#bonferroni correction
corrected_p = -1 * np.log10(
0.01 / float(len(consensus_sequence)))
cutoff_plot = ax2.plot([0, len(consensus_sequence)],
[corrected_p, corrected_p],
color='k',
dashes=(5, 10),
label='Bonferronni corrected cutoff')
plots = diff_plot + pval_plot + cutoff_plot
diff_y_min, diff_y_max = ax1.get_ylim()
p_y_min, p_y_max = ax2.get_ylim()
if cut_points:
for idx, cut_point in enumerate(cut_points):
if idx == 0:
plot_cleavage = ax1.plot(
[cut_point, cut_point],
[diff_y_min, diff_y_max],
'--k',
lw=2,
label='Predicted cleavage position')
ax2.plot([cut_point, cut_point],
[p_y_min, p_y_max],
'--k',
lw=2,
label='Predicted cleavage position')
plots = plots + plot_cleavage
else:
ax1.plot([cut_point, cut_point],
[diff_y_min, diff_y_max],
'--k',
lw=2,
label='_nolegend_')
ax2.plot([cut_point, cut_point],
[diff_y_min, diff_y_max],
'--k',
lw=2,
label='_nolegend_')
for idx, sgRNA_int in enumerate(sgRNA_intervals):
if idx == 0:
p2 = ax1.plot([sgRNA_int[0], sgRNA_int[1]],
[diff_y_min, diff_y_min],
lw=10,
c=(0, 0, 0, 0.15),
label='sgRNA')
ax2.plot([sgRNA_int[0], sgRNA_int[1]],
[p_y_min, p_y_min],
lw=10,
c=(0, 0, 0, 0.15),
label='sgRNA')
plots = plots + p2
else:
ax1.plot([sgRNA_int[0], sgRNA_int[1]],
[diff_y_min, diff_y_min],
lw=10,
c=(0, 0, 0, 0.15),
label='_nolegend_')
ax2.plot([sgRNA_int[0], sgRNA_int[1]],
[p_y_min, p_y_min],
lw=10,
c=(0, 0, 0, 0.15),
label='_nolegend_')
labs = [p.get_label() for p in plots]
lgd = plt.legend(plots,
labs,
loc='upper center',
bbox_to_anchor=(0.5, -0.2),
ncol=1,
fancybox=True,
shadow=False)
plot_name = '2.' + amplicon_plot_name + mod + '_quantification'
plt.savefig(_jp(plot_name + '.pdf'),
bbox_inches='tight',
bbox_extra_artists=(lgd, ))
if save_png:
plt.savefig(_jp(plot_name + '.png'),
bbox_inches='tight',
bbox_extra_artists=(lgd, ))
crispresso2_info['summary_plot_names'].append(plot_name)
crispresso2_info['summary_plot_titles'][
plot_name] = mod_name + ' locations'
crispresso2_info['summary_plot_labels'][
plot_name] = mod_name + ' location comparison for amplicon ' + amplicon_name + '; Top: percent difference; Bottom: p-value.'
crispresso2_info['summary_plot_datas'][plot_name] = [
(mod_name + ' quantification',
os.path.basename(mod_filename))
]
#create merged heatmaps for each cut site
allele_files_1 = amplicon_info_1[amplicon_name]['allele_files']
allele_files_2 = amplicon_info_2[amplicon_name]['allele_files']
for allele_file_1 in allele_files_1:
allele_file_1_name = os.path.split(allele_file_1)[
1] #get file part of path
for allele_file_2 in allele_files_2:
allele_file_2_name = os.path.split(allele_file_2)[
1] #get file part of path
#if files are the same (same amplicon, cut site, guide), run comparison
if allele_file_1_name == allele_file_2_name:
df1 = pd.read_csv(allele_file_1, sep="\t")
df2 = pd.read_csv(allele_file_2, sep="\t")
#find unmodified reference for comparison (if it exists)
ref_seq_around_cut = ""
if len(df1.loc[df1['Reference_Sequence'].str.contains(
'-') == False]) > 0:
ref_seq_around_cut = df1.loc[
df1['Reference_Sequence'].str.contains('-') ==
False]['Reference_Sequence'].iloc[0]
#otherwise figure out which sgRNA was used for this comparison
elif len(df2.loc[df2['Reference_Sequence'].str.
contains('-') == False]) > 0:
ref_seq_around_cut = df2.loc[
df2['Reference_Sequence'].str.contains('-') ==
False]['Reference_Sequence'].iloc[0]
else:
seq_len = df2[df2['Unedited'] ==
True]['Reference_Sequence'].iloc[0]
for sgRNA_interval, cut_point in zip(
sgRNA_intervals, cut_points):
sgRNA_seq = consensus_sequence[
sgRNA_interval[0]:sgRNA_interval[1]]
if sgRNA_seq in allele_file_1_name:
this_sgRNA_seq = sgRNA_seq
this_cut_point = cut_point
ref_seq_around_cut = consensus_sequence[max(
0, this_cut_point -
args.offset_around_cut_to_plot +
1):min(
len(reference_seq), cut_point +
args.offset_around_cut_to_plot +
1)]
break
merged = pd.merge(df1,
df2,
on=[
'Aligned_Sequence',
'Reference_Sequence', 'Unedited',
'n_deleted', 'n_inserted',
'n_mutated'
],
suffixes=('_' + sample_1_name,
'_' + sample_2_name),
how='outer')
quant_cols = [
'#Reads_' + sample_1_name,
'%Reads_' + sample_1_name,
'#Reads_' + sample_2_name,
'%Reads_' + sample_2_name
]
merged[quant_cols] = merged[quant_cols].fillna(0)
lfc_error = 0.1
merged['each_LFC'] = np.log2(
((merged['%Reads_' + sample_1_name] + lfc_error) /
(merged['%Reads_' + sample_2_name] + lfc_error)
).astype(float)).replace([np.inf, np.NaN], 0)
merged = merged.reset_index().set_index(
'Aligned_Sequence')
output_root = allele_file_1_name.replace(".txt", "")
allele_comparison_file = _jp(output_root + '.txt')
merged.to_csv(allele_comparison_file,
sep="\t",
index=None)
plot_name = '3.' + output_root + '_top'
CRISPRessoPlot.plot_alleles_table_compare(
ref_seq_around_cut,
merged.sort_values(['each_LFC'], ascending=True),
sample_1_name,
sample_2_name,
_jp(plot_name),
MIN_FREQUENCY=args.
min_frequency_alleles_around_cut_to_plot,
MAX_N_ROWS=args.
max_rows_alleles_around_cut_to_plot,
SAVE_ALSO_PNG=save_png)
crispresso2_info['summary_plot_names'].append(
plot_name)
crispresso2_info['summary_plot_titles'][
plot_name] = 'Alleles enriched in ' + sample_1_name
crispresso2_info['summary_plot_labels'][plot_name] = 'Distribution comparison of alleles. Nucleotides are indicated by unique colors (A = green; C = red; G = yellow; T = purple). Substitutions are shown in bold font. Red rectangles highlight inserted sequences. Horizontal dashed lines indicate deleted sequences. The vertical dashed line indicates the predicted cleavage site. '+ \
'The proportion and number of reads is shown for each sample on the right, with the values for ' + sample_1_name + ' followed by the values for ' + sample_2_name +'. Alleles are sorted for enrichment in ' + sample_1_name+'.'
crispresso2_info['summary_plot_datas'][plot_name] = [
('Allele comparison table',
os.path.basename(allele_comparison_file))
]
plot_name = '3.' + output_root + '_bottom'
CRISPRessoPlot.plot_alleles_table_compare(
ref_seq_around_cut,
merged.sort_values(['each_LFC'], ascending=False),
sample_1_name,
sample_2_name,
_jp(plot_name),
MIN_FREQUENCY=args.
min_frequency_alleles_around_cut_to_plot,
MAX_N_ROWS=args.
max_rows_alleles_around_cut_to_plot,
SAVE_ALSO_PNG=save_png)
crispresso2_info['summary_plot_names'].append(
plot_name)
crispresso2_info['summary_plot_titles'][
plot_name] = 'Alleles enriched in ' + sample_2_name
crispresso2_info['summary_plot_labels'][plot_name] = 'Distribution comparison of alleles. Nucleotides are indicated by unique colors (A = green; C = red; G = yellow; T = purple). Substitutions are shown in bold font. Red rectangles highlight inserted sequences. Horizontal dashed lines indicate deleted sequences. The vertical dashed line indicates the predicted cleavage site. '+ \
'The proportion and number of reads is shown for each sample on the right, with the values for ' + sample_1_name + ' followed by the values for ' + sample_2_name +'. Alleles are sorted for enrichment in ' + sample_2_name+'.'
crispresso2_info['summary_plot_datas'][plot_name] = [
('Allele comparison table',
os.path.basename(allele_comparison_file))
]
if not args.suppress_report:
if (args.place_report_in_output_folder):
report_name = _jp("CRISPResso2Batch_report.html")
else:
report_name = OUTPUT_DIRECTORY + '.html'
CRISPRessoReport.make_compare_report_from_folder(
report_name, crispresso2_info, OUTPUT_DIRECTORY, _ROOT)
crispresso2_info['report_location'] = report_name
crispresso2_info['report_filename'] = os.path.basename(report_name)
cp.dump(crispresso2_info, open(crispresso2Compare_info_file, 'wb'))
info('Analysis Complete!')
print(CRISPRessoShared.get_crispresso_footer())
sys.exit(0)
except Exception as e:
debug_flag = False
if 'args' in vars() and 'debug' in args:
debug_flag = args.debug
if debug_flag:
traceback.print_exc(file=sys.stdout)
error('\n\nERROR: %s' % e)
sys.exit(-1)