def main():
(_, select_range, cc_type, antisense) = \
tuple(sys.argv)
antisense = antisense.lower() == 'true'
strand = 'sense' if not antisense else 'antisense'
select_range = select_range.split('_')
select_range = int(select_range[0]), int(select_range[1])
cross_corr_path = cross_corr_sense_path
orfs = paper_orfs
if antisense:
cross_corr_path = cross_corr_antisense_path
orfs = antisense_orfs
child_name = get_name(select_range, cc_type)
print_fl("Running entropy for %d-%d, %s, %s" %
(select_range[0], select_range[1], cc_type, strand))
name = 'entropy_%s' % strand
timer = Timer()
print_fl("Reading Cross correlation...", end='')
cross_correlation = pd.read_hdf(cross_corr_path, 'cross_correlation')
print_fl("Done.")
timer.print_time()
print_fl()
print_fl("Computing entropy")
calculate_cc_summary_measure_range_type(orfs, cross_correlation, cc_type,
select_range, strand, timer)
child_done(name, WATCH_TMP_DIR, child_name)
def collect_mnase(mnase_seq, window, pos_chr_df,
pos_key='position', chrom_key='chromosome',
strand=None, set_index=False, log=False):
collected_mnase_eq = pd.DataFrame()
win_2 = window/2
timer = Timer()
if log:
print_fl("Collecting MNase-seq fragments for %d entries" % len(pos_chr_df))
print_fl("around a %d window" % window)
i = 0
for chrom in range(1, 17):
# get chromosome specific nucleosoems and MNase-seq
chrom_entries = pos_chr_df[pos_chr_df[chrom_key] == chrom]
if len(chrom_entries) == 0: continue
chrom_mnase = filter_mnase(mnase_seq, chrom=chrom)
# for each element in the dataset
for idx, entry in chrom_entries.iterrows():
# get MNase-seq fragments at pos_chr_df
# and 0 center
center = entry[pos_key]
nuc_mnase = filter_mnase(chrom_mnase, start=center-win_2, end=center+win_2)
# orient properly left to right (upstream to downstream)
if strand is None or entry[strand] == '+':
nuc_mnase.loc[:, 'mid'] = nuc_mnase.mid - center
# crick strand, flip
else:
nuc_mnase.loc[:, 'mid'] = center - nuc_mnase.mid
select_columns = ['chr', 'length', 'mid', 'time']
if set_index:
nuc_mnase['parent'] = idx
select_columns.append('parent')
# append to MNase-seq
collected_mnase_eq = collected_mnase_eq.append(nuc_mnase[select_columns])
# print_fl progress
if log and i % 200 == 0: print_fl("%d/%d - %s" % (i, len(pos_chr_df),
timer.get_time()))
i += 1
if log: timer.print_time()
return collected_mnase_eq
def main():
from src.kernel_fitter import compute_triple_kernel
(_, chrom, antisense) = \
tuple(sys.argv)
antisense = antisense.lower() == 'true'
chrom = int(chrom)
print_fl("Running cross correlation on chromosome %d, antisense: %s" %
(chrom, str(antisense)))
name = task_name(antisense)
timer = Timer()
nuc_kernel = MNaseSeqDensityKernel(filepath=nuc_kernel_path)
sm_kernel = MNaseSeqDensityKernel(filepath=sm_kernel_path)
triple_kernel = compute_triple_kernel(nuc_kernel)
print_fl("Reading MNase-seq...", end='')
all_mnase_data = pd.read_hdf(mnase_seq_path, 'mnase_data')
print_fl("Done.")
timer.print_time()
print_fl()
if not antisense:
cc_dir = cc_sense_chrom_dir
cc_orfs = read_orfs_data("%s/orfs_cd_paper_dataset.csv" % OUTPUT_DIR)
else:
cc_dir = cc_antisense_chrom_dir
cc_orfs = antisense_orfs
calculate_cross_correlation_chr(all_mnase_data,
cc_orfs,
chrom,
antisense,
nuc_kernel,
sm_kernel,
triple_kernel,
cc_dir,
log=True,
timer=timer)
child_done(name, WATCH_TMP_DIR, chrom)
def collect_small_peaks():
from src.small_peak_calling import call_orf_small_peaks
from src.timer import Timer
orfs = all_orfs_TSS_PAS()
timer = Timer()
all_peaks = pd.DataFrame()
for chrom in range(1, 17):
print("Chromosome %d" % chrom)
chr_orfs = orfs[orfs.chr == chrom]
# load relevant cross correlations
chrom_cross_correlation = pd.read_hdf(
'%s/cross_correlation_chr%d.h5.z' %
(cc_sense_chrom_dir, chrom))
small_cc = -1 * chrom_cross_correlation.loc['diff']
for idx, orf in chr_orfs.iterrows():
try:
peaks = call_orf_small_peaks(small_cc, orf)
except KeyError:
continue
all_peaks = all_peaks.append(peaks)
timer.print_time()
all_peaks = all_peaks.reset_index(drop=True)
all_peaks['name'] = all_peaks['orf'] + '_' + all_peaks['time'].astype(str) + '_' + \
all_peaks['chr'].astype(str) + '_' + all_peaks['original_mid'].astype(str)
all_peaks = all_peaks.set_index('name')
return all_peaks
def run_model(name, save_dir, predict_TPM=True):
timer = Timer()
print_fl("Loading %s model" % name)
print_fl("Predicting TPM: %s" % predict_TPM)
sample_N = None
model_fun = get_model_funs()[name]
model = model_fun(sample_N=sample_N)
folds = 10
print_fl("Fitting %d folds.." % folds)
model.fit_cv(log=True, k=folds)
# save models to disk
res = model.Y.join(model.Y_predict, lsuffix='_true', rsuffix='_predicted')
res.to_csv('%s/%s_results.csv' % (save_dir, name))
res = pd.DataFrame({'r2': model.r2, 'mse': model.mse})
res.to_csv('%s/res_%s.csv' % (save_dir, name))
timer.print_time()
