def get_variants_in_samples(gemini_db, samples, annotations, min_allele_freq, min_alt_depth, min_depth, max_aaf_all, somatic=False):
"""
Returns a dataframe with variants from all samples.
"""
if somatic:
get_vars_fn = gem_ops.get_somatic_vars_in_sample
else:
get_vars_fn = gem_ops.get_vars_in_sample
print >> sys.stderr, "Samples to process: ", ", ".join(samples)
all_vars = []
for sample in samples:
start = time.time()
sample_vars = get_vars_fn(gemini_db, annotations, sample, min_allele_freq=min_allele_freq,
min_alt_depth=min_alt_depth, min_depth=min_depth, max_aaf_all=max_aaf_all)
end = time.time()
all_vars.append(sample_vars)
print >> sys.stderr, sample, len(sample_vars), end-start
# Combine all variants together and reset index so adding metadata is easy.
all_vars_df = gem_ops.convert_cols(pd.concat(all_vars))
all_vars_df.reset_index(inplace=True, drop=True)
# Using sample column, create and populate columns for id, plate, tissue, and replicate.
sample_attrs = pd.DataFrame(list(all_vars_df["sample"].apply(lambda s: split_id(s))), columns=["id", "plate", "tissue", "replicate"])
for i, col in enumerate(["id", "plate", "tissue", "replicate"]):
all_vars_df.insert(i+1, col, pd.Series())
all_vars_df[col] = sample_attrs[col]
return all_vars_df
# Write results to file.
if sample_pattern == ".*":
sample_pattern = "all"
out_filename = "find_vars_results_%s_minaf%.2f_ad%i_d%i.txt" % (sample_pattern, min_allele_freq, min_alt_depth, min_depth)
out_file = open(out_filename, "w")
out_file.write( all_vars_df.to_csv(sep="\t", index=False, float_format='%.3f') )
out_file.close()
print "Wrote results to file %s" % out_filename
elif operation == "augment_vars":
# Augment variants with updated and joint information.
# Read results into dataframe.
results_df = gem_ops.convert_cols( pd.read_csv(results_file, sep="\t") )
results_df = filter_and_augment_variants(results_df, min_allele_freq, min_alt_depth, min_depth, max_num_het, tissue, add_joint_cols)
# Print augmented results.
augmented_out_file = open("augmented_" + results_file, "w")
augmented_out_file.write(results_df.to_csv(sep="\t", index=False))
augmented_out_file.close()
print "Wrote augmented variants to file %s" % augmented_out_file
# Print joint variants.
if add_joint:
joint_out_file = open("joint_" + results_file, "w")
print_joint_variants(results_df, joint_out_file, all_cols=True)
joint_out_file.close()