def combine_spikein(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
sailfish_dir = os.path.join(work_dir, "spikein")
dont_combine, to_combine = partition(dd.get_spikein_counts,
dd.sample_data_iterator(samples),
True)
if not to_combine:
return samples
tidy_file = os.path.join(sailfish_dir, "spikein.sf")
if not file_exists(tidy_file):
logger.info("Combining count files into %s." % tidy_file)
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_spikein_counts(data)
samplename = dd.get_sample_name(data)
new_df = sailfish._sailfish_expression_parser(
sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
df["id"] = df.index
# some versions of the transcript annotations can have duplicated entries
df = df.drop_duplicates(["id", "sample"])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
logger.info("Finished combining count files into %s." % tidy_file)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_spikein_counts(data, tidy_file)
updated_samples.append([data])
return updated_samples
def counts_spikein(data):
data = utils.to_single_data(data)
samplename = dd.get_sample_name(data)
work_dir = dd.get_work_dir(data)
salmon_dir = os.path.join(work_dir, "spikein", samplename)
fasta_file = dd.get_spikein_fasta(data)
if not fasta_file:
return data
files = dd.get_input_sequence_files(data)
if len(files) == 2:
fq1, fq2 = files
else:
fq1, fq2 = files[0], None
assert file_exists(fasta_file), "%s was not found, exiting." % fasta_file
readlength = fastq.estimate_read_length(fq1)
if readlength % 2 == 0:
readlength -= 1
kmersize = min(readlength, 31)
logger.info("kmersize used for salmon index at spikein quant: %s" %
kmersize)
kmersize = kmersize if not dd.get_analysis(
data).lower() == "smallrna-seq" else 15
fasta_index = _index_spikein(fasta_file, salmon_dir, data, kmersize)
out_file = _salmon_quant_reads(fq1, fq2, salmon_dir, fasta_index, data)
data = dd.set_spikein_counts(data, out_file)
return data
def combine_spikein(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
sailfish_dir = os.path.join(work_dir, "spikein")
dont_combine, to_combine = partition(dd.get_spikein_counts,
dd.sample_data_iterator(samples), True)
if not to_combine:
return samples
tidy_file = os.path.join(sailfish_dir, "spikein.sf")
if not file_exists(tidy_file):
logger.info("Combining count files into %s." % tidy_file)
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_spikein_counts(data)
samplename = dd.get_sample_name(data)
new_df = sailfish._sailfish_expression_parser(sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
df["id"] = df.index
# some versions of the transcript annotations can have duplicated entries
df = df.drop_duplicates(["id", "sample"])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
logger.info("Finished combining count files into %s." % tidy_file)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_spikein_counts(data, tidy_file)
updated_samples.append([data])
return updated_samples
def counts_spikein(data):
data = utils.to_single_data(data)
samplename = dd.get_sample_name(data)
work_dir = dd.get_work_dir(data)
salmon_dir = os.path.join(work_dir, "spikein", samplename)
fasta_file = dd.get_spikein_fasta(data)
if not fasta_file:
return data
files = dd.get_input_sequence_files(data)
if len(files) == 2:
fq1, fq2 = files
else:
fq1, fq2 = files[0], None
assert file_exists(fasta_file), "%s was not found, exiting." % fasta_file
kmer = 31 if not dd.get_analysis(data).lower() == "smallrna-seq" else 15
fasta_index = _index_spikein(fasta_file, salmon_dir, data, kmer)
out_file = _salmon_quant_reads(fq1, fq2, salmon_dir, fasta_index, data)
data = dd.set_spikein_counts(data, out_file)
return data
def counts_spikein(data):
data = utils.to_single_data(data)
samplename = dd.get_sample_name(data)
work_dir = dd.get_work_dir(data)
salmon_dir = os.path.join(work_dir, "spikein", samplename)
fasta_file = dd.get_spikein_fasta(data)
if not fasta_file:
return data
files = dd.get_input_sequence_files(data)
if len(files) == 2:
fq1, fq2 = files
else:
fq1, fq2 = files[0], None
assert file_exists(fasta_file), "%s was not found, exiting." % fasta_file
kmer = 31 if not dd.get_analysis(data).lower() == "smallrna-seq" else 15
fasta_index = _index_spikein(fasta_file, salmon_dir, data, kmer)
out_file = _salmon_quant_reads(fq1, fq2, salmon_dir, fasta_index, data)
data = dd.set_spikein_counts(data, out_file)
return data
def counts_spikein(data):
data = utils.to_single_data(data)
samplename = dd.get_sample_name(data)
work_dir = dd.get_work_dir(data)
salmon_dir = os.path.join(work_dir, "spikein", samplename)
fasta_file = dd.get_spikein_fasta(data)
if not fasta_file:
return data
files = dd.get_input_sequence_files(data)
if len(files) == 2:
fq1, fq2 = files
else:
fq1, fq2 = files[0], None
assert file_exists(fasta_file), "%s was not found, exiting." % fasta_file
readlength = fastq.estimate_read_length(fq1)
if readlength % 2 == 0:
readlength -= 1
kmersize = min(readlength, 31)
logger.info("kmersize used for salmon index at spikein quant: %s" % kmersize)
kmersize = kmersize if not dd.get_analysis(data).lower() == "smallrna-seq" else 15
fasta_index = _index_spikein(fasta_file, salmon_dir, data, kmersize)
out_file = _salmon_quant_reads(fq1, fq2, salmon_dir, fasta_index, data)
data = dd.set_spikein_counts(data, out_file)
return data
