def combine_sailfish(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
sailfish_dir = os.path.join(work_dir, "sailfish")
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
dont_combine, to_combine = partition(dd.get_sailfish,
dd.sample_data_iterator(samples),
True)
if not to_combine:
return samples
tidy_file = os.path.join(sailfish_dir, "combined.sf")
transcript_tpm_file = os.path.join(sailfish_dir, "combined.isoform.sf.tpm")
gene_tpm_file = os.path.join(sailfish_dir, "combined.gene.sf.tpm")
tx2gene = os.path.join(sailfish_dir, "tx2gene.csv")
if not all([
file_exists(x)
for x in [gene_tpm_file, tidy_file, transcript_tpm_file, tx2gene]
]):
logger.info("Combining count files into %s." % tidy_file)
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_sailfish(data)
samplename = dd.get_sample_name(data)
new_df = _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")
with file_transaction(transcript_tpm_file) as tx_out_file:
df.pivot("id", "sample", "tpm").to_csv(tx_out_file, sep="\t")
with file_transaction(gene_tpm_file) as tx_out_file:
pivot = df.pivot("id", "sample", "tpm")
tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
orient="index")
tdf.columns = ["gene_id"]
pivot = pivot.join(tdf)
pivot = pivot.groupby("gene_id").agg(np.sum)
pivot.to_csv(tx_out_file, sep="\t")
tx2gene = gtf.tx2genefile(gtf_file, tx2gene)
logger.info("Finished combining count files into %s." % tidy_file)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_sailfish_tidy(data, tidy_file)
data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
data = dd.set_tx2gene(data, tx2gene)
updated_samples.append([data])
return updated_samples
def combine_sailfish(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
sailfish_dir = os.path.join(work_dir, "sailfish")
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
dont_combine, to_combine = partition(dd.get_sailfish,
dd.sample_data_iterator(samples), True)
if not to_combine:
return samples
tidy_file = os.path.join(sailfish_dir, "combined.sf")
transcript_tpm_file = os.path.join(sailfish_dir, "combined.isoform.sf.tpm")
gene_tpm_file = os.path.join(sailfish_dir, "combined.gene.sf.tpm")
tx2gene = os.path.join(sailfish_dir, "tx2gene.csv")
if not all([file_exists(x) for x in [gene_tpm_file, tidy_file,
transcript_tpm_file, tx2gene]]):
logger.info("Combining count files into %s." % tidy_file)
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_sailfish(data)
samplename = dd.get_sample_name(data)
new_df = _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")
with file_transaction(transcript_tpm_file) as tx_out_file:
df.pivot("id", "sample", "tpm").to_csv(tx_out_file, sep="\t")
with file_transaction(gene_tpm_file) as tx_out_file:
pivot = df.pivot("id", "sample", "tpm")
tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
orient="index")
tdf.columns = ["gene_id"]
pivot = pivot.join(tdf)
pivot = pivot.groupby("gene_id").agg(np.sum)
pivot.to_csv(tx_out_file, sep="\t")
tx2gene = gtf.tx2genefile(gtf_file, tx2gene)
logger.info("Finished combining count files into %s." % tidy_file)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_sailfish_tidy(data, tidy_file)
data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
data = dd.set_tx2gene(data, tx2gene)
updated_samples.append([data])
return updated_samples
def combine_sailfish(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
dont_combine, to_combine = partition(dd.get_sailfish,
dd.sample_data_iterator(samples), True)
if not to_combine:
return samples
tidy_file = os.path.join(work_dir, "sailfish", "combined.sf")
transcript_tpm_file = os.path.join(work_dir, "sailfish",
"combined.isoform.sf.tpm")
gene_tpm_file = os.path.join(work_dir, "sailfish",
"combined.gene.sf.tpm")
if not all([file_exists(x) for x in [gene_tpm_file, tidy_file,
transcript_tpm_file]]):
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_sailfish(data)
samplename = dd.get_sample_name(data)
new_df = _sailfish_expression_parser(sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
with file_transaction(transcript_tpm_file) as tx_out_file:
df.pivot(None, "sample", "tpm").to_csv(tx_out_file, sep="\t")
with file_transaction(gene_tpm_file) as tx_out_file:
pivot = df.pivot(None, "sample", "tpm")
tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
orient="index")
tdf.columns = ["gene_id"]
pivot = pivot.join(tdf)
pivot = pivot.groupby("gene_id").agg(np.sum)
pivot.to_csv(tx_out_file, sep="\t")
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_sailfish_tidy(data, tidy_file)
data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
updated_samples.append([data])
return updated_samples
def combine_sailfish(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
dont_combine, to_combine = partition(dd.get_sailfish,
dd.sample_data_iterator(samples), True)
if not to_combine:
return samples
tidy_file = os.path.join(work_dir, "sailfish", "combined.sf")
transcript_tpm_file = os.path.join(work_dir, "sailfish",
"combined.isoform.sf.tpm")
gene_tpm_file = os.path.join(work_dir, "sailfish",
"combined.gene.sf.tpm")
if not all([file_exists(x) for x in [gene_tpm_file, tidy_file,
transcript_tpm_file]]):
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_sailfish(data)
samplename = dd.get_sample_name(data)
new_df = _sailfish_expression_parser(sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
with file_transaction(transcript_tpm_file) as tx_out_file:
df.pivot(None, "sample", "tpm").to_csv(tx_out_file, sep="\t")
with file_transaction(gene_tpm_file) as tx_out_file:
pivot = df.pivot(None, "sample", "tpm")
tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
orient="index")
tdf.columns = ["gene_id"]
pivot = pivot.join(tdf)
pivot = pivot.groupby("gene_id").agg(np.sum)
pivot.to_csv(tx_out_file, sep="\t")
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_sailfish_tidy(data, tidy_file)
data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
updated_samples.append([data])
return updated_samples
