def _load_gtf_as_dataframe(self, usecols=None, features=None):
"""
Parse this genome source's GTF file and load it as a Pandas DataFrame
"""
logger.info("Reading GTF from %s", self.gtf_path)
df = read_gtf(
self.gtf_path,
column_converters={
"seqname": normalize_chromosome,
"strand": normalize_strand,
},
infer_biotype_column=True,
usecols=usecols,
features=features)
column_names = set(df.keys())
expect_gene_feature = features is None or "gene" in features
expect_transcript_feature = features is None or "transcript" in features
observed_features = set(df["feature"])
# older Ensembl releases don't have "gene" or "transcript"
# features, so fill in those rows if they're missing
if expect_gene_feature and "gene" not in observed_features:
# if we have to reconstruct gene feature rows then
# fill in values for 'gene_name' and 'gene_biotype'
# but only if they're actually present in the GTF
logger.info("Creating missing gene features...")
df = create_missing_features(
dataframe=df,
unique_keys={"gene": "gene_id"},
extra_columns={
"gene": {
"gene_name",
"gene_biotype"
}.intersection(column_names),
},
missing_value="")
logger.info("Done.")
if expect_transcript_feature and "transcript" not in observed_features:
logger.info("Creating missing transcript features...")
df = create_missing_features(
dataframe=df,
unique_keys={"transcript": "transcript_id"},
extra_columns={
"transcript": {
"gene_id",
"gene_name",
"gene_biotype",
"transcript_name",
"transcript_biotype",
"protein_id",
}.intersection(column_names)
},
missing_value="")
logger.info("Done.")
return df
def _load_gtf_as_dataframe(self, usecols=None, features=None):
"""
Parse this genome source's GTF file and load it as a Pandas DataFrame
"""
logger.info("Reading GTF from %s", self.gtf_path)
df = read_gtf(self.gtf_path,
column_converters={
"seqname": normalize_chromosome,
"strand": normalize_strand,
},
infer_biotype_column=True,
usecols=usecols,
features=features)
column_names = set(df.keys())
expect_gene_feature = features is None or "gene" in features
expect_transcript_feature = features is None or "transcript" in features
observed_features = set(df["feature"])
# older Ensembl releases don't have "gene" or "transcript"
# features, so fill in those rows if they're missing
if expect_gene_feature and "gene" not in observed_features:
# if we have to reconstruct gene feature rows then
# fill in values for 'gene_name' and 'gene_biotype'
# but only if they're actually present in the GTF
logger.info("Creating missing gene features...")
df = create_missing_features(dataframe=df,
unique_keys={"gene": "gene_id"},
extra_columns={
"gene":
{"gene_name", "gene_biotype"
}.intersection(column_names),
},
missing_value="")
logger.info("Done.")
if expect_transcript_feature and "transcript" not in observed_features:
logger.info("Creating missing transcript features...")
df = create_missing_features(
dataframe=df,
unique_keys={"transcript": "transcript_id"},
extra_columns={
"transcript": {
"gene_id",
"gene_name",
"gene_biotype",
"transcript_name",
"transcript_biotype",
"protein_id",
}.intersection(column_names)
},
missing_value="")
logger.info("Done.")
return df
def _load_full_dataframe_from_gtf(self):
"""
Parse this genome source's GTF file and load it as a Pandas DataFrame
"""
print("Reading GTF from %s" % self.gtf_path)
df = read_gtf_as_dataframe(
self.gtf_path,
column_converters={
"seqname": normalize_chromosome,
"strand": normalize_strand,
},
infer_biotype_column=True)
features = set(df["feature"])
column_names = set(df.keys())
# older Ensembl releases don't have "gene" or "transcript"
# features, so fill in those rows if they're missing
if "gene" not in features:
# if we have to reconstruct gene feature rows then
# fill in values for 'gene_name' and 'gene_biotype'
# but only if they're actually present in the GTF
df = create_missing_features(
dataframe=df,
unique_keys={"gene": "gene_id"},
extra_columns={
"gene": {
"gene_name",
"gene_biotype"
}.intersection(column_names),
},
missing_value="")
if "transcript" not in features:
df = create_missing_features(
dataframe=df,
unique_keys={"transcript": "transcript_id"},
extra_columns={
"transcript": {
"gene_id",
"gene_name",
"gene_biotype",
"transcript_name",
"transcript_biotype",
"protein_id",
}.intersection(column_names)
},
missing_value="")
return df
def _load_full_dataframe_from_gtf(self):
"""
Parse this genome source's GTF file and load it as a Pandas DataFrame
"""
df = read_gtf_as_dataframe(
self.gtf_path,
column_converters={
"seqname": normalize_chromosome,
"strand": normalize_strand,
},
infer_biotype_column=True)
features = set(df["feature"])
column_names = set(df.keys())
# older Ensembl releases don't have "gene" or "transcript"
# features, so fill in those rows if they're missing
if "gene" not in features:
# if we have to reconstruct gene feature rows then
# fill in values for 'gene_name' and 'gene_biotype'
# but only if they're actually present in the GTF
df = create_missing_features(
dataframe=df,
unique_keys={"gene": "gene_id"},
extra_columns={
"gene": {
"gene_name",
"gene_biotype"
}.intersection(column_names),
},
missing_value="")
if "transcript" not in features:
df = create_missing_features(
dataframe=df,
unique_keys={"transcript": "transcript_id"},
extra_columns={
"transcript": {
"gene_id",
"gene_name",
"gene_biotype",
"transcript_name",
"transcript_biotype",
"protein_id",
}.intersection(column_names)
},
missing_value="")
return df
def test_create_missing_features():
assert "gene" not in set(GTF_DATAFRAME["feature"]), \
"Original GTF should not contain gene feature"
assert "transcript" not in set(GTF_DATAFRAME["feature"]), \
"Original GTF should not contain transcript feature"
df_extra_features = create_missing_features(
GTF_DATAFRAME,
unique_keys={
"gene": "gene_id",
"transcript": "transcript_id"
},
extra_columns={
"gene": {"gene_name"},
"transcript": {"gene_id", "gene_name", "transcript_name"},
})
_check_expanded_dataframe(df_extra_features)
def test_create_missing_features():
assert "gene" not in set(GTF_DATAFRAME["feature"]), \
"Original GTF should not contain gene feature"
assert "transcript" not in set(GTF_DATAFRAME["feature"]), \
"Original GTF should not contain transcript feature"
df_extra_features = create_missing_features(
GTF_DATAFRAME,
unique_keys={
"gene": "gene_id",
"transcript": "transcript_id"
},
extra_columns={
"gene": {"gene_name"},
"transcript": {"gene_id", "gene_name", "transcript_name"},
})
_check_expanded_dataframe(df_extra_features)
def test_create_missing_features_identity():
df_should_be_same = create_missing_features(GTF_DATAFRAME, {})
assert len(GTF_DATAFRAME) == len(df_should_be_same), \
"GTF DataFrames should be same size"
def test_create_missing_features_identity():
df_should_be_same = create_missing_features(GTF_DATAFRAME, {})
assert len(GTF_DATAFRAME) == len(df_should_be_same), \
"GTF DataFrames should be same size"