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Improve dbnsfp parser.
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@everaldorodrigo
everaldorodrigo committed Mar 5, 2024
1 parent d24c509 commit 661fa2f
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Showing 2 changed files with 52 additions and 36 deletions.
44 changes: 26 additions & 18 deletions src/hub/dataload/sources/dbnsfp/dbnsfp_parser_46a_v1.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -642,19 +642,19 @@ def prune_hgvsc_hgvsp(raw_doc: dict, hgvsc_columns: list[Column], hgvsp_columns:
return raw_doc


def prune_gtex_sqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
def prune_gtex(new_doc_key: str, raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
"""
Map each GTEx gene name and tissue name from the raw document into a dictionary,
and assign all such dictionaries to the raw document's top "gtex" field.
E.g. with the following input value:
E.g. with the following input value for GTEx eQTL:
row["gtex_gene"] = "ENOSF1|ENOSF1"
row["gtex_tissue"] = "Adipose_Subcutaneous|Muscle_Skeletal"
raw_doc will be assigned as:
row["gtex.sqtl"] = [
row["gtex.eqtl"] = [
{'gene': 'ENOSF1', 'tissue': 'Adipose_Subcutaneous'},
{'gene': 'ENOSF1', 'tissue': 'Muscle_Skeletal'}
]
Expand All @@ -668,15 +668,15 @@ def prune_gtex_sqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, n
gtex_result = [{"gene": acc, "tissue": entry} for (acc, entry) in split_zip(gene_value, tissue_value, sep=r"|", na_values=na_values)]
gtex_result = _check_length(gtex_result)
if gtex_result is not None:
raw_doc["gtex.sqtl"] = gtex_result
raw_doc[new_doc_key] = gtex_result

del raw_doc[gene_column.dest]
del raw_doc[tissue_column.dest]

return raw_doc


def prune_gtex_eqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
def prune_gtex_sqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
"""
Map each GTEx gene name and tissue name from the raw document into a dictionary,
and assign all such dictionaries to the raw document's top "gtex" field.
Expand All @@ -688,26 +688,34 @@ def prune_gtex_eqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, n
raw_doc will be assigned as:
row["gtex.eqtl"] = [
row["gtex.sqtl"] = [
{'gene': 'ENOSF1', 'tissue': 'Adipose_Subcutaneous'},
{'gene': 'ENOSF1', 'tissue': 'Muscle_Skeletal'}
]
"""
# when these two keys are not present in the doc, it means the responding two values in tsv files are NA values
if (gene_column.dest in raw_doc) and (tissue_column.dest in raw_doc):
gene_value = raw_doc[gene_column.dest]
tissue_value = raw_doc[tissue_column.dest]
doc = prune_gtex(new_doc_key="gtex.sqtl", raw_doc=raw_doc, gene_column=gene_column, tissue_column=tissue_column, na_values=na_values)
return doc

# special separator "|" for GTEx
gtex_result = [{"gene": acc, "tissue": entry} for (acc, entry) in split_zip(gene_value, tissue_value, sep=r"|", na_values=na_values)]
gtex_result = _check_length(gtex_result)
if gtex_result is not None:
raw_doc["gtex.eqtl"] = gtex_result

del raw_doc[gene_column.dest]
del raw_doc[tissue_column.dest]
def prune_gtex_eqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
"""
Map each GTEx gene name and tissue name from the raw document into a dictionary,
and assign all such dictionaries to the raw document's top "gtex" field.
return raw_doc
E.g. with the following input value:
row["gtex_gene"] = "ENOSF1|ENOSF1"
row["gtex_tissue"] = "Adipose_Subcutaneous|Muscle_Skeletal"
raw_doc will be assigned as:
row["gtex.eqtl"] = [
{'gene': 'ENOSF1', 'tissue': 'Adipose_Subcutaneous'},
{'gene': 'ENOSF1', 'tissue': 'Muscle_Skeletal'}
]
"""
doc = prune_gtex(new_doc_key="gtex.eqtl", raw_doc=raw_doc, gene_column=gene_column, tissue_column=tissue_column, na_values=na_values)
return doc


def prune_hg19_doc(doc: dict, na_values: set = NA_VALUES):
Expand Down
44 changes: 26 additions & 18 deletions src/hub/dataload/sources/dbnsfp/dbnsfp_parser_46a_v2.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -638,19 +638,19 @@ def normalize_hg38_row(row: dict):
return normalize_hg19_row(row)


def prune_gtex_sqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
def prune_gtex(new_doc_key: str, raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
"""
Map each GTEx gene name and tissue name from the raw document into a dictionary,
and assign all such dictionaries to the raw document's top "gtex" field.
E.g. with the following input value:
E.g. with the following input value for GTEx eQTL:
row["gtex_gene"] = "ENOSF1|ENOSF1"
row["gtex_tissue"] = "Adipose_Subcutaneous|Muscle_Skeletal"
raw_doc will be assigned as:
row["gtex.sqtl"] = [
row["gtex.eqtl"] = [
{'gene': 'ENOSF1', 'tissue': 'Adipose_Subcutaneous'},
{'gene': 'ENOSF1', 'tissue': 'Muscle_Skeletal'}
]
Expand All @@ -664,15 +664,15 @@ def prune_gtex_sqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, n
gtex_result = [{"gene": acc, "tissue": entry} for (acc, entry) in split_zip([gene_value, tissue_value], sep=r"|", na_values=na_values)]
gtex_result = _check_length(gtex_result)
if gtex_result is not None:
raw_doc["gtex.sqtl"] = gtex_result
raw_doc[new_doc_key] = gtex_result

del raw_doc[gene_column.dest]
del raw_doc[tissue_column.dest]

return raw_doc


def prune_gtex_eqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
def prune_gtex_sqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
"""
Map each GTEx gene name and tissue name from the raw document into a dictionary,
and assign all such dictionaries to the raw document's top "gtex" field.
Expand All @@ -684,26 +684,34 @@ def prune_gtex_eqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, n
raw_doc will be assigned as:
row["gtex.eqtl"] = [
row["gtex.sqtl"] = [
{'gene': 'ENOSF1', 'tissue': 'Adipose_Subcutaneous'},
{'gene': 'ENOSF1', 'tissue': 'Muscle_Skeletal'}
]
"""
# when these two keys are not present in the doc, it means the responding two values in tsv files are NA values
if (gene_column.dest in raw_doc) and (tissue_column.dest in raw_doc):
gene_value = raw_doc[gene_column.dest]
tissue_value = raw_doc[tissue_column.dest]
doc = prune_gtex(new_doc_key="gtex.sqtl", raw_doc=raw_doc, gene_column=gene_column, tissue_column=tissue_column, na_values=na_values)
return doc

# special separator "|" for GTEx
gtex_result = [{"gene": acc, "tissue": entry} for (acc, entry) in split_zip([gene_value, tissue_value], sep=r"|", na_values=na_values)]
gtex_result = _check_length(gtex_result)
if gtex_result is not None:
raw_doc["gtex.eqtl"] = gtex_result

del raw_doc[gene_column.dest]
del raw_doc[tissue_column.dest]
def prune_gtex_eqtl(raw_doc: dict, gene_column: Column, tissue_column: Column, na_values: set = NA_VALUES):
"""
Map each GTEx gene name and tissue name from the raw document into a dictionary,
and assign all such dictionaries to the raw document's top "gtex" field.
return raw_doc
E.g. with the following input value:
row["gtex_gene"] = "ENOSF1|ENOSF1"
row["gtex_tissue"] = "Adipose_Subcutaneous|Muscle_Skeletal"
raw_doc will be assigned as:
row["gtex.eqtl"] = [
{'gene': 'ENOSF1', 'tissue': 'Adipose_Subcutaneous'},
{'gene': 'ENOSF1', 'tissue': 'Muscle_Skeletal'}
]
"""
doc = prune_gtex(new_doc_key="gtex.eqtl", raw_doc=raw_doc, gene_column=gene_column, tissue_column=tissue_column, na_values=na_values)
return doc


def prune_mutation_taster(raw_doc: dict, aae_column: Column, model_column: Column, pred_column: Column, score_column: Column, na_values: set = NA_VALUES):
Expand Down

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