diff --git a/src/find_interaction_cluster/ppi_scripts/config_ppi.py b/src/find_interaction_cluster/ppi_scripts/config_ppi.py
index 312baea9d862909fa409ab7aed53984c2e50e702..d61820b4b05874c7d86013efed9c5018f6407623 100644
--- a/src/find_interaction_cluster/ppi_scripts/config_ppi.py
+++ b/src/find_interaction_cluster/ppi_scripts/config_ppi.py
@@ -17,7 +17,7 @@ class ConfigPPI:
output_folder = ConfigGraph.output_folder / "ppi_analysis"
data = ConfigGraph.data
bed_ensembl = data / "ppi_data" / "liftover_hg38gene2hg19.bed"
- ensembl_gene = data / "ppi_data" / "mart_export_hg38_proteins_genes.txt"
+ ensembl_gene = data / "ppi_data" / "mart_export_hg37_proteins_genes.txt"
ppi_string = data / "ppi_data" / "9606.protein.links.v11.0.txt.gz"
protein_gene = output_folder / "protein_gene.txt"
gene_ppi_file = output_folder / "ppi_gene_file.txt"
diff --git a/src/find_interaction_cluster/ppi_scripts/create_gene_interaction_file.py b/src/find_interaction_cluster/ppi_scripts/create_gene_interaction_file.py
index 59221d693e4fa8d558d4b2018b94f8cc94964bfa..3c3bebabfea017d47bcfa3ae0cdef0dc53949518 100644
--- a/src/find_interaction_cluster/ppi_scripts/create_gene_interaction_file.py
+++ b/src/find_interaction_cluster/ppi_scripts/create_gene_interaction_file.py
@@ -27,43 +27,27 @@ def load_string_ppi_file(ppi_file: Path) -> pd.DataFrame:
return df
-def load_protein_gene_links(prot_gene_file: Path) -> Tuple[pd.DataFrame, Dict]:
+def load_protein_gene_links(prot_gene_file: Path) -> pd.DataFrame:
"""
Load the file linking each gene to it's hg19 coordinates and encoded \
proteins.
:param prot_gene_file: A file linking each gene to it's hg19 \
coordinates and encoded proteins.
- :return: The loaded file and a dictionary of synonyms
+ :return: The loaded file
"""
- df = pd.read_csv(prot_gene_file, sep="\t",
- dtype={"Gene_stable_ID": str,
- "Protein_stable_ID": str,
- "Gene_name": str,
- "Gene_Synonym": str,
- "chr": str,
- "start": int,
- "stop": int,
- "strand": str})
- dic_synonym = {}
- for i in range(df.shape[0]):
- mseries = df.iloc[i, :]
- if isinstance(mseries["Gene_Synonym"], float):
- dic_synonym[mseries["Gene_name"].upper()] = \
- mseries["Gene_name"].upper()
- else:
- dic_synonym[mseries["Gene_Synonym"].upper()] = \
- mseries["Gene_name"].upper()
- return df, dic_synonym
-
-
-def load_fasterdb_gene(bed_gene: Path, dic_synonym: Dict) -> Dict:
+ df = pd.read_csv(prot_gene_file, sep="\t")
+ df.rename({"Chromosome-scaffold_name": "chr", "Gene_start": "start",
+ "Gene_end": "stop", "Strand": "strand"}, axis=1, inplace=True)
+ df['Gene_name'] = df['Gene_name'].str.upper()
+ return df
+
+
+def load_fasterdb_gene(bed_gene: Path) -> Dict:
"""
Load a bed file containing all fasterDB gene.
:param bed_gene: A bed file containing all fasterdb gene
- :param dic_synonym: A dictionary of synonyms used to replace \
- the old fasterDB names by the new ones
:return: A dictionary linking each fasterDB gene name to its coordinates
"""
dic = {}
@@ -71,8 +55,6 @@ def load_fasterdb_gene(bed_gene: Path, dic_synonym: Dict) -> Dict:
for line in inbed:
line = line.replace("\n", "").split("\t")
gene_name = line[4].upper()
- if gene_name in dic_synonym.keys():
- gene_name = dic_synonym[gene_name]
if gene_name not in dic.keys():
dic[gene_name] = [[line[0], int(line[1]), int(line[2]),
int(line[3]), gene_name, line[5]]]
@@ -93,12 +75,8 @@ def find_fasterdb_id(mseries: pd.Series, dic: Dict) -> Optional[int]:
:return: The fasterDB id of the ensembl gene.
"""
if mseries['Gene_name'].upper() not in dic.keys():
- if isinstance(mseries['Gene_Synonym'], float) or \
- mseries['Gene_Synonym'].upper() not in dic.keys():
return None
- genes = dic[mseries['Gene_Synonym'].upper()]
- else:
- genes = dic[mseries['Gene_name'].upper()]
+ genes = dic[mseries['Gene_name'].upper()]
if len(genes) == 1:
return genes[0][3]
elif len(genes) >= 1:
@@ -127,8 +105,7 @@ def add_gene_id_column(prot_gene_df: pd.DataFrame, dic: Dict) -> pd.DataFrame:
axis=1, dic=dic)
df = prot_gene_df.loc[-prot_gene_df['FasterDB_id'].isna(), :].copy()
df['FasterDB_id'] = df['FasterDB_id'].astype(int)
- df.drop(['Gene_Synonym'], axis=1, inplace=True)
- return df.drop_duplicates()
+ return df
def add_fasterdb_id_to_ppi_file(ppi_df: pd.DataFrame,
@@ -164,9 +141,8 @@ def get_protein_interaction_table():
"""
link ensemble gene and their encoding protein to a fasterDB gene id.
"""
- df_prot_gene, dic_synonyms = \
- load_protein_gene_links(ConfigPPI.protein_gene)
- dic_gene = load_fasterdb_gene(Config.bed_gene, dic_synonyms)
+ df_prot_gene = load_protein_gene_links(ConfigPPI.protein_gene)
+ dic_gene = load_fasterdb_gene(Config.bed_gene)
df_fasterdb_id = add_gene_id_column(df_prot_gene, dic_gene)
df_fasterdb_id.to_csv(ConfigPPI.protein_gene_fasterdb, sep="\t",
index=False)
diff --git a/src/find_interaction_cluster/ppi_scripts/get_ensembl_hg19_gene_ccoordinates.py b/src/find_interaction_cluster/ppi_scripts/get_ensembl_hg19_gene_ccoordinates.py
index a2ecaeda470c7c56d08d951d1a9bb7ec3c9f0255..0f2c9fa94c751d03bde3351dff9a3f5367ab6a3d 100644
--- a/src/find_interaction_cluster/ppi_scripts/get_ensembl_hg19_gene_ccoordinates.py
+++ b/src/find_interaction_cluster/ppi_scripts/get_ensembl_hg19_gene_ccoordinates.py
@@ -4,7 +4,7 @@
"""
Description: The goal of this script is to get only coding gene from \
-ensemble with their protein id and their coordinate in hg19.
+ensembl..
"""
from .config_ppi import ConfigPPI
@@ -25,49 +25,16 @@ def load_genes(ensembl_file: Path) -> pd.DataFrame:
"""
df = pd.read_csv(ensembl_file, sep="\t",
dtype={"Gene_stable_ID": str,
- "Gene_stable_ID_version": str,
"Protein_stable_ID": str,
"Chromosome-scaffold_name": str,
"Gene_start": int,
"Gene_end": int,
"Strand": str,
- "Gene_name": str,
- "Gene_Synonym": str})
+ "Gene_name": str})
df = df.loc[-df['Protein_stable_ID'].isna(), :]
- return df[["Gene_stable_ID", "Protein_stable_ID", "Gene_name",
- "Gene_Synonym"]]
-
-
-def load_bed(ensembl_bed: Path) -> pd.DataFrame:
- """
- Load a bed file containing ensembl hg38 gene on hg19 coordinates.
-
- :param ensembl_bed: A bed file containing ensembl gene
- :return: the bed file
- """
- df = pd.read_csv(ensembl_bed, names=["chr", "start", "stop", "name",
- "score", "strand"], sep="\t")
- df['chr'] = df["chr"].str.replace("chr", "")
- df = df.drop_duplicates()
- names = np.unique(df['name'].values, return_counts=True)
- good_names = [cname for i, cname in enumerate(list(names[0]))
- if names[1][i] == 1]
- return df.loc[df['name'].isin(good_names), :]
-
-
-def merge_tables(df_gene: pd.DataFrame, df_bed: pd.DataFrame) -> pd.DataFrame:
- """
- Merges `df_gene` and `df_bed`together.
-
- :param df_gene: table containing all the human protein of ensembl \
- database and the name and location of their genes.
- :param df_bed: A bed file containing ensembl gene
- :return: The merged table
- """
- df_gene = df_gene.loc[
- df_gene["Gene_name"].isin(list(df_bed['name'].unique())), :]
- df_bed.rename({'name': "Gene_name"}, inplace=True, axis=1)
- return df_gene.merge(df_bed, how="left", on="Gene_name")
+ df = df.loc[df["Chromosome-scaffold_name"].isin(
+ list(map(str, range(1, 23))) + ["X", "Y"]), :]
+ return df.drop_duplicates()
def create_protein_gene_file():
@@ -77,7 +44,5 @@ def create_protein_gene_file():
"""
df_gene = load_genes(ConfigPPI.ensembl_gene)
- df_bed = load_bed(ConfigPPI.bed_ensembl)
- df = merge_tables(df_gene, df_bed)
ConfigPPI.protein_gene.parent.mkdir(parents=True, exist_ok=True)
- df.to_csv(ConfigPPI.protein_gene, sep="\t", index=False)
+ df_gene.to_csv(ConfigPPI.protein_gene, sep="\t", index=False)