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-rw-r--r--gn3/computations/partial_correlations.py131
-rw-r--r--gn3/db/correlations.py5
-rw-r--r--gn3/db/traits.py47
3 files changed, 100 insertions, 83 deletions
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py
index 869bee4..231b0a7 100644
--- a/gn3/computations/partial_correlations.py
+++ b/gn3/computations/partial_correlations.py
@@ -14,12 +14,20 @@ import pingouin
from scipy.stats import pearsonr, spearmanr
from gn3.settings import TEXTDIR
+from gn3.random import random_string
from gn3.function_helpers import compose
from gn3.data_helpers import parse_csv_line
from gn3.db.traits import export_informative
from gn3.db.traits import retrieve_trait_info, retrieve_trait_data
from gn3.db.species import species_name, translate_to_mouse_gene_id
-from gn3.db.correlations import get_filename, fetch_all_database_data
+from gn3.db.correlations import (
+ get_filename,
+ fetch_all_database_data,
+ check_for_literature_info,
+ fetch_tissue_correlations,
+ fetch_literature_correlations,
+ check_symbol_for_tissue_correlation,
+ fetch_gene_symbol_tissue_value_dict_for_trait)
def control_samples(controls: Sequence[dict], sampleslist: Sequence[str]):
"""
@@ -311,7 +319,7 @@ def compute_partial(
zero_order_corr = pingouin.corr(
datafrm["x"], datafrm["y"], method=(
- "pearson" if "pearson" in method.lower() else "spearman"))
+ "pearson" if "pearson" in method.lower() else "spearman"))
if math.isnan(pc_coeff):
return (
@@ -371,9 +379,10 @@ def partial_correlations_normal(# pylint: disable=R0913
return len(trait_database), all_correlations
-def partial_corrs(
- conn, samples , primary_vals, control_vals, return_number, species, input_trait_geneid,
- input_trait_symbol, tissue_probeset_freeze_id, method, dataset, database_filename):
+def partial_corrs(# pylint: disable=[R0913]
+ conn, samples, primary_vals, control_vals, return_number, species,
+ input_trait_geneid, input_trait_symbol, tissue_probeset_freeze_id,
+ method, dataset, database_filename):
"""
Compute the partial correlations, selecting the fast or normal method
depending on the existence of the database text file.
@@ -404,8 +413,7 @@ def partial_corrs(
data_start_pos, dataset, method)
def literature_correlation_by_list(
- conn: Any, input_trait_mouse_geneid: int, species: str,
- trait_list: Tuple[dict]) -> Tuple[dict]:
+ conn: Any, species: str, trait_list: Tuple[dict]) -> Tuple[dict]:
"""
This is a migration of the
`web.webqtl.correlation.CorrelationPage.getLiteratureCorrelationByList`
@@ -415,16 +423,16 @@ def literature_correlation_by_list(
bool(t.get("tissue_corr")) and
bool(t.get("tissue_p_value"))))(trait)
for trait in trait_list):
- temp_table_name = f"LITERATURE{random_string(8)}"
- q1 = (
+ temporary_table_name = f"LITERATURE{random_string(8)}"
+ query1 = (
f"CREATE TEMPORARY TABLE {temporary_table_name} "
"(GeneId1 INT(12) UNSIGNED, GeneId2 INT(12) UNSIGNED PRIMARY KEY, "
"value DOUBLE)")
- q2 = (
+ query2 = (
f"INSERT INTO {temporary_table_name}(GeneId1, GeneId2, value) "
"SELECT GeneId1, GeneId2, value FROM LCorrRamin3 "
"WHERE GeneId1=%(geneid)s")
- q3 = (
+ query3 = (
"INSERT INTO {temporary_table_name}(GeneId1, GeneId2, value) "
"SELECT GeneId2, GeneId1, value FROM LCorrRamin3 "
"WHERE GeneId2=%s AND GeneId1 != %(geneid)s")
@@ -433,7 +441,8 @@ def literature_correlation_by_list(
if trait.get("geneid"):
return {
**trait,
- "mouse_geneid": translate_to_mouse_gene_id(trait.get("geneid"))
+ "mouse_geneid": translate_to_mouse_gene_id(
+ species, trait.get("geneid"), conn)
}
return {**trait, "mouse_geneid": 0}
@@ -441,13 +450,13 @@ def literature_correlation_by_list(
cursor.execute(
f"SELECT GeneId2, value FROM {temporary_table_name} "
"WHERE GeneId2 IN %(geneids)s",
- geneids = geneids)
- return {geneid: value for geneid, value in cursor.fetchall()}
+ geneids=geneids)
+ return dict(cursor.fetchall())
with conn.cursor() as cursor:
- cursor.execute(q1)
- cursor.execute(q2)
- cursor.execute(q3)
+ cursor.execute(query1)
+ cursor.execute(query2)
+ cursor.execute(query3)
traits = tuple(__set_mouse_geneid__(trait) for trait in trait_list)
lcorrs = __retrieve_lcorr__(
@@ -470,9 +479,9 @@ def tissue_correlation_by_list(
`web.webqtl.correlation.CorrelationPage.getTissueCorrelationByList`
function in GeneNetwork1.
"""
- def __add_tissue_corr__(trait, primary_trait_value, trait_value):
+ def __add_tissue_corr__(trait, primary_trait_values, trait_values):
result = pingouin.corr(
- primary_trait_values, target_trait_values,
+ primary_trait_values, trait_values,
method=("spearman" if "spearman" in method.lower() else "pearson"))
return {
**trait,
@@ -484,7 +493,8 @@ def tissue_correlation_by_list(
prim_trait_symbol_value_dict = fetch_gene_symbol_tissue_value_dict_for_trait(
(primary_trait_symbol,), tissue_probeset_freeze_id, conn)
if primary_trait_symbol.lower() in prim_trait_symbol_value_dict:
- primary_trait_value = prim_trait_symbol_value_dict[prim_trait_symbol.lower()]
+ primary_trait_value = prim_trait_symbol_value_dict[
+ primary_trait_symbol.lower()]
gene_symbol_list = tuple(
trait for trait in trait_list if "symbol" in trait.keys())
symbol_value_dict = fetch_gene_symbol_tissue_value_dict_for_trait(
@@ -504,7 +514,7 @@ def tissue_correlation_by_list(
} for trait in trait_list)
return trait_list
-def partial_correlations_entry(
+def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911]
conn: Any, primary_trait_name: str,
control_trait_names: Tuple[str, ...], method: str,
criteria: int, group: str, target_db_name: str) -> dict:
@@ -524,7 +534,7 @@ def partial_correlations_entry(
primary_trait = retrieve_trait_info(threshold, primary_trait_name, conn)
primary_trait_data = retrieve_trait_data(primary_trait, conn)
- primary_samples, primary_values, primary_variances = export_informative(
+ primary_samples, primary_values, _primary_variances = export_informative(
primary_trait_data)
cntrl_traits = tuple(
@@ -537,8 +547,8 @@ def partial_correlations_entry(
(cntrl_samples,
cntrl_values,
- cntrl_variances,
- cntrl_ns) = control_samples(cntrl_traits_data, primary_samples)
+ _cntrl_variances,
+ _cntrl_ns) = control_samples(cntrl_traits_data, primary_samples)
common_primary_control_samples = primary_samples
fixed_primary_vals = primary_values
@@ -547,8 +557,8 @@ def partial_correlations_entry(
(common_primary_control_samples,
fixed_primary_vals,
fixed_control_vals,
- primary_variances,
- cntrl_variances) = fix_samples(primary_trait, cntrl_traits)
+ _primary_variances,
+ _cntrl_variances) = fix_samples(primary_trait, cntrl_traits)
if len(common_primary_control_samples) < corr_min_informative:
return {
@@ -580,7 +590,6 @@ def partial_correlations_entry(
tissue_probeset_freeze_id = 1
db_type = primary_trait["db"]["dataset_type"]
- db_name = primary_trait["db"]["dataset_name"]
if db_type == "ProbeSet" and method.lower() in (
"sgo literature correlation",
@@ -605,10 +614,11 @@ def partial_correlations_entry(
"associated Literature Information."),
"error_type": "Literature Correlation"}
- if (method.lower() in (
- "tissue correlation, pearson's r",
- "tissue correlation, spearman's rho")
- and input_trait_symbol is None):
+ if (
+ method.lower() in (
+ "tissue correlation, pearson's r",
+ "tissue correlation, spearman's rho")
+ and input_trait_symbol is None):
return {
"status": "error",
"message": (
@@ -616,11 +626,12 @@ def partial_correlations_entry(
"any associated Tissue Correlation Information."),
"error_type": "Tissue Correlation"}
- if (method.lower() in (
- "tissue correlation, pearson's r",
- "tissue correlation, spearman's rho")
- and check_symbol_for_tissue_correlation(
- conn, tissue_probeset_freeze_id, input_trait_symbol)):
+ if (
+ method.lower() in (
+ "tissue correlation, pearson's r",
+ "tissue correlation, spearman's rho")
+ and check_symbol_for_tissue_correlation(
+ conn, tissue_probeset_freeze_id, input_trait_symbol)):
return {
"status": "error",
"message": (
@@ -629,7 +640,7 @@ def partial_correlations_entry(
"error_type": "Tissue Correlation"}
database_filename = get_filename(conn, target_db_name, TEXTDIR)
- total_traits, all_correlations = partial_corrs(
+ _total_traits, all_correlations = partial_corrs(
conn, common_primary_control_samples, fixed_primary_vals,
fixed_control_vals, len(fixed_primary_vals), species,
input_trait_geneid, input_trait_symbol, tissue_probeset_freeze_id,
@@ -637,11 +648,11 @@ def partial_correlations_entry(
def __make_sorter__(method):
- def __sort_6__(x):
- return x[6]
+ def __sort_6__(row):
+ return row[6]
- def __sort_3__(x):
- return x[3]
+ def __sort_3__(row):
+ return row[3]
if "literature" in method.lower():
return __sort_6__
@@ -655,33 +666,31 @@ def partial_correlations_entry(
all_correlations, key=__make_sorter__(method))
add_lit_corr_and_tiss_corr = compose(
- partial(
- literature_correlation_by_list, conn, input_trait_mouse_geneid,
- species),
+ partial(literature_correlation_by_list, conn, species),
partial(
tissue_correlation_by_list, conn, input_trait_symbol,
tissue_probeset_freeze_id, method))
trait_list = add_lit_corr_and_tiss_corr(tuple(
- {
- **retrieve_trait_info(
- threshold,
- f"{primary_trait['db']['dataset_name']}::{item[0]}",
- conn),
- "noverlap": item[1],
- "partial_corr": item[2],
- "partial_corr_p_value": item[3],
- "corr": item[4],
- "corr_p_value": item[5],
- "rank_order": (1 if "spearman" in method.lower() else 0),
- **({
- "tissue_corr": item[6],
- "tissue_p_value": item[7]}
+ {
+ **retrieve_trait_info(
+ threshold,
+ f"{primary_trait['db']['dataset_name']}::{item[0]}",
+ conn),
+ "noverlap": item[1],
+ "partial_corr": item[2],
+ "partial_corr_p_value": item[3],
+ "corr": item[4],
+ "corr_p_value": item[5],
+ "rank_order": (1 if "spearman" in method.lower() else 0),
+ **({
+ "tissue_corr": item[6],
+ "tissue_p_value": item[7]}
if len(item) == 8 else {}),
- **({"l_corr": item[6]}
+ **({"l_corr": item[6]}
if len(item) == 7 else {})
- }
+ }
for item in
- sorted_correlations[:min(criteria, len(all_correlations))]))
+ sorted_correlations[:min(criteria, len(all_correlations))]))
return trait_list
diff --git a/gn3/db/correlations.py b/gn3/db/correlations.py
index 2a38bae..3d12019 100644
--- a/gn3/db/correlations.py
+++ b/gn3/db/correlations.py
@@ -29,7 +29,7 @@ def get_filename(conn: Any, target_db_name: str, text_files_dir: str) -> Union[
filename = "ProbeSetFreezeId_{tid}_FullName_{fname}.txt".format(
tid=result[0],
fname=result[1].replace(' ', '_').replace('/', '_'))
- return ((filename in os.listdir(text_file_dir))
+ return ((filename in os.listdir(text_files_dir))
and f"{text_files_dir}/{filename}")
return False
@@ -280,7 +280,8 @@ def build_temporary_tissue_correlations_table(
# We should probably pass the `correlations_of_all_tissue_traits` function
# as an argument to this function and get rid of the one call immediately
# following this comment.
- from gn3.computations.partial_correlations import correlations_of_all_tissue_traits
+ from gn3.computations.partial_correlations import (#pylint: disable=[C0415, R0401]
+ correlations_of_all_tissue_traits)
# This import above is necessary within the function to avoid
# circular-imports.
#
diff --git a/gn3/db/traits.py b/gn3/db/traits.py
index 75de4f4..d4a96f0 100644
--- a/gn3/db/traits.py
+++ b/gn3/db/traits.py
@@ -1,9 +1,10 @@
"""This class contains functions relating to trait data manipulation"""
import os
-import MySQLdb
from functools import reduce
from typing import Any, Dict, Union, Sequence
+import MySQLdb
+
from gn3.settings import TMPDIR
from gn3.random import random_string
from gn3.function_helpers import compose
@@ -81,10 +82,10 @@ def export_trait_data(
def get_trait_csv_sample_data(conn: Any,
trait_name: int, phenotype_id: int):
"""Fetch a trait and return it as a csv string"""
- def __float_strip(n):
- if str(n)[-2:] == ".0":
- return str(int(n))
- return str(n)
+ def __float_strip(num_str):
+ if str(num_str)[-2:] == ".0":
+ return str(int(num_str))
+ return str(num_str)
sql = ("SELECT DISTINCT Strain.Name, PublishData.value, "
"PublishSE.error, NStrain.count FROM "
"(PublishData, Strain, PublishXRef, PublishFreeze) "
@@ -108,7 +109,7 @@ def get_trait_csv_sample_data(conn: Any,
return "\n".join(csv_data)
-def update_sample_data(conn: Any,
+def update_sample_data(conn: Any, #pylint: disable=[R0913]
trait_name: str,
strain_name: str,
phenotype_id: int,
@@ -219,7 +220,7 @@ def delete_sample_data(conn: Any,
"WHERE StrainId = %s AND DataId = %s" %
(strain_id, data_id)))
deleted_n_strains = cursor.rowcount
- except Exception as e:
+ except Exception as e: #pylint: disable=[C0103, W0612]
conn.rollback()
raise MySQLdb.Error
conn.commit()
@@ -230,7 +231,7 @@ def delete_sample_data(conn: Any,
deleted_se_data, deleted_n_strains)
-def insert_sample_data(conn: Any,
+def insert_sample_data(conn: Any, #pylint: disable=[R0913]
trait_name: str,
strain_name: str,
phenotype_id: int,
@@ -272,7 +273,7 @@ def insert_sample_data(conn: Any,
"VALUES (%s, %s, %s)") %
(strain_id, data_id, count))
inserted_n_strains = cursor.rowcount
- except Exception as e:
+ except Exception as e: #pylint: disable=[C0103, W0612]
conn.rollback()
raise MySQLdb.Error
return (inserted_published_data,
@@ -450,7 +451,7 @@ def set_homologene_id_field(trait_type, trait_info, conn):
Common postprocessing function for all trait types.
Sets the value for the 'homologene' key."""
- def set_to_null(ti): return {**ti, "homologeneid": None}
+ def set_to_null(ti): return {**ti, "homologeneid": None} # pylint: disable=[C0103, C0321]
functions_table = {
"Temp": set_to_null,
"Geno": set_to_null,
@@ -656,8 +657,9 @@ def retrieve_temp_trait_data(trait_info: dict, conn: Any):
query,
{"trait_name": trait_info["trait_name"]})
return [dict(zip(
- ["sample_name", "value", "se_error", "nstrain", "id"], row))
- for row in cursor.fetchall()]
+ ["sample_name", "value", "se_error", "nstrain", "id"],
+ row))
+ for row in cursor.fetchall()]
return []
@@ -696,8 +698,10 @@ def retrieve_geno_trait_data(trait_info: Dict, conn: Any):
"dataset_name": trait_info["db"]["dataset_name"],
"species_id": retrieve_species_id(
trait_info["db"]["group"], conn)})
- return [dict(zip(
- ["sample_name", "value", "se_error", "id"], row))
+ return [
+ dict(zip(
+ ["sample_name", "value", "se_error", "id"],
+ row))
for row in cursor.fetchall()]
return []
@@ -728,8 +732,9 @@ def retrieve_publish_trait_data(trait_info: Dict, conn: Any):
query,
{"trait_name": trait_info["trait_name"],
"dataset_id": trait_info["db"]["dataset_id"]})
- return [dict(zip(
- ["sample_name", "value", "se_error", "nstrain", "id"], row))
+ return [
+ dict(zip(
+ ["sample_name", "value", "se_error", "nstrain", "id"], row))
for row in cursor.fetchall()]
return []
@@ -762,8 +767,9 @@ def retrieve_cellid_trait_data(trait_info: Dict, conn: Any):
{"cellid": trait_info["cellid"],
"trait_name": trait_info["trait_name"],
"dataset_id": trait_info["db"]["dataset_id"]})
- return [dict(zip(
- ["sample_name", "value", "se_error", "id"], row))
+ return [
+ dict(zip(
+ ["sample_name", "value", "se_error", "id"], row))
for row in cursor.fetchall()]
return []
@@ -792,8 +798,9 @@ def retrieve_probeset_trait_data(trait_info: Dict, conn: Any):
query,
{"trait_name": trait_info["trait_name"],
"dataset_name": trait_info["db"]["dataset_name"]})
- return [dict(zip(
- ["sample_name", "value", "se_error", "id"], row))
+ return [
+ dict(zip(
+ ["sample_name", "value", "se_error", "id"], row))
for row in cursor.fetchall()]
return []
generated by cgit v1.2.3 (git 2.44.0) at 2024-11-28 02:04:20 +0000