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authorBonfaceKilz2022-01-05 17:01:24 +0300
committerGitHub2022-01-05 17:01:24 +0300
commit0b1643e87cf4303db3673dcf3cf240aeb4d518cb (patch)
tree62ed5cae507303a469c040eb47a8b5b70b3a1a93 /gn3
parentac28fb48e4e3197de6bfeef332198b70689837c9 (diff)
parentaf52afa4318feadfa3cd1cc4dcdd3d86907f68a4 (diff)
downloadgenenetwork3-0b1643e87cf4303db3673dcf3cf240aeb4d518cb.tar.gz
Merge branch 'main' into fix/check-for-duplicates-before-deletions-or-insertions
Diffstat (limited to 'gn3')
-rw-r--r--gn3/api/correlation.py21
-rw-r--r--gn3/computations/correlations.py5
-rw-r--r--gn3/computations/partial_correlations.py93
-rw-r--r--gn3/db/datasets.py12
4 files changed, 106 insertions, 25 deletions
diff --git a/gn3/api/correlation.py b/gn3/api/correlation.py
index e936eaf..1caf31f 100644
--- a/gn3/api/correlation.py
+++ b/gn3/api/correlation.py
@@ -1,7 +1,9 @@
"""Endpoints for running correlations"""
+import json
from flask import jsonify
from flask import Blueprint
from flask import request
+from flask import make_response
from gn3.computations.correlations import compute_all_sample_correlation
from gn3.computations.correlations import compute_all_lit_correlation
@@ -87,15 +89,28 @@ def compute_tissue_corr(corr_method="pearson"):
@correlation.route("/partial", methods=["POST"])
def partial_correlation():
+ """API endpoint for partial correlations."""
def trait_fullname(trait):
return f"{trait['dataset']}::{trait['name']}"
+ class OutputEncoder(json.JSONEncoder):
+ """
+ Class to encode output into JSON, for objects which the default
+ json.JSONEncoder class does not have default encoding for.
+ """
+ def default(self, obj):
+ if isinstance(obj, bytes):
+ return str(obj, encoding="utf-8")
+ return json.JSONEncoder.default(self, obj)
+
args = request.get_json()
conn, _cursor_object = database_connector()
corr_results = partial_correlations_entry(
conn, trait_fullname(args["primary_trait"]),
tuple(trait_fullname(trait) for trait in args["control_traits"]),
args["method"], int(args["criteria"]), args["target_db"])
- return make_response(
- jsonify(corr_results),
- 400)
+ response = make_response(
+ json.dumps(corr_results, cls=OutputEncoder).replace(": NaN", ": null"),
+ 400 if "error" in corr_results.keys() else 200)
+ response.headers["Content-Type"] = "application/json"
+ return response
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py
index d38946e..1b4b3a4 100644
--- a/gn3/computations/correlations.py
+++ b/gn3/computations/correlations.py
@@ -7,6 +7,7 @@ from typing import List
from typing import Tuple
from typing import Optional
from typing import Callable
+from typing import Generator
import scipy.stats
import pingouin as pg
@@ -79,7 +80,7 @@ def compute_sample_r_correlation(trait_name, corr_method, trait_vals,
zip(*list(normalize_values(trait_vals, target_samples_vals))))
num_overlap = len(normalized_traits_vals)
except ValueError:
- return
+ return None
if num_overlap > 5:
@@ -106,7 +107,7 @@ package :not packaged in guix
def filter_shared_sample_keys(this_samplelist,
- target_samplelist) -> Tuple[List, List]:
+ target_samplelist) -> Generator:
"""Given primary and target sample-list for two base and target trait select
filter the values using the shared keys
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py
index 13c411a..984c15a 100644
--- a/gn3/computations/partial_correlations.py
+++ b/gn3/computations/partial_correlations.py
@@ -217,7 +217,7 @@ def good_dataset_samples_indexes(
def partial_correlations_fast(# pylint: disable=[R0913, R0914]
samples, primary_vals, control_vals, database_filename,
fetched_correlations, method: str, correlation_type: str) -> Tuple[
- float, Tuple[float, ...]]:
+ int, Tuple[float, ...]]:
"""
Computes partial correlation coefficients using data from a CSV file.
@@ -350,7 +350,9 @@ def compute_partial(
def partial_correlations_normal(# pylint: disable=R0913
primary_vals, control_vals, input_trait_gene_id, trait_database,
data_start_pos: int, db_type: str, method: str) -> Tuple[
- float, Tuple[float, ...]]:
+ int, Tuple[Union[
+ Tuple[str, int, float, float, float, float], None],
+ ...]]:#Tuple[float, ...]
"""
Computes the correlation coefficients.
@@ -485,7 +487,7 @@ def literature_correlation_by_list(
def tissue_correlation_by_list(
conn: Any, primary_trait_symbol: str, tissue_probeset_freeze_id: int,
- method: str, trait_list: Tuple[dict]) -> Tuple[dict]:
+ method: str, trait_list: Tuple[dict]) -> Tuple[dict, ...]:
"""
This is a migration of the
`web.webqtl.correlation.CorrelationPage.getTissueCorrelationByList`
@@ -508,7 +510,7 @@ def tissue_correlation_by_list(
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())
+ trait["symbol"] for trait in trait_list if "symbol" in trait.keys())
symbol_value_dict = fetch_gene_symbol_tissue_value_dict_for_trait(
gene_symbol_list, tissue_probeset_freeze_id, conn)
return tuple(
@@ -526,6 +528,54 @@ def tissue_correlation_by_list(
} for trait in trait_list)
return trait_list
+def trait_for_output(trait):
+ """
+ Process a trait for output.
+
+ Removes a lot of extraneous data from the trait, that is not needed for
+ the display of partial correlation results.
+ This function also removes all key-value pairs, for which the value is
+ `None`, because it is a waste of network resources to transmit the key-value
+ pair just to indicate it does not exist.
+ """
+ trait = {
+ "trait_type": trait["trait_type"],
+ "dataset_name": trait["db"]["dataset_name"],
+ "dataset_type": trait["db"]["dataset_type"],
+ "group": trait["db"]["group"],
+ "trait_fullname": trait["trait_fullname"],
+ "trait_name": trait["trait_name"],
+ "symbol": trait.get("symbol"),
+ "description": trait.get("description"),
+ "pre_publication_description": trait.get(
+ "pre_publication_description"),
+ "post_publication_description": trait.get(
+ "post_publication_description"),
+ "original_description": trait.get(
+ "original_description"),
+ "authors": trait.get("authors"),
+ "year": trait.get("year"),
+ "probe_target_description": trait.get(
+ "probe_target_description"),
+ "chr": trait.get("chr"),
+ "mb": trait.get("mb"),
+ "geneid": trait.get("geneid"),
+ "homologeneid": trait.get("homologeneid"),
+ "noverlap": trait.get("noverlap"),
+ "partial_corr": trait.get("partial_corr"),
+ "partial_corr_p_value": trait.get("partial_corr_p_value"),
+ "corr": trait.get("corr"),
+ "corr_p_value": trait.get("corr_p_value"),
+ "rank_order": trait.get("rank_order"),
+ "delta": (
+ None if trait.get("partial_corr") is None
+ else (trait.get("partial_corr") - trait.get("corr"))),
+ "l_corr": trait.get("l_corr"),
+ "tissue_corr": trait.get("tissue_corr"),
+ "tissue_p_value": trait.get("tissue_p_value")
+ }
+ return {key: val for key, val in trait.items() if val is not None}
+
def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911]
conn: Any, primary_trait_name: str,
control_trait_names: Tuple[str, ...], method: str,
@@ -669,19 +719,30 @@ def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911]
def __make_sorter__(method):
- def __sort_6__(row):
- return row[6]
-
- def __sort_3__(row):
+ def __compare_lit_or_tiss_correlation_values_(row):
+ # Index Content
+ # 0 trait name
+ # 1 N
+ # 2 partial correlation coefficient
+ # 3 p value of partial correlation
+ # 6 literature/tissue correlation value
+ return (row[6], row[3])
+
+ def __compare_partial_correlation_p_values__(row):
+ # Index Content
+ # 0 trait name
+ # 1 partial correlation coefficient
+ # 2 N
+ # 3 p value of partial correlation
return row[3]
if "literature" in method.lower():
- return __sort_6__
+ return __compare_lit_or_tiss_correlation_values_
if "tissue" in method.lower():
- return __sort_6__
+ return __compare_lit_or_tiss_correlation_values_
- return __sort_3__
+ return __compare_partial_correlation_p_values__
sorted_correlations = sorted(
all_correlations, key=__make_sorter__(method))
@@ -717,7 +778,11 @@ def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911]
return {
"status": "success",
"results": {
- "primary_trait": primary_trait,
- "control_traits": cntrl_traits,
- "correlations": trait_list
+ "primary_trait": trait_for_output(primary_trait),
+ "control_traits": tuple(
+ trait_for_output(trait) for trait in cntrl_traits),
+ "correlations": tuple(
+ trait_for_output(trait) for trait in trait_list),
+ "dataset_type": target_dataset["type"],
+ "method": "spearman" if "spearman" in method.lower() else "pearson"
}}
diff --git a/gn3/db/datasets.py b/gn3/db/datasets.py
index c50e148..a41e228 100644
--- a/gn3/db/datasets.py
+++ b/gn3/db/datasets.py
@@ -3,7 +3,7 @@ This module contains functions relating to specific trait dataset manipulation
"""
import re
from string import Template
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional
from SPARQLWrapper import JSON, SPARQLWrapper
from gn3.settings import SPARQL_ENDPOINT
@@ -297,7 +297,7 @@ def retrieve_trait_dataset(trait_type, trait, threshold, conn):
**group
}
-def sparql_query(query: str) -> Dict[str, Any]:
+def sparql_query(query: str) -> List[Dict[str, Any]]:
"""Run a SPARQL query and return the bound variables."""
sparql = SPARQLWrapper(SPARQL_ENDPOINT)
sparql.setQuery(query)
@@ -328,7 +328,7 @@ WHERE {
OPTIONAL { ?dataset gn:geoSeries ?geo_series } .
}
""",
- """
+ """
PREFIX gn: <http://genenetwork.org/>
SELECT ?platform_name ?normalization_name ?species_name ?inbred_set_name ?tissue_name
WHERE {
@@ -341,7 +341,7 @@ WHERE {
OPTIONAL { ?dataset gn:datasetOfPlatform / gn:name ?platform_name } .
}
""",
- """
+ """
PREFIX gn: <http://genenetwork.org/>
SELECT ?specifics ?summary ?about_cases ?about_tissue ?about_platform
?about_data_processing ?notes ?experiment_design ?contributors
@@ -362,8 +362,8 @@ WHERE {
OPTIONAL { ?dataset gn:acknowledgment ?acknowledgment . }
}
"""]
- result = {'accession_id': accession_id,
- 'investigator': {}}
+ result: Dict[str, Any] = {'accession_id': accession_id,
+ 'investigator': {}}
query_result = {}
for query in queries:
if sparql_result := sparql_query(Template(query).substitute(accession_id=accession_id)):