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authorFrederick Muriuki Muriithi2021-12-06 14:04:59 +0300
committerFrederick Muriuki Muriithi2021-12-06 14:04:59 +0300
commit66406115f41594ba40e3fbbc6f69aace2d11800f (patch)
tree0f3de09b74a3f47918dd4a192665c8a06c508144
parent77099cac68e8f4792bf54d8e1f7ce6f315bedfa7 (diff)
parent5d2248f1dabbc7dd04f48aafcc9f327817a9c92c (diff)
downloadgenenetwork3-66406115f41594ba40e3fbbc6f69aace2d11800f.tar.gz
Merge branch 'partial-correlations'
-rw-r--r--.github/PULL_REQUEST_TEMPLATE.md2
-rw-r--r--README.md12
-rw-r--r--gn3/api/heatmaps.py6
-rw-r--r--gn3/app.py13
-rw-r--r--gn3/authentication.py165
-rw-r--r--gn3/computations/biweight.py27
-rw-r--r--gn3/computations/correlations.py41
-rw-r--r--gn3/computations/correlations2.py36
-rw-r--r--gn3/computations/partial_correlations.py696
-rw-r--r--gn3/computations/wgcna.py49
-rw-r--r--gn3/data_helpers.py52
-rw-r--r--gn3/db/correlations.py564
-rw-r--r--gn3/db/species.py44
-rw-r--r--gn3/db/traits.py361
-rw-r--r--gn3/heatmaps.py202
-rw-r--r--gn3/settings.py21
-rw-r--r--guix.scm97
-rw-r--r--mypy.ini9
-rw-r--r--requirements.txt1
-rw-r--r--scripts/calculate_biweight.R43
-rw-r--r--scripts/wgcna_analysis.R17
-rw-r--r--setup.py1
-rw-r--r--sql/update/json_diff_update.sql22
-rw-r--r--tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv101
-rw-r--r--tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv101
-rw-r--r--tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.txt1009
-rw-r--r--tests/unit/computations/test_biweight.py21
-rw-r--r--tests/unit/computations/test_correlation.py50
-rw-r--r--tests/unit/computations/test_partial_correlations.py299
-rw-r--r--tests/unit/computations/test_wgcna.py14
-rw-r--r--tests/unit/db/test_correlation.py96
-rw-r--r--tests/unit/db/test_traits.py176
-rw-r--r--tests/unit/test_authentication.py101
-rw-r--r--tests/unit/test_data_helpers.py89
-rw-r--r--tests/unit/test_heatmaps.py94
35 files changed, 4101 insertions, 531 deletions
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 926b054..e9a2425 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,3 +1,5 @@
+Please fill out the template below, and delete items not applicable to your pull request.
+
#### Description
<!--Brief description of the PR. What does this PR do? -->
diff --git a/README.md b/README.md
index 84a7a54..62e8aeb 100644
--- a/README.md
+++ b/README.md
@@ -9,8 +9,8 @@ Install GNU Guix - this can be done on every running Linux system.
There are at least three ways to start GeneNetwork3 with GNU Guix:
-1. Create an environment with `guix environment`
-2. Create a container with `guix environment -C`
+1. Create an environment with `guix shell`
+2. Create a container with `guix shell -C`
3. Use a profile and shell settings with `source ~/opt/genenetwork3/etc/profile`
#### Create an environment:
@@ -18,13 +18,13 @@ There are at least three ways to start GeneNetwork3 with GNU Guix:
Simply load up the environment (for development purposes):
```bash
-guix environment --load=guix.scm
+guix shell -Df guix.scm
```
Also, make sure you have the [guix-bioinformatics](https://git.genenetwork.org/guix-bioinformatics/guix-bioinformatics) channel set up.
```bash
-env GUIX_PACKAGE_PATH=~/guix-bioinformatics/ ~/.config/guix/current/bin/guix environment --expose=$HOME/genotype_files/ --load=guix.scm
+guix shell --expose=$HOME/genotype_files/ -Df guix.scm
python3
import redis
```
@@ -32,7 +32,7 @@ python3
#### Run a Guix container
```
-env GUIX_PACKAGE_PATH=~/guix-bioinformatics/ ~/.config/guix/current/bin/guix environment -C --network --expose=$HOME/genotype_files/ --load=guix.scm
+guix shell -C --network --expose=$HOME/genotype_files/ -Df guix.scm
```
@@ -41,7 +41,7 @@ env GUIX_PACKAGE_PATH=~/guix-bioinformatics/ ~/.config/guix/current/bin/guix env
Create a new profile with
```
-env GUIX_PACKAGE_PATH=~/guix-bioinformatics/ ~/.config/guix/current/bin/guix package -i genenetwork3 -p ~/opt/genenetwork3
+guix package -i genenetwork3 -p ~/opt/genenetwork3
```
and load the profile settings with
diff --git a/gn3/api/heatmaps.py b/gn3/api/heatmaps.py
index 62ca2ad..633a061 100644
--- a/gn3/api/heatmaps.py
+++ b/gn3/api/heatmaps.py
@@ -17,7 +17,9 @@ def clustered_heatmaps():
Parses the incoming data and responds with the JSON-serialized plotly figure
representing the clustered heatmap.
"""
- traits_names = request.get_json().get("traits_names", tuple())
+ heatmap_request = request.get_json()
+ traits_names = heatmap_request.get("traits_names", tuple())
+ vertical = heatmap_request.get("vertical", False)
if len(traits_names) < 2:
return jsonify({
"message": "You need to provide at least two trait names."
@@ -30,7 +32,7 @@ def clustered_heatmaps():
traits_fullnames = [parse_trait_fullname(trait) for trait in traits_names]
with io.StringIO() as io_str:
- _filename, figure = build_heatmap(traits_fullnames, conn)
+ figure = build_heatmap(traits_fullnames, conn, vertical=vertical)
figure.write_json(io_str)
fig_json = io_str.getvalue()
return fig_json, 200
diff --git a/gn3/app.py b/gn3/app.py
index a25332c..3d68b3f 100644
--- a/gn3/app.py
+++ b/gn3/app.py
@@ -21,12 +21,6 @@ def create_app(config: Union[Dict, str, None] = None) -> Flask:
# Load default configuration
app.config.from_object("gn3.settings")
- CORS(
- app,
- origins=app.config["CORS_ORIGINS"],
- allow_headers=app.config["CORS_HEADERS"],
- supports_credentials=True, intercept_exceptions=False)
-
# Load environment configuration
if "GN3_CONF" in os.environ:
app.config.from_envvar('GN3_CONF')
@@ -37,6 +31,13 @@ def create_app(config: Union[Dict, str, None] = None) -> Flask:
app.config.update(config)
elif config.endswith(".py"):
app.config.from_pyfile(config)
+
+ CORS(
+ app,
+ origins=app.config["CORS_ORIGINS"],
+ allow_headers=app.config["CORS_HEADERS"],
+ supports_credentials=True, intercept_exceptions=False)
+
app.register_blueprint(general, url_prefix="/api/")
app.register_blueprint(gemma, url_prefix="/api/gemma")
app.register_blueprint(rqtl, url_prefix="/api/rqtl")
diff --git a/gn3/authentication.py b/gn3/authentication.py
new file mode 100644
index 0000000..a6372c1
--- /dev/null
+++ b/gn3/authentication.py
@@ -0,0 +1,165 @@
+"""Methods for interacting with gn-proxy."""
+import functools
+import json
+import uuid
+import datetime
+
+from urllib.parse import urljoin
+from enum import Enum, unique
+from typing import Dict, List, Optional, Union
+
+from redis import Redis
+import requests
+
+
+@functools.total_ordering
+class OrderedEnum(Enum):
+ """A class that ordered Enums in order of position"""
+ @classmethod
+ @functools.lru_cache(None)
+ def _member_list(cls):
+ return list(cls)
+
+ def __lt__(self, other):
+ if self.__class__ is other.__class__:
+ member_list = self.__class__._member_list()
+ return member_list.index(self) < member_list.index(other)
+ return NotImplemented
+
+
+@unique
+class DataRole(OrderedEnum):
+ """Enums for Data Access"""
+ NO_ACCESS = "no-access"
+ VIEW = "view"
+ EDIT = "edit"
+
+
+@unique
+class AdminRole(OrderedEnum):
+ """Enums for Admin status"""
+ NOT_ADMIN = "not-admin"
+ EDIT_ACCESS = "edit-access"
+ EDIT_ADMINS = "edit-admins"
+
+
+def get_user_membership(conn: Redis, user_id: str,
+ group_id: str) -> Dict:
+ """Return a dictionary that indicates whether the `user_id` is a
+ member or admin of `group_id`.
+
+ Args:
+ - conn: a Redis Connection with the responses decoded.
+ - user_id: a user's unique id
+ e.g. '8ad942fe-490d-453e-bd37-56f252e41603'
+ - group_id: a group's unique id
+ e.g. '7fa95d07-0e2d-4bc5-b47c-448fdc1260b2'
+
+ Returns:
+ A dict indicating whether the user is an admin or a member of
+ the group: {"member": True, "admin": False}
+
+ """
+ results = {"member": False, "admin": False}
+ for key, value in conn.hgetall('groups').items():
+ if key == group_id:
+ group_info = json.loads(value)
+ if user_id in group_info.get("admins"):
+ results["admin"] = True
+ if user_id in group_info.get("members"):
+ results["member"] = True
+ break
+ return results
+
+
+def get_highest_user_access_role(
+ resource_id: str,
+ user_id: str,
+ gn_proxy_url: str = "http://localhost:8080") -> Dict:
+ """Get the highest access roles for a given user
+
+ Args:
+ - resource_id: The unique id of a given resource.
+ - user_id: The unique id of a given user.
+ - gn_proxy_url: The URL where gn-proxy is running.
+
+ Returns:
+ A dict indicating the highest access role the user has.
+
+ """
+ role_mapping: Dict[str, Union[DataRole, AdminRole]] = {}
+ for data_role, admin_role in zip(DataRole, AdminRole):
+ role_mapping.update({data_role.value: data_role, })
+ role_mapping.update({admin_role.value: admin_role, })
+ access_role = {}
+ response = requests.get(urljoin(gn_proxy_url,
+ ("available?resource="
+ f"{resource_id}&user={user_id}")))
+ for key, value in json.loads(response.content).items():
+ access_role[key] = max(map(lambda role: role_mapping[role], value))
+ return access_role
+
+
+def get_groups_by_user_uid(user_uid: str, conn: Redis) -> Dict:
+ """Given a user uid, get the groups in which they are a member or admin of.
+
+ Args:
+ - user_uid: A user's unique id
+ - conn: A redis connection
+
+ Returns:
+ - A dictionary containing the list of groups the user is part of e.g.:
+ {"admin": [], "member": ["ce0dddd1-6c50-4587-9eec-6c687a54ad86"]}
+ """
+ admin = []
+ member = []
+ for group_uuid, group_info in conn.hgetall("groups").items():
+ group_info = json.loads(group_info)
+ group_info["uuid"] = group_uuid
+ if user_uid in group_info.get('admins'):
+ admin.append(group_info)
+ if user_uid in group_info.get('members'):
+ member.append(group_info)
+ return {
+ "admin": admin,
+ "member": member,
+ }
+
+
+def get_user_info_by_key(key: str, value: str,
+ conn: Redis) -> Optional[Dict]:
+ """Given a key, get a user's information if value is matched"""
+ if key != "user_id":
+ for user_uuid, user_info in conn.hgetall("users").items():
+ user_info = json.loads(user_info)
+ if (key in user_info and user_info.get(key) == value):
+ user_info["user_id"] = user_uuid
+ return user_info
+ elif key == "user_id":
+ if user_info := conn.hget("users", value):
+ user_info = json.loads(user_info)
+ user_info["user_id"] = value
+ return user_info
+ return None
+
+
+def create_group(conn: Redis, group_name: Optional[str],
+ admin_user_uids: List = None,
+ member_user_uids: List = None) -> Optional[Dict]:
+ """Create a group given the group name, members and admins of that group."""
+ if admin_user_uids is None:
+ admin_user_uids = []
+ if member_user_uids is None:
+ member_user_uids = []
+ if group_name and bool(admin_user_uids + member_user_uids):
+ timestamp = datetime.datetime.utcnow().strftime('%b %d %Y %I:%M%p')
+ group = {
+ "id": (group_id := str(uuid.uuid4())),
+ "admins": admin_user_uids,
+ "members": member_user_uids,
+ "name": group_name,
+ "created_timestamp": timestamp,
+ "changed_timestamp": timestamp,
+ }
+ conn.hset("groups", group_id, json.dumps(group))
+ return group
diff --git a/gn3/computations/biweight.py b/gn3/computations/biweight.py
deleted file mode 100644
index 7accd0c..0000000
--- a/gn3/computations/biweight.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""module contains script to call biweight midcorrelation in R"""
-import subprocess
-
-from typing import List
-from typing import Tuple
-
-from gn3.settings import BIWEIGHT_RSCRIPT
-
-
-def calculate_biweight_corr(trait_vals: List,
- target_vals: List,
- path_to_script: str = BIWEIGHT_RSCRIPT,
- command: str = "Rscript"
- ) -> Tuple[float, float]:
- """biweight function"""
-
- args_1 = ' '.join(str(trait_val) for trait_val in trait_vals)
- args_2 = ' '.join(str(target_val) for target_val in target_vals)
- cmd = [command, path_to_script] + [args_1] + [args_2]
-
- results = subprocess.check_output(cmd, universal_newlines=True)
- try:
- (corr_coeff, p_val) = tuple(
- [float(y.strip()) for y in results.split()])
- return (corr_coeff, p_val)
- except Exception as error:
- raise error
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py
index bb13ff1..c5c56db 100644
--- a/gn3/computations/correlations.py
+++ b/gn3/computations/correlations.py
@@ -1,6 +1,7 @@
"""module contains code for correlations"""
import math
import multiprocessing
+from contextlib import closing
from typing import List
from typing import Tuple
@@ -8,7 +9,7 @@ from typing import Optional
from typing import Callable
import scipy.stats
-from gn3.computations.biweight import calculate_biweight_corr
+import pingouin as pg
def map_shared_keys_to_values(target_sample_keys: List,
@@ -49,13 +50,9 @@ def normalize_values(a_values: List,
([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3)
"""
- a_new = []
- b_new = []
for a_val, b_val in zip(a_values, b_values):
if (a_val and b_val is not None):
- a_new.append(a_val)
- b_new.append(b_val)
- return a_new, b_new, len(a_new)
+ yield a_val, b_val
def compute_corr_coeff_p_value(primary_values: List, target_values: List,
@@ -81,8 +78,10 @@ def compute_sample_r_correlation(trait_name, corr_method, trait_vals,
correlation coeff and p value
"""
- (sanitized_traits_vals, sanitized_target_vals,
- num_overlap) = normalize_values(trait_vals, target_samples_vals)
+
+ sanitized_traits_vals, sanitized_target_vals = list(
+ zip(*list(normalize_values(trait_vals, target_samples_vals))))
+ num_overlap = len(sanitized_traits_vals)
if num_overlap > 5:
@@ -102,11 +101,10 @@ package :not packaged in guix
"""
- try:
- results = calculate_biweight_corr(x_val, y_val)
- return results
- except Exception as error:
- raise error
+ results = pg.corr(x_val, y_val, method="bicor")
+ corr_coeff = results["r"].values[0]
+ p_val = results["p-val"].values[0]
+ return (corr_coeff, p_val)
def filter_shared_sample_keys(this_samplelist,
@@ -115,13 +113,9 @@ def filter_shared_sample_keys(this_samplelist,
filter the values using the shared keys
"""
- this_vals = []
- target_vals = []
for key, value in target_samplelist.items():
if key in this_samplelist:
- target_vals.append(value)
- this_vals.append(this_samplelist[key])
- return (this_vals, target_vals)
+ yield this_samplelist[key], value
def fast_compute_all_sample_correlation(this_trait,
@@ -140,9 +134,10 @@ def fast_compute_all_sample_correlation(this_trait,
for target_trait in target_dataset:
trait_name = target_trait.get("trait_id")
target_trait_data = target_trait["trait_sample_data"]
- processed_values.append((trait_name, corr_method, *filter_shared_sample_keys(
- this_trait_samples, target_trait_data)))
- with multiprocessing.Pool(4) as pool:
+ processed_values.append((trait_name, corr_method,
+ list(zip(*list(filter_shared_sample_keys(
+ this_trait_samples, target_trait_data))))))
+ with closing(multiprocessing.Pool()) as pool:
results = pool.starmap(compute_sample_r_correlation, processed_values)
for sample_correlation in results:
@@ -173,8 +168,8 @@ def compute_all_sample_correlation(this_trait,
for target_trait in target_dataset:
trait_name = target_trait.get("trait_id")
target_trait_data = target_trait["trait_sample_data"]
- this_vals, target_vals = filter_shared_sample_keys(
- this_trait_samples, target_trait_data)
+ this_vals, target_vals = list(zip(*list(filter_shared_sample_keys(
+ this_trait_samples, target_trait_data))))
sample_correlation = compute_sample_r_correlation(
trait_name=trait_name,
diff --git a/gn3/computations/correlations2.py b/gn3/computations/correlations2.py
index 93db3fa..d0222ae 100644
--- a/gn3/computations/correlations2.py
+++ b/gn3/computations/correlations2.py
@@ -6,45 +6,21 @@ FUNCTIONS:
compute_correlation:
TODO: Describe what the function does..."""
-from math import sqrt
-from functools import reduce
+from scipy import stats
## From GN1: mostly for clustering and heatmap generation
def __items_with_values(dbdata, userdata):
"""Retains only corresponding items in the data items that are not `None` values.
This should probably be renamed to something sensible"""
- def both_not_none(item1, item2):
- """Check that both items are not the value `None`."""
- if (item1 is not None) and (item2 is not None):
- return (item1, item2)
- return None
- def split_lists(accumulator, item):
- """Separate the 'x' and 'y' items."""
- return [accumulator[0] + [item[0]], accumulator[1] + [item[1]]]
- return reduce(
- split_lists,
- filter(lambda x: x is not None, map(both_not_none, dbdata, userdata)),
- [[], []])
+ filtered = [x for x in zip(dbdata, userdata) if x[0] is not None and x[1] is not None]
+ return tuple(zip(*filtered)) if filtered else ([], [])
def compute_correlation(dbdata, userdata):
- """Compute some form of correlation.
+ """Compute the Pearson correlation coefficient.
This is extracted from
https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/utility/webqtlUtil.py#L622-L647
"""
x_items, y_items = __items_with_values(dbdata, userdata)
- if len(x_items) < 6:
- return (0.0, len(x_items))
- meanx = sum(x_items)/len(x_items)
- meany = sum(y_items)/len(y_items)
- def cal_corr_vals(acc, item):
- xitem, yitem = item
- return [
- acc[0] + ((xitem - meanx) * (yitem - meany)),
- acc[1] + ((xitem - meanx) * (xitem - meanx)),
- acc[2] + ((yitem - meany) * (yitem - meany))]
- xyd, sxd, syd = reduce(cal_corr_vals, zip(x_items, y_items), [0.0, 0.0, 0.0])
- try:
- return ((xyd/(sqrt(sxd)*sqrt(syd))), len(x_items))
- except ZeroDivisionError:
- return(0, len(x_items))
+ correlation = stats.pearsonr(x_items, y_items)[0] if len(x_items) >= 6 else 0
+ return (correlation, len(x_items))
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py
new file mode 100644
index 0000000..231b0a7
--- /dev/null
+++ b/gn3/computations/partial_correlations.py
@@ -0,0 +1,696 @@
+"""
+This module deals with partial correlations.
+
+It is an attempt to migrate over the partial correlations feature from
+GeneNetwork1.
+"""
+
+import math
+from functools import reduce, partial
+from typing import Any, Tuple, Union, Sequence
+
+import pandas
+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,
+ 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]):
+ """
+ Fetches data for the control traits.
+
+ This migrates `web/webqtl/correlation/correlationFunction.controlStrain` in
+ GN1, with a few modifications to the arguments passed in.
+
+ PARAMETERS:
+ controls: A map of sample names to trait data. Equivalent to the `cvals`
+ value in the corresponding source function in GN1.
+ sampleslist: A list of samples. Equivalent to `strainlst` in the
+ corresponding source function in GN1
+ """
+ def __process_control__(trait_data):
+ def __process_sample__(acc, sample):
+ if sample in trait_data["data"].keys():
+ sample_item = trait_data["data"][sample]
+ val = sample_item["value"]
+ if val is not None:
+ return (
+ acc[0] + (sample,),
+ acc[1] + (val,),
+ acc[2] + (sample_item["variance"],))
+ return acc
+ return reduce(
+ __process_sample__, sampleslist, (tuple(), tuple(), tuple()))
+
+ return reduce(
+ lambda acc, item: (
+ acc[0] + (item[0],),
+ acc[1] + (item[1],),
+ acc[2] + (item[2],),
+ acc[3] + (len(item[0]),),
+ ),
+ [__process_control__(trait_data) for trait_data in controls],
+ (tuple(), tuple(), tuple(), tuple()))
+
+def dictify_by_samples(samples_vals_vars: Sequence[Sequence]) -> Sequence[dict]:
+ """
+ Build a sequence of dictionaries from a sequence of separate sequences of
+ samples, values and variances.
+
+ This is a partial migration of
+ `web.webqtl.correlation.correlationFunction.fixStrains` function in GN1.
+ This implementation extracts code that will find common use, and that will
+ find use in more than one place.
+ """
+ return tuple(
+ {
+ sample: {"sample_name": sample, "value": val, "variance": var}
+ for sample, val, var in zip(*trait_line)
+ } for trait_line in zip(*(samples_vals_vars[0:3])))
+
+def fix_samples(primary_trait: dict, control_traits: Sequence[dict]) -> Sequence[Sequence[Any]]:
+ """
+ Corrects sample_names, values and variance such that they all contain only
+ those samples that are common to the reference trait and all control traits.
+
+ This is a partial migration of the
+ `web.webqtl.correlation.correlationFunction.fixStrain` function in GN1.
+ """
+ primary_samples = tuple(
+ present[0] for present in
+ ((sample, all(sample in control.keys() for control in control_traits))
+ for sample in primary_trait.keys())
+ if present[1])
+ control_vals_vars: tuple = reduce(
+ lambda acc, x: (acc[0] + (x[0],), acc[1] + (x[1],)),
+ ((item["value"], item["variance"])
+ for sublist in [tuple(control.values()) for control in control_traits]
+ for item in sublist),
+ (tuple(), tuple()))
+ return (
+ primary_samples,
+ tuple(primary_trait[sample]["value"] for sample in primary_samples),
+ control_vals_vars[0],
+ tuple(primary_trait[sample]["variance"] for sample in primary_samples),
+ control_vals_vars[1])
+
+def find_identical_traits(
+ primary_name: str, primary_value: float, control_names: Tuple[str, ...],
+ control_values: Tuple[float, ...]) -> Tuple[str, ...]:
+ """
+ Find traits that have the same value when the values are considered to
+ 3 decimal places.
+
+ This is a migration of the
+ `web.webqtl.correlation.correlationFunction.findIdenticalTraits` function in
+ GN1.
+ """
+ def __merge_identicals__(
+ acc: Tuple[str, ...],
+ ident: Tuple[str, Tuple[str, ...]]) -> Tuple[str, ...]:
+ return acc + ident[1]
+
+ def __dictify_controls__(acc, control_item):
+ ckey = tuple("{:.3f}".format(item) for item in control_item[0])
+ return {**acc, ckey: acc.get(ckey, tuple()) + (control_item[1],)}
+
+ return (reduce(## for identical control traits
+ __merge_identicals__,
+ (item for item in reduce(# type: ignore[var-annotated]
+ __dictify_controls__, zip(control_values, control_names),
+ {}).items() if len(item[1]) > 1),
+ tuple())
+ or
+ reduce(## If no identical control traits, try primary and controls
+ __merge_identicals__,
+ (item for item in reduce(# type: ignore[var-annotated]
+ __dictify_controls__,
+ zip((primary_value,) + control_values,
+ (primary_name,) + control_names), {}).items()
+ if len(item[1]) > 1),
+ tuple()))
+
+def tissue_correlation(
+ primary_trait_values: Tuple[float, ...],
+ target_trait_values: Tuple[float, ...],
+ method: str) -> Tuple[float, float]:
+ """
+ Compute the correlation between the primary trait values, and the values of
+ a single target value.
+
+ This migrates the `cal_tissue_corr` function embedded in the larger
+ `web.webqtl.correlation.correlationFunction.batchCalTissueCorr` function in
+ GeneNetwork1.
+ """
+ def spearman_corr(*args):
+ result = spearmanr(*args)
+ return (result.correlation, result.pvalue)
+
+ method_fns = {"pearson": pearsonr, "spearman": spearman_corr}
+
+ assert len(primary_trait_values) == len(target_trait_values), (
+ "The lengths of the `primary_trait_values` and `target_trait_values` "
+ "must be equal")
+ assert method in method_fns.keys(), (
+ "Method must be one of: {}".format(",".join(method_fns.keys())))
+
+ corr, pvalue = method_fns[method](primary_trait_values, target_trait_values)
+ return (corr, pvalue)
+
+def batch_computed_tissue_correlation(
+ primary_trait_values: Tuple[float, ...], target_traits_dict: dict,
+ method: str) -> Tuple[dict, dict]:
+ """
+ This is a migration of the
+ `web.webqtl.correlation.correlationFunction.batchCalTissueCorr` function in
+ GeneNetwork1
+ """
+ def __corr__(acc, target):
+ corr = tissue_correlation(primary_trait_values, target[1], method)
+ return ({**acc[0], target[0]: corr[0]}, {**acc[0], target[1]: corr[1]})
+ return reduce(__corr__, target_traits_dict.items(), ({}, {}))
+
+def correlations_of_all_tissue_traits(
+ primary_trait_symbol_value_dict: dict, symbol_value_dict: dict,
+ method: str) -> Tuple[dict, dict]:
+ """
+ Computes and returns the correlation of all tissue traits.
+
+ This is a migration of the
+ `web.webqtl.correlation.correlationFunction.calculateCorrOfAllTissueTrait`
+ function in GeneNetwork1.
+ """
+ primary_trait_values = tuple(primary_trait_symbol_value_dict.values())[0]
+ return batch_computed_tissue_correlation(
+ primary_trait_values, symbol_value_dict, method)
+
+def good_dataset_samples_indexes(
+ samples: Tuple[str, ...],
+ samples_from_file: Tuple[str, ...]) -> Tuple[int, ...]:
+ """
+ Return the indexes of the items in `samples_from_files` that are also found
+ in `samples`.
+
+ This is a partial migration of the
+ `web.webqtl.correlation.PartialCorrDBPage.getPartialCorrelationsFast`
+ function in GeneNetwork1.
+ """
+ return tuple(sorted(
+ samples_from_file.index(good) for good in
+ set(samples).intersection(set(samples_from_file))))
+
+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, ...]]:
+ """
+ Computes partial correlation coefficients using data from a CSV file.
+
+ This is a partial migration of the
+ `web.webqtl.correlation.PartialCorrDBPage.getPartialCorrelationsFast`
+ function in GeneNetwork1.
+ """
+ assert method in ("spearman", "pearson")
+ with open(database_filename, "r") as dataset_file:
+ dataset = tuple(dataset_file.readlines())
+
+ good_dataset_samples = good_dataset_samples_indexes(
+ samples, parse_csv_line(dataset[0])[1:])
+
+ def __process_trait_names_and_values__(acc, line):
+ trait_line = parse_csv_line(line)
+ trait_name = trait_line[0]
+ trait_data = trait_line[1:]
+ if trait_name in fetched_correlations.keys():
+ return (
+ acc[0] + (trait_name,),
+ acc[1] + tuple(
+ trait_data[i] if i in good_dataset_samples else None
+ for i in range(len(trait_data))))
+ return acc
+
+ processed_trait_names_values: tuple = reduce(
+ __process_trait_names_and_values__, dataset[1:], (tuple(), tuple()))
+ all_target_trait_names: Tuple[str, ...] = processed_trait_names_values[0]
+ all_target_trait_values: Tuple[float, ...] = processed_trait_names_values[1]
+
+ all_correlations = compute_partial(
+ primary_vals, control_vals, all_target_trait_names,
+ all_target_trait_values, method)
+ ## Line 772 to 779 in GN1 are the cause of the weird complexity in the
+ ## return below. Once the surrounding code is successfully migrated and
+ ## reworked, this complexity might go away, by getting rid of the
+ ## `correlation_type` parameter
+ return len(all_correlations), tuple(
+ corr + (
+ (fetched_correlations[corr[0]],) if correlation_type == "literature"
+ else fetched_correlations[corr[0]][0:2])
+ for idx, corr in enumerate(all_correlations))
+
+def build_data_frame(
+ xdata: Tuple[float, ...], ydata: Tuple[float, ...],
+ zdata: Union[
+ Tuple[float, ...],
+ Tuple[Tuple[float, ...], ...]]) -> pandas.DataFrame:
+ """
+ Build a pandas DataFrame object from xdata, ydata and zdata
+ """
+ x_y_df = pandas.DataFrame({"x": xdata, "y": ydata})
+ if isinstance(zdata[0], float):
+ return x_y_df.join(pandas.DataFrame({"z": zdata}))
+ interm_df = x_y_df.join(pandas.DataFrame(
+ {"z{}".format(i): val for i, val in enumerate(zdata)}))
+ if interm_df.shape[1] == 3:
+ return interm_df.rename(columns={"z0": "z"})
+ return interm_df
+
+def compute_partial(
+ primary_vals, control_vals, target_vals, target_names,
+ method: str) -> Tuple[
+ Union[
+ Tuple[str, int, float, float, float, float], None],
+ ...]:
+ """
+ Compute the partial correlations.
+
+ This is a re-implementation of the
+ `web.webqtl.correlation.correlationFunction.determinePartialsByR` function
+ in GeneNetwork1.
+
+ This implementation reworks the child function `compute_partial` which will
+ then be used in the place of `determinPartialsByR`.
+
+ TODO: moving forward, we might need to use the multiprocessing library to
+ speed up the computations, in case they are found to be slow.
+ """
+ # replace the R code with `pingouin.partial_corr`
+ def __compute_trait_info__(target):
+ targ_vals = target[0]
+ targ_name = target[1]
+ primary = [
+ prim for targ, prim in zip(targ_vals, primary_vals)
+ if targ is not None]
+
+ datafrm = build_data_frame(
+ primary,
+ tuple(targ for targ in targ_vals if targ is not None),
+ tuple(cont for i, cont in enumerate(control_vals)
+ if target[i] is not None))
+ covariates = "z" if datafrm.shape[1] == 3 else [
+ col for col in datafrm.columns if col not in ("x", "y")]
+ ppc = pingouin.partial_corr(
+ data=datafrm, x="x", y="y", covar=covariates, method=(
+ "pearson" if "pearson" in method.lower() else "spearman"))
+ pc_coeff = ppc["r"][0]
+
+ zero_order_corr = pingouin.corr(
+ datafrm["x"], datafrm["y"], method=(
+ "pearson" if "pearson" in method.lower() else "spearman"))
+
+ if math.isnan(pc_coeff):
+ return (
+ targ_name, len(primary), pc_coeff, 1, zero_order_corr["r"][0],
+ zero_order_corr["p-val"][0])
+ return (
+ targ_name, len(primary), pc_coeff,
+ (ppc["p-val"][0] if not math.isnan(ppc["p-val"][0]) else (
+ 0 if (abs(pc_coeff - 1) < 0.0000001) else 1)),
+ zero_order_corr["r"][0], zero_order_corr["p-val"][0])
+
+ return tuple(
+ __compute_trait_info__(target)
+ for target in zip(target_vals, target_names))
+
+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, ...]]:
+ """
+ Computes the correlation coefficients.
+
+ This is a migration of the
+ `web.webqtl.correlation.PartialCorrDBPage.getPartialCorrelationsNormal`
+ function in GeneNetwork1.
+ """
+ def __add_lit_and_tiss_corr__(item):
+ if method.lower() == "sgo literature correlation":
+ # if method is 'SGO Literature Correlation', `compute_partial`
+ # would give us LitCorr in the [1] position
+ return tuple(item) + trait_database[1]
+ if method.lower() in (
+ "tissue correlation, pearson's r",
+ "tissue correlation, spearman's rho"):
+ # if method is 'Tissue Correlation, *', `compute_partial` would give
+ # us Tissue Corr in the [1] position and Tissue Corr P Value in the
+ # [2] position
+ return tuple(item) + (trait_database[1], trait_database[2])
+ return item
+
+ target_trait_names, target_trait_vals = reduce(
+ lambda acc, item: (acc[0]+(item[0],), acc[1]+(item[data_start_pos:],)),
+ trait_database, (tuple(), tuple()))
+
+ all_correlations = compute_partial(
+ primary_vals, control_vals, target_trait_vals, target_trait_names,
+ method)
+
+ if (input_trait_gene_id and db_type == "ProbeSet" and method.lower() in (
+ "sgo literature correlation", "tissue correlation, pearson's r",
+ "tissue correlation, spearman's rho")):
+ return (
+ len(trait_database),
+ tuple(
+ __add_lit_and_tiss_corr__(item)
+ for idx, item in enumerate(all_correlations)))
+
+ return len(trait_database), all_correlations
+
+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.
+
+ This is a partial migration of the
+ `web.webqtl.correlation.PartialCorrDBPage.__init__` function in
+ GeneNetwork1.
+ """
+ if database_filename:
+ return partial_correlations_fast(
+ samples, primary_vals, control_vals, database_filename,
+ (
+ fetch_literature_correlations(
+ species, input_trait_geneid, dataset, return_number, conn)
+ if "literature" in method.lower() else
+ fetch_tissue_correlations(
+ dataset, input_trait_symbol, tissue_probeset_freeze_id,
+ method, return_number, conn)),
+ method,
+ ("literature" if method.lower() == "sgo literature correlation"
+ else ("tissue" if "tissue" in method.lower() else "genetic")))
+
+ trait_database, data_start_pos = fetch_all_database_data(
+ conn, species, input_trait_geneid, input_trait_symbol, samples, dataset,
+ method, return_number, tissue_probeset_freeze_id)
+ return partial_correlations_normal(
+ primary_vals, control_vals, input_trait_geneid, trait_database,
+ data_start_pos, dataset, method)
+
+def literature_correlation_by_list(
+ conn: Any, species: str, trait_list: Tuple[dict]) -> Tuple[dict]:
+ """
+ This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.getLiteratureCorrelationByList`
+ function in GeneNetwork1.
+ """
+ if any((lambda t: (
+ bool(t.get("tissue_corr")) and
+ bool(t.get("tissue_p_value"))))(trait)
+ for trait in trait_list):
+ 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)")
+ query2 = (
+ f"INSERT INTO {temporary_table_name}(GeneId1, GeneId2, value) "
+ "SELECT GeneId1, GeneId2, value FROM LCorrRamin3 "
+ "WHERE GeneId1=%(geneid)s")
+ query3 = (
+ "INSERT INTO {temporary_table_name}(GeneId1, GeneId2, value) "
+ "SELECT GeneId2, GeneId1, value FROM LCorrRamin3 "
+ "WHERE GeneId2=%s AND GeneId1 != %(geneid)s")
+
+ def __set_mouse_geneid__(trait):
+ if trait.get("geneid"):
+ return {
+ **trait,
+ "mouse_geneid": translate_to_mouse_gene_id(
+ species, trait.get("geneid"), conn)
+ }
+ return {**trait, "mouse_geneid": 0}
+
+ def __retrieve_lcorr__(cursor, geneids):
+ cursor.execute(
+ f"SELECT GeneId2, value FROM {temporary_table_name} "
+ "WHERE GeneId2 IN %(geneids)s",
+ geneids=geneids)
+ return dict(cursor.fetchall())
+
+ with conn.cursor() as cursor:
+ cursor.execute(query1)
+ cursor.execute(query2)
+ cursor.execute(query3)
+
+ traits = tuple(__set_mouse_geneid__(trait) for trait in trait_list)
+ lcorrs = __retrieve_lcorr__(
+ cursor, (
+ trait["mouse_geneid"] for trait in traits
+ if (trait["mouse_geneid"] != 0 and
+ trait["mouse_geneid"].find(";") < 0)))
+ return tuple(
+ {**trait, "l_corr": lcorrs.get(trait["mouse_geneid"], None)}
+ for trait in traits)
+
+ return trait_list
+ return trait_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]:
+ """
+ This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.getTissueCorrelationByList`
+ function in GeneNetwork1.
+ """
+ def __add_tissue_corr__(trait, primary_trait_values, trait_values):
+ result = pingouin.corr(
+ primary_trait_values, trait_values,
+ method=("spearman" if "spearman" in method.lower() else "pearson"))
+ return {
+ **trait,
+ "tissue_corr": result["r"],
+ "tissue_p_value": result["p-val"]
+ }
+
+ if any((lambda t: bool(t.get("l_corr")))(trait) for trait in trait_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[
+ 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(
+ gene_symbol_list, tissue_probeset_freeze_id, conn)
+ return tuple(
+ __add_tissue_corr__(
+ trait, primary_trait_value,
+ symbol_value_dict[trait["symbol"].lower()])
+ for trait in trait_list
+ if ("symbol" in trait and
+ bool(trait["symbol"]) and
+ trait["symbol"].lower() in symbol_value_dict))
+ return tuple({
+ **trait,
+ "tissue_corr": None,
+ "tissue_p_value": None
+ } for trait in trait_list)
+ return trait_list
+
+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:
+ """
+ This is the 'ochestration' function for the partial-correlation feature.
+
+ This function will dispatch the functions doing data fetches from the
+ database (and various other places) and feed that data to the functions
+ doing the conversions and computations. It will then return the results of
+ all of that work.
+
+ This function is doing way too much. Look into splitting out the
+ functionality into smaller functions that do fewer things.
+ """
+ threshold = 0
+ corr_min_informative = 4
+
+ 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_trait_data)
+
+ cntrl_traits = tuple(
+ retrieve_trait_info(threshold, trait_full_name, conn)
+ for trait_full_name in control_trait_names)
+ cntrl_traits_data = tuple(
+ retrieve_trait_data(cntrl_trait, conn)
+ for cntrl_trait in cntrl_traits)
+ species = species_name(conn, group)
+
+ (cntrl_samples,
+ cntrl_values,
+ _cntrl_variances,
+ _cntrl_ns) = control_samples(cntrl_traits_data, primary_samples)
+
+ common_primary_control_samples = primary_samples
+ fixed_primary_vals = primary_values
+ fixed_control_vals = cntrl_values
+ if not all(cnt_smp == primary_samples for cnt_smp in cntrl_samples):
+ (common_primary_control_samples,
+ fixed_primary_vals,
+ fixed_control_vals,
+ _primary_variances,
+ _cntrl_variances) = fix_samples(primary_trait, cntrl_traits)
+
+ if len(common_primary_control_samples) < corr_min_informative:
+ return {
+ "status": "error",
+ "message": (
+ f"Fewer than {corr_min_informative} samples data entered for "
+ f"{group} dataset. No calculation of correlation has been "
+ "attempted."),
+ "error_type": "Inadequate Samples"}
+
+ identical_traits_names = find_identical_traits(
+ primary_trait_name, primary_values, control_trait_names, cntrl_values)
+ if len(identical_traits_names) > 0:
+ return {
+ "status": "error",
+ "message": (
+ f"{identical_traits_names[0]} and {identical_traits_names[1]} "
+ "have the same values for the {len(fixed_primary_vals)} "
+ "samples that will be used to compute the partial correlation "
+ "(common for all primary and control traits). In such cases, "
+ "partial correlation cannot be computed. Please re-select your "
+ "traits."),
+ "error_type": "Identical Traits"}
+
+ input_trait_geneid = primary_trait.get("geneid")
+ input_trait_symbol = primary_trait.get("symbol")
+ input_trait_mouse_geneid = translate_to_mouse_gene_id(
+ species, input_trait_geneid, conn)
+
+ tissue_probeset_freeze_id = 1
+ db_type = primary_trait["db"]["dataset_type"]
+
+ if db_type == "ProbeSet" and method.lower() in (
+ "sgo literature correlation",
+ "tissue correlation, pearson's r",
+ "tissue correlation, spearman's rho"):
+ return {
+ "status": "error",
+ "message": (
+ "Wrong correlation type: It is not possible to compute the "
+ f"{method} between your trait and data in the {target_db_name} "
+ "database. Please try again after selecting another type of "
+ "correlation."),
+ "error_type": "Correlation Type"}
+
+ if (method.lower() == "sgo literature correlation" and (
+ input_trait_geneid is None or
+ check_for_literature_info(conn, input_trait_mouse_geneid))):
+ return {
+ "status": "error",
+ "message": (
+ "No Literature Information: This gene does not have any "
+ "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):
+ return {
+ "status": "error",
+ "message": (
+ "No Tissue Correlation Information: This gene does not have "
+ "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)):
+ return {
+ "status": "error",
+ "message": (
+ "No Tissue Correlation Information: This gene does not have "
+ "any associated Tissue Correlation Information."),
+ "error_type": "Tissue Correlation"}
+
+ database_filename = get_filename(conn, target_db_name, TEXTDIR)
+ _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,
+ method, primary_trait["db"], database_filename)
+
+
+ def __make_sorter__(method):
+ def __sort_6__(row):
+ return row[6]
+
+ def __sort_3__(row):
+ return row[3]
+
+ if "literature" in method.lower():
+ return __sort_6__
+
+ if "tissue" in method.lower():
+ return __sort_6__
+
+ return __sort_3__
+
+ sorted_correlations = sorted(
+ all_correlations, key=__make_sorter__(method))
+
+ add_lit_corr_and_tiss_corr = compose(
+ 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]}
+ if len(item) == 8 else {}),
+ **({"l_corr": item[6]}
+ if len(item) == 7 else {})
+ }
+ for item in
+ sorted_correlations[:min(criteria, len(all_correlations))]))
+
+ return trait_list
diff --git a/gn3/computations/wgcna.py b/gn3/computations/wgcna.py
index fd508fa..ab12fe7 100644
--- a/gn3/computations/wgcna.py
+++ b/gn3/computations/wgcna.py
@@ -3,8 +3,11 @@
import os
import json
import uuid
-from gn3.settings import TMPDIR
+import subprocess
+import base64
+
+from gn3.settings import TMPDIR
from gn3.commands import run_cmd
@@ -14,12 +17,46 @@ def dump_wgcna_data(request_data: dict):
temp_file_path = os.path.join(TMPDIR, filename)
+ request_data["TMPDIR"] = TMPDIR
+
with open(temp_file_path, "w") as output_file:
json.dump(request_data, output_file)
return temp_file_path
+def stream_cmd_output(socketio, request_data, cmd: str):
+ """function to stream in realtime"""
+ # xtodo syncing and closing /edge cases
+
+ socketio.emit("output", {"data": f"calling you script {cmd}"},
+ namespace="/", room=request_data["socket_id"])
+ results = subprocess.Popen(
+ cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
+
+ if results.stdout is not None:
+
+ for line in iter(results.stdout.readline, b""):
+ socketio.emit("output",
+ {"data": line.decode("utf-8").rstrip()},
+ namespace="/", room=request_data["socket_id"])
+
+ socketio.emit(
+ "output", {"data":
+ "parsing the output results"}, namespace="/",
+ room=request_data["socket_id"])
+
+
+def process_image(image_loc: str) -> bytes:
+ """encode the image"""
+
+ try:
+ with open(image_loc, "rb") as image_file:
+ return base64.b64encode(image_file.read())
+ except FileNotFoundError:
+ return b""
+
+
def compose_wgcna_cmd(rscript_path: str, temp_file_path: str):
"""function to componse wgcna cmd"""
# (todo):issue relative paths to abs paths
@@ -32,6 +69,8 @@ def call_wgcna_script(rscript_path: str, request_data: dict):
generated_file = dump_wgcna_data(request_data)
cmd = compose_wgcna_cmd(rscript_path, generated_file)
+ # stream_cmd_output(request_data, cmd) disable streaming of data
+
try:
run_cmd_results = run_cmd(cmd)
@@ -40,8 +79,14 @@ def call_wgcna_script(rscript_path: str, request_data: dict):
if run_cmd_results["code"] != 0:
return run_cmd_results
+
+ output_file_data = json.load(outputfile)
+ output_file_data["output"]["image_data"] = process_image(
+ output_file_data["output"]["imageLoc"]).decode("ascii")
+ # json format only supports unicode string// to get image data reconvert
+
return {
- "data": json.load(outputfile),
+ "data": output_file_data,
**run_cmd_results
}
except FileNotFoundError:
diff --git a/gn3/data_helpers.py b/gn3/data_helpers.py
new file mode 100644
index 0000000..b72fbc5
--- /dev/null
+++ b/gn3/data_helpers.py
@@ -0,0 +1,52 @@
+"""
+This module will hold generic functions that can operate on a wide-array of
+data structures.
+"""
+
+from math import ceil
+from functools import reduce
+from typing import Any, Tuple, Sequence, Optional
+
+def partition_all(num: int, items: Sequence[Any]) -> Tuple[Tuple[Any, ...], ...]:
+ """
+ Given a sequence `items`, return a new sequence of the same type as `items`
+ with the data partitioned into sections of `n` items per partition.
+
+ This is an approximation of clojure's `partition-all` function.
+ """
+ def __compute_start_stop__(acc, iteration):
+ start = iteration * num
+ return acc + ((start, start + num),)
+
+ iterations = range(ceil(len(items) / num))
+ return tuple([# type: ignore[misc]
+ tuple(items[start:stop]) for start, stop # type: ignore[has-type]
+ in reduce(
+ __compute_start_stop__, iterations, tuple())])
+
+def partition_by(partition_fn, items):
+ """
+ Given a sequence `items`, return a tuple of tuples, each of which contain
+ the values in `items` partitioned such that the first item in each internal
+ tuple, when passed to `partition_function` returns True.
+
+ This is an approximation of Clojure's `partition-by` function.
+ """
+ def __partitioner__(accumulator, item):
+ if partition_fn(item):
+ return accumulator + ((item,),)
+ return accumulator[:-1] + (accumulator[-1] + (item,),)
+
+ return reduce(__partitioner__, items, tuple())
+
+def parse_csv_line(
+ line: str, delimiter: str = ",",
+ quoting: Optional[str] = '"') -> Tuple[str, ...]:
+ """
+ Parses a line from a CSV file into a tuple of strings.
+
+ This is a migration of the `web.webqtl.utility.webqtlUtil.readLineCSV`
+ function in GeneNetwork1.
+ """
+ return tuple(
+ col.strip("{} \t\n".format(quoting)) for col in line.split(delimiter))
diff --git a/gn3/db/correlations.py b/gn3/db/correlations.py
new file mode 100644
index 0000000..3d12019
--- /dev/null
+++ b/gn3/db/correlations.py
@@ -0,0 +1,564 @@
+"""
+This module will hold functions that are used in the (partial) correlations
+feature to access the database to retrieve data needed for computations.
+"""
+import os
+from functools import reduce
+from typing import Any, Dict, Tuple, Union
+
+from gn3.random import random_string
+from gn3.data_helpers import partition_all
+from gn3.db.species import translate_to_mouse_gene_id
+
+def get_filename(conn: Any, target_db_name: str, text_files_dir: str) -> Union[
+ str, bool]:
+ """
+ Retrieve the name of the reference database file with which correlations are
+ computed.
+
+ This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.getFileName` function in
+ GeneNetwork1.
+ """
+ with conn.cursor() as cursor:
+ cursor.execute(
+ "SELECT Id, FullName from ProbeSetFreeze WHERE Name=%s",
+ (target_db_name,))
+ result = cursor.fetchone()
+ if result:
+ filename = "ProbeSetFreezeId_{tid}_FullName_{fname}.txt".format(
+ tid=result[0],
+ fname=result[1].replace(' ', '_').replace('/', '_'))
+ return ((filename in os.listdir(text_files_dir))
+ and f"{text_files_dir}/{filename}")
+
+ return False
+
+def build_temporary_literature_table(
+ conn: Any, species: str, gene_id: int, return_number: int) -> str:
+ """
+ Build and populate a temporary table to hold the literature correlation data
+ to be used in computations.
+
+ "This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.getTempLiteratureTable` function in
+ GeneNetwork1.
+ """
+ def __translated_species_id(row, cursor):
+ if species == "mouse":
+ return row[1]
+ query = {
+ "rat": "SELECT rat FROM GeneIDXRef WHERE mouse=%s",
+ "human": "SELECT human FROM GeneIDXRef WHERE mouse=%d"}
+ if species in query.keys():
+ cursor.execute(query[species], row[1])
+ record = cursor.fetchone()
+ if record:
+ return record[0]
+ return None
+ return None
+
+ temp_table_name = f"TOPLITERATURE{random_string(8)}"
+ with conn.cursor as cursor:
+ mouse_geneid = translate_to_mouse_gene_id(species, gene_id, conn)
+ data_query = (
+ "SELECT GeneId1, GeneId2, value FROM LCorrRamin3 "
+ "WHERE GeneId1 = %(mouse_gene_id)s "
+ "UNION ALL "
+ "SELECT GeneId2, GeneId1, value FROM LCorrRamin3 "
+ "WHERE GeneId2 = %(mouse_gene_id)s "
+ "AND GeneId1 != %(mouse_gene_id)s")
+ cursor.execute(
+ (f"CREATE TEMPORARY TABLE {temp_table_name} ("
+ "GeneId1 int(12) unsigned, "
+ "GeneId2 int(12) unsigned PRIMARY KEY, "
+ "value double)"))
+ cursor.execute(data_query, mouse_gene_id=mouse_geneid)
+ literature_data = [
+ {"GeneId1": row[0], "GeneId2": row[1], "value": row[2]}
+ for row in cursor.fetchall()
+ if __translated_species_id(row, cursor)]
+
+ cursor.execute(
+ (f"INSERT INTO {temp_table_name} "
+ "VALUES (%(GeneId1)s, %(GeneId2)s, %(value)s)"),
+ literature_data[0:(2 * return_number)])
+
+ return temp_table_name
+
+def fetch_geno_literature_correlations(temp_table: str) -> str:
+ """
+ Helper function for `fetch_literature_correlations` below, to build query
+ for `Geno*` tables.
+ """
+ return (
+ f"SELECT Geno.Name, {temp_table}.value "
+ "FROM Geno, GenoXRef, GenoFreeze "
+ f"LEFT JOIN {temp_table} ON {temp_table}.GeneId2=ProbeSet.GeneId "
+ "WHERE ProbeSet.GeneId IS NOT NULL "
+ f"AND {temp_table}.value IS NOT NULL "
+ "AND GenoXRef.GenoFreezeId = GenoFreeze.Id "
+ "AND GenoFreeze.Name = %(db_name)s "
+ "AND Geno.Id=GenoXRef.GenoId "
+ "ORDER BY Geno.Id")
+
+def fetch_probeset_literature_correlations(temp_table: str) -> str:
+ """
+ Helper function for `fetch_literature_correlations` below, to build query
+ for `ProbeSet*` tables.
+ """
+ return (
+ f"SELECT ProbeSet.Name, {temp_table}.value "
+ "FROM ProbeSet, ProbeSetXRef, ProbeSetFreeze "
+ "LEFT JOIN {temp_table} ON {temp_table}.GeneId2=ProbeSet.GeneId "
+ "WHERE ProbeSet.GeneId IS NOT NULL "
+ "AND {temp_table}.value IS NOT NULL "
+ "AND ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id "
+ "AND ProbeSetFreeze.Name = %(db_name)s "
+ "AND ProbeSet.Id=ProbeSetXRef.ProbeSetId "
+ "ORDER BY ProbeSet.Id")
+
+def fetch_literature_correlations(
+ species: str, gene_id: int, dataset: dict, return_number: int,
+ conn: Any) -> dict:
+ """
+ Gather the literature correlation data and pair it with trait id string(s).
+
+ This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.fetchLitCorrelations` function in
+ GeneNetwork1.
+ """
+ temp_table = build_temporary_literature_table(
+ conn, species, gene_id, return_number)
+ query_fns = {
+ "Geno": fetch_geno_literature_correlations,
+ # "Temp": fetch_temp_literature_correlations,
+ # "Publish": fetch_publish_literature_correlations,
+ "ProbeSet": fetch_probeset_literature_correlations}
+ with conn.cursor as cursor:
+ cursor.execute(
+ query_fns[dataset["dataset_type"]](temp_table),
+ db_name=dataset["dataset_name"])
+ results = cursor.fetchall()
+ cursor.execute("DROP TEMPORARY TABLE %s", temp_table)
+ return dict(results)
+
+def fetch_symbol_value_pair_dict(
+ symbol_list: Tuple[str, ...], data_id_dict: dict,
+ conn: Any) -> Dict[str, Tuple[float, ...]]:
+ """
+ Map each gene symbols to the corresponding tissue expression data.
+
+ This is a migration of the
+ `web.webqtl.correlation.correlationFunction.getSymbolValuePairDict` function
+ in GeneNetwork1.
+ """
+ data_ids = {
+ symbol: data_id_dict.get(symbol) for symbol in symbol_list
+ if data_id_dict.get(symbol) is not None
+ }
+ query = "SELECT Id, value FROM TissueProbeSetData WHERE Id IN %(data_ids)s"
+ with conn.cursor() as cursor:
+ cursor.execute(
+ query,
+ data_ids=tuple(data_ids.values()))
+ value_results = cursor.fetchall()
+ return {
+ key: tuple(row[1] for row in value_results if row[0] == key)
+ for key in data_ids.keys()
+ }
+
+ return {}
+
+def fetch_gene_symbol_tissue_value_dict(
+ symbol_list: Tuple[str, ...], data_id_dict: dict, conn: Any,
+ limit_num: int = 1000) -> dict:#getGeneSymbolTissueValueDict
+ """
+ Wrapper function for `gn3.db.correlations.fetch_symbol_value_pair_dict`.
+
+ This is a migrations of the
+ `web.webqtl.correlation.correlationFunction.getGeneSymbolTissueValueDict` in
+ GeneNetwork1.
+ """
+ count = len(symbol_list)
+ if count != 0 and count <= limit_num:
+ return fetch_symbol_value_pair_dict(symbol_list, data_id_dict, conn)
+
+ if count > limit_num:
+ return {
+ key: value for dct in [
+ fetch_symbol_value_pair_dict(sl, data_id_dict, conn)
+ for sl in partition_all(limit_num, symbol_list)]
+ for key, value in dct.items()
+ }
+
+ return {}
+
+def fetch_tissue_probeset_xref_info(
+ gene_name_list: Tuple[str, ...], probeset_freeze_id: int,
+ conn: Any) -> Tuple[tuple, dict, dict, dict, dict, dict, dict]:
+ """
+ Retrieve the ProbeSet XRef information for tissues.
+
+ This is a migration of the
+ `web.webqtl.correlation.correlationFunction.getTissueProbeSetXRefInfo`
+ function in GeneNetwork1."""
+ with conn.cursor() as cursor:
+ if len(gene_name_list) == 0:
+ query = (
+ "SELECT t.Symbol, t.GeneId, t.DataId, t.Chr, t.Mb, "
+ "t.description, t.Probe_Target_Description "
+ "FROM "
+ "("
+ " SELECT Symbol, max(Mean) AS maxmean "
+ " FROM TissueProbeSetXRef "
+ " WHERE TissueProbeSetFreezeId=%(probeset_freeze_id)s "
+ " AND Symbol != '' "
+ " AND Symbol IS NOT NULL "
+ " GROUP BY Symbol"
+ ") AS x "
+ "INNER JOIN TissueProbeSetXRef AS t ON t.Symbol = x.Symbol "
+ "AND t.Mean = x.maxmean")
+ cursor.execute(query, probeset_freeze_id=probeset_freeze_id)
+ else:
+ query = (
+ "SELECT t.Symbol, t.GeneId, t.DataId, t.Chr, t.Mb, "
+ "t.description, t.Probe_Target_Description "
+ "FROM "
+ "("
+ " SELECT Symbol, max(Mean) AS maxmean "
+ " FROM TissueProbeSetXRef "
+ " WHERE TissueProbeSetFreezeId=%(probeset_freeze_id)s "
+ " AND Symbol in %(symbols)s "
+ " GROUP BY Symbol"
+ ") AS x "
+ "INNER JOIN TissueProbeSetXRef AS t ON t.Symbol = x.Symbol "
+ "AND t.Mean = x.maxmean")
+ cursor.execute(
+ query, probeset_freeze_id=probeset_freeze_id,
+ symbols=tuple(gene_name_list))
+
+ results = cursor.fetchall()
+
+ return reduce(
+ lambda acc, item: (
+ acc[0] + (item[0],),
+ {**acc[1], item[0].lower(): item[1]},
+ {**acc[1], item[0].lower(): item[2]},
+ {**acc[1], item[0].lower(): item[3]},
+ {**acc[1], item[0].lower(): item[4]},
+ {**acc[1], item[0].lower(): item[5]},
+ {**acc[1], item[0].lower(): item[6]}),
+ results or tuple(),
+ (tuple(), {}, {}, {}, {}, {}, {}))
+
+def fetch_gene_symbol_tissue_value_dict_for_trait(
+ gene_name_list: Tuple[str, ...], probeset_freeze_id: int,
+ conn: Any) -> dict:
+ """
+ Fetches a map of the gene symbols to the tissue values.
+
+ This is a migration of the
+ `web.webqtl.correlation.correlationFunction.getGeneSymbolTissueValueDictForTrait`
+ function in GeneNetwork1.
+ """
+ xref_info = fetch_tissue_probeset_xref_info(
+ gene_name_list, probeset_freeze_id, conn)
+ if xref_info[0]:
+ return fetch_gene_symbol_tissue_value_dict(xref_info[0], xref_info[2], conn)
+ return {}
+
+def build_temporary_tissue_correlations_table(
+ conn: Any, trait_symbol: str, probeset_freeze_id: int, method: str,
+ return_number: int) -> str:
+ """
+ Build a temporary table to hold the tissue correlations data.
+
+ This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.getTempTissueCorrTable` function in
+ GeneNetwork1."""
+ # 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 (#pylint: disable=[C0415, R0401]
+ correlations_of_all_tissue_traits)
+ # This import above is necessary within the function to avoid
+ # circular-imports.
+ #
+ #
+ # This import above is indicative of convoluted code, with the computation
+ # being interwoven with the data retrieval. This needs to be changed, such
+ # that the function being imported here is no longer necessary, or have the
+ # imported function passed to this function as an argument.
+ symbol_corr_dict, symbol_p_value_dict = correlations_of_all_tissue_traits(
+ fetch_gene_symbol_tissue_value_dict_for_trait(
+ (trait_symbol,), probeset_freeze_id, conn),
+ fetch_gene_symbol_tissue_value_dict_for_trait(
+ tuple(), probeset_freeze_id, conn),
+ method)
+
+ symbol_corr_list = sorted(
+ symbol_corr_dict.items(), key=lambda key_val: key_val[1])
+
+ temp_table_name = f"TOPTISSUE{random_string(8)}"
+ create_query = (
+ "CREATE TEMPORARY TABLE {temp_table_name}"
+ "(Symbol varchar(100) PRIMARY KEY, Correlation float, PValue float)")
+ insert_query = (
+ f"INSERT INTO {temp_table_name}(Symbol, Correlation, PValue) "
+ " VALUES (%(symbol)s, %(correlation)s, %(pvalue)s)")
+
+ with conn.cursor() as cursor:
+ cursor.execute(create_query)
+ cursor.execute(
+ insert_query,
+ tuple({
+ "symbol": symbol,
+ "correlation": corr,
+ "pvalue": symbol_p_value_dict[symbol]
+ } for symbol, corr in symbol_corr_list[0: 2 * return_number]))
+
+ return temp_table_name
+
+def fetch_tissue_correlations(# pylint: disable=R0913
+ dataset: dict, trait_symbol: str, probeset_freeze_id: int, method: str,
+ return_number: int, conn: Any) -> dict:
+ """
+ Pair tissue correlations data with a trait id string.
+
+ This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.fetchTissueCorrelations` function in
+ GeneNetwork1.
+ """
+ temp_table = build_temporary_tissue_correlations_table(
+ conn, trait_symbol, probeset_freeze_id, method, return_number)
+ with conn.cursor() as cursor:
+ cursor.execute(
+ (
+ f"SELECT ProbeSet.Name, {temp_table}.Correlation, "
+ f"{temp_table}.PValue "
+ "FROM (ProbeSet, ProbeSetXRef, ProbeSetFreeze) "
+ "LEFT JOIN {temp_table} ON {temp_table}.Symbol=ProbeSet.Symbol "
+ "WHERE ProbeSetFreeze.Name = %(db_name) "
+ "AND ProbeSetFreeze.Id=ProbeSetXRef.ProbeSetFreezeId "
+ "AND ProbeSet.Id = ProbeSetXRef.ProbeSetId "
+ "AND ProbeSet.Symbol IS NOT NULL "
+ "AND %s.Correlation IS NOT NULL"),
+ db_name=dataset["dataset_name"])
+ results = cursor.fetchall()
+ cursor.execute("DROP TEMPORARY TABLE %s", temp_table)
+ return {
+ trait_name: (tiss_corr, tiss_p_val)
+ for trait_name, tiss_corr, tiss_p_val in results}
+
+def check_for_literature_info(conn: Any, geneid: int) -> bool:
+ """
+ Checks the database to find out whether the trait with `geneid` has any
+ associated literature.
+
+ This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.checkForLitInfo` function in
+ GeneNetwork1.
+ """
+ query = "SELECT 1 FROM LCorrRamin3 WHERE GeneId1=%s LIMIT 1"
+ with conn.cursor() as cursor:
+ cursor.execute(query, geneid)
+ result = cursor.fetchone()
+ if result:
+ return True
+
+ return False
+
+def check_symbol_for_tissue_correlation(
+ conn: Any, tissue_probeset_freeze_id: int, symbol: str = "") -> bool:
+ """
+ Checks whether a symbol has any associated tissue correlations.
+
+ This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.checkSymbolForTissueCorr` function
+ in GeneNetwork1.
+ """
+ query = (
+ "SELECT 1 FROM TissueProbeSetXRef "
+ "WHERE TissueProbeSetFreezeId=%(probeset_freeze_id)s "
+ "AND Symbol=%(symbol)s LIMIT 1")
+ with conn.cursor() as cursor:
+ cursor.execute(
+ query, probeset_freeze_id=tissue_probeset_freeze_id, symbol=symbol)
+ result = cursor.fetchone()
+ if result:
+ return True
+
+ return False
+
+def fetch_sample_ids(
+ conn: Any, sample_names: Tuple[str, ...], species_name: str) -> Tuple[
+ int, ...]:
+ """
+ Given a sequence of sample names, and a species name, return the sample ids
+ that correspond to both.
+
+ This is a partial migration of the
+ `web.webqtl.correlation.CorrelationPage.fetchAllDatabaseData` function in
+ GeneNetwork1.
+ """
+ query = (
+ "SELECT Strain.Id FROM Strain, Species "
+ "WHERE Strain.Name IN %(samples_names)s "
+ "AND Strain.SpeciesId=Species.Id "
+ "AND Species.name=%(species_name)s")
+ with conn.cursor() as cursor:
+ cursor.execute(
+ query,
+ {
+ "samples_names": tuple(sample_names),
+ "species_name": species_name
+ })
+ return tuple(row[0] for row in cursor.fetchall())
+
+def build_query_sgo_lit_corr(
+ db_type: str, temp_table: str, sample_id_columns: str,
+ joins: Tuple[str, ...]) -> str:
+ """
+ Build query for `SGO Literature Correlation` data, when querying the given
+ `temp_table` temporary table.
+
+ This is a partial migration of the
+ `web.webqtl.correlation.CorrelationPage.fetchAllDatabaseData` function in
+ GeneNetwork1.
+ """
+ return (
+ (f"SELECT {db_type}.Name, {temp_table}.value, " +
+ sample_id_columns +
+ f" FROM ({db_type}, {db_type}XRef, {db_type}Freeze) " +
+ f"LEFT JOIN {temp_table} ON {temp_table}.GeneId2=ProbeSet.GeneId " +
+ " ".join(joins) +
+ " WHERE ProbeSet.GeneId IS NOT NULL " +
+ f"AND {temp_table}.value IS NOT NULL " +
+ f"AND {db_type}XRef.{db_type}FreezeId = {db_type}Freeze.Id " +
+ f"AND {db_type}Freeze.Name = %(db_name)s " +
+ f"AND {db_type}.Id = {db_type}XRef.{db_type}Id " +
+ f"ORDER BY {db_type}.Id"),
+ 2)
+
+def build_query_tissue_corr(db_type, temp_table, sample_id_columns, joins):
+ """
+ Build query for `Tissue Correlation` data, when querying the given
+ `temp_table` temporary table.
+
+ This is a partial migration of the
+ `web.webqtl.correlation.CorrelationPage.fetchAllDatabaseData` function in
+ GeneNetwork1.
+ """
+ return (
+ (f"SELECT {db_type}.Name, {temp_table}.Correlation, " +
+ f"{temp_table}.PValue, " +
+ sample_id_columns +
+ f" FROM ({db_type}, {db_type}XRef, {db_type}Freeze) " +
+ f"LEFT JOIN {temp_table} ON {temp_table}.Symbol=ProbeSet.Symbol " +
+ " ".join(joins) +
+ " WHERE ProbeSet.Symbol IS NOT NULL " +
+ f"AND {temp_table}.Correlation IS NOT NULL " +
+ f"AND {db_type}XRef.{db_type}FreezeId = {db_type}Freeze.Id " +
+ f"AND {db_type}Freeze.Name = %(db_name)s " +
+ f"AND {db_type}.Id = {db_type}XRef.{db_type}Id "
+ f"ORDER BY {db_type}.Id"),
+ 3)
+
+def fetch_all_database_data(# pylint: disable=[R0913, R0914]
+ conn: Any, species: str, gene_id: int, trait_symbol: str,
+ samples: Tuple[str, ...], dataset: dict, method: str,
+ return_number: int, probeset_freeze_id: int) -> Tuple[
+ Tuple[float], int]:
+ """
+ This is a migration of the
+ `web.webqtl.correlation.CorrelationPage.fetchAllDatabaseData` function in
+ GeneNetwork1.
+ """
+ db_type = dataset["dataset_type"]
+ db_name = dataset["dataset_name"]
+ def __build_query__(sample_ids, temp_table):
+ sample_id_columns = ", ".join(f"T{smpl}.value" for smpl in sample_ids)
+ if db_type == "Publish":
+ joins = tuple(
+ ("LEFT JOIN PublishData AS T{item} "
+ "ON T{item}.Id = PublishXRef.DataId "
+ "AND T{item}.StrainId = %(T{item}_sample_id)s")
+ for item in sample_ids)
+ return (
+ ("SELECT PublishXRef.Id, " +
+ sample_id_columns +
+ "FROM (PublishXRef, PublishFreeze) " +
+ " ".join(joins) +
+ " WHERE PublishXRef.InbredSetId = PublishFreeze.InbredSetId "
+ "AND PublishFreeze.Name = %(db_name)s"),
+ 1)
+ if temp_table is not None:
+ joins = tuple(
+ (f"LEFT JOIN {db_type}Data AS T{item} "
+ f"ON T{item}.Id = {db_type}XRef.DataId "
+ f"AND T{item}.StrainId=%(T{item}_sample_id)s")
+ for item in sample_ids)
+ if method.lower() == "sgo literature correlation":
+ return build_query_sgo_lit_corr(
+ sample_ids, temp_table, sample_id_columns, joins)
+ if method.lower() in (
+ "tissue correlation, pearson's r",
+ "tissue correlation, spearman's rho"):
+ return build_query_tissue_corr(
+ sample_ids, temp_table, sample_id_columns, joins)
+ joins = tuple(
+ (f"LEFT JOIN {db_type}Data AS T{item} "
+ f"ON T{item}.Id = {db_type}XRef.DataId "
+ f"AND T{item}.StrainId = %(T{item}_sample_id)s")
+ for item in sample_ids)
+ return (
+ (
+ f"SELECT {db_type}.Name, " +
+ sample_id_columns +
+ f" FROM ({db_type}, {db_type}XRef, {db_type}Freeze) " +
+ " ".join(joins) +
+ f" WHERE {db_type}XRef.{db_type}FreezeId = {db_type}Freeze.Id " +
+ f"AND {db_type}Freeze.Name = %(db_name)s " +
+ f"AND {db_type}.Id = {db_type}XRef.{db_type}Id " +
+ f"ORDER BY {db_type}.Id"),
+ 1)
+
+ def __fetch_data__(sample_ids, temp_table):
+ query, data_start_pos = __build_query__(sample_ids, temp_table)
+ with conn.cursor() as cursor:
+ cursor.execute(
+ query,
+ {"db_name": db_name,
+ **{f"T{item}_sample_id": item for item in sample_ids}})
+ return (cursor.fetchall(), data_start_pos)
+
+ sample_ids = tuple(
+ # look into graduating this to an argument and removing the `samples`
+ # and `species` argument: function currying and compositions might help
+ # with this
+ f"{sample_id}" for sample_id in
+ fetch_sample_ids(conn, samples, species))
+
+ temp_table = None
+ if gene_id and db_type == "probeset":
+ if method.lower() == "sgo literature correlation":
+ temp_table = build_temporary_literature_table(
+ conn, species, gene_id, return_number)
+ if method.lower() in (
+ "tissue correlation, pearson's r",
+ "tissue correlation, spearman's rho"):
+ temp_table = build_temporary_tissue_correlations_table(
+ conn, trait_symbol, probeset_freeze_id, method, return_number)
+
+ trait_database = tuple(
+ item for sublist in
+ (__fetch_data__(ssample_ids, temp_table)
+ for ssample_ids in partition_all(25, sample_ids))
+ for item in sublist)
+
+ if temp_table:
+ with conn.cursor() as cursor:
+ cursor.execute(f"DROP TEMPORARY TABLE {temp_table}")
+
+ return (trait_database[0], trait_database[1])
diff --git a/gn3/db/species.py b/gn3/db/species.py
index 0deae4e..5b8e096 100644
--- a/gn3/db/species.py
+++ b/gn3/db/species.py
@@ -30,3 +30,47 @@ def get_chromosome(name: str, is_species: bool, conn: Any) -> Optional[Tuple]:
with conn.cursor() as cursor:
cursor.execute(_sql)
return cursor.fetchall()
+
+def translate_to_mouse_gene_id(species: str, geneid: int, conn: Any) -> int:
+ """
+ Translate rat or human geneid to mouse geneid
+
+ This is a migration of the
+ `web.webqtl.correlation/CorrelationPage.translateToMouseGeneID` function in
+ GN1
+ """
+ assert species in ("rat", "mouse", "human"), "Invalid species"
+ if geneid is None:
+ return 0
+
+ if species == "mouse":
+ return geneid
+
+ with conn.cursor as cursor:
+ query = {
+ "rat": "SELECT mouse FROM GeneIDXRef WHERE rat = %s",
+ "human": "SELECT mouse FROM GeneIDXRef WHERE human = %s"
+ }
+ cursor.execute(query[species], geneid)
+ translated_gene_id = cursor.fetchone()
+ if translated_gene_id:
+ return translated_gene_id[0]
+
+ return 0 # default if all else fails
+
+def species_name(conn: Any, group: str) -> str:
+ """
+ Retrieve the name of the species, given the group (RISet).
+
+ This is a migration of the
+ `web.webqtl.dbFunction.webqtlDatabaseFunction.retrieveSpecies` function in
+ GeneNetwork1.
+ """
+ with conn.cursor() as cursor:
+ cursor.execute(
+ ("SELECT Species.Name FROM Species, InbredSet "
+ "WHERE InbredSet.Name = %(group_name)s "
+ "AND InbredSet.SpeciesId = Species.Id"),
+ {"group_name": group})
+ return cursor.fetchone()[0]
+ return None
diff --git a/gn3/db/traits.py b/gn3/db/traits.py
index f2673c8..4098b08 100644
--- a/gn3/db/traits.py
+++ b/gn3/db/traits.py
@@ -1,17 +1,93 @@
"""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
from gn3.db.datasets import retrieve_trait_dataset
+def export_trait_data(
+ trait_data: dict, samplelist: Sequence[str], dtype: str = "val",
+ var_exists: bool = False, n_exists: bool = False):
+ """
+ Export data according to `samplelist`. Mostly used in calculating
+ correlations.
+
+ DESCRIPTION:
+ Migrated from
+ https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/base/webqtlTrait.py#L166-L211
+
+ PARAMETERS
+ trait: (dict)
+ The dictionary of key-value pairs representing a trait
+ samplelist: (list)
+ A list of sample names
+ dtype: (str)
+ ... verify what this is ...
+ var_exists: (bool)
+ A flag indicating existence of variance
+ n_exists: (bool)
+ A flag indicating existence of ndata
+ """
+ def __export_all_types(tdata, sample):
+ sample_data = []
+ if tdata[sample]["value"]:
+ sample_data.append(tdata[sample]["value"])
+ if var_exists:
+ if tdata[sample]["variance"]:
+ sample_data.append(tdata[sample]["variance"])
+ else:
+ sample_data.append(None)
+ if n_exists:
+ if tdata[sample]["ndata"]:
+ sample_data.append(tdata[sample]["ndata"])
+ else:
+ sample_data.append(None)
+ else:
+ if var_exists and n_exists:
+ sample_data += [None, None, None]
+ elif var_exists or n_exists:
+ sample_data += [None, None]
+ else:
+ sample_data.append(None)
+
+ return tuple(sample_data)
+
+ def __exporter(accumulator, sample):
+ # pylint: disable=[R0911]
+ if sample in trait_data["data"]:
+ if dtype == "val":
+ return accumulator + (trait_data["data"][sample]["value"], )
+ if dtype == "var":
+ return accumulator + (trait_data["data"][sample]["variance"], )
+ if dtype == "N":
+ return accumulator + (trait_data["data"][sample]["ndata"], )
+ if dtype == "all":
+ return accumulator + __export_all_types(trait_data["data"], sample)
+ raise KeyError("Type `%s` is incorrect" % dtype)
+ if var_exists and n_exists:
+ return accumulator + (None, None, None)
+ if var_exists or n_exists:
+ return accumulator + (None, None)
+ return accumulator + (None,)
+
+ return reduce(__exporter, samplelist, tuple())
+
+
def get_trait_csv_sample_data(conn: Any,
trait_name: int, phenotype_id: int):
"""Fetch a trait and return it as a csv string"""
- sql = ("SELECT DISTINCT Strain.Id, PublishData.Id, Strain.Name, "
- "PublishData.value, "
+ 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) "
"LEFT JOIN PublishSE ON "
@@ -23,65 +99,189 @@ def get_trait_csv_sample_data(conn: Any,
"PublishData.Id = PublishXRef.DataId AND "
"PublishXRef.Id = %s AND PublishXRef.PhenotypeId = %s "
"AND PublishData.StrainId = Strain.Id Order BY Strain.Name")
- csv_data = ["Strain Id,Strain Name,Value,SE,Count"]
- publishdata_id = ""
+ csv_data = ["Strain Name,Value,SE,Count"]
with conn.cursor() as cursor:
cursor.execute(sql, (trait_name, phenotype_id,))
for record in cursor.fetchall():
- (strain_id, publishdata_id,
- strain_name, value, error, count) = record
+ (strain_name, value, error, count) = record
csv_data.append(
- ",".join([str(val) if val else "x"
- for val in (strain_id, strain_name,
- value, error, count)]))
- return f"# Publish Data Id: {publishdata_id}\n\n" + "\n".join(csv_data)
+ ",".join([__float_strip(val) if val else "x"
+ for val in (strain_name, value, error, count)]))
+ return "\n".join(csv_data)
+
+def update_sample_data(conn: Any, #pylint: disable=[R0913]
-def update_sample_data(conn: Any,
+ trait_name: str,
strain_name: str,
- strain_id: int,
- publish_data_id: int,
+ phenotype_id: int,
value: Union[int, float, str],
error: Union[int, float, str],
count: Union[int, str]):
"""Given the right parameters, update sample-data from the relevant
table."""
- # pylint: disable=[R0913, R0914, C0103]
- STRAIN_ID_SQL: str = "UPDATE Strain SET Name = %s WHERE Id = %s"
- PUBLISH_DATA_SQL: str = ("UPDATE PublishData SET value = %s "
- "WHERE StrainId = %s AND Id = %s")
- PUBLISH_SE_SQL: str = ("UPDATE PublishSE SET error = %s "
- "WHERE StrainId = %s AND DataId = %s")
- N_STRAIN_SQL: str = ("UPDATE NStrain SET count = %s "
- "WHERE StrainId = %s AND DataId = %s")
-
- updated_strains: int = 0
+ strain_id, data_id = "", ""
+
+ with conn.cursor() as cursor:
+ cursor.execute(
+ ("SELECT Strain.Id, PublishData.Id FROM "
+ "(PublishData, Strain, PublishXRef, PublishFreeze) "
+ "LEFT JOIN PublishSE ON "
+ "(PublishSE.DataId = PublishData.Id AND "
+ "PublishSE.StrainId = PublishData.StrainId) "
+ "LEFT JOIN NStrain ON "
+ "(NStrain.DataId = PublishData.Id AND "
+ "NStrain.StrainId = PublishData.StrainId) "
+ "WHERE PublishXRef.InbredSetId = "
+ "PublishFreeze.InbredSetId AND "
+ "PublishData.Id = PublishXRef.DataId AND "
+ "PublishXRef.Id = %s AND "
+ "PublishXRef.PhenotypeId = %s "
+ "AND PublishData.StrainId = Strain.Id "
+ "AND Strain.Name = \"%s\"") % (trait_name,
+ phenotype_id,
+ str(strain_name)))
+ strain_id, data_id = cursor.fetchone()
updated_published_data: int = 0
updated_se_data: int = 0
updated_n_strains: int = 0
with conn.cursor() as cursor:
- # Update the Strains table
- cursor.execute(STRAIN_ID_SQL, (strain_name, strain_id))
- updated_strains = cursor.rowcount
# Update the PublishData table
- cursor.execute(PUBLISH_DATA_SQL,
+ cursor.execute(("UPDATE PublishData SET value = %s "
+ "WHERE StrainId = %s AND Id = %s"),
(None if value == "x" else value,
- strain_id, publish_data_id))
+ strain_id, data_id))
updated_published_data = cursor.rowcount
+
# Update the PublishSE table
- cursor.execute(PUBLISH_SE_SQL,
+ cursor.execute(("UPDATE PublishSE SET error = %s "
+ "WHERE StrainId = %s AND DataId = %s"),
(None if error == "x" else error,
- strain_id, publish_data_id))
+ strain_id, data_id))
updated_se_data = cursor.rowcount
+
# Update the NStrain table
- cursor.execute(N_STRAIN_SQL,
+ cursor.execute(("UPDATE NStrain SET count = %s "
+ "WHERE StrainId = %s AND DataId = %s"),
(None if count == "x" else count,
- strain_id, publish_data_id))
+ strain_id, data_id))
updated_n_strains = cursor.rowcount
- return (updated_strains, updated_published_data,
+ return (updated_published_data,
updated_se_data, updated_n_strains)
+
+def delete_sample_data(conn: Any,
+ trait_name: str,
+ strain_name: str,
+ phenotype_id: int):
+ """Given the right parameters, delete sample-data from the relevant
+ table."""
+ strain_id, data_id = "", ""
+
+ deleted_published_data: int = 0
+ deleted_se_data: int = 0
+ deleted_n_strains: int = 0
+
+ with conn.cursor() as cursor:
+ # Delete the PublishData table
+ try:
+ cursor.execute(
+ ("SELECT Strain.Id, PublishData.Id FROM "
+ "(PublishData, Strain, PublishXRef, PublishFreeze) "
+ "LEFT JOIN PublishSE ON "
+ "(PublishSE.DataId = PublishData.Id AND "
+ "PublishSE.StrainId = PublishData.StrainId) "
+ "LEFT JOIN NStrain ON "
+ "(NStrain.DataId = PublishData.Id AND "
+ "NStrain.StrainId = PublishData.StrainId) "
+ "WHERE PublishXRef.InbredSetId = "
+ "PublishFreeze.InbredSetId AND "
+ "PublishData.Id = PublishXRef.DataId AND "
+ "PublishXRef.Id = %s AND "
+ "PublishXRef.PhenotypeId = %s "
+ "AND PublishData.StrainId = Strain.Id "
+ "AND Strain.Name = \"%s\"") % (trait_name,
+ phenotype_id,
+ str(strain_name)))
+ strain_id, data_id = cursor.fetchone()
+
+ cursor.execute(("DELETE FROM PublishData "
+ "WHERE StrainId = %s AND Id = %s")
+ % (strain_id, data_id))
+ deleted_published_data = cursor.rowcount
+
+ # Delete the PublishSE table
+ cursor.execute(("DELETE FROM PublishSE "
+ "WHERE StrainId = %s AND DataId = %s") %
+ (strain_id, data_id))
+ deleted_se_data = cursor.rowcount
+
+ # Delete the NStrain table
+ cursor.execute(("DELETE FROM NStrain "
+ "WHERE StrainId = %s AND DataId = %s" %
+ (strain_id, data_id)))
+ deleted_n_strains = cursor.rowcount
+ except Exception as e: #pylint: disable=[C0103, W0612]
+ conn.rollback()
+ raise MySQLdb.Error
+ conn.commit()
+ cursor.close()
+ cursor.close()
+
+ return (deleted_published_data,
+ deleted_se_data, deleted_n_strains)
+
+
+def insert_sample_data(conn: Any, #pylint: disable=[R0913]
+ trait_name: str,
+ strain_name: str,
+ phenotype_id: int,
+ value: Union[int, float, str],
+ error: Union[int, float, str],
+ count: Union[int, str]):
+ """Given the right parameters, insert sample-data to the relevant table.
+
+ """
+
+ inserted_published_data, inserted_se_data, inserted_n_strains = 0, 0, 0
+ with conn.cursor() as cursor:
+ try:
+ cursor.execute("SELECT DataId FROM PublishXRef WHERE Id = %s AND "
+ "PhenotypeId = %s", (trait_name, phenotype_id))
+ data_id = cursor.fetchone()
+
+ cursor.execute("SELECT Id FROM Strain WHERE Name = %s",
+ (strain_name,))
+ strain_id = cursor.fetchone()
+
+ # Insert the PublishData table
+ cursor.execute(("INSERT INTO PublishData (Id, StrainId, value)"
+ "VALUES (%s, %s, %s)"),
+ (data_id, strain_id, value))
+ inserted_published_data = cursor.rowcount
+
+ # Insert into the PublishSE table if error is specified
+ if error and error != "x":
+ cursor.execute(("INSERT INTO PublishSE (StrainId, DataId, "
+ " error) VALUES (%s, %s, %s)") %
+ (strain_id, data_id, error))
+ inserted_se_data = cursor.rowcount
+
+ # Insert into the NStrain table
+ if count and count != "x":
+ cursor.execute(("INSERT INTO NStrain "
+ "(StrainId, DataId, error) "
+ "VALUES (%s, %s, %s)") %
+ (strain_id, data_id, count))
+ inserted_n_strains = cursor.rowcount
+ except Exception as e: #pylint: disable=[C0103, W0612]
+ conn.rollback()
+ raise MySQLdb.Error
+ return (inserted_published_data,
+ inserted_se_data, inserted_n_strains)
+
+
def retrieve_publish_trait_info(trait_data_source: Dict[str, Any], conn: Any):
"""Retrieve trait information for type `Publish` traits.
@@ -121,11 +321,12 @@ def retrieve_publish_trait_info(trait_data_source: Dict[str, Any], conn: Any):
cursor.execute(
query,
{
- k:v for k, v in trait_data_source.items()
+ k: v for k, v in trait_data_source.items()
if k in ["trait_name", "trait_dataset_id"]
})
return dict(zip([k.lower() for k in keys], cursor.fetchone()))
+
def set_confidential_field(trait_type, trait_info):
"""Post processing function for 'Publish' trait types.
@@ -138,6 +339,7 @@ def set_confidential_field(trait_type, trait_info):
and not trait_info.get("pubmed_id", None)) else 0}
return trait_info
+
def retrieve_probeset_trait_info(trait_data_source: Dict[str, Any], conn: Any):
"""Retrieve trait information for type `ProbeSet` traits.
@@ -165,11 +367,12 @@ def retrieve_probeset_trait_info(trait_data_source: Dict[str, Any], conn: Any):
cursor.execute(
query,
{
- k:v for k, v in trait_data_source.items()
+ k: v for k, v in trait_data_source.items()
if k in ["trait_name", "trait_dataset_name"]
})
return dict(zip(keys, cursor.fetchone()))
+
def retrieve_geno_trait_info(trait_data_source: Dict[str, Any], conn: Any):
"""Retrieve trait information for type `Geno` traits.
@@ -189,11 +392,12 @@ def retrieve_geno_trait_info(trait_data_source: Dict[str, Any], conn: Any):
cursor.execute(
query,
{
- k:v for k, v in trait_data_source.items()
+ k: v for k, v in trait_data_source.items()
if k in ["trait_name", "trait_dataset_name"]
})
return dict(zip(keys, cursor.fetchone()))
+
def retrieve_temp_trait_info(trait_data_source: Dict[str, Any], conn: Any):
"""Retrieve trait information for type `Temp` traits.
@@ -206,11 +410,12 @@ def retrieve_temp_trait_info(trait_data_source: Dict[str, Any], conn: Any):
cursor.execute(
query,
{
- k:v for k, v in trait_data_source.items()
+ k: v for k, v in trait_data_source.items()
if k in ["trait_name"]
})
return dict(zip(keys, cursor.fetchone()))
+
def set_haveinfo_field(trait_info):
"""
Common postprocessing function for all trait types.
@@ -218,6 +423,7 @@ def set_haveinfo_field(trait_info):
Sets the value for the 'haveinfo' field."""
return {**trait_info, "haveinfo": 1 if trait_info else 0}
+
def set_homologene_id_field_probeset(trait_info, conn):
"""
Postprocessing function for 'ProbeSet' traits.
@@ -233,7 +439,7 @@ def set_homologene_id_field_probeset(trait_info, conn):
cursor.execute(
query,
{
- k:v for k, v in trait_info.items()
+ k: v for k, v in trait_info.items()
if k in ["geneid", "group"]
})
res = cursor.fetchone()
@@ -241,12 +447,13 @@ def set_homologene_id_field_probeset(trait_info, conn):
return {**trait_info, "homologeneid": res[0]}
return {**trait_info, "homologeneid": None}
+
def set_homologene_id_field(trait_type, trait_info, conn):
"""
Common postprocessing function for all trait types.
Sets the value for the 'homologene' key."""
- set_to_null = lambda ti: {**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,
@@ -255,6 +462,7 @@ def set_homologene_id_field(trait_type, trait_info, conn):
}
return functions_table[trait_type](trait_info)
+
def load_publish_qtl_info(trait_info, conn):
"""
Load extra QTL information for `Publish` traits
@@ -275,6 +483,7 @@ def load_publish_qtl_info(trait_info, conn):
return dict(zip(["locus", "lrs", "additive"], cursor.fetchone()))
return {"locus": "", "lrs": "", "additive": ""}
+
def load_probeset_qtl_info(trait_info, conn):
"""
Load extra QTL information for `ProbeSet` traits
@@ -297,6 +506,7 @@ def load_probeset_qtl_info(trait_info, conn):
["locus", "lrs", "pvalue", "mean", "additive"], cursor.fetchone()))
return {"locus": "", "lrs": "", "pvalue": "", "mean": "", "additive": ""}
+
def load_qtl_info(qtl, trait_type, trait_info, conn):
"""
Load extra QTL information for traits
@@ -325,6 +535,7 @@ def load_qtl_info(qtl, trait_type, trait_info, conn):
return qtl_info_functions[trait_type](trait_info, conn)
+
def build_trait_name(trait_fullname):
"""
Initialises the trait's name, and other values from the search data provided
@@ -351,6 +562,7 @@ def build_trait_name(trait_fullname):
"cellid": name_parts[2] if len(name_parts) == 3 else ""
}
+
def retrieve_probeset_sequence(trait, conn):
"""
Retrieve a 'ProbeSet' trait's sequence information
@@ -372,6 +584,7 @@ def retrieve_probeset_sequence(trait, conn):
seq = cursor.fetchone()
return {**trait, "sequence": seq[0] if seq else ""}
+
def retrieve_trait_info(
threshold: int, trait_full_name: str, conn: Any,
qtl=None):
@@ -427,6 +640,7 @@ def retrieve_trait_info(
}
return trait_info
+
def retrieve_temp_trait_data(trait_info: dict, conn: Any):
"""
Retrieve trait data for `Temp` traits.
@@ -445,10 +659,12 @@ 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))
+ ["sample_name", "value", "se_error", "nstrain", "id"],
+ row))
for row in cursor.fetchall()]
return []
+
def retrieve_species_id(group, conn: Any):
"""
Retrieve a species id given the Group value
@@ -460,6 +676,7 @@ def retrieve_species_id(group, conn: Any):
return cursor.fetchone()[0]
return None
+
def retrieve_geno_trait_data(trait_info: Dict, conn: Any):
"""
Retrieve trait data for `Geno` traits.
@@ -483,11 +700,14 @@ 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))
- for row in cursor.fetchall()]
+ return [
+ dict(zip(
+ ["sample_name", "value", "se_error", "id"],
+ row))
+ for row in cursor.fetchall()]
return []
+
def retrieve_publish_trait_data(trait_info: Dict, conn: Any):
"""
Retrieve trait data for `Publish` traits.
@@ -514,11 +734,13 @@ 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))
- for row in cursor.fetchall()]
+ return [
+ dict(zip(
+ ["sample_name", "value", "se_error", "nstrain", "id"], row))
+ for row in cursor.fetchall()]
return []
+
def retrieve_cellid_trait_data(trait_info: Dict, conn: Any):
"""
Retrieve trait data for `Probe Data` types.
@@ -547,11 +769,13 @@ 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))
- for row in cursor.fetchall()]
+ return [
+ dict(zip(
+ ["sample_name", "value", "se_error", "id"], row))
+ for row in cursor.fetchall()]
return []
+
def retrieve_probeset_trait_data(trait_info: Dict, conn: Any):
"""
Retrieve trait data for `ProbeSet` traits.
@@ -576,11 +800,13 @@ 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))
- for row in cursor.fetchall()]
+ return [
+ dict(zip(
+ ["sample_name", "value", "se_error", "id"], row))
+ for row in cursor.fetchall()]
return []
+
def with_samplelist_data_setup(samplelist: Sequence[str]):
"""
Build function that computes the trait data from provided list of samples.
@@ -607,6 +833,7 @@ def with_samplelist_data_setup(samplelist: Sequence[str]):
return None
return setup_fn
+
def without_samplelist_data_setup():
"""
Build function that computes the trait data.
@@ -627,6 +854,7 @@ def without_samplelist_data_setup():
return None
return setup_fn
+
def retrieve_trait_data(trait: dict, conn: Any, samplelist: Sequence[str] = tuple()):
"""
Retrieve trait data
@@ -666,11 +894,38 @@ def retrieve_trait_data(trait: dict, conn: Any, samplelist: Sequence[str] = tupl
"data": dict(map(
lambda x: (
x["sample_name"],
- {k:v for k, v in x.items() if x != "sample_name"}),
+ {k: v for k, v in x.items() if x != "sample_name"}),
data))}
return {}
+
def generate_traits_filename(base_path: str = TMPDIR):
"""Generate a unique filename for use with generated traits files."""
return "{}/traits_test_file_{}.txt".format(
os.path.abspath(base_path), random_string(10))
+
+
+def export_informative(trait_data: dict, inc_var: bool = False) -> tuple:
+ """
+ Export informative strain
+
+ This is a migration of the `exportInformative` function in
+ web/webqtl/base/webqtlTrait.py module in GeneNetwork1.
+
+ There is a chance that the original implementation has a bug, especially
+ dealing with the `inc_var` value. It the `inc_var` value is meant to control
+ the inclusion of the `variance` value, then the current implementation, and
+ that one in GN1 have a bug.
+ """
+ def __exporter__(acc, data_item):
+ if not inc_var or data_item["variance"] is not None:
+ return (
+ acc[0] + (data_item["sample_name"],),
+ acc[1] + (data_item["value"],),
+ acc[2] + (data_item["variance"],))
+ return acc
+ return reduce(
+ __exporter__,
+ filter(lambda td: td["value"] is not None,
+ trait_data["data"].values()),
+ (tuple(), tuple(), tuple()))
diff --git a/gn3/heatmaps.py b/gn3/heatmaps.py
index adbfbc6..f0af409 100644
--- a/gn3/heatmaps.py
+++ b/gn3/heatmaps.py
@@ -14,6 +14,7 @@ from plotly.subplots import make_subplots # type: ignore
from gn3.settings import TMPDIR
from gn3.random import random_string
from gn3.computations.slink import slink
+from gn3.db.traits import export_trait_data
from gn3.computations.correlations2 import compute_correlation
from gn3.db.genotypes import (
build_genotype_file, load_genotype_samples)
@@ -26,72 +27,6 @@ from gn3.computations.qtlreaper import (
parse_reaper_main_results,
organise_reaper_main_results)
-def export_trait_data(
- trait_data: dict, samplelist: Sequence[str], dtype: str = "val",
- var_exists: bool = False, n_exists: bool = False):
- """
- Export data according to `samplelist`. Mostly used in calculating
- correlations.
-
- DESCRIPTION:
- Migrated from
- https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/base/webqtlTrait.py#L166-L211
-
- PARAMETERS
- trait: (dict)
- The dictionary of key-value pairs representing a trait
- samplelist: (list)
- A list of sample names
- dtype: (str)
- ... verify what this is ...
- var_exists: (bool)
- A flag indicating existence of variance
- n_exists: (bool)
- A flag indicating existence of ndata
- """
- def __export_all_types(tdata, sample):
- sample_data = []
- if tdata[sample]["value"]:
- sample_data.append(tdata[sample]["value"])
- if var_exists:
- if tdata[sample]["variance"]:
- sample_data.append(tdata[sample]["variance"])
- else:
- sample_data.append(None)
- if n_exists:
- if tdata[sample]["ndata"]:
- sample_data.append(tdata[sample]["ndata"])
- else:
- sample_data.append(None)
- else:
- if var_exists and n_exists:
- sample_data += [None, None, None]
- elif var_exists or n_exists:
- sample_data += [None, None]
- else:
- sample_data.append(None)
-
- return tuple(sample_data)
-
- def __exporter(accumulator, sample):
- # pylint: disable=[R0911]
- if sample in trait_data["data"]:
- if dtype == "val":
- return accumulator + (trait_data["data"][sample]["value"], )
- if dtype == "var":
- return accumulator + (trait_data["data"][sample]["variance"], )
- if dtype == "N":
- return accumulator + (trait_data["data"][sample]["ndata"], )
- if dtype == "all":
- return accumulator + __export_all_types(trait_data["data"], sample)
- raise KeyError("Type `%s` is incorrect" % dtype)
- if var_exists and n_exists:
- return accumulator + (None, None, None)
- if var_exists or n_exists:
- return accumulator + (None, None)
- return accumulator + (None,)
-
- return reduce(__exporter, samplelist, tuple())
def trait_display_name(trait: Dict):
"""
@@ -129,11 +64,7 @@ def cluster_traits(traits_data_list: Sequence[Dict]):
def __compute_corr(tdata_i, tdata_j):
if tdata_i[0] == tdata_j[0]:
return 0.0
- corr_vals = compute_correlation(tdata_i[1], tdata_j[1])
- corr = corr_vals[0]
- if (1 - corr) < 0:
- return 0.0
- return 1 - corr
+ return 1 - compute_correlation(tdata_i[1], tdata_j[1])[0]
def __cluster(tdata_i):
return tuple(
@@ -168,7 +99,9 @@ def get_loci_names(
__get_trait_loci, [v[1] for v in organised.items()], {})
return tuple(loci_dict[_chr] for _chr in chromosome_names)
-def build_heatmap(traits_names, conn: Any):
+def build_heatmap(
+ traits_names: Sequence[str], conn: Any,
+ vertical: bool = False) -> go.Figure:
"""
heatmap function
@@ -220,17 +153,21 @@ def build_heatmap(traits_names, conn: Any):
zip(traits_ids,
[traits[idx]["trait_fullname"] for idx in traits_order]))
- return generate_clustered_heatmap(
+ return clustered_heatmap(
process_traits_data_for_heatmap(
organised, traits_ids, chromosome_names),
clustered,
- "single_heatmap_{}".format(random_string(10)),
- y_axis=tuple(
- ordered_traits_names[traits_ids[order]]
- for order in traits_order),
- y_label="Traits",
- x_axis=chromosome_names,
- x_label="Chromosomes",
+ x_axis={
+ "label": "Chromosomes",
+ "data": chromosome_names
+ },
+ y_axis={
+ "label": "Traits",
+ "data": tuple(
+ ordered_traits_names[traits_ids[order]]
+ for order in traits_order)
+ },
+ vertical=vertical,
loci_names=get_loci_names(organised, chromosome_names))
def compute_traits_order(slink_data, neworder: tuple = tuple()):
@@ -349,68 +286,81 @@ def process_traits_data_for_heatmap(data, trait_names, chromosome_names):
for chr_name in chromosome_names]
return hdata
-def generate_clustered_heatmap(
- data, clustering_data, image_filename_prefix, x_axis=None,
- x_label: str = "", y_axis=None, y_label: str = "",
+def clustered_heatmap(
+ data: Sequence[Sequence[float]], clustering_data: Sequence[float],
+ x_axis,#: Dict[Union[str, int], Union[str, Sequence[str]]],
+ y_axis: Dict[str, Union[str, Sequence[str]]],
loci_names: Sequence[Sequence[str]] = tuple(),
- output_dir: str = TMPDIR,
- colorscale=((0.0, '#0000FF'), (0.5, '#00FF00'), (1.0, '#FF0000'))):
+ vertical: bool = False,
+ colorscale: Sequence[Sequence[Union[float, str]]] = (
+ (0.0, '#0000FF'), (0.5, '#00FF00'), (1.0, '#FF0000'))) -> go.Figure:
"""
Generate a dendrogram, and heatmaps for each chromosome, and put them all
into one plot.
"""
# pylint: disable=[R0913, R0914]
- num_cols = 1 + len(x_axis)
+ x_axis_data = x_axis["data"]
+ y_axis_data = y_axis["data"]
+ num_plots = 1 + len(x_axis_data)
fig = make_subplots(
- rows=1,
- cols=num_cols,
- shared_yaxes="rows",
+ rows=num_plots if vertical else 1,
+ cols=1 if vertical else num_plots,
+ shared_xaxes="columns" if vertical else False,
+ shared_yaxes=False if vertical else "rows",
+ vertical_spacing=0.010,
horizontal_spacing=0.001,
- subplot_titles=["distance"] + x_axis,
+ subplot_titles=["" if vertical else x_axis["label"]] + [
+ "Chromosome: {}".format(chromo) if vertical else chromo
+ for chromo in x_axis_data],#+ x_axis_data,
figure=ff.create_dendrogram(
- np.array(clustering_data), orientation="right", labels=y_axis))
+ np.array(clustering_data),
+ orientation="bottom" if vertical else "right",
+ labels=y_axis_data))
hms = [go.Heatmap(
name=chromo,
- x=loci,
- y=y_axis,
+ x=y_axis_data if vertical else loci,
+ y=loci if vertical else y_axis_data,
z=data_array,
+ transpose=vertical,
showscale=False)
for chromo, data_array, loci
- in zip(x_axis, data, loci_names)]
+ in zip(x_axis_data, data, loci_names)]
for i, heatmap in enumerate(hms):
- fig.add_trace(heatmap, row=1, col=(i + 2))
-
- fig.update_layout(
- {
- "width": 1500,
- "height": 800,
- "xaxis": {
+ fig.add_trace(
+ heatmap,
+ row=((i + 2) if vertical else 1),
+ col=(1 if vertical else (i + 2)))
+
+ axes_layouts = {
+ "{axis}axis{count}".format(
+ axis=("y" if vertical else "x"),
+ count=(i+1 if i > 0 else "")): {
"mirror": False,
- "showgrid": True,
- "title": x_label
- },
- "yaxis": {
- "title": y_label
+ "showticklabels": i == 0,
+ "ticks": "outside" if i == 0 else ""
}
- })
+ for i in range(num_plots)}
- x_axes_layouts = {
- "xaxis{}".format(i+1 if i > 0 else ""): {
- "mirror": False,
- "showticklabels": i == 0,
- "ticks": "outside" if i == 0 else ""
- }
- for i in range(num_cols)}
+ print("vertical?: {} ==> {}".format("T" if vertical else "F", axes_layouts))
- fig.update_layout(
- {
- "width": 4000,
- "height": 800,
- "yaxis": {
- "mirror": False,
- "ticks": ""
- },
- **x_axes_layouts})
+ fig.update_layout({
+ "width": 800 if vertical else 4000,
+ "height": 4000 if vertical else 800,
+ "{}axis".format("x" if vertical else "y"): {
+ "mirror": False,
+ "ticks": "",
+ "side": "top" if vertical else "left",
+ "title": y_axis["label"],
+ "tickangle": 90 if vertical else 0,
+ "ticklabelposition": "outside top" if vertical else "outside left"
+ },
+ "{}axis".format("y" if vertical else "x"): {
+ "mirror": False,
+ "showgrid": True,
+ "title": "Distance",
+ "side": "right" if vertical else "top"
+ },
+ **axes_layouts})
fig.update_traces(
showlegend=False,
colorscale=colorscale,
@@ -418,7 +368,5 @@ def generate_clustered_heatmap(
fig.update_traces(
showlegend=True,
showscale=True,
- selector={"name": x_axis[-1]})
- image_filename = "{}/{}.html".format(output_dir, image_filename_prefix)
- fig.write_html(image_filename)
- return image_filename, fig
+ selector={"name": x_axis_data[-1]})
+ return fig
diff --git a/gn3/settings.py b/gn3/settings.py
index 150d96d..0ac6698 100644
--- a/gn3/settings.py
+++ b/gn3/settings.py
@@ -17,14 +17,10 @@ RQTL_WRAPPER = "rqtl_wrapper.R"
SQL_URI = os.environ.get(
"SQL_URI", "mysql://webqtlout:webqtlout@localhost/db_webqtl")
SECRET_KEY = "password"
-SQLALCHEMY_TRACK_MODIFICATIONS = False
# gn2 results only used in fetching dataset info
GN2_BASE_URL = "http://www.genenetwork.org/"
-# biweight script
-BIWEIGHT_RSCRIPT = "~/genenetwork3/scripts/calculate_biweight.R"
-
# wgcna script
WGCNA_RSCRIPT = "wgcna_analysis.R"
# qtlreaper command
@@ -35,13 +31,26 @@ GENOTYPE_FILES = os.environ.get(
"GENOTYPE_FILES", "{}/genotype_files/genotype".format(os.environ.get("HOME")))
# CROSS-ORIGIN SETUP
-CORS_ORIGINS = [
+def parse_env_cors(default):
+ """Parse comma-separated configuration into list of strings."""
+ origins_str = os.environ.get("CORS_ORIGINS", None)
+ if origins_str:
+ return [
+ origin.strip() for origin in origins_str.split(",") if origin != ""]
+ return default
+
+CORS_ORIGINS = parse_env_cors([
"http://localhost:*",
"http://127.0.0.1:*"
-]
+])
CORS_HEADERS = [
"Content-Type",
"Authorization",
"Access-Control-Allow-Credentials"
]
+
+GNSHARE = os.environ.get("GNSHARE", "/gnshare/gn/")
+TEXTDIR = f"{GNSHARE}/web/ProbeSetFreeze_DataMatrix"
+
+ROUND_TO = 10
diff --git a/guix.scm b/guix.scm
index 9b8f399..9bf23c8 100644
--- a/guix.scm
+++ b/guix.scm
@@ -17,72 +17,47 @@
;;; You should have received a copy of the GNU General Public License
;;; along with genenetwork3. If not, see https://www.gnu.org/licenses/.
-;; To use this file to build HEAD of gemma:
-;;
-;; env GUIX_PACKAGE_PATH=~/guix-bioinformatics/ guix build -f guix.scm
-;;
-;; After checking out the git repo
-;; cd ~ ; git clone https://git.genenetwork.org/guix-bioinformatics/guix-bioinformatics
+;; Make sure you have the
+;; https://git.genenetwork.org/guix-bioinformatics/guix-bioinformatics channel
+;; set up.
;;
;; To get a development container (e.g., run in emacs shell).
;;
-;; env GUIX_PACKAGE_PATH=~/guix-bioinformatics/ guix environment -C -l guix.scm
+;; guix shell -C -Df guix.scm
-(use-modules
- (srfi srfi-1)
- (srfi srfi-26)
- (ice-9 match)
- (ice-9 popen)
- (ice-9 rdelim)
- (gn packages gemma)
- (gn packages python)
- (gnu packages base)
- (gnu packages check)
- (gnu packages graph)
- (gnu packages cran)
- (gnu packages databases)
- (gnu packages statistics)
- (gnu packages bioconductor)
- (gnu packages golang)
- (gn packages genenetwork)
- (gnu packages python)
- (gnu packages python-check)
- (gnu packages python-crypto)
- (gnu packages python-web)
- (gnu packages python-xyz)
- (gnu packages python-science)
- ((guix build utils) #:select (with-directory-excursion))
- (guix build-system python)
- (guix gexp)
- (guix git-download)
- (guix licenses)
- (guix packages))
+(use-modules (gn packages gemma)
+ (gn packages python)
+ (gnu packages base)
+ (gnu packages check)
+ (gnu packages graph)
+ (gnu packages cran)
+ (gnu packages databases)
+ (gnu packages statistics)
+ (gnu packages bioconductor)
+ (gnu packages golang)
+ (gn packages genenetwork)
+ (gnu packages python)
+ (gnu packages python-check)
+ (gnu packages python-crypto)
+ (gnu packages python-web)
+ (gnu packages python-xyz)
+ (gnu packages python-science)
+ ((guix build utils) #:select (with-directory-excursion))
+ (guix build-system python)
+ (guix gexp)
+ (guix git-download)
+ (guix licenses)
+ (guix packages))
(define %source-dir (dirname (current-filename)))
-(define git-file?
- (let* ((pipe (with-directory-excursion %source-dir
- (open-pipe* OPEN_READ "git" "ls-files")))
- (files (let loop ((lines '()))
- (match (read-line pipe)
- ((? eof-object?)
- (reverse lines))
- (line
- (loop (cons line lines))))))
- (status (close-pipe pipe)))
- (lambda (file stat)
- (match (stat:type stat)
- ('directory #t)
- ((or 'regular 'symlink)
- (any (cut string-suffix? <> file) files))
- (_ #f)))))
(package
(name "genenetwork3.git")
- (version "0.0.1")
- (source (local-file %source-dir
+ (version "0.1.0")
+ (source (local-file %source-dir "genenetwork3-checkout"
#:recursive? #t
- #:select? git-file?))
+ #:select? (git-predicate %source-dir)))
(propagated-inputs `(("coreutils" ,coreutils)
("gemma-wrapper" ,gemma-wrapper)
("gunicorn" ,gunicorn)
@@ -90,27 +65,25 @@
("python-bcrypt" ,python-bcrypt)
("python-flask" ,python-flask)
("python-flask-cors" ,python-flask-cors)
+ ("python-flask-socketio" ,python-flask-socketio)
("python-ipfshttpclient" ,python-ipfshttpclient)
("python-mypy" ,python-mypy)
("python-mypy-extensions" ,python-mypy-extensions)
("python-mysqlclient" ,python-mysqlclient)
("python-numpy" ,python-numpy)
+ ("python-pandas" ,python-pandas)
+ ("python-pingouin" ,python-pingouin)
("python-plotly" ,python-plotly)
("python-pylint" ,python-pylint)
("python-redis" ,python-redis)
("python-requests" ,python-requests)
("python-scipy" ,python-scipy)
- ("python-sqlalchemy-stubs"
- ,python-sqlalchemy-stubs)
("r-optparse" ,r-optparse)
("r-qtl" ,r-qtl)
+ ("r-rjson" ,r-rjson)
("r-stringi" ,r-stringi)
("r-wgcna" ,r-wgcna)
- ("r-rjson" ,r-rjson)
- ("python-plotly" ,python-plotly)
- ("python-pandas" ,python-pandas)
- ("rust-qtlreaper" ,rust-qtlreaper)
- ("python-flask-cors" ,python-flask-cors)))
+ ("rust-qtlreaper" ,rust-qtlreaper)))
(build-system python-build-system)
(home-page "https://github.com/genenetwork/genenetwork3")
(synopsis "GeneNetwork3 API for data science and machine learning.")
diff --git a/mypy.ini b/mypy.ini
index 5d66812..b0c48df 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -11,3 +11,12 @@ ignore_missing_imports = True
[mypy-ipfshttpclient.*]
ignore_missing_imports = True
+
+[mypy-pingouin.*]
+ignore_missing_imports = True
+
+[mypy-redis.*]
+ignore_missing_imports = True
+
+[mypy-requests.*]
+ignore_missing_imports = True \ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index d332a96..e268674 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -35,3 +35,4 @@ Werkzeug==1.0.1
wrapt==1.12.1
plotly==4.14.3
flask-cors==3.0.9
+pingouin
diff --git a/scripts/calculate_biweight.R b/scripts/calculate_biweight.R
deleted file mode 100644
index 8d8366e..0000000
--- a/scripts/calculate_biweight.R
+++ /dev/null
@@ -1,43 +0,0 @@
-
-library(testthat)
-library(WGCNA)
-
-arg_values <- commandArgs(trailingOnly = TRUE)
-ParseArgs <- function(args){
-
- trait_vals <- as.numeric(unlist(strsplit(args[1], split=" ")))
- target_vals <- as.numeric(unlist(strsplit(args[2], split=" ")))
-
- return(list(trait_vals= c(trait_vals),target_vals = c(target_vals)))
-
-}
-BiweightMidCorrelation <- function(trait_val,target_val){
-
- results <-bicorAndPvalue(as.numeric(unlist(trait_val)),as.numeric(unlist(target_val)))
- return ((c(c(results$bicor)[1],c(results$p)[1])))
-
-}
-
-
-
-test_that("biweight results"),{
- vec_1 <- c(1,2,3,4)
- vec_2 <- c(1,2,3,4)
-
- results <- BiweightMidCorrelation(vec_1,vec_2)
- expect_equal(c(1.0,0.0),results)
-}
-
-
-test_that("parsing args "),{
- my_args <- c("1 2 3 4","5 6 7 8")
- results <- ParseArgs(my_args)
-
- expect_equal(results[1],c(1,2,3,4))
- expect_equal(results[2],c(5,6,7,8))
-}
-
-parsed_values <- ParseArgs(arg_values)
-
-
-cat(BiweightMidCorrelation(parsed_values[1],parsed_values[2])) \ No newline at end of file
diff --git a/scripts/wgcna_analysis.R b/scripts/wgcna_analysis.R
index 17b3537..b0d25a9 100644
--- a/scripts/wgcna_analysis.R
+++ b/scripts/wgcna_analysis.R
@@ -6,11 +6,13 @@ library(rjson)
options(stringsAsFactors = FALSE);
-imgDir = Sys.getenv("GENERATED_IMAGE_DIR")
+cat("Running the wgcna analysis script\n")
+
# load expression data **assumes from json files row(traits)(columns info+samples)
# pass the file_path as arg
# pass the file path to read json data
+
args = commandArgs(trailingOnly=TRUE)
if (length(args)==0) {
@@ -21,6 +23,7 @@ if (length(args)==0) {
}
inputData <- fromJSON(file = json_file_path)
+imgDir = inputData$TMPDIR
trait_sample_data <- do.call(rbind, inputData$trait_sample_data)
@@ -83,6 +86,11 @@ network <- blockwiseModules(dataExpr,
+cat("Generated network \n")
+
+network
+
+
genImageRandStr <- function(prefix){
randStr <- paste(prefix,stri_rand_strings(1, 9, pattern = "[A-Za-z0-9]"),sep="_")
@@ -90,14 +98,19 @@ genImageRandStr <- function(prefix){
return(paste(randStr,".png",sep=""))
}
+
mergedColors <- labels2colors(network$colors)
imageLoc <- file.path(imgDir,genImageRandStr("WGCNAoutput"))
png(imageLoc,width=1000,height=600,type='cairo-png')
+
+cat("Generating the CLuster dendrogram\n")
+
+
plotDendroAndColors(network$dendrograms[[1]],mergedColors[network$blockGenes[[1]]],
"Module colors",
-dendroLabels = FALSE, hang = 0.03,
+dendroLabels = NULL, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)
diff --git a/setup.py b/setup.py
index 98a076f..55b4be2 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,6 @@ setup(author='Bonface M. K.',
"redis==3.5.3"
"requests==2.25.1"
"scipy==1.6.0"
- "sqlalchemy-stubs==0.4"
"plotly==4.14.3"
"flask-cors==3.0.9"
],
diff --git a/sql/update/json_diff_update.sql b/sql/update/json_diff_update.sql
new file mode 100644
index 0000000..e18b285
--- /dev/null
+++ b/sql/update/json_diff_update.sql
@@ -0,0 +1,22 @@
+-- metadata_audit.sql ---
+
+-- Copyright (C) 2021 Bonface Munyoki K. <me@bonfacemunyoki.com>
+
+-- Author: Bonface Munyoki K. <me@bonfacemunyoki.com>
+
+-- This program is free software; you can redistribute it and/or
+-- modify it under the terms of the GNU General Public License
+-- as published by the Free Software Foundation; either version 3
+-- of the License, or (at your option) any later version.
+
+-- This program is distributed in the hope that it will be useful,
+-- but WITHOUT ANY WARRANTY; without even the implied warranty of
+-- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+-- GNU General Public License for more details.
+
+-- You should have received a copy of the GNU General Public License
+-- along with this program. If not, see <http://www.gnu.org/licenses/>.
+
+-- Update the json_diff_data column to TEXT
+ALTER TABLE metadata_audit
+MODIFY json_diff_data TEXT NOT NULL;
diff --git a/tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv b/tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv
new file mode 100644
index 0000000..a1558a0
--- /dev/null
+++ b/tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv
@@ -0,0 +1,101 @@
+"function_name","count","x","y","z","method","rm","result"
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diff --git a/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv b/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv
new file mode 100644
index 0000000..a1558a0
--- /dev/null
+++ b/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv
@@ -0,0 +1,101 @@
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new file mode 100644
index 0000000..6254902
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+------------------------------------------------------------
diff --git a/tests/unit/computations/test_biweight.py b/tests/unit/computations/test_biweight.py
deleted file mode 100644
index ad404f1..0000000
--- a/tests/unit/computations/test_biweight.py
+++ /dev/null
@@ -1,21 +0,0 @@
-"""test for biweight script"""
-from unittest import TestCase
-from unittest import mock
-
-from gn3.computations.biweight import calculate_biweight_corr
-
-
-class TestBiweight(TestCase):
- """test class for biweight"""
-
- @mock.patch("gn3.computations.biweight.subprocess.check_output")
- def test_calculate_biweight_corr(self, mock_check_output):
- """test for calculate_biweight_corr func"""
- mock_check_output.return_value = "0.1 0.5"
- results = calculate_biweight_corr(command="Rscript",
- path_to_script="./r_script.R",
- trait_vals=[
- 1.2, 1.1, 1.9],
- target_vals=[1.9, 0.4, 1.1])
-
- self.assertEqual(results, (0.1, 0.5))
diff --git a/tests/unit/computations/test_correlation.py b/tests/unit/computations/test_correlation.py
index fc52ec1..d60dd62 100644
--- a/tests/unit/computations/test_correlation.py
+++ b/tests/unit/computations/test_correlation.py
@@ -1,14 +1,17 @@
"""Module contains the tests for correlation"""
from unittest import TestCase
from unittest import mock
+import unittest
from collections import namedtuple
+import math
+from numpy.testing import assert_almost_equal
from gn3.computations.correlations import normalize_values
-from gn3.computations.correlations import do_bicor
from gn3.computations.correlations import compute_sample_r_correlation
from gn3.computations.correlations import compute_all_sample_correlation
from gn3.computations.correlations import filter_shared_sample_keys
+
from gn3.computations.correlations import tissue_correlation_for_trait
from gn3.computations.correlations import lit_correlation_for_trait
from gn3.computations.correlations import fetch_lit_correlation_data
@@ -94,20 +97,11 @@ class TestCorrelation(TestCase):
results = normalize_values([2.3, None, None, 3.2, 4.1, 5],
[3.4, 7.2, 1.3, None, 6.2, 4.1])
- expected_results = ([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3)
-
- self.assertEqual(results, expected_results)
-
- @mock.patch("gn3.computations.correlations.calculate_biweight_corr")
- def test_bicor(self, mock_biweight):
- """Test for doing biweight mid correlation """
- mock_biweight.return_value = (1.0, 0.0)
-
- results = do_bicor(x_val=[1, 2, 3], y_val=[4, 5, 6])
+ expected_results = [(2.3, 4.1, 5), (3.4, 6.2, 4.1)]
- self.assertEqual(results, (1.0, 0.0)
- )
+ self.assertEqual(list(zip(*list(results))), expected_results)
+ @unittest.skip("reason for skipping")
@mock.patch("gn3.computations.correlations.compute_corr_coeff_p_value")
@mock.patch("gn3.computations.correlations.normalize_values")
def test_compute_sample_r_correlation(self, norm_vals, compute_corr):
@@ -163,22 +157,23 @@ class TestCorrelation(TestCase):
}
- filtered_target_samplelist = ["1.23", "6.565", "6.456"]
- filtered_this_samplelist = ["6.266", "6.565", "6.456"]
+ filtered_target_samplelist = ("1.23", "6.565", "6.456")
+ filtered_this_samplelist = ("6.266", "6.565", "6.456")
results = filter_shared_sample_keys(
this_samplelist=this_samplelist, target_samplelist=target_samplelist)
- self.assertEqual(results, (filtered_this_samplelist,
- filtered_target_samplelist))
+ self.assertEqual(list(zip(*list(results))), [filtered_this_samplelist,
+ filtered_target_samplelist])
@mock.patch("gn3.computations.correlations.compute_sample_r_correlation")
@mock.patch("gn3.computations.correlations.filter_shared_sample_keys")
def test_compute_all_sample(self, filter_shared_samples, sample_r_corr):
"""Given target dataset compute all sample r correlation"""
- filter_shared_samples.return_value = (["1.23", "6.565", "6.456"], [
- "6.266", "6.565", "6.456"])
+ filter_shared_samples.return_value = [iter(val) for val in [(
+ "1.23", "6.266"), ("6.565", "6.565"), ("6.456", "6.456")]]
+
sample_r_corr.return_value = (["1419792_at", -1.0, 0.9, 6])
this_trait_data = {
@@ -210,10 +205,8 @@ class TestCorrelation(TestCase):
this_trait=this_trait_data, target_dataset=traits_dataset), sample_all_results)
sample_r_corr.assert_called_once_with(
trait_name='1419792_at',
- corr_method="pearson", trait_vals=['1.23', '6.565', '6.456'],
- target_samples_vals=['6.266', '6.565', '6.456'])
- filter_shared_samples.assert_called_once_with(
- this_trait_data.get("trait_sample_data"), traits_dataset[0].get("trait_sample_data"))
+ corr_method="pearson", trait_vals=('1.23', '6.565', '6.456'),
+ target_samples_vals=('6.266', '6.565', '6.456'))
@mock.patch("gn3.computations.correlations.compute_corr_coeff_p_value")
def test_tissue_correlation_for_trait(self, mock_compute_corr_coeff):
@@ -479,10 +472,10 @@ class TestCorrelation(TestCase):
[None, None, None, None, None, None, None, None, None, 0],
(0.0, 1)],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
- (0, 10)],
+ (math.nan, 10)],
[[9.87, 9.87, 9.87, 9.87, 9.87, 9.87, 9.87, 9.87, 9.87, 9.87],
[9.87, 9.87, 9.87, 9.87, 9.87, 9.87, 9.87, 9.87, 9.87, 9.87],
- (0.9999999999999998, 10)],
+ (math.nan, 10)],
[[9.3, 2.2, 5.4, 7.2, 6.4, 7.6, 3.8, 1.8, 8.4, 0.2],
[0.6, 3.97, 5.82, 8.21, 1.65, 4.55, 6.72, 9.5, 7.33, 2.34],
(-0.12720361919462056, 10)],
@@ -490,5 +483,8 @@ class TestCorrelation(TestCase):
[None, None, None, None, 2, None, None, 3, None, None],
(0.0, 2)]]:
with self.subTest(dbdata=dbdata, userdata=userdata):
- self.assertEqual(compute_correlation(
- dbdata, userdata), expected)
+ actual = compute_correlation(dbdata, userdata)
+ with self.subTest("correlation coefficient"):
+ assert_almost_equal(actual[0], expected[0])
+ with self.subTest("overlap"):
+ self.assertEqual(actual[1], expected[1])
diff --git a/tests/unit/computations/test_partial_correlations.py b/tests/unit/computations/test_partial_correlations.py
new file mode 100644
index 0000000..3690ca4
--- /dev/null
+++ b/tests/unit/computations/test_partial_correlations.py
@@ -0,0 +1,299 @@
+"""Module contains tests for gn3.partial_correlations"""
+
+from unittest import TestCase
+
+import pandas
+from numpy.testing import assert_allclose
+
+from gn3.computations.partial_correlations import (
+ fix_samples,
+ control_samples,
+ build_data_frame,
+ dictify_by_samples,
+ tissue_correlation,
+ find_identical_traits,
+ good_dataset_samples_indexes)
+
+sampleslist = ["B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"]
+control_traits = (
+ {
+ "mysqlid": 36688172,
+ "data": {
+ "B6cC3-1": {
+ "sample_name": "B6cC3-1", "value": 7.51879, "variance": None,
+ "ndata": None},
+ "BXD1": {
+ "sample_name": "BXD1", "value": 7.77141, "variance": None,
+ "ndata": None},
+ "BXD12": {
+ "sample_name": "BXD12", "value": 8.39265, "variance": None,
+ "ndata": None},
+ "BXD16": {
+ "sample_name": "BXD16", "value": 8.17443, "variance": None,
+ "ndata": None},
+ "BXD19": {
+ "sample_name": "BXD19", "value": 8.30401, "variance": None,
+ "ndata": None},
+ "BXD2": {
+ "sample_name": "BXD2", "value": 7.80944, "variance": None,
+ "ndata": None}}},
+ {
+ "mysqlid": 36688172,
+ "data": {
+ "B6cC3-21": {
+ "sample_name": "B6cC3-1", "value": 7.51879, "variance": None,
+ "ndata": None},
+ "BXD21": {
+ "sample_name": "BXD1", "value": 7.77141, "variance": None,
+ "ndata": None},
+ "BXD12": {
+ "sample_name": "BXD12", "value": 8.39265, "variance": None,
+ "ndata": None},
+ "BXD16": {
+ "sample_name": "BXD16", "value": 8.17443, "variance": None,
+ "ndata": None},
+ "BXD19": {
+ "sample_name": "BXD19", "value": 8.30401, "variance": None,
+ "ndata": None},
+ "BXD2": {
+ "sample_name": "BXD2", "value": 7.80944, "variance": None,
+ "ndata": None}}},
+ {
+ "mysqlid": 36688172,
+ "data": {
+ "B6cC3-1": {
+ "sample_name": "B6cC3-1", "value": 7.51879, "variance": None,
+ "ndata": None},
+ "BXD1": {
+ "sample_name": "BXD1", "value": 7.77141, "variance": None,
+ "ndata": None},
+ "BXD12": {
+ "sample_name": "BXD12", "value": None, "variance": None,
+ "ndata": None},
+ "BXD16": {
+ "sample_name": "BXD16", "value": None, "variance": None,
+ "ndata": None},
+ "BXD19": {
+ "sample_name": "BXD19", "value": None, "variance": None,
+ "ndata": None},
+ "BXD2": {
+ "sample_name": "BXD2", "value": 7.80944, "variance": None,
+ "ndata": None}}})
+
+dictified_control_samples = (
+ {"B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879, "variance": None},
+ "BXD1": {"sample_name": "BXD1", "value": 7.77141, "variance": None},
+ "BXD12": {"sample_name": "BXD12", "value": 8.39265, "variance": None},
+ "BXD16": {"sample_name": "BXD16", "value": 8.17443, "variance": None},
+ "BXD19": {"sample_name": "BXD19", "value": 8.30401, "variance": None},
+ "BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None}},
+ {"BXD12": {"sample_name": "BXD12", "value": 8.39265, "variance": None},
+ "BXD16": {"sample_name": "BXD16", "value": 8.17443, "variance": None},
+ "BXD19": {"sample_name": "BXD19", "value": 8.30401, "variance": None},
+ "BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None}},
+ {"B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879, "variance": None},
+ "BXD1": {"sample_name": "BXD1", "value": 7.77141, "variance": None},
+ "BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None}})
+
+class TestPartialCorrelations(TestCase):
+ """Class for testing partial correlations computation functions"""
+
+ def test_control_samples(self):
+ """Test that the control_samples works as expected."""
+ self.assertEqual(
+ control_samples(control_traits, sampleslist),
+ ((("B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"),
+ ("BXD12", "BXD16", "BXD19", "BXD2"),
+ ("B6cC3-1", "BXD1", "BXD2")),
+ ((7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944),
+ (8.39265, 8.17443, 8.30401, 7.80944),
+ (7.51879, 7.77141, 7.80944)),
+ ((None, None, None, None, None, None), (None, None, None, None),
+ (None, None, None)),
+ (6, 4, 3)))
+
+ def test_dictify_by_samples(self):
+ """
+ Test that `dictify_by_samples` generates the appropriate dict
+
+ Given:
+ a sequence of sequences with sample names, values and variances, as
+ in the output of `gn3.partial_correlations.control_samples` or
+ the output of `gn3.db.traits.export_informative`
+ When:
+ the sequence is passed as an argument into the
+ `gn3.partial_correlations.dictify_by_sample`
+ Then:
+ return a sequence of dicts with keys being the values of the sample
+ names, and each of who's values being sub-dicts with the keys
+ 'sample_name', 'value' and 'variance' whose values correspond to the
+ values passed in.
+ """
+ self.assertEqual(
+ dictify_by_samples(
+ ((("B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"),
+ ("BXD12", "BXD16", "BXD19", "BXD2"),
+ ("B6cC3-1", "BXD1", "BXD2")),
+ ((7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944),
+ (8.39265, 8.17443, 8.30401, 7.80944),
+ (7.51879, 7.77141, 7.80944)),
+ ((None, None, None, None, None, None), (None, None, None, None),
+ (None, None, None)),
+ (6, 4, 3))),
+ dictified_control_samples)
+
+ def test_fix_samples(self):
+ """
+ Test that `fix_samples` returns only the common samples
+
+ Given:
+ - A primary trait
+ - A sequence of control samples
+ When:
+ - The two arguments are passed to `fix_samples`
+ Then:
+ - Only the names of the samples present in the primary trait that
+ are also present in ALL the control traits are present in the
+ return value
+ - Only the values of the samples present in the primary trait that
+ are also present in ALL the control traits are present in the
+ return value
+ - ALL the values for ALL the control traits are present in the
+ return value
+ - Only the variances of the samples present in the primary trait
+ that are also present in ALL the control traits are present in the
+ return value
+ - ALL the variances for ALL the control traits are present in the
+ return value
+ - The return value is a tuple of the above items, in the following
+ order:
+ ((sample_names, ...), (primary_trait_values, ...),
+ (control_traits_values, ...), (primary_trait_variances, ...)
+ (control_traits_variances, ...))
+ """
+ self.assertEqual(
+ fix_samples(
+ {"B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879,
+ "variance": None},
+ "BXD1": {"sample_name": "BXD1", "value": 7.77141,
+ "variance": None},
+ "BXD2": {"sample_name": "BXD2", "value": 7.80944,
+ "variance": None}},
+ dictified_control_samples),
+ (("BXD2",), (7.80944,),
+ (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944, 8.39265,
+ 8.17443, 8.30401, 7.80944, 7.51879, 7.77141, 7.80944),
+ (None,),
+ (None, None, None, None, None, None, None, None, None, None, None,
+ None, None)))
+
+ def test_find_identical_traits(self):
+ """
+ Test `gn3.partial_correlations.find_identical_traits`.
+
+ Given:
+ - the name of a primary trait
+ - a sequence of values for the primary trait
+ - a sequence of names of control traits
+ - a sequence of values of control traits
+ When:
+ - the arguments above are passed to the `find_identical_traits`
+ function
+ Then:
+ - Return ALL trait names that have the same value when up to three
+ decimal places are considered
+ """
+ for primn, primv, contn, contv, expected in (
+ ("pt", (12.98395,), ("ct0", "ct1", "ct2"),
+ ((0.1234, 2.3456, 3.4567),), tuple()),
+ ("pt", (12.98395, 2.3456, 3.4567), ("ct0", "ct1", "ct2"),
+ ((12.98354, 2.3456, 3.4567), (64.2334, 6.3256, 64.2364),
+ (4.2374, 67.2345, 7.48234)), ("pt", "ct0")),
+ ("pt", (12.98395, 75.52382), ("ct0", "ct1", "ct2", "ct3"),
+ ((0.1234, 2.3456), (0.3621, 6543.572), (0.1234, 2.3456),
+ (0.1233, 4.5678)), ("ct0", "ct2"))
+ ):
+ with self.subTest(
+ primary_name=primn, primary_value=primv,
+ control_names=contn, control_values=contv):
+ self.assertEqual(
+ find_identical_traits(primn, primv, contn, contv), expected)
+
+ def test_tissue_correlation_error(self):
+ """
+ Test that `tissue_correlation` raises specific exceptions for particular
+ error conditions.
+ """
+ for primary, target, method, error, error_msg in (
+ ((1, 2, 3), (4, 5, 6, 7), "pearson",
+ AssertionError,
+ (
+ "The lengths of the `primary_trait_values` and "
+ "`target_trait_values` must be equal")),
+ ((1, 2, 3), (4, 5, 6, 7), "spearman",
+ AssertionError,
+ (
+ "The lengths of the `primary_trait_values` and "
+ "`target_trait_values` must be equal")),
+ ((1, 2, 3, 4), (5, 6, 7), "pearson",
+ AssertionError,
+ (
+ "The lengths of the `primary_trait_values` and "
+ "`target_trait_values` must be equal")),
+ ((1, 2, 3, 4), (5, 6, 7), "spearman",
+ AssertionError,
+ (
+ "The lengths of the `primary_trait_values` and "
+ "`target_trait_values` must be equal")),
+ ((1, 2, 3), (4, 5, 6), "nonexistentmethod",
+ AssertionError,
+ (
+ "Method must be one of: pearson, spearman"))):
+ with self.subTest(primary=primary, target=target, method=method):
+ with self.assertRaises(error, msg=error_msg):
+ tissue_correlation(primary, target, method)
+
+ def test_tissue_correlation(self): # pylint: disable=R0201
+ """
+ Test that the correct correlation values are computed for the given:
+ - primary trait
+ - target trait
+ - method
+ """
+ for primary, target, method, expected in (
+ ((12.34, 18.36, 42.51), (37.25, 46.25, 46.56), "pearson",
+ (0.6761779252651052, 0.5272701133657985)),
+ ((1, 2, 3, 4, 5), (5, 6, 7, 8, 7), "spearman",
+ (0.8207826816681233, 0.08858700531354381))):
+ with self.subTest(primary=primary, target=target, method=method):
+ assert_allclose(
+ tissue_correlation(primary, target, method), expected)
+
+ def test_good_dataset_samples_indexes(self):
+ """
+ Test that `good_dataset_samples_indexes` returns correct indices.
+ """
+ self.assertEqual(
+ good_dataset_samples_indexes(
+ ("a", "e", "i", "k"),
+ ("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l")),
+ (0, 4, 8, 10))
+
+ def test_build_data_frame(self):
+ """
+ Check that the function builds the correct data frame.
+ """
+ for xdata, ydata, zdata, expected in (
+ ((0.1, 1.1, 2.1), (2.1, 3.1, 4.1), (5.1, 6.1, 7.1),
+ pandas.DataFrame({
+ "x": (0.1, 1.1, 2.1), "y": (2.1, 3.1, 4.1),
+ "z": (5.1, 6.1, 7.1)})),
+ ((0.1, 1.1, 2.1), (2.1, 3.1, 4.1),
+ ((5.1, 6.1, 7.1), (5.2, 6.2, 7.2), (5.3, 6.3, 7.3)),
+ pandas.DataFrame({
+ "x": (0.1, 1.1, 2.1), "y": (2.1, 3.1, 4.1),
+ "z0": (5.1, 6.1, 7.1), "z1": (5.2, 6.2, 7.2),
+ "z2": (5.3, 6.3, 7.3)}))):
+ with self.subTest(xdata=xdata, ydata=ydata, zdata=zdata):
+ self.assertTrue(
+ build_data_frame(xdata, ydata, zdata).equals(expected))
diff --git a/tests/unit/computations/test_wgcna.py b/tests/unit/computations/test_wgcna.py
index ec81d94..5f23a86 100644
--- a/tests/unit/computations/test_wgcna.py
+++ b/tests/unit/computations/test_wgcna.py
@@ -10,13 +10,16 @@ from gn3.computations.wgcna import call_wgcna_script
class TestWgcna(TestCase):
"""test class for wgcna"""
+ @mock.patch("gn3.computations.wgcna.process_image")
@mock.patch("gn3.computations.wgcna.run_cmd")
@mock.patch("gn3.computations.wgcna.compose_wgcna_cmd")
@mock.patch("gn3.computations.wgcna.dump_wgcna_data")
def test_call_wgcna_script(self,
mock_dumping_data,
mock_compose_wgcna,
- mock_run_cmd):
+ mock_run_cmd,
+ mock_img,
+ ):
"""test for calling wgcna script"""
# pylint: disable = line-too-long
@@ -50,7 +53,7 @@ class TestWgcna(TestCase):
"output": "Flagging genes and samples with too many missing values...\n ..step 1\nAllowing parallel execution with up to 3 working processes.\npickSoftThreshold: will use block size 7.\n pickSoftThreshold: calculating connectivity for given powers...\n ..working on genes 1 through 7 of 7\n Flagging genes and samples with too many missing values...\n ..step 1\n ..Working on block 1 .\n TOM calculation: adjacency..\n ..will not use multithreading.\nclustering..\n ....detecting modules..\n ....calculating module eigengenes..\n ....checking kME in modules..\n ..merging modules that are too close..\n mergeCloseModules: Merging modules whose distance is less than 0.15\n mergeCloseModules: less than two proper modules.\n ..color levels are turquoise\n ..there is nothing to merge.\n Calculating new MEs...\n"
}
- json_output = "{\"inputdata\":{\"trait_sample_data \":{},\"minModuleSize\":30,\"TOMtype\":\"unsigned\"},\"outputdata\":{\"eigengenes\":[],\"colors\":[]}}"
+ json_output = "{\"inputdata\":{\"trait_sample_data \":{},\"minModuleSize\":30,\"TOMtype\":\"unsigned\"},\"output\":{\"eigengenes\":[],\"imageLoc\":[],\"colors\":[]}}"
expected_output = {
@@ -61,9 +64,11 @@ class TestWgcna(TestCase):
"TOMtype": "unsigned"
},
- "outputdata": {
+ "output": {
"eigengenes": [],
- "colors": []
+ "imageLoc": [],
+ "colors": [],
+ "image_data": "AFDSFNBSDGJJHH"
}
},
@@ -74,6 +79,7 @@ class TestWgcna(TestCase):
with mock.patch("builtins.open", mock.mock_open(read_data=json_output)):
mock_run_cmd.return_value = mock_run_cmd_results
+ mock_img.return_value = b"AFDSFNBSDGJJHH"
results = call_wgcna_script(
"Rscript/GUIX_PATH/scripts/r_file.R", request_data)
diff --git a/tests/unit/db/test_correlation.py b/tests/unit/db/test_correlation.py
new file mode 100644
index 0000000..3f940b2
--- /dev/null
+++ b/tests/unit/db/test_correlation.py
@@ -0,0 +1,96 @@
+"""
+Tests for the gn3.db.correlations module
+"""
+
+from unittest import TestCase
+
+from gn3.db.correlations import (
+ build_query_sgo_lit_corr,
+ build_query_tissue_corr)
+
+class TestCorrelation(TestCase):
+ """Test cases for correlation data fetching functions"""
+ maxDiff = None
+
+ def test_build_query_sgo_lit_corr(self):
+ """
+ Test that the literature correlation query is built correctly.
+ """
+ self.assertEqual(
+ build_query_sgo_lit_corr(
+ "Probeset",
+ "temp_table_xy45i7wd",
+ "T1.value, T2.value, T3.value",
+ (("LEFT JOIN ProbesetData AS T1 "
+ "ON T1.Id = ProbesetXRef.DataId "
+ "AND T1.StrainId=%(T1_sample_id)s"),
+ (
+ "LEFT JOIN ProbesetData AS T2 "
+ "ON T2.Id = ProbesetXRef.DataId "
+ "AND T2.StrainId=%(T2_sample_id)s"),
+ (
+ "LEFT JOIN ProbesetData AS T3 "
+ "ON T3.Id = ProbesetXRef.DataId "
+ "AND T3.StrainId=%(T3_sample_id)s"))),
+ (("SELECT Probeset.Name, temp_table_xy45i7wd.value, "
+ "T1.value, T2.value, T3.value "
+ "FROM (Probeset, ProbesetXRef, ProbesetFreeze) "
+ "LEFT JOIN temp_table_xy45i7wd ON temp_table_xy45i7wd.GeneId2=ProbeSet.GeneId "
+ "LEFT JOIN ProbesetData AS T1 "
+ "ON T1.Id = ProbesetXRef.DataId "
+ "AND T1.StrainId=%(T1_sample_id)s "
+ "LEFT JOIN ProbesetData AS T2 "
+ "ON T2.Id = ProbesetXRef.DataId "
+ "AND T2.StrainId=%(T2_sample_id)s "
+ "LEFT JOIN ProbesetData AS T3 "
+ "ON T3.Id = ProbesetXRef.DataId "
+ "AND T3.StrainId=%(T3_sample_id)s "
+ "WHERE ProbeSet.GeneId IS NOT NULL "
+ "AND temp_table_xy45i7wd.value IS NOT NULL "
+ "AND ProbesetXRef.ProbesetFreezeId = ProbesetFreeze.Id "
+ "AND ProbesetFreeze.Name = %(db_name)s "
+ "AND Probeset.Id = ProbesetXRef.ProbesetId "
+ "ORDER BY Probeset.Id"),
+ 2))
+
+ def test_build_query_tissue_corr(self):
+ """
+ Test that the tissue correlation query is built correctly.
+ """
+ self.assertEqual(
+ build_query_tissue_corr(
+ "Probeset",
+ "temp_table_xy45i7wd",
+ "T1.value, T2.value, T3.value",
+ (("LEFT JOIN ProbesetData AS T1 "
+ "ON T1.Id = ProbesetXRef.DataId "
+ "AND T1.StrainId=%(T1_sample_id)s"),
+ (
+ "LEFT JOIN ProbesetData AS T2 "
+ "ON T2.Id = ProbesetXRef.DataId "
+ "AND T2.StrainId=%(T2_sample_id)s"),
+ (
+ "LEFT JOIN ProbesetData AS T3 "
+ "ON T3.Id = ProbesetXRef.DataId "
+ "AND T3.StrainId=%(T3_sample_id)s"))),
+ (("SELECT Probeset.Name, temp_table_xy45i7wd.Correlation, "
+ "temp_table_xy45i7wd.PValue, "
+ "T1.value, T2.value, T3.value "
+ "FROM (Probeset, ProbesetXRef, ProbesetFreeze) "
+ "LEFT JOIN temp_table_xy45i7wd ON temp_table_xy45i7wd.Symbol=ProbeSet.Symbol "
+ "LEFT JOIN ProbesetData AS T1 "
+ "ON T1.Id = ProbesetXRef.DataId "
+ "AND T1.StrainId=%(T1_sample_id)s "
+ "LEFT JOIN ProbesetData AS T2 "
+ "ON T2.Id = ProbesetXRef.DataId "
+ "AND T2.StrainId=%(T2_sample_id)s "
+ "LEFT JOIN ProbesetData AS T3 "
+ "ON T3.Id = ProbesetXRef.DataId "
+ "AND T3.StrainId=%(T3_sample_id)s "
+ "WHERE ProbeSet.Symbol IS NOT NULL "
+ "AND temp_table_xy45i7wd.Correlation IS NOT NULL "
+ "AND ProbesetXRef.ProbesetFreezeId = ProbesetFreeze.Id "
+ "AND ProbesetFreeze.Name = %(db_name)s "
+ "AND Probeset.Id = ProbesetXRef.ProbesetId "
+ "ORDER BY Probeset.Id"),
+ 3))
diff --git a/tests/unit/db/test_traits.py b/tests/unit/db/test_traits.py
index 8af8e82..4aa9389 100644
--- a/tests/unit/db/test_traits.py
+++ b/tests/unit/db/test_traits.py
@@ -2,6 +2,8 @@
from unittest import mock, TestCase
from gn3.db.traits import (
build_trait_name,
+ export_trait_data,
+ export_informative,
set_haveinfo_field,
update_sample_data,
retrieve_trait_info,
@@ -12,6 +14,38 @@ from gn3.db.traits import (
retrieve_publish_trait_info,
retrieve_probeset_trait_info)
+samplelist = ["B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"]
+trait_data = {
+ "mysqlid": 36688172,
+ "data": {
+ "B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879, "variance": None, "ndata": None},
+ "BXD1": {"sample_name": "BXD1", "value": 7.77141, "variance": None, "ndata": None},
+ "BXD12": {"sample_name": "BXD12", "value": 8.39265, "variance": None, "ndata": None},
+ "BXD16": {"sample_name": "BXD16", "value": 8.17443, "variance": None, "ndata": None},
+ "BXD19": {"sample_name": "BXD19", "value": 8.30401, "variance": None, "ndata": None},
+ "BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None, "ndata": None},
+ "BXD21": {"sample_name": "BXD21", "value": 8.93809, "variance": None, "ndata": None},
+ "BXD24": {"sample_name": "BXD24", "value": 7.99415, "variance": None, "ndata": None},
+ "BXD27": {"sample_name": "BXD27", "value": 8.12177, "variance": None, "ndata": None},
+ "BXD28": {"sample_name": "BXD28", "value": 7.67688, "variance": None, "ndata": None},
+ "BXD32": {"sample_name": "BXD32", "value": 7.79062, "variance": None, "ndata": None},
+ "BXD39": {"sample_name": "BXD39", "value": 8.27641, "variance": None, "ndata": None},
+ "BXD40": {"sample_name": "BXD40", "value": 8.18012, "variance": None, "ndata": None},
+ "BXD42": {"sample_name": "BXD42", "value": 7.82433, "variance": None, "ndata": None},
+ "BXD6": {"sample_name": "BXD6", "value": 8.09718, "variance": None, "ndata": None},
+ "BXH14": {"sample_name": "BXH14", "value": 7.97475, "variance": None, "ndata": None},
+ "BXH19": {"sample_name": "BXH19", "value": 7.67223, "variance": None, "ndata": None},
+ "BXH2": {"sample_name": "BXH2", "value": 7.93622, "variance": None, "ndata": None},
+ "BXH22": {"sample_name": "BXH22", "value": 7.43692, "variance": None, "ndata": None},
+ "BXH4": {"sample_name": "BXH4", "value": 7.96336, "variance": None, "ndata": None},
+ "BXH6": {"sample_name": "BXH6", "value": 7.75132, "variance": None, "ndata": None},
+ "BXH7": {"sample_name": "BXH7", "value": 8.12927, "variance": None, "ndata": None},
+ "BXH8": {"sample_name": "BXH8", "value": 6.77338, "variance": None, "ndata": None},
+ "BXH9": {"sample_name": "BXH9", "value": 8.03836, "variance": None, "ndata": None},
+ "C3H/HeJ": {"sample_name": "C3H/HeJ", "value": 7.42795, "variance": None, "ndata": None},
+ "C57BL/6J": {"sample_name": "C57BL/6J", "value": 7.50606, "variance": None, "ndata": None},
+ "DBA/2J": {"sample_name": "DBA/2J", "value": 7.72588, "variance": None, "ndata": None}}}
+
class TestTraitsDBFunctions(TestCase):
"Test cases for traits functions"
@@ -226,3 +260,145 @@ class TestTraitsDBFunctions(TestCase):
with self.subTest(trait_info=trait_info, expected=expected):
self.assertEqual(
set_confidential_field(trait_type, trait_info), expected)
+
+ def test_export_trait_data_dtype(self):
+ """
+ Test `export_trait_data` with different values for the `dtype` keyword
+ argument
+ """
+ for dtype, expected in [
+ ["val", (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
+ ["var", (None, None, None, None, None, None)],
+ ["N", (None, None, None, None, None, None)],
+ ["all", (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)]]:
+ with self.subTest(dtype=dtype):
+ self.assertEqual(
+ export_trait_data(trait_data, samplelist, dtype=dtype),
+ expected)
+
+ def test_export_trait_data_dtype_all_flags(self):
+ """
+ Test `export_trait_data` with different values for the `dtype` keyword
+ argument and the different flags set up
+ """
+ for dtype, vflag, nflag, expected in [
+ ["val", False, False,
+ (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
+ ["val", False, True,
+ (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
+ ["val", True, False,
+ (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
+ ["val", True, True,
+ (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
+ ["var", False, False, (None, None, None, None, None, None)],
+ ["var", False, True, (None, None, None, None, None, None)],
+ ["var", True, False, (None, None, None, None, None, None)],
+ ["var", True, True, (None, None, None, None, None, None)],
+ ["N", False, False, (None, None, None, None, None, None)],
+ ["N", False, True, (None, None, None, None, None, None)],
+ ["N", True, False, (None, None, None, None, None, None)],
+ ["N", True, True, (None, None, None, None, None, None)],
+ ["all", False, False,
+ (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
+ ["all", False, True,
+ (7.51879, None, 7.77141, None, 8.39265, None, 8.17443, None,
+ 8.30401, None, 7.80944, None)],
+ ["all", True, False,
+ (7.51879, None, 7.77141, None, 8.39265, None, 8.17443, None,
+ 8.30401, None, 7.80944, None)],
+ ["all", True, True,
+ (7.51879, None, None, 7.77141, None, None, 8.39265, None, None,
+ 8.17443, None, None, 8.30401, None, None, 7.80944, None, None)]
+ ]:
+ with self.subTest(dtype=dtype, vflag=vflag, nflag=nflag):
+ self.assertEqual(
+ export_trait_data(
+ trait_data, samplelist, dtype=dtype, var_exists=vflag,
+ n_exists=nflag),
+ expected)
+
+ def test_export_informative(self):
+ """Test that the function exports appropriate data."""
+ # pylint: disable=W0621
+ for trait_data, inc_var, expected in [
+ [{"data": {
+ "sample1": {
+ "sample_name": "sample1", "value": 9, "variance": None,
+ "ndata": 13
+ },
+ "sample2": {
+ "sample_name": "sample2", "value": 8, "variance": None,
+ "ndata": 13
+ },
+ "sample3": {
+ "sample_name": "sample3", "value": 7, "variance": None,
+ "ndata": 13
+ },
+ "sample4": {
+ "sample_name": "sample4", "value": 6, "variance": None,
+ "ndata": 13
+ },
+ }}, 0, (
+ ("sample1", "sample2", "sample3", "sample4"), (9, 8, 7, 6),
+ (None, None, None, None))],
+ [{"data": {
+ "sample1": {
+ "sample_name": "sample1", "value": 9, "variance": None,
+ "ndata": 13
+ },
+ "sample2": {
+ "sample_name": "sample2", "value": 8, "variance": None,
+ "ndata": 13
+ },
+ "sample3": {
+ "sample_name": "sample3", "value": None, "variance": None,
+ "ndata": 13
+ },
+ "sample4": {
+ "sample_name": "sample4", "value": 6, "variance": None,
+ "ndata": 13
+ },
+ }}, 0, (
+ ("sample1", "sample2", "sample4"), (9, 8, 6),
+ (None, None, None))],
+ [{"data": {
+ "sample1": {
+ "sample_name": "sample1", "value": 9, "variance": None,
+ "ndata": 13
+ },
+ "sample2": {
+ "sample_name": "sample2", "value": 8, "variance": None,
+ "ndata": 13
+ },
+ "sample3": {
+ "sample_name": "sample3", "value": 7, "variance": None,
+ "ndata": 13
+ },
+ "sample4": {
+ "sample_name": "sample4", "value": 6, "variance": None,
+ "ndata": 13
+ },
+ }}, True, (tuple(), tuple(), tuple())],
+ [{"data": {
+ "sample1": {
+ "sample_name": "sample1", "value": 9, "variance": None,
+ "ndata": 13
+ },
+ "sample2": {
+ "sample_name": "sample2", "value": 8, "variance": 0.657,
+ "ndata": 13
+ },
+ "sample3": {
+ "sample_name": "sample3", "value": 7, "variance": None,
+ "ndata": 13
+ },
+ "sample4": {
+ "sample_name": "sample4", "value": 6, "variance": None,
+ "ndata": 13
+ },
+ }}, 0, (
+ ("sample1", "sample2", "sample3", "sample4"), (9, 8, 7, 6),
+ (None, 0.657, None, None))]]:
+ with self.subTest(trait_data=trait_data):
+ self.assertEqual(
+ export_informative(trait_data, inc_var), expected)
diff --git a/tests/unit/test_authentication.py b/tests/unit/test_authentication.py
new file mode 100644
index 0000000..061b684
--- /dev/null
+++ b/tests/unit/test_authentication.py
@@ -0,0 +1,101 @@
+"""Test cases for authentication.py"""
+import json
+import unittest
+
+from unittest import mock
+from gn3.authentication import AdminRole
+from gn3.authentication import DataRole
+from gn3.authentication import get_highest_user_access_role
+from gn3.authentication import get_user_membership
+
+
+class TestGetUserMembership(unittest.TestCase):
+ """Test cases for `get_user_membership`"""
+
+ def setUp(self):
+ conn = mock.MagicMock()
+ conn.hgetall.return_value = {
+ '7fa95d07-0e2d-4bc5-b47c-448fdc1260b2': (
+ '{"name": "editors", '
+ '"admins": ["8ad942fe-490d-453e-bd37-56f252e41604", "rand"], '
+ '"members": ["8ad942fe-490d-453e-bd37-56f252e41603", '
+ '"rand"], '
+ '"changed_timestamp": "Oct 06 2021 06:39PM", '
+ '"created_timestamp": "Oct 06 2021 06:39PM"}')}
+ self.conn = conn
+
+ def test_user_is_group_member_only(self):
+ """Test that a user is only a group member"""
+ self.assertEqual(
+ get_user_membership(
+ conn=self.conn,
+ user_id="8ad942fe-490d-453e-bd37-56f252e41603",
+ group_id="7fa95d07-0e2d-4bc5-b47c-448fdc1260b2"),
+ {"member": True,
+ "admin": False})
+
+ def test_user_is_group_admin_only(self):
+ """Test that a user is a group admin only"""
+ self.assertEqual(
+ get_user_membership(
+ conn=self.conn,
+ user_id="8ad942fe-490d-453e-bd37-56f252e41604",
+ group_id="7fa95d07-0e2d-4bc5-b47c-448fdc1260b2"),
+ {"member": False,
+ "admin": True})
+
+ def test_user_is_both_group_member_and_admin(self):
+ """Test that a user is both an admin and member of a group"""
+ self.assertEqual(
+ get_user_membership(
+ conn=self.conn,
+ user_id="rand",
+ group_id="7fa95d07-0e2d-4bc5-b47c-448fdc1260b2"),
+ {"member": True,
+ "admin": True})
+
+
+class TestCheckUserAccessRole(unittest.TestCase):
+ """Test cases for `get_highest_user_access_role`"""
+
+ @mock.patch("gn3.authentication.requests.get")
+ def test_edit_access(self, requests_mock):
+ """Test that the right access roles are set if the user has edit access"""
+ response = mock.PropertyMock(return_value=json.dumps(
+ {
+ 'data': ['no-access', 'view', 'edit', ],
+ 'metadata': ['no-access', 'view', 'edit', ],
+ 'admin': ['not-admin', 'edit-access', ],
+ }
+ ))
+ type(requests_mock.return_value).content = response
+ self.assertEqual(
+ get_highest_user_access_role(
+ resource_id="0196d92e1665091f202f",
+ user_id="8ad942fe-490d-453e-bd37"),
+ {
+ "data": DataRole.EDIT,
+ "metadata": DataRole.EDIT,
+ "admin": AdminRole.EDIT_ACCESS,
+ })
+
+ @mock.patch("gn3.authentication.requests.get")
+ def test_no_access(self, requests_mock):
+ """Test that the right access roles are set if the user has no access"""
+ response = mock.PropertyMock(return_value=json.dumps(
+ {
+ 'data': ['no-access', ],
+ 'metadata': ['no-access', ],
+ 'admin': ['not-admin', ],
+ }
+ ))
+ type(requests_mock.return_value).content = response
+ self.assertEqual(
+ get_highest_user_access_role(
+ resource_id="0196d92e1665091f202f",
+ user_id=""),
+ {
+ "data": DataRole.NO_ACCESS,
+ "metadata": DataRole.NO_ACCESS,
+ "admin": AdminRole.NOT_ADMIN,
+ })
diff --git a/tests/unit/test_data_helpers.py b/tests/unit/test_data_helpers.py
new file mode 100644
index 0000000..88ea469
--- /dev/null
+++ b/tests/unit/test_data_helpers.py
@@ -0,0 +1,89 @@
+"""
+Test functions in gn3.data_helpers
+"""
+
+from unittest import TestCase
+
+from gn3.data_helpers import partition_by, partition_all, parse_csv_line
+
+class TestDataHelpers(TestCase):
+ """
+ Test functions in gn3.data_helpers
+ """
+
+ def test_partition_all(self):
+ """
+ Test that `gn3.data_helpers.partition_all` partitions sequences as expected.
+
+ Given:
+ - `num`: The number of items per partition
+ - `items`: A sequence of items
+ When:
+ - The arguments above are passed to the `gn3.data_helpers.partition_all`
+ Then:
+ - Return a new sequence with partitions, each of which has `num`
+ items in the same order as those in `items`, save for the last
+ partition which might have fewer items than `num`.
+ """
+ for count, items, expected in (
+ (1, [0, 1, 2, 3], ((0,), (1,), (2,), (3,))),
+ (3, (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),
+ ((0, 1, 2), (3, 4, 5), (6, 7, 8), (9, ))),
+ (4, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
+ ((0, 1, 2, 3), (4, 5, 6, 7), (8, 9))),
+ (13, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
+ ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9), ))):
+ with self.subTest(n=count, items=items):
+ self.assertEqual(partition_all(count, items), expected)
+
+ def test_parse_csv_line(self):
+ """
+ Test parsing a single line from a CSV file
+
+ Given:
+ - `line`: a line read from a csv file
+ - `delimiter`: the expected delimiter in the csv file
+ - `quoting`: the quoting enclosing each column in the csv file
+ When:
+ - `line` is parsed with the `parse_csv_file` with the given
+ parameters
+ Then:
+ - return a tuple of the columns in the CSV file, without the
+ delimiter and quoting
+ """
+ for line, delimiter, quoting, expected in (
+ ('"this","is","a","test"', ",", '"', ("this", "is", "a", "test")),
+ ('"this","is","a","test"', ",", None, ('"this"', '"is"', '"a"', '"test"'))):
+ with self.subTest(line=line, delimiter=delimiter, quoting=quoting):
+ self.assertEqual(
+ parse_csv_line(
+ line=line, delimiter=delimiter, quoting=quoting),
+ expected)
+
+ def test_partition_by(self):
+ """
+ Test that `partition_by` groups the data using the given predicate
+
+ Given:
+ - `part_fn`: a predicate funtion that return boolean True/False
+ - `items`: a sequence of items
+ When:
+ - the partitioning predicate function and the sequence of items are
+ passed to the `partition_by` function
+ Then:
+ - the result is a tuple, with sub-tuples containing the data in the
+ original sequence. Each sub-tuple is a partition, ending as soon as
+ the next value in the sequence, when passed to `part_fn`, returns
+ boolean `True`.
+ """
+ for part_fn, items, expected in (
+ (lambda s: s.startswith("----"),
+ ("------", "a", "b", "-----", "c", "----", "d", "e", "---",
+ "f"),
+ (("------", "a", "b"), ("-----", "c"),
+ ("----", "d", "e", "---", "f"))),
+ (lambda x: (x % 2) == 0,
+ (0, 1, 3, 2, 4, 5, 7, 6, 9, 1),
+ ((0, 1, 3), (2,), (4, 5, 7), (6, 9, 1))),):
+ with self.subTest(items=items):
+ self.assertEqual(partition_by(part_fn, items), expected)
diff --git a/tests/unit/test_heatmaps.py b/tests/unit/test_heatmaps.py
index 7b66688..a88341b 100644
--- a/tests/unit/test_heatmaps.py
+++ b/tests/unit/test_heatmaps.py
@@ -1,46 +1,18 @@
"""Module contains tests for gn3.heatmaps.heatmaps"""
from unittest import TestCase
+
+from numpy.testing import assert_allclose
+
from gn3.heatmaps import (
cluster_traits,
get_loci_names,
get_lrs_from_chr,
- export_trait_data,
compute_traits_order,
retrieve_samples_and_values,
process_traits_data_for_heatmap)
from tests.unit.sample_test_data import organised_trait_1, organised_trait_2
samplelist = ["B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"]
-trait_data = {
- "mysqlid": 36688172,
- "data": {
- "B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879, "variance": None, "ndata": None},
- "BXD1": {"sample_name": "BXD1", "value": 7.77141, "variance": None, "ndata": None},
- "BXD12": {"sample_name": "BXD12", "value": 8.39265, "variance": None, "ndata": None},
- "BXD16": {"sample_name": "BXD16", "value": 8.17443, "variance": None, "ndata": None},
- "BXD19": {"sample_name": "BXD19", "value": 8.30401, "variance": None, "ndata": None},
- "BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None, "ndata": None},
- "BXD21": {"sample_name": "BXD21", "value": 8.93809, "variance": None, "ndata": None},
- "BXD24": {"sample_name": "BXD24", "value": 7.99415, "variance": None, "ndata": None},
- "BXD27": {"sample_name": "BXD27", "value": 8.12177, "variance": None, "ndata": None},
- "BXD28": {"sample_name": "BXD28", "value": 7.67688, "variance": None, "ndata": None},
- "BXD32": {"sample_name": "BXD32", "value": 7.79062, "variance": None, "ndata": None},
- "BXD39": {"sample_name": "BXD39", "value": 8.27641, "variance": None, "ndata": None},
- "BXD40": {"sample_name": "BXD40", "value": 8.18012, "variance": None, "ndata": None},
- "BXD42": {"sample_name": "BXD42", "value": 7.82433, "variance": None, "ndata": None},
- "BXD6": {"sample_name": "BXD6", "value": 8.09718, "variance": None, "ndata": None},
- "BXH14": {"sample_name": "BXH14", "value": 7.97475, "variance": None, "ndata": None},
- "BXH19": {"sample_name": "BXH19", "value": 7.67223, "variance": None, "ndata": None},
- "BXH2": {"sample_name": "BXH2", "value": 7.93622, "variance": None, "ndata": None},
- "BXH22": {"sample_name": "BXH22", "value": 7.43692, "variance": None, "ndata": None},
- "BXH4": {"sample_name": "BXH4", "value": 7.96336, "variance": None, "ndata": None},
- "BXH6": {"sample_name": "BXH6", "value": 7.75132, "variance": None, "ndata": None},
- "BXH7": {"sample_name": "BXH7", "value": 8.12927, "variance": None, "ndata": None},
- "BXH8": {"sample_name": "BXH8", "value": 6.77338, "variance": None, "ndata": None},
- "BXH9": {"sample_name": "BXH9", "value": 8.03836, "variance": None, "ndata": None},
- "C3H/HeJ": {"sample_name": "C3H/HeJ", "value": 7.42795, "variance": None, "ndata": None},
- "C57BL/6J": {"sample_name": "C57BL/6J", "value": 7.50606, "variance": None, "ndata": None},
- "DBA/2J": {"sample_name": "DBA/2J", "value": 7.72588, "variance": None, "ndata": None}}}
slinked = (
(((0, 2, 0.16381088984330505),
@@ -55,63 +27,7 @@ slinked = (
class TestHeatmap(TestCase):
"""Class for testing heatmap computation functions"""
- def test_export_trait_data_dtype(self):
- """
- Test `export_trait_data` with different values for the `dtype` keyword
- argument
- """
- for dtype, expected in [
- ["val", (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
- ["var", (None, None, None, None, None, None)],
- ["N", (None, None, None, None, None, None)],
- ["all", (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)]]:
- with self.subTest(dtype=dtype):
- self.assertEqual(
- export_trait_data(trait_data, samplelist, dtype=dtype),
- expected)
-
- def test_export_trait_data_dtype_all_flags(self):
- """
- Test `export_trait_data` with different values for the `dtype` keyword
- argument and the different flags set up
- """
- for dtype, vflag, nflag, expected in [
- ["val", False, False,
- (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
- ["val", False, True,
- (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
- ["val", True, False,
- (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
- ["val", True, True,
- (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
- ["var", False, False, (None, None, None, None, None, None)],
- ["var", False, True, (None, None, None, None, None, None)],
- ["var", True, False, (None, None, None, None, None, None)],
- ["var", True, True, (None, None, None, None, None, None)],
- ["N", False, False, (None, None, None, None, None, None)],
- ["N", False, True, (None, None, None, None, None, None)],
- ["N", True, False, (None, None, None, None, None, None)],
- ["N", True, True, (None, None, None, None, None, None)],
- ["all", False, False,
- (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944)],
- ["all", False, True,
- (7.51879, None, 7.77141, None, 8.39265, None, 8.17443, None,
- 8.30401, None, 7.80944, None)],
- ["all", True, False,
- (7.51879, None, 7.77141, None, 8.39265, None, 8.17443, None,
- 8.30401, None, 7.80944, None)],
- ["all", True, True,
- (7.51879, None, None, 7.77141, None, None, 8.39265, None, None,
- 8.17443, None, None, 8.30401, None, None, 7.80944, None, None)]
- ]:
- with self.subTest(dtype=dtype, vflag=vflag, nflag=nflag):
- self.assertEqual(
- export_trait_data(
- trait_data, samplelist, dtype=dtype, var_exists=vflag,
- n_exists=nflag),
- expected)
-
- def test_cluster_traits(self):
+ def test_cluster_traits(self): # pylint: disable=R0201
"""
Test that the clustering is working as expected.
"""
@@ -126,7 +42,7 @@ class TestHeatmap(TestCase):
(6.84118, 7.08432, 7.59844, 7.08229, 7.26774, 7.24991),
(9.45215, 10.6943, 8.64719, 10.1592, 7.75044, 8.78615),
(7.04737, 6.87185, 7.58586, 6.92456, 6.84243, 7.36913)]
- self.assertEqual(
+ assert_allclose(
cluster_traits(traits_data_list),
((0.0, 0.20337048635536847, 0.16381088984330505, 1.7388553629398245,
1.5025235756329178, 0.6952839500255574, 1.271661230252733,