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| author | zsloan | 2021-11-11 11:23:39 -0600 |
|---|---|---|
| committer | GitHub | 2021-11-11 11:23:39 -0600 |
| commit | 8c77af63efae6f06d7c7c3269fc0e41811a8037a (patch) | |
| tree | 9ffa4b84fd36f09e772db3e218bc980999324c41 | |
| parent | 607c6e627c23c1bce3b199b145855182ab51b211 (diff) | |
| parent | 249b85102063debfeeb1b0565956059b8a3af1cf (diff) | |
| download | genenetwork3-8c77af63efae6f06d7c7c3269fc0e41811a8037a.tar.gz | |
Merge branch 'main' into feature/add_rqtl_pairscan
31 files changed, 2204 insertions, 425 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? --> @@ -24,7 +24,7 @@ guix environment --load=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=$HOME/genotype_files/ --load=guix.scm +guix environment --expose=$HOME/genotype_files/ --load=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=$HOME/genotype_files/ --load=guix.scm +guix environment -C --network --expose=$HOME/genotype_files/ --load=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 @@ -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..6719631 --- /dev/null +++ b/gn3/authentication.py @@ -0,0 +1,162 @@ +"""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 uuid, group_info in conn.hgetall("groups").items(): + group_info = json.loads(group_info) + group_info["uuid"] = 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 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"] = 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 = [], + member_user_uids: List = []) -> Optional[Dict]: + """Create a group given the group name, members and admins of that group.""" + 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/partial_correlations.py b/gn3/computations/partial_correlations.py new file mode 100644 index 0000000..07dc16d --- /dev/null +++ b/gn3/computations/partial_correlations.py @@ -0,0 +1,289 @@ +""" +This module deals with partial correlations. + +It is an attempt to migrate over the partial correlations feature from +GeneNetwork1. +""" + +from functools import reduce +from typing import Any, Tuple, Sequence +from scipy.stats import pearsonr, spearmanr + +from gn3.settings import TEXTDIR +from gn3.data_helpers import parse_csv_line + +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 = "{:.3f}".format(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 (round(corr, 10), round(pvalue, 10)) + +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 determine_partials( + primary_vals, control_vals, all_target_trait_names, + all_target_trait_values, method): + """ + This **WILL** be a migration of + `web.webqtl.correlation.correlationFunction.determinePartialsByR` function + in GeneNetwork1. + + The function in GeneNetwork1 contains code written in R that is then used to + compute the partial correlations. + """ + ## This function is not implemented at this stage + return tuple( + primary_vals, control_vals, all_target_trait_names, + all_target_trait_values, method) + +def compute_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, ...]]: + """ + This is a partial migration of the + `web.webqtl.correlation.PartialCorrDBPage.getPartialCorrelationsFast` + function in GeneNetwork1. + """ + assert method in ("spearman", "pearson") + with open(f"{TEXTDIR}/{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 = determine_partials( + 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 partial_correlation_matrix( + xdata: Tuple[float, ...], ydata: Tuple[float, ...], + zdata: Tuple[float, ...], method: str = "pearsons", + omit_nones: bool = True) -> float: + """ + Computes the partial correlation coefficient using the + 'variance-covariance matrix' method + + This is a partial migration of the + `web.webqtl.correlation.correlationFunction.determinPartialsByR` function in + GeneNetwork1, specifically the `pcor.mat` function written in the R + programming language. + """ + return 0 + +def partial_correlation_recursive( + xdata: Tuple[float, ...], ydata: Tuple[float, ...], + zdata: Tuple[float, ...], method: str = "pearsons", + omit_nones: bool = True) -> float: + """ + Computes the partial correlation coefficient using the 'recursive formula' + method + + This is a partial migration of the + `web.webqtl.correlation.correlationFunction.determinPartialsByR` function in + GeneNetwork1, specifically the `pcor.rec` function written in the R + programming language. + """ + return 0 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..d3f942b --- /dev/null +++ b/gn3/data_helpers.py @@ -0,0 +1,37 @@ +""" +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 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..06b3310 --- /dev/null +++ b/gn3/db/correlations.py @@ -0,0 +1,381 @@ +""" +This module will hold functions that are used in the (partial) correlations +feature to access the database to retrieve data needed for computations. +""" + +from functools import reduce +from typing import Any, Dict, Tuple + +from gn3.random import random_string +from gn3.data_helpers import partition_all +from gn3.db.species import translate_to_mouse_gene_id + +from gn3.computations.partial_correlations import correlations_of_all_tissue_traits + +def get_filename(target_db_name: str, conn: Any) -> str: + """ + 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: + return "ProbeSetFreezeId_{tid}_FullName_{fname}.txt".format( + tid=result[0], + fname=result[1].replace(' ', '_').replace('/', '_')) + + return "" + +def build_temporary_literature_table( + species: str, gene_id: int, return_number: int, conn: Any) -> 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( + species, gene_id, return_number, conn) + 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( + trait_symbol: str, probeset_freeze_id: int, method: str, + return_number: int, conn: Any) -> 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. + 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( + trait_symbol, probeset_freeze_id, method, return_number, conn) + 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 diff --git a/gn3/db/species.py b/gn3/db/species.py index 0deae4e..702a9a8 100644 --- a/gn3/db/species.py +++ b/gn3/db/species.py @@ -30,3 +30,30 @@ 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 diff --git a/gn3/db/traits.py b/gn3/db/traits.py index f2673c8..1c6aaa7 100644 --- a/gn3/db/traits.py +++ b/gn3/db/traits.py @@ -1,12 +1,81 @@ """This class contains functions relating to trait data manipulation""" import os +from functools import reduce from typing import Any, Dict, Union, Sequence + 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""" @@ -674,3 +743,27 @@ 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..bf9dfd1 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): """ @@ -168,7 +103,9 @@ 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 +157,21 @@ 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 +290,81 @@ 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 +372,5 @@ 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..57c63df 100644 --- a/gn3/settings.py +++ b/gn3/settings.py @@ -22,9 +22,6 @@ SQLALCHEMY_TRACK_MODIFICATIONS = False 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 +32,24 @@ 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" @@ -28,61 +28,39 @@ ;; ;; env GUIX_PACKAGE_PATH=~/guix-bioinformatics/ guix environment -C -l 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) @@ -100,6 +78,7 @@ ("python-redis" ,python-redis) ("python-requests" ,python-requests) ("python-scipy" ,python-scipy) + ("python-flask-socketio" ,python-flask-socketio) ("python-sqlalchemy-stubs" ,python-sqlalchemy-stubs) ("r-optparse" ,r-optparse) @@ -109,8 +88,8 @@ ("r-rjson" ,r-rjson) ("python-plotly" ,python-plotly) ("python-pandas" ,python-pandas) - ("rust-qtlreaper" ,rust-qtlreaper) - ("python-flask-cors" ,python-flask-cors))) + ("python-pingouin" ,python-pingouin) + ("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.") @@ -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..54c04a6 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==0.3.12 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/rqtl_wrapper.R b/scripts/rqtl_wrapper.R index ffff5b9..eb660b5 100644 --- a/scripts/rqtl_wrapper.R +++ b/scripts/rqtl_wrapper.R @@ -9,6 +9,7 @@ option_list = list( make_option(c("-g", "--geno"), type="character", help=".geno file containing a dataset's genotypes"), make_option(c("-p", "--pheno"), type="character", help="File containing two columns - sample names and values"), make_option(c("-c", "--addcovar"), action="store_true", default=NULL, help="Use covariates (included as extra columns in the phenotype input file)"), + make_option(c("--covarstruct"), type="character", help="File detailing which covariates are categorical or numerical"), make_option(c("--model"), type="character", default="normal", help="Mapping Model - Normal or Non-Parametric"), make_option(c("--method"), type="character", default="hk", help="Mapping Method - hk (Haley Knott), ehk (Extended Haley Knott), mr (Marker Regression), em (Expectation-Maximization), imp (Imputation)"), make_option(c("--pairscan"), action="store_true", default=NULL, help="Run Pair Scan - the R/qtl function scantwo"), @@ -34,6 +35,20 @@ verbose_print <- function(...){ } } +adjustXprobs <- function(cross){ + sex <- getsex(cross)$sex + pr <- cross$geno[["X"]]$prob + stopifnot(!is.null(pr), !is.null(sex)) + + for(i in 1:ncol(pr)) { + pr[sex==0,i,3:4] <- 0 + pr[sex==1,i,1:2] <- 0 + pr[,i,] <- pr[,i,]/rowSums(pr[,i,]) + } + cross$geno[["X"]]$prob <- pr + invisible(cross) +} + if (is.null(opt$geno) || is.null(opt$pheno)){ print_help(opt_parser) stop("Both a genotype and phenotype file must be provided.", call.=FALSE) @@ -52,7 +67,7 @@ get_geno_code <- function(header, name = 'unk'){ return(trim(strsplit(header[mat],':')[[1]][2])) } -geno_to_csvr <- function(genotypes, trait_names, trait_vals, out, sex = NULL, +geno_to_csvr <- function(genotypes, trait_names, trait_vals, out, type, sex = NULL, mapping_scale = "Mb", verbose = FALSE){ # Assume a geno header is not longer than 40 lines header = readLines(genotypes, 40) @@ -149,8 +164,12 @@ for (i in 1:length(trait_names)) { trait_names[i] = paste("T_", this_trait, sep = "") } +# Get type of genotypes, since it needs to be checked before calc.genoprob +header = readLines(geno_file, 40) +type <- get_geno_code(header, 'type') + verbose_print('Generating cross object\n') -cross_object = geno_to_csvr(geno_file, trait_names, trait_vals, cross_file) +cross_object = geno_to_csvr(geno_file, trait_names, trait_vals, cross_file, type) # Calculate genotype probabilities if (!is.null(opt$interval)) { @@ -164,17 +183,41 @@ if (!is.null(opt$interval)) { cross_object <- calc.genoprob(cross_object) } +# If 4way, adjust X chromosome genotype probabilities +if (type == "4-way") { + verbose_print('Adjusting genotype probabilities for 4way cross') + cross_object <- adjustXprobs(cross_object) +} + # Pull covariates out of cross object, if they exist -covars = vector(mode = "list", length = length(trait_names) - 1) +covars <- c() # Holds the covariates which should be passed to R/qtl if (!is.null(opt$addcovar)) { verbose_print('Pulling covariates out of cross object\n') - #If perm strata are being used, it'll be included as the final column in the phenotype file + # If perm strata are being used, it'll be included as the final column in the phenotype file if (!is.null(opt$pstrata)) { - covar_names = trait_names[3:length(trait_names) - 1] + covar_names = trait_names[2:(length(trait_names)-1)] } else { covar_names = trait_names[2:length(trait_names)] } covars <- pull.pheno(cross_object, covar_names) + # Read in the covar description file + covarDescr <- read.table(opt$covarstruct, sep="\t", header=FALSE) + for(x in 1:nrow(covarDescr)){ + cat(covarDescr[x, 1]) + name <- paste0("T_", covarDescr[x, 1]) # The covar description file doesn't have T_ in trait names (the cross object does) + type <- covarDescr[x, 2] + if(type == "categorical"){ + if(length(table(covars[,name])) > 2){ # More then 2 levels create the model matrix for the factor + mdata <- data.frame(toExpand = as.factor(covars[, name])) + options(na.action='na.pass') + modelmatrix <- model.matrix(~ toExpand + 0, mdata)[,-1] + covars <- cbind(covars, modelmatrix) + }else{ # 2 levels? just bind the trait as covar + verbose_print('Binding covars to covars\n') + covars <- cbind(covars, covars[,name]) + } + } + } } # Pull permutation strata out of cross object, if it is being used @@ -250,5 +293,51 @@ if (!is.null(opt$pairscan)) { write.csv(qtl_results[1], out_file) write.csv(qtl_results[2], map_out_file) } else { + # QTL main effects on adjusted longevity + getEffects <- function(sdata, gtsprob, marker = "1_24042124", model = "longevity ~ sex + site + cohort + treatment", trait = "longevity"){ + rownames(sdata) <- 1:nrow(sdata) + rownames(gtsprob) <- 1:nrow(gtsprob) + mp <- gtsprob[, grep(marker, colnames(gtsprob))] + gts <- unlist(lapply(lapply(lapply(apply(mp,1,function(x){which(x > 0.85)}),names), strsplit, ":"), function(x){ + if(length(x) > 0){ return(x[[1]][2]); }else{ return(NA) } + })) + + ismissing <- which(apply(sdata, 1, function(x){any(is.na(x))})) + if(length(ismissing) > 0){ + sdata <- sdata[-ismissing, ] + gts <- gts[-ismissing] + } + + mlm <- lm(as.formula(model), data = sdata) + pheAdj <- rep(NA, nrow(sdata)) + adj <- residuals(mlm) + mean(sdata[, trait]) + pheAdj[as.numeric(names(adj))] <- adj + means <- c(mean(pheAdj[which(gts == "AC")],na.rm=TRUE),mean(pheAdj[which(gts == "AD")],na.rm=TRUE),mean(pheAdj[which(gts == "BC")],na.rm=TRUE),mean(pheAdj[which(gts == "BD")],na.rm=TRUE)) + std <- function(x) sd(x,na.rm=TRUE)/sqrt(length(x)) + stderrs <- c(std(pheAdj[which(gts == "AC")]),std(pheAdj[which(gts == "AD")]),std(pheAdj[which(gts == "BC")]),std(pheAdj[which(gts == "BD")])) + paste0(round(means,0), " ± ", round(stderrs,2)) + } + + if (type == "4-way") { + verbose_print("Get phenotype name + genoprob + all phenotypes + models for 4-way crosses") + traitname <- colnames(pull.pheno(cross_object))[1] + gtsp <- pull.genoprob(cross_object) + allpheno <- pull.pheno(cross_object) + if (!is.null(opt$addcovar)) { + model <- paste0(traitname, " ~ ", paste0(covar_names, sep="", collapse=" + ")) + } else { + model <- paste0(traitname, " ~ 1 ") + } + + meffects <- c() + verbose_print("Getting QTL main effects for 4-way crosses") + for(marker in rownames(qtl_results)){ + meff <- getEffects(allpheno, gtsp, marker = marker, model, trait = traitname) + meffects <- rbind(meffects, meff) + } + qtl_results <- cbind(data.frame(qtl_results[,1:3]), meffects) + colnames(qtl_results)[4:7] <- c("AC", "AD", "BC", "BD") + } + write.csv(qtl_results, out_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/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" +"pcor.mat",13,"-89.1427156049758, -70.2603165991604, 52.4590492714196, -57.2846714872867, -26.3361851219088, -0.000567594543099403, 17.9622953757644, -64.1319836024195, 39.2497098539025, 20.8931459579617, 74.1897551808506, -35.3126015048474, -55.9552798978984","47.3745858296752, -68.9209029078484, -31.9765134248883, -45.0486478861421, -85.1716816890985, 65.432888455689, 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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..706520a 100644 --- a/tests/unit/computations/test_correlation.py +++ b/tests/unit/computations/test_correlation.py @@ -1,14 +1,15 @@ """Module contains the tests for correlation""" from unittest import TestCase from unittest import mock +import unittest from collections import namedtuple 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 +95,11 @@ 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 +155,23 @@ 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 +203,8 @@ 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): diff --git a/tests/unit/computations/test_partial_correlations.py b/tests/unit/computations/test_partial_correlations.py new file mode 100644 index 0000000..83cb9d9 --- /dev/null +++ b/tests/unit/computations/test_partial_correlations.py @@ -0,0 +1,335 @@ +"""Module contains tests for gn3.partial_correlations""" + +import csv +from unittest import TestCase, skip +from gn3.computations.partial_correlations import ( + fix_samples, + control_samples, + dictify_by_samples, + tissue_correlation, + find_identical_traits, + partial_correlation_matrix, + good_dataset_samples_indexes, + partial_correlation_recursive) + +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}}) + +def parse_test_data_csv(filename): + """ + Parse test data csv files for R -> Python conversion of some functions. + """ + def __str__to_tuple(line, field): + return tuple(float(s.strip()) for s in line[field].split(",")) + + with open(filename, newline="\n") as csvfile: + reader = csv.DictReader(csvfile, delimiter=",", quotechar='"') + lines = tuple(row for row in reader) + + methods = {"p": "pearson", "s": "spearman", "k": "kendall"} + return tuple({ + **line, + "x": __str__to_tuple(line, "x"), + "y": __str__to_tuple(line, "y"), + "z": __str__to_tuple(line, "z"), + "method": methods[line["method"]], + "rm": line["rm"] == "TRUE", + "result": float(line["result"]) + } for line in lines) + + +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 + - the value of a 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, ("ct0", "ct1", "ct2"), + (12.98354, 2.3456, 3.4567), ("pt", "ct0")), + ("pt", 12.98395, ("ct0", "ct1", "ct2", "ct3"), + (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): + """ + 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", + (0.6761779253, 0.5272701134)), + ((1, 2, 3, 4, 5), (5, 6, 7, 8, 7), "spearman", + (0.8207826817, 0.0885870053))): + with self.subTest(primary=primary, target=target, method=method): + self.assertEqual( + 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)) + + @skip + def test_partial_correlation_matrix(self): + """ + Test that `partial_correlation_matrix` computes the appropriate + correlation value. + """ + for sample in parse_test_data_csv( + ("tests/unit/computations/partial_correlations_test_data/" + "pcor_mat_blackbox_test.csv")): + with self.subTest( + xdata=sample["x"], ydata=sample["y"], zdata=sample["z"], + method=sample["method"], omit_nones=sample["rm"]): + self.assertEqual( + partial_correlation_matrix( + sample["x"], sample["y"], sample["z"], + method=sample["method"], omit_nones=sample["rm"]), + sample["result"]) + + @skip + def test_partial_correlation_recursive(self): + """ + Test that `partial_correlation_recursive` computes the appropriate + correlation value. + """ + for sample in parse_test_data_csv( + ("tests/unit/computations/partial_correlations_test_data/" + "pcor_rec_blackbox_test.csv")): + with self.subTest( + xdata=sample["x"], ydata=sample["y"], zdata=sample["z"], + method=sample["method"], omit_nones=sample["rm"]): + self.assertEqual( + partial_correlation_recursive( + sample["x"], sample["y"], sample["z"], + method=sample["method"], omit_nones=sample["rm"]), + sample["result"]) 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_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..39aea45 --- /dev/null +++ b/tests/unit/test_data_helpers.py @@ -0,0 +1,61 @@ +""" +Test functions in gn3.data_helpers +""" + +from unittest import TestCase + +from gn3.data_helpers import 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) diff --git a/tests/unit/test_heatmaps.py b/tests/unit/test_heatmaps.py index 7b66688..03fd4a6 100644 --- a/tests/unit/test_heatmaps.py +++ b/tests/unit/test_heatmaps.py @@ -4,43 +4,12 @@ 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), @@ -55,62 +24,6 @@ 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): """ Test that the clustering is working as expected. |