Mercurial > repos > bimib > marea_2_0
changeset 22:b0436f014861 draft
Uploaded
| author | bimib |
|---|---|
| date | Thu, 23 May 2024 16:50:05 +0000 |
| parents | ac405d3fb28e |
| children | 0f02bfde697c |
| files | marea_2_0/marea.py |
| diffstat | 1 files changed, 906 insertions(+), 904 deletions(-) [+] |
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--- a/marea_2_0/marea.py Thu May 23 15:10:01 2024 +0000 +++ b/marea_2_0/marea.py Thu May 23 16:50:05 2024 +0000 @@ -1,905 +1,907 @@ -from __future__ import division -import csv -from enum import Enum -import re -import sys -import numpy as np -import pandas as pd -import itertools as it -import scipy.stats as st -import lxml.etree as ET -import math -import os -import argparse -import pyvips -import utils.general_utils as utils -from PIL import Image -from typing import Tuple, Union, Optional, List, Dict - -ERRORS = [] -########################## argparse ########################################## -ARGS :argparse.Namespace -def process_args() -> argparse.Namespace: - """ - Interfaces the script of a module with its frontend, making the user's choices for various parameters available as values in code. - - Args: - args : Always obtained (in file) from sys.argv - - Returns: - Namespace : An object containing the parsed arguments - """ - parser = argparse.ArgumentParser( - usage = "%(prog)s [options]", - description = "process some value's genes to create a comparison's map.") - - #General: - parser.add_argument( - '-td', '--tool_dir', - type = str, - required = True, - help = 'your tool directory') - - parser.add_argument('-on', '--control', type = str) - parser.add_argument('-ol', '--out_log', help = "Output log") - - #Computation details: - parser.add_argument( - '-co', '--comparison', - type = str, - default = '1vs1', - choices = ['manyvsmany', 'onevsrest', 'onevsmany']) - - parser.add_argument( - '-pv' ,'--pValue', - type = float, - default = 0.1, - help = 'P-Value threshold (default: %(default)s)') - - parser.add_argument( - '-fc', '--fChange', - type = float, - default = 1.5, - help = 'Fold-Change threshold (default: %(default)s)') - - parser.add_argument( - "-ne", "--net", - type = utils.Bool("net"), default = False, - help = "choose if you want net enrichment for RPS") - - parser.add_argument( - '-op', '--option', - type = str, - choices = ['datasets', 'dataset_class'], - help='dataset or dataset and class') - - #RAS: - parser.add_argument( - "-ra", "--using_RAS", - type = utils.Bool("using_RAS"), default = True, - help = "choose whether to use RAS datasets.") - - parser.add_argument( - '-id', '--input_data', - type = str, - help = 'input dataset') - - parser.add_argument( - '-ic', '--input_class', - type = str, - help = 'sample group specification') - - parser.add_argument( - '-ids', '--input_datas', - type = str, - nargs = '+', - help = 'input datasets') - - parser.add_argument( - '-na', '--names', - type = str, - nargs = '+', - help = 'input names') - - #RPS: - parser.add_argument( - "-rp", "--using_RPS", - type = utils.Bool("using_RPS"), default = False, - help = "choose whether to use RPS datasets.") - - parser.add_argument( - '-idr', '--input_data_rps', - type = str, - help = 'input dataset rps') - - parser.add_argument( - '-icr', '--input_class_rps', - type = str, - help = 'sample group specification rps') - - parser.add_argument( - '-idsr', '--input_datas_rps', - type = str, - nargs = '+', - help = 'input datasets rps') - - parser.add_argument( - '-nar', '--names_rps', - type = str, - nargs = '+', - help = 'input names rps') - - #Output: - parser.add_argument( - "-gs", "--generate_svg", - type = utils.Bool("generate_svg"), default = True, - help = "choose whether to use RAS datasets.") - - parser.add_argument( - "-gp", "--generate_pdf", - type = utils.Bool("generate_pdf"), default = True, - help = "choose whether to use RAS datasets.") - - parser.add_argument( - '-cm', '--custom_map', - type = str, - help='custom map to use') - - parser.add_argument( - '-mc', '--choice_map', - type = utils.Model, default = utils.Model.HMRcore, - choices = [utils.Model.HMRcore, utils.Model.ENGRO2, utils.Model.Custom]) - - args :argparse.Namespace = parser.parse_args() - if args.using_RAS and not args.using_RPS: args.net = False - - return args - -############################ dataset input #################################### -def read_dataset(data :str, name :str) -> pd.DataFrame: - """ - Tries to read the dataset from its path (data) as a tsv and turns it into a DataFrame. - - Args: - data : filepath of a dataset (from frontend input params or literals upon calling) - name : name associated with the dataset (from frontend input params or literals upon calling) - - Returns: - pd.DataFrame : dataset in a runtime operable shape - - Raises: - sys.exit : if there's no data (pd.errors.EmptyDataError) or if the dataset has less than 2 columns - """ - try: - dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python') - except pd.errors.EmptyDataError: - sys.exit('Execution aborted: wrong format of ' + name + '\n') - if len(dataset.columns) < 2: - sys.exit('Execution aborted: wrong format of ' + name + '\n') - return dataset - -############################ dataset name ##################################### -def name_dataset(name_data :str, count :int) -> str: - """ - Produces a unique name for a dataset based on what was provided by the user. The default name for any dataset is "Dataset", thus if the user didn't change it this function appends f"_{count}" to make it unique. - - Args: - name_data : name associated with the dataset (from frontend input params) - count : counter from 1 to make these names unique (external) - - Returns: - str : the name made unique - """ - if str(name_data) == 'Dataset': - return str(name_data) + '_' + str(count) - else: - return str(name_data) - -############################ map_methods ###################################### -FoldChange = Union[float, int, str] # Union[float, Literal[0, "-INF", "INF"]] -def fold_change(avg1 :float, avg2 :float) -> FoldChange: - """ - Calculates the fold change between two gene expression values. - - Args: - avg1 : average expression value from one dataset avg2 : average expression value from the other dataset - - Returns: - FoldChange : - 0 : when both input values are 0 - "-INF" : when avg1 is 0 - "INF" : when avg2 is 0 - float : for any other combination of values - """ - if avg1 == 0 and avg2 == 0: - return 0 - elif avg1 == 0: - return '-INF' - elif avg2 == 0: - return 'INF' - else: - return math.log(avg1 / avg2, 2) - -def fix_style(l :str, col :Optional[str], width :str, dash :str) -> str: - """ - Produces a "fixed" style string to assign to a reaction arrow in the SVG map, assigning style properties to the corresponding values passed as input params. - - Args: - l : current style string of an SVG element - col : new value for the "stroke" style property - width : new value for the "stroke-width" style property - dash : new value for the "stroke-dasharray" style property - - Returns: - str : the fixed style string - """ - tmp = l.split(';') - flag_col = False - flag_width = False - flag_dash = False - for i in range(len(tmp)): - if tmp[i].startswith('stroke:'): - tmp[i] = 'stroke:' + col - flag_col = True - if tmp[i].startswith('stroke-width:'): - tmp[i] = 'stroke-width:' + width - flag_width = True - if tmp[i].startswith('stroke-dasharray:'): - tmp[i] = 'stroke-dasharray:' + dash - flag_dash = True - if not flag_col: - tmp.append('stroke:' + col) - if not flag_width: - tmp.append('stroke-width:' + width) - if not flag_dash: - tmp.append('stroke-dasharray:' + dash) - return ';'.join(tmp) - -# The type of d values is collapsed, losing precision, because the dict containst lists instead of tuples, please fix! -def fix_map(d :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, threshold_P_V :float, threshold_F_C :float, max_F_C :float) -> ET.ElementTree: - """ - Edits the selected SVG map based on the p-value and fold change data (d) and some significance thresholds also passed as inputs. - - Args: - d : dictionary mapping a p-value and a fold-change value (values) to each reaction ID as encoded in the SVG map (keys) - core_map : SVG map to modify - threshold_P_V : threshold for a p-value to be considered significant - threshold_F_C : threshold for a fold change value to be considered significant - max_F_C : highest fold change (absolute value) - - Returns: - ET.ElementTree : the modified core_map - - Side effects: - core_map : mut - """ - maxT = 12 - minT = 2 - grey = '#BEBEBE' - blue = '#0000FF' - red = '#E41A1C' - for el in core_map.iter(): - el_id = str(el.get('id')) - if el_id.startswith('R_'): - tmp = d.get(el_id[2:]) - if tmp != None: - p_val :float = tmp[0] - f_c = tmp[1] - if p_val < threshold_P_V: - if not isinstance(f_c, str): - if abs(f_c) < math.log(threshold_F_C, 2): - col = grey - width = str(minT) - else: - if f_c < 0: - col = blue - elif f_c > 0: - col = red - width = str(max((abs(f_c) * maxT) / max_F_C, minT)) - else: - if f_c == '-INF': - col = blue - elif f_c == 'INF': - col = red - width = str(maxT) - dash = 'none' - else: - dash = '5,5' - col = grey - width = str(minT) - el.set('style', fix_style(el.get('style', ""), col, width, dash)) - return core_map - -def getElementById(reactionId :str, metabMap :ET.ElementTree) -> utils.Result[ET.Element, utils.Result.ResultErr]: - """ - Finds any element in the given map with the given ID. ID uniqueness in an svg file is recommended but - not enforced, if more than one element with the exact ID is found only the first will be returned. - - Args: - reactionId (str): exact ID of the requested element. - metabMap (ET.ElementTree): metabolic map containing the element. - - Returns: - utils.Result[ET.Element, ResultErr]: result of the search, either the first match found or a ResultErr. - """ - return utils.Result.Ok( - f"//*[@id=\"{reactionId}\"]").map( - lambda xPath : metabMap.xpath(xPath)[0]).mapErr( - lambda _ : utils.Result.ResultErr(f"No elements with ID \"{reactionId}\" found in map")) - # ^^^ we shamelessly ignore the contents of the IndexError, it offers nothing to the user. - -def styleMapElement(element :ET.Element, styleStr :str) -> None: - currentStyles :str = element.get("style", "") - if re.search(r";stroke:[^;]+;stroke-width:[^;]+;stroke-dasharray:[^;]+$", currentStyles): - currentStyles = ';'.join(currentStyles.split(';')[:-3]) - - element.set("style", currentStyles + styleStr) - -class ReactionDirection(Enum): - Unknown = "" - Direct = "_F" - Inverse = "_B" - - @classmethod - def fromDir(cls, s :str) -> "ReactionDirection": - # vvv as long as there's so few variants I actually condone the if spam: - if s == ReactionDirection.Direct.value: return ReactionDirection.Direct - if s == ReactionDirection.Inverse.value: return ReactionDirection.Inverse - return ReactionDirection.Unknown - - @classmethod - def fromReactionId(cls, reactionId :str) -> "ReactionDirection": - return ReactionDirection.fromDir(reactionId[-2:]) - -def getArrowBodyElementId(reactionId :str) -> str: - if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV - elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: reactionId = reactionId[:-2] - return f"R_{reactionId}" - -def getArrowHeadElementId(reactionId :str) -> Tuple[str, str]: - """ - We attempt extracting the direction information from the provided reaction ID, if unsuccessful we provide the IDs of both directions. - - Args: - reactionId : the provided reaction ID. - - Returns: - Tuple[str, str]: either a single str ID for the correct arrow head followed by an empty string or both options to try. - """ - if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV - elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: return reactionId[:-3:-1] + reactionId[:-2], "" - return f"F_{reactionId}", f"B_{reactionId}" - -class ArrowColor(Enum): - """ - Encodes possible arrow colors based on their meaning in the enrichment process. - """ - Invalid = "#BEBEBE" # gray, fold-change under treshold - UpRegulated = "#E41A1C" # red, up-regulated reaction - DownRegulated = "#0000FF" # blue, down-regulated reaction - - UpRegulatedInv = "#FF7A00" - # ^^^ different shade of red (actually orange), up-regulated net value for a reversible reaction with - # conflicting enrichment in the two directions. - - DownRegulatedInv = "#B22CF1" - # ^^^ different shade of blue (actually purple), down-regulated net value for a reversible reaction with - # conflicting enrichment in the two directions. - - @classmethod - def fromFoldChangeSign(cls, foldChange :float, *, useAltColor = False) -> "ArrowColor": - colors = (cls.DownRegulated, cls.DownRegulatedInv) if foldChange < 0 else (cls.UpRegulated, cls.UpRegulatedInv) - return colors[useAltColor] - - def __str__(self) -> str: return self.value - -class Arrow: - """ - Models the properties of a reaction arrow that change based on enrichment. - """ - MIN_W = 2 - MAX_W = 12 - - def __init__(self, width :int, col: ArrowColor, *, isDashed = False) -> None: - """ - (Private) Initializes an instance of Arrow. - - Args: - width : width of the arrow, ideally to be kept within Arrow.MIN_W and Arrow.MAX_W (not enforced). - col : color of the arrow. - isDashed : whether the arrow should be dashed, meaning the associated pValue resulted not significant. - - Returns: - None : practically, a Arrow instance. - """ - self.w = width - self.col = col - self.dash = isDashed - - def applyTo(self, reactionId :str, metabMap :ET.ElementTree, styleStr :str) -> None: - if getElementById(reactionId, metabMap).map(lambda el : styleMapElement(el, styleStr)).isErr: - ERRORS.append(reactionId) - - def styleReactionElements(self, metabMap :ET.ElementTree, reactionId :str, *, mindReactionDir = True) -> None: - # If We're dealing with RAS data or in general don't care about the direction of the reaction we only style the arrow body - if not mindReactionDir: - return self.applyTo(getArrowBodyElementId(reactionId), metabMap, self.toStyleStr()) - - # Now we style the arrow head(s): - idOpt1, idOpt2 = getArrowHeadElementId(reactionId) - self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True)) - if idOpt2: self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True)) - - def getMapReactionId(self, reactionId :str, mindReactionDir :bool) -> str: - """ - Computes the reaction ID as encoded in the map for a given reaction ID from the dataset. - - Args: - reactionId: the reaction ID, as encoded in the dataset. - mindReactionDir: if True forward (F_) and backward (B_) directions will be encoded in the result. - - Returns: - str : the ID of an arrow's body or tips in the map. - """ - # we assume the reactionIds also don't encode reaction dir if they don't mind it when styling the map. - if not mindReactionDir: return "R_" + reactionId - - #TODO: this is clearly something we need to make consistent in RPS - return (reactionId[:-3:-1] + reactionId[:-2]) if reactionId[:-2] in ["_F", "_B"] else f"F_{reactionId}" # "Pyr_F" --> "F_Pyr" - - def toStyleStr(self, *, downSizedForTips = False) -> str: - """ - Collapses the styles of this Arrow into a str, ready to be applied as part of the "style" property on an svg element. - - Returns: - str : the styles string. - """ - width = self.w - if downSizedForTips: width *= 0.15 - return f";stroke:{self.col};stroke-width:{width};stroke-dasharray:{'5,5' if self.dash else 'none'}" - -# vvv These constants could be inside the class itself a static properties, but python -# was built by brainless organisms so here we are! -INVALID_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid) -INSIGNIFICANT_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid, isDashed = True) - -def applyRpsEnrichmentToMap(rpsEnrichmentRes :Dict[str, Union[Tuple[float, FoldChange], Tuple[float, FoldChange, float, float]]], metabMap :ET.ElementTree, maxNumericFoldChange :float) -> None: - """ - Applies RPS enrichment results to the provided metabolic map. - - Args: - rpsEnrichmentRes : RPS enrichment results. - metabMap : the metabolic map to edit. - maxNumericFoldChange : biggest finite fold-change value found. - - Side effects: - metabMap : mut - - Returns: - None - """ - for reactionId, values in rpsEnrichmentRes.items(): - pValue = values[0] - foldChange = values[1] - - if isinstance(foldChange, str): foldChange = float(foldChange) - if pValue >= ARGS.pValue: # pValue above tresh: dashed arrow - INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId) - continue - - if abs(foldChange) < math.log(ARGS.fChange, 2): - INVALID_ARROW.styleReactionElements(metabMap, reactionId) - continue - - width = Arrow.MAX_W - if not math.isinf(foldChange): - try: width = max(abs(foldChange * Arrow.MAX_W) / maxNumericFoldChange, Arrow.MIN_W) - except ZeroDivisionError: pass - - if not reactionId.endswith("_RV"): # RV stands for reversible reactions - Arrow(width, ArrowColor.fromFoldChangeSign(foldChange)).styleReactionElements(metabMap, reactionId) - continue - - reactionId = reactionId[:-3] # Remove "_RV" - - inversionScore = (values[2] < 0) + (values[3] < 0) # Compacts the signs of averages into 1 easy to check score - if inversionScore == 2: foldChange *= -1 - # ^^^ Style the inverse direction with the opposite sign netValue - - # If the score is 1 (opposite signs) we use alternative colors vvv - arrow = Arrow(width, ArrowColor.fromFoldChangeSign(foldChange, useAltColor = inversionScore == 1)) - - # vvv These 2 if statements can both be true and can both happen - if ARGS.net: # style arrow head(s): - arrow.styleReactionElements(metabMap, reactionId + ("_B" if inversionScore == 2 else "_F")) - - if not ARGS.using_RAS: # style arrow body - arrow.styleReactionElements(metabMap, reactionId, mindReactionDir = False) - -############################ split class ###################################### -def split_class(classes :pd.DataFrame, resolve_rules :Dict[str, List[float]]) -> Dict[str, List[List[float]]]: - """ - Generates a :dict that groups together data from a :DataFrame based on classes the data is related to. - - Args: - classes : a :DataFrame of only string values, containing class information (rows) and keys to query the resolve_rules :dict - resolve_rules : a :dict containing :float data - - Returns: - dict : the dict with data grouped by class - - Side effects: - classes : mut - """ - class_pat :Dict[str, List[List[float]]] = {} - for i in range(len(classes)): - classe :str = classes.iloc[i, 1] - if pd.isnull(classe): continue - - l :List[List[float]] = [] - for j in range(i, len(classes)): - if classes.iloc[j, 1] == classe: - pat_id :str = classes.iloc[j, 0] - tmp = resolve_rules.get(pat_id, None) - if tmp != None: - l.append(tmp) - classes.iloc[j, 1] = None - - if l: - class_pat[classe] = list(map(list, zip(*l))) - continue - - utils.logWarning( - f"Warning: no sample found in class \"{classe}\", the class has been disregarded", ARGS.out_log) - - return class_pat - -############################ conversion ############################################## -#conversion from svg to png -def svg_to_png_with_background(svg_path :utils.FilePath, png_path :utils.FilePath, dpi :int = 72, scale :int = 1, size :Optional[float] = None) -> None: - """ - Internal utility to convert an SVG to PNG (forced opaque) to aid in PDF conversion. - - Args: - svg_path : path to SVG file - png_path : path for new PNG file - dpi : dots per inch of the generated PNG - scale : scaling factor for the generated PNG, computed internally when a size is provided - size : final effective width of the generated PNG - - Returns: - None - """ - if size: - image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=1) - scale = size / image.width - image = image.resize(scale) - else: - image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=scale) - - white_background = pyvips.Image.black(image.width, image.height).new_from_image([255, 255, 255]) - white_background = white_background.affine([scale, 0, 0, scale]) - - if white_background.bands != image.bands: - white_background = white_background.extract_band(0) - - composite_image = white_background.composite2(image, 'over') - composite_image.write_to_file(png_path.show()) - -#funzione unica, lascio fuori i file e li passo in input -#conversion from png to pdf -def convert_png_to_pdf(png_file :utils.FilePath, pdf_file :utils.FilePath) -> None: - """ - Internal utility to convert a PNG to PDF to aid from SVG conversion. - - Args: - png_file : path to PNG file - pdf_file : path to new PDF file - - Returns: - None - """ - image = Image.open(png_file.show()) - image = image.convert("RGB") - image.save(pdf_file.show(), "PDF", resolution=100.0) - -#function called to reduce redundancy in the code -def convert_to_pdf(file_svg :utils.FilePath, file_png :utils.FilePath, file_pdf :utils.FilePath) -> None: - """ - Converts the SVG map at the provided path to PDF. - - Args: - file_svg : path to SVG file - file_png : path to PNG file - file_pdf : path to new PDF file - - Returns: - None - """ - svg_to_png_with_background(file_svg, file_png) - try: - convert_png_to_pdf(file_png, file_pdf) - print(f'PDF file {file_pdf.filePath} successfully generated.') - - except Exception as e: - raise utils.DataErr(file_pdf.show(), f'Error generating PDF file: {e}') - -############################ map ############################################## -def buildOutputPath(dataset1Name :str, dataset2Name = "rest", *, details = "", ext :utils.FileFormat) -> utils.FilePath: - """ - Builds a FilePath instance from the names of confronted datasets ready to point to a location in the - "result/" folder, used by this tool for output files in collections. - - Args: - dataset1Name : _description_ - dataset2Name : _description_. Defaults to "rest". - details : _description_ - ext : _description_ - - Returns: - utils.FilePath : _description_ - """ - # This function returns a util data structure but is extremely specific to this module. - # RAS also uses collections as output and as such might benefit from a method like this, but I'd wait - # TODO: until a third tool with multiple outputs appears before porting this to utils. - return utils.FilePath( - f"{dataset1Name}_vs_{dataset2Name}" + (f" ({details})" if details else ""), - # ^^^ yes this string is built every time even if the form is the same for the same 2 datasets in - # all output files: I don't care, this was never the performance bottleneck of the tool and - # there is no other net gain in saving and re-using the built string. - ext, - prefix = "result") - -FIELD_NOT_AVAILABLE = '/' -def writeToCsv(rows: List[list], fieldNames :List[str], outPath :utils.FilePath) -> None: - fieldsAmt = len(fieldNames) - with open(outPath.show(), "w", newline = "") as fd: - writer = csv.DictWriter(fd, fieldnames = fieldNames, delimiter = '\t') - writer.writeheader() - - for row in rows: - sizeMismatch = fieldsAmt - len(row) - if sizeMismatch > 0: row.extend([FIELD_NOT_AVAILABLE] * sizeMismatch) - writer.writerow({ field : data for field, data in zip(fieldNames, row) }) - -OldEnrichedScores = Dict[str, List[Union[float, FoldChange]]] #TODO: try to use Tuple whenever possible -def writeTabularResult(enrichedScores : OldEnrichedScores, ras_enrichment: bool, outPath :utils.FilePath) -> None: - fieldNames = ["ids", "P_Value", "Log2(fold change)"] - if not ras_enrichment: fieldNames.extend(["average_1", "average_2"]) - - writeToCsv([ [reactId] + values for reactId, values in enrichedScores.items() ], fieldNames, outPath) - -def temp_thingsInCommon(tmp :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, max_F_C :float, dataset1Name :str, dataset2Name = "rest", ras_enrichment = True) -> None: - # this function compiles the things always in common between comparison modes after enrichment. - # TODO: organize, name better. - writeTabularResult(tmp, ras_enrichment, buildOutputPath(dataset1Name, dataset2Name, details = "Tabular Result", ext = utils.FileFormat.TSV)) - - if ras_enrichment: - fix_map(tmp, core_map, ARGS.pValue, ARGS.fChange, max_F_C) - return - - for reactId, enrichData in tmp.items(): tmp[reactId] = tuple(enrichData) - applyRpsEnrichmentToMap(tmp, core_map, max_F_C) - -def computePValue(dataset1Data :List[float], dataset2Data :List[float]) -> float: - """ - Computes the statistical significance score (P-value) of the comparison between coherent data - from two datasets. The data is supposed to, in both datasets: - - be related to the same reaction ID; - - be ordered by sample, such that the item at position i in both lists is related to the - same sample or cell line. - - Args: - dataset1Data : data from the 1st dataset. - dataset2Data : data from the 2nd dataset. - - Returns: - float: P-value from a Kolmogorov-Smirnov test on the provided data. - """ - return st.ks_2samp(dataset1Data, dataset2Data)[1] - -def compareDatasetPair(dataset1Data :List[List[float]], dataset2Data :List[List[float]], ids :List[str]) -> Tuple[Dict[str, List[Union[float, FoldChange]]], float]: - #TODO: the following code still suffers from "dumbvarnames-osis" - tmp :Dict[str, List[Union[float, FoldChange]]] = {} - count = 0 - max_F_C = 0 - - for l1, l2 in zip(dataset1Data, dataset2Data): - reactId = ids[count] - count += 1 - if not reactId: continue # we skip ids that have already been processed - - try: #TODO: identify the source of these errors and minimize code in the try block - reactDir = ReactionDirection.fromReactionId(reactId) - # Net score is computed only for reversible reactions when user wants it on arrow tips or when RAS datasets aren't used - if (ARGS.net or not ARGS.using_RAS) and reactDir is not ReactionDirection.Unknown: - try: position = ids.index(reactId[:-1] + ('B' if reactDir is ReactionDirection.Direct else 'F')) - except ValueError: continue # we look for the complementary id, if not found we skip - - nets1 = np.subtract(l1, dataset1Data[position]) - nets2 = np.subtract(l2, dataset2Data[position]) - - p_value = computePValue(nets1, nets2) - avg1 = sum(nets1) / len(nets1) - avg2 = sum(nets2) / len(nets2) - net = (avg1 - avg2) / abs(avg2) - - if math.isnan(net): continue - tmp[reactId[:-1] + "RV"] = [p_value, net, avg1, avg2] - - # vvv complementary directional ids are set to None once processed if net is to be applied to tips - if ARGS.net: - ids[position] = None - continue - - # fallthrough is intended, regular scores need to be computed when tips aren't net but RAS datasets aren't used - p_value = computePValue(l1, l2) - avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2)) - if not isinstance(avg, str) and max_F_C < abs(avg): max_F_C = abs(avg) - tmp[reactId] = [float(p_value), avg] - - except (TypeError, ZeroDivisionError): continue - - return tmp, max_F_C - -def computeEnrichment(metabMap :ET.ElementTree, class_pat :Dict[str, List[List[float]]], ids :List[str], *, fromRAS = True) -> None: - """ - Compares clustered data based on a given comparison mode and applies enrichment-based styling on the - provided metabolic map. - - Args: - metabMap : SVG map to modify. - class_pat : the clustered data. - ids : ids for data association. - fromRAS : whether the data to enrich consists of RAS scores. - - Returns: - None - - Raises: - sys.exit : if there are less than 2 classes for comparison - - Side effects: - metabMap : mut - ids : mut - """ - class_pat = { k.strip() : v for k, v in class_pat.items() } - #TODO: simplfy this stuff vvv and stop using sys.exit (raise the correct utils error) - if (not class_pat) or (len(class_pat.keys()) < 2): sys.exit('Execution aborted: classes provided for comparisons are less than two\n') - - if ARGS.comparison == "manyvsmany": - for i, j in it.combinations(class_pat.keys(), 2): - #TODO: these 2 functions are always called in pair and in this order and need common data, - # some clever refactoring would be appreciated. - comparisonDict, max_F_C = compareDatasetPair(class_pat.get(i), class_pat.get(j), ids) - temp_thingsInCommon(comparisonDict, metabMap, max_F_C, i, j, fromRAS) - - elif ARGS.comparison == "onevsrest": - for single_cluster in class_pat.keys(): - t :List[List[List[float]]] = [] - for k in class_pat.keys(): - if k != single_cluster: - t.append(class_pat.get(k)) - - rest :List[List[float]] = [] - for i in t: - rest = rest + i - - comparisonDict, max_F_C = compareDatasetPair(class_pat.get(single_cluster), rest, ids) - temp_thingsInCommon(comparisonDict, metabMap, max_F_C, single_cluster, fromRAS) - - elif ARGS.comparison == "onevsmany": - controlItems = class_pat.get(ARGS.control) - for otherDataset in class_pat.keys(): - if otherDataset == ARGS.control: continue - - comparisonDict, max_F_C = compareDatasetPair(controlItems, class_pat.get(otherDataset), ids) - temp_thingsInCommon(comparisonDict, metabMap, max_F_C, ARGS.control, otherDataset, fromRAS) - -def createOutputMaps(dataset1Name :str, dataset2Name :str, core_map :ET.ElementTree) -> None: - svgFilePath = buildOutputPath(dataset1Name, dataset2Name, details = "SVG Map", ext = utils.FileFormat.SVG) - utils.writeSvg(svgFilePath, core_map) - - if ARGS.generate_pdf: - pngPath = buildOutputPath(dataset1Name, dataset2Name, details = "PNG Map", ext = utils.FileFormat.PNG) - pdfPath = buildOutputPath(dataset1Name, dataset2Name, details = "PDF Map", ext = utils.FileFormat.PDF) - convert_to_pdf(svgFilePath, pngPath, pdfPath) - - if not ARGS.generate_svg: os.remove(svgFilePath.show()) - -ClassPat = Dict[str, List[List[float]]] -def getClassesAndIdsFromDatasets(datasetsPaths :List[str], datasetPath :str, classPath :str, names :List[str]) -> Tuple[List[str], ClassPat]: - # TODO: I suggest creating dicts with ids as keys instead of keeping class_pat and ids separate, - # for the sake of everyone's sanity. - class_pat :ClassPat = {} - if ARGS.option == 'datasets': - num = 1 #TODO: the dataset naming function could be a generator - for path, name in zip(datasetsPaths, names): - name = name_dataset(name, num) - resolve_rules_float, ids = getDatasetValues(path, name) - if resolve_rules_float != None: - class_pat[name] = list(map(list, zip(*resolve_rules_float.values()))) - - num += 1 - - elif ARGS.option == "dataset_class": - classes = read_dataset(classPath, "class") - classes = classes.astype(str) - - resolve_rules_float, ids = getDatasetValues(datasetPath, "Dataset Class (not actual name)") - if resolve_rules_float != None: class_pat = split_class(classes, resolve_rules_float) - - return ids, class_pat - #^^^ TODO: this could be a match statement over an enum, make it happen future marea dev with python 3.12! (it's why I kept the ifs) - -#TODO: create these damn args as FilePath objects -def getDatasetValues(datasetPath :str, datasetName :str) -> Tuple[ClassPat, List[str]]: - """ - Opens the dataset at the given path and extracts the values (expected nullable numerics) and the IDs. - - Args: - datasetPath : path to the dataset - datasetName (str): dataset name, used in error reporting - - Returns: - Tuple[ClassPat, List[str]]: values and IDs extracted from the dataset - """ - dataset = read_dataset(datasetPath, datasetName) - IDs = pd.Series.tolist(dataset.iloc[:, 0].astype(str)) - - dataset = dataset.drop(dataset.columns[0], axis = "columns").to_dict("list") - return { id : list(map(utils.Float("Dataset values, not an argument"), values)) for id, values in dataset.items() }, IDs - -############################ MAIN ############################################# -def main() -> None: - """ - Initializes everything and sets the program in motion based on the fronted input arguments. - - Returns: - None - - Raises: - sys.exit : if a user-provided custom map is in the wrong format (ET.XMLSyntaxError, ET.XMLSchemaParseError) - """ - global ARGS - ARGS = process_args() - - if os.path.isdir('result') == False: os.makedirs('result') - - core_map :ET.ElementTree = ARGS.choice_map.getMap( - ARGS.tool_dir, - utils.FilePath.fromStrPath(ARGS.custom_map) if ARGS.custom_map else None) - # TODO: ^^^ ugly but fine for now, the argument is None if the model isn't custom because no file was given. - # getMap will None-check the customPath and panic when the model IS custom but there's no file (good). A cleaner - # solution can be derived from my comment in FilePath.fromStrPath - - if ARGS.using_RAS: - ids, class_pat = getClassesAndIdsFromDatasets(ARGS.input_datas, ARGS.input_data, ARGS.input_class, ARGS.names) - computeEnrichment(core_map, class_pat, ids) - - if ARGS.using_RPS: - ids, class_pat = getClassesAndIdsFromDatasets(ARGS.input_datas_rps, ARGS.input_data_rps, ARGS.input_class_rps, ARGS.names_rps) - computeEnrichment(core_map, class_pat, ids, fromRAS = False) - - # create output files: TODO: this is the same comparison happening in "maps", find a better way to organize this - if ARGS.comparison == "manyvsmany": - for i, j in it.combinations(class_pat.keys(), 2): createOutputMaps(i, j, core_map) - return - - if ARGS.comparison == "onevsrest": - for single_cluster in class_pat.keys(): createOutputMaps(single_cluster, "rest", core_map) - return - - for otherDataset in class_pat.keys(): - if otherDataset != ARGS.control: createOutputMaps(i, j, core_map) - - if not ERRORS: return - utils.logWarning( - f"The following reaction IDs were mentioned in the dataset but weren't found in the map: {ERRORS}", - ARGS.out_log) - - print('Execution succeded') - -############################################################################### -if __name__ == "__main__": +from __future__ import division +import csv +from enum import Enum +import re +import sys +import numpy as np +import pandas as pd +import itertools as it +import scipy.stats as st +import lxml.etree as ET +import math +import os +import argparse +import pyvips +import utils.general_utils as utils +from PIL import Image +from typing import Tuple, Union, Optional, List, Dict + +ERRORS = [] +########################## argparse ########################################## +ARGS :argparse.Namespace +def process_args() -> argparse.Namespace: + """ + Interfaces the script of a module with its frontend, making the user's choices for various parameters available as values in code. + + Args: + args : Always obtained (in file) from sys.argv + + Returns: + Namespace : An object containing the parsed arguments + """ + parser = argparse.ArgumentParser( + usage = "%(prog)s [options]", + description = "process some value's genes to create a comparison's map.") + + #General: + parser.add_argument( + '-td', '--tool_dir', + type = str, + required = True, + help = 'your tool directory') + + parser.add_argument('-on', '--control', type = str) + parser.add_argument('-ol', '--out_log', help = "Output log") + + #Computation details: + parser.add_argument( + '-co', '--comparison', + type = str, + default = '1vs1', + choices = ['manyvsmany', 'onevsrest', 'onevsmany']) + + parser.add_argument( + '-pv' ,'--pValue', + type = float, + default = 0.1, + help = 'P-Value threshold (default: %(default)s)') + + parser.add_argument( + '-fc', '--fChange', + type = float, + default = 1.5, + help = 'Fold-Change threshold (default: %(default)s)') + + parser.add_argument( + "-ne", "--net", + type = utils.Bool("net"), default = False, + help = "choose if you want net enrichment for RPS") + + parser.add_argument( + '-op', '--option', + type = str, + choices = ['datasets', 'dataset_class'], + help='dataset or dataset and class') + + #RAS: + parser.add_argument( + "-ra", "--using_RAS", + type = utils.Bool("using_RAS"), default = True, + help = "choose whether to use RAS datasets.") + + parser.add_argument( + '-id', '--input_data', + type = str, + help = 'input dataset') + + parser.add_argument( + '-ic', '--input_class', + type = str, + help = 'sample group specification') + + parser.add_argument( + '-ids', '--input_datas', + type = str, + nargs = '+', + help = 'input datasets') + + parser.add_argument( + '-na', '--names', + type = str, + nargs = '+', + help = 'input names') + + #RPS: + parser.add_argument( + "-rp", "--using_RPS", + type = utils.Bool("using_RPS"), default = False, + help = "choose whether to use RPS datasets.") + + parser.add_argument( + '-idr', '--input_data_rps', + type = str, + help = 'input dataset rps') + + parser.add_argument( + '-icr', '--input_class_rps', + type = str, + help = 'sample group specification rps') + + parser.add_argument( + '-idsr', '--input_datas_rps', + type = str, + nargs = '+', + help = 'input datasets rps') + + parser.add_argument( + '-nar', '--names_rps', + type = str, + nargs = '+', + help = 'input names rps') + + #Output: + parser.add_argument( + "-gs", "--generate_svg", + type = utils.Bool("generate_svg"), default = True, + help = "choose whether to use RAS datasets.") + + parser.add_argument( + "-gp", "--generate_pdf", + type = utils.Bool("generate_pdf"), default = True, + help = "choose whether to use RAS datasets.") + + parser.add_argument( + '-cm', '--custom_map', + type = str, + help='custom map to use') + + parser.add_argument( + '-mc', '--choice_map', + type = utils.Model, default = utils.Model.HMRcore, + choices = [utils.Model.HMRcore, utils.Model.ENGRO2, utils.Model.Custom]) + + args :argparse.Namespace = parser.parse_args() + if args.using_RAS and not args.using_RPS: args.net = False + + return args + +############################ dataset input #################################### +def read_dataset(data :str, name :str) -> pd.DataFrame: + """ + Tries to read the dataset from its path (data) as a tsv and turns it into a DataFrame. + + Args: + data : filepath of a dataset (from frontend input params or literals upon calling) + name : name associated with the dataset (from frontend input params or literals upon calling) + + Returns: + pd.DataFrame : dataset in a runtime operable shape + + Raises: + sys.exit : if there's no data (pd.errors.EmptyDataError) or if the dataset has less than 2 columns + """ + try: + dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python') + except pd.errors.EmptyDataError: + sys.exit('Execution aborted: wrong format of ' + name + '\n') + if len(dataset.columns) < 2: + sys.exit('Execution aborted: wrong format of ' + name + '\n') + return dataset + +############################ dataset name ##################################### +def name_dataset(name_data :str, count :int) -> str: + """ + Produces a unique name for a dataset based on what was provided by the user. The default name for any dataset is "Dataset", thus if the user didn't change it this function appends f"_{count}" to make it unique. + + Args: + name_data : name associated with the dataset (from frontend input params) + count : counter from 1 to make these names unique (external) + + Returns: + str : the name made unique + """ + if str(name_data) == 'Dataset': + return str(name_data) + '_' + str(count) + else: + return str(name_data) + +############################ map_methods ###################################### +FoldChange = Union[float, int, str] # Union[float, Literal[0, "-INF", "INF"]] +def fold_change(avg1 :float, avg2 :float) -> FoldChange: + """ + Calculates the fold change between two gene expression values. + + Args: + avg1 : average expression value from one dataset avg2 : average expression value from the other dataset + + Returns: + FoldChange : + 0 : when both input values are 0 + "-INF" : when avg1 is 0 + "INF" : when avg2 is 0 + float : for any other combination of values + """ + if avg1 == 0 and avg2 == 0: + return 0 + elif avg1 == 0: + return '-INF' + elif avg2 == 0: + return 'INF' + else: + return math.log(avg1 / avg2, 2) + +def fix_style(l :str, col :Optional[str], width :str, dash :str) -> str: + """ + Produces a "fixed" style string to assign to a reaction arrow in the SVG map, assigning style properties to the corresponding values passed as input params. + + Args: + l : current style string of an SVG element + col : new value for the "stroke" style property + width : new value for the "stroke-width" style property + dash : new value for the "stroke-dasharray" style property + + Returns: + str : the fixed style string + """ + tmp = l.split(';') + flag_col = False + flag_width = False + flag_dash = False + for i in range(len(tmp)): + if tmp[i].startswith('stroke:'): + tmp[i] = 'stroke:' + col + flag_col = True + if tmp[i].startswith('stroke-width:'): + tmp[i] = 'stroke-width:' + width + flag_width = True + if tmp[i].startswith('stroke-dasharray:'): + tmp[i] = 'stroke-dasharray:' + dash + flag_dash = True + if not flag_col: + tmp.append('stroke:' + col) + if not flag_width: + tmp.append('stroke-width:' + width) + if not flag_dash: + tmp.append('stroke-dasharray:' + dash) + return ';'.join(tmp) + +# The type of d values is collapsed, losing precision, because the dict containst lists instead of tuples, please fix! +def fix_map(d :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, threshold_P_V :float, threshold_F_C :float, max_F_C :float) -> ET.ElementTree: + """ + Edits the selected SVG map based on the p-value and fold change data (d) and some significance thresholds also passed as inputs. + + Args: + d : dictionary mapping a p-value and a fold-change value (values) to each reaction ID as encoded in the SVG map (keys) + core_map : SVG map to modify + threshold_P_V : threshold for a p-value to be considered significant + threshold_F_C : threshold for a fold change value to be considered significant + max_F_C : highest fold change (absolute value) + + Returns: + ET.ElementTree : the modified core_map + + Side effects: + core_map : mut + """ + maxT = 12 + minT = 2 + grey = '#BEBEBE' + blue = '#0000FF' + red = '#E41A1C' + for el in core_map.iter(): + el_id = str(el.get('id')) + if el_id.startswith('R_'): + tmp = d.get(el_id[2:]) + if tmp != None: + p_val :float = tmp[0] + f_c = tmp[1] + if p_val < threshold_P_V: + if not isinstance(f_c, str): + if abs(f_c) < math.log(threshold_F_C, 2): + col = grey + width = str(minT) + else: + if f_c < 0: + col = blue + elif f_c > 0: + col = red + width = str(max((abs(f_c) * maxT) / max_F_C, minT)) + else: + if f_c == '-INF': + col = blue + elif f_c == 'INF': + col = red + width = str(maxT) + dash = 'none' + else: + dash = '5,5' + col = grey + width = str(minT) + el.set('style', fix_style(el.get('style', ""), col, width, dash)) + return core_map + +def getElementById(reactionId :str, metabMap :ET.ElementTree) -> utils.Result[ET.Element, utils.Result.ResultErr]: + """ + Finds any element in the given map with the given ID. ID uniqueness in an svg file is recommended but + not enforced, if more than one element with the exact ID is found only the first will be returned. + + Args: + reactionId (str): exact ID of the requested element. + metabMap (ET.ElementTree): metabolic map containing the element. + + Returns: + utils.Result[ET.Element, ResultErr]: result of the search, either the first match found or a ResultErr. + """ + return utils.Result.Ok( + f"//*[@id=\"{reactionId}\"]").map( + lambda xPath : metabMap.xpath(xPath)[0]).mapErr( + lambda _ : utils.Result.ResultErr(f"No elements with ID \"{reactionId}\" found in map")) + # ^^^ we shamelessly ignore the contents of the IndexError, it offers nothing to the user. + +def styleMapElement(element :ET.Element, styleStr :str) -> None: + currentStyles :str = element.get("style", "") + if re.search(r";stroke:[^;]+;stroke-width:[^;]+;stroke-dasharray:[^;]+$", currentStyles): + currentStyles = ';'.join(currentStyles.split(';')[:-3]) + + element.set("style", currentStyles + styleStr) + +class ReactionDirection(Enum): + Unknown = "" + Direct = "_F" + Inverse = "_B" + + @classmethod + def fromDir(cls, s :str) -> "ReactionDirection": + # vvv as long as there's so few variants I actually condone the if spam: + if s == ReactionDirection.Direct.value: return ReactionDirection.Direct + if s == ReactionDirection.Inverse.value: return ReactionDirection.Inverse + return ReactionDirection.Unknown + + @classmethod + def fromReactionId(cls, reactionId :str) -> "ReactionDirection": + return ReactionDirection.fromDir(reactionId[-2:]) + +def getArrowBodyElementId(reactionId :str) -> str: + if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV + elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: reactionId = reactionId[:-2] + return f"R_{reactionId}" + +def getArrowHeadElementId(reactionId :str) -> Tuple[str, str]: + """ + We attempt extracting the direction information from the provided reaction ID, if unsuccessful we provide the IDs of both directions. + + Args: + reactionId : the provided reaction ID. + + Returns: + Tuple[str, str]: either a single str ID for the correct arrow head followed by an empty string or both options to try. + """ + if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV + elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: return reactionId[:-3:-1] + reactionId[:-2], "" + return f"F_{reactionId}", f"B_{reactionId}" + +class ArrowColor(Enum): + """ + Encodes possible arrow colors based on their meaning in the enrichment process. + """ + Invalid = "#BEBEBE" # gray, fold-change under treshold + UpRegulated = "#E41A1C" # red, up-regulated reaction + DownRegulated = "#0000FF" # blue, down-regulated reaction + + UpRegulatedInv = "#FF7A00" + # ^^^ different shade of red (actually orange), up-regulated net value for a reversible reaction with + # conflicting enrichment in the two directions. + + DownRegulatedInv = "#B22CF1" + # ^^^ different shade of blue (actually purple), down-regulated net value for a reversible reaction with + # conflicting enrichment in the two directions. + + @classmethod + def fromFoldChangeSign(cls, foldChange :float, *, useAltColor = False) -> "ArrowColor": + colors = (cls.DownRegulated, cls.DownRegulatedInv) if foldChange < 0 else (cls.UpRegulated, cls.UpRegulatedInv) + return colors[useAltColor] + + def __str__(self) -> str: return self.value + +class Arrow: + """ + Models the properties of a reaction arrow that change based on enrichment. + """ + MIN_W = 2 + MAX_W = 12 + + def __init__(self, width :int, col: ArrowColor, *, isDashed = False) -> None: + """ + (Private) Initializes an instance of Arrow. + + Args: + width : width of the arrow, ideally to be kept within Arrow.MIN_W and Arrow.MAX_W (not enforced). + col : color of the arrow. + isDashed : whether the arrow should be dashed, meaning the associated pValue resulted not significant. + + Returns: + None : practically, a Arrow instance. + """ + self.w = width + self.col = col + self.dash = isDashed + + def applyTo(self, reactionId :str, metabMap :ET.ElementTree, styleStr :str) -> None: + if getElementById(reactionId, metabMap).map(lambda el : styleMapElement(el, styleStr)).isErr: + ERRORS.append(reactionId) + + def styleReactionElements(self, metabMap :ET.ElementTree, reactionId :str, *, mindReactionDir = True) -> None: + # If We're dealing with RAS data or in general don't care about the direction of the reaction we only style the arrow body + if not mindReactionDir: + return self.applyTo(getArrowBodyElementId(reactionId), metabMap, self.toStyleStr()) + + # Now we style the arrow head(s): + idOpt1, idOpt2 = getArrowHeadElementId(reactionId) + self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True)) + if idOpt2: self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True)) + + def getMapReactionId(self, reactionId :str, mindReactionDir :bool) -> str: + """ + Computes the reaction ID as encoded in the map for a given reaction ID from the dataset. + + Args: + reactionId: the reaction ID, as encoded in the dataset. + mindReactionDir: if True forward (F_) and backward (B_) directions will be encoded in the result. + + Returns: + str : the ID of an arrow's body or tips in the map. + """ + # we assume the reactionIds also don't encode reaction dir if they don't mind it when styling the map. + if not mindReactionDir: return "R_" + reactionId + + #TODO: this is clearly something we need to make consistent in RPS + return (reactionId[:-3:-1] + reactionId[:-2]) if reactionId[:-2] in ["_F", "_B"] else f"F_{reactionId}" # "Pyr_F" --> "F_Pyr" + + def toStyleStr(self, *, downSizedForTips = False) -> str: + """ + Collapses the styles of this Arrow into a str, ready to be applied as part of the "style" property on an svg element. + + Returns: + str : the styles string. + """ + width = self.w + if downSizedForTips: width *= 0.15 + return f";stroke:{self.col};stroke-width:{width};stroke-dasharray:{'5,5' if self.dash else 'none'}" + +# vvv These constants could be inside the class itself a static properties, but python +# was built by brainless organisms so here we are! +INVALID_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid) +INSIGNIFICANT_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid, isDashed = True) + +def applyRpsEnrichmentToMap(rpsEnrichmentRes :Dict[str, Union[Tuple[float, FoldChange], Tuple[float, FoldChange, float, float]]], metabMap :ET.ElementTree, maxNumericFoldChange :float) -> None: + """ + Applies RPS enrichment results to the provided metabolic map. + + Args: + rpsEnrichmentRes : RPS enrichment results. + metabMap : the metabolic map to edit. + maxNumericFoldChange : biggest finite fold-change value found. + + Side effects: + metabMap : mut + + Returns: + None + """ + for reactionId, values in rpsEnrichmentRes.items(): + pValue = values[0] + foldChange = values[1] + + if isinstance(foldChange, str): foldChange = float(foldChange) + if pValue >= ARGS.pValue: # pValue above tresh: dashed arrow + INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId) + continue + + if abs(foldChange) < math.log(ARGS.fChange, 2): + INVALID_ARROW.styleReactionElements(metabMap, reactionId) + continue + + width = Arrow.MAX_W + if not math.isinf(foldChange): + try: width = max(abs(foldChange * Arrow.MAX_W) / maxNumericFoldChange, Arrow.MIN_W) + except ZeroDivisionError: pass + + if not reactionId.endswith("_RV"): # RV stands for reversible reactions + Arrow(width, ArrowColor.fromFoldChangeSign(foldChange)).styleReactionElements(metabMap, reactionId) + continue + + reactionId = reactionId[:-3] # Remove "_RV" + + inversionScore = (values[2] < 0) + (values[3] < 0) # Compacts the signs of averages into 1 easy to check score + if inversionScore == 2: foldChange *= -1 + # ^^^ Style the inverse direction with the opposite sign netValue + + # If the score is 1 (opposite signs) we use alternative colors vvv + arrow = Arrow(width, ArrowColor.fromFoldChangeSign(foldChange, useAltColor = inversionScore == 1)) + + # vvv These 2 if statements can both be true and can both happen + if ARGS.net: # style arrow head(s): + arrow.styleReactionElements(metabMap, reactionId + ("_B" if inversionScore == 2 else "_F")) + + if not ARGS.using_RAS: # style arrow body + arrow.styleReactionElements(metabMap, reactionId, mindReactionDir = False) + +############################ split class ###################################### +def split_class(classes :pd.DataFrame, resolve_rules :Dict[str, List[float]]) -> Dict[str, List[List[float]]]: + """ + Generates a :dict that groups together data from a :DataFrame based on classes the data is related to. + + Args: + classes : a :DataFrame of only string values, containing class information (rows) and keys to query the resolve_rules :dict + resolve_rules : a :dict containing :float data + + Returns: + dict : the dict with data grouped by class + + Side effects: + classes : mut + """ + class_pat :Dict[str, List[List[float]]] = {} + for i in range(len(classes)): + classe :str = classes.iloc[i, 1] + if pd.isnull(classe): continue + + l :List[List[float]] = [] + for j in range(i, len(classes)): + if classes.iloc[j, 1] == classe: + pat_id :str = classes.iloc[j, 0] + tmp = resolve_rules.get(pat_id, None) + if tmp != None: + l.append(tmp) + classes.iloc[j, 1] = None + + if l: + class_pat[classe] = list(map(list, zip(*l))) + continue + + utils.logWarning( + f"Warning: no sample found in class \"{classe}\", the class has been disregarded", ARGS.out_log) + + return class_pat + +############################ conversion ############################################## +#conversion from svg to png +def svg_to_png_with_background(svg_path :utils.FilePath, png_path :utils.FilePath, dpi :int = 72, scale :int = 1, size :Optional[float] = None) -> None: + """ + Internal utility to convert an SVG to PNG (forced opaque) to aid in PDF conversion. + + Args: + svg_path : path to SVG file + png_path : path for new PNG file + dpi : dots per inch of the generated PNG + scale : scaling factor for the generated PNG, computed internally when a size is provided + size : final effective width of the generated PNG + + Returns: + None + """ + if size: + image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=1) + scale = size / image.width + image = image.resize(scale) + else: + image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=scale) + + white_background = pyvips.Image.black(image.width, image.height).new_from_image([255, 255, 255]) + white_background = white_background.affine([scale, 0, 0, scale]) + + if white_background.bands != image.bands: + white_background = white_background.extract_band(0) + + composite_image = white_background.composite2(image, 'over') + composite_image.write_to_file(png_path.show()) + +#funzione unica, lascio fuori i file e li passo in input +#conversion from png to pdf +def convert_png_to_pdf(png_file :utils.FilePath, pdf_file :utils.FilePath) -> None: + """ + Internal utility to convert a PNG to PDF to aid from SVG conversion. + + Args: + png_file : path to PNG file + pdf_file : path to new PDF file + + Returns: + None + """ + image = Image.open(png_file.show()) + image = image.convert("RGB") + image.save(pdf_file.show(), "PDF", resolution=100.0) + +#function called to reduce redundancy in the code +def convert_to_pdf(file_svg :utils.FilePath, file_png :utils.FilePath, file_pdf :utils.FilePath) -> None: + """ + Converts the SVG map at the provided path to PDF. + + Args: + file_svg : path to SVG file + file_png : path to PNG file + file_pdf : path to new PDF file + + Returns: + None + """ + svg_to_png_with_background(file_svg, file_png) + try: + convert_png_to_pdf(file_png, file_pdf) + print(f'PDF file {file_pdf.filePath} successfully generated.') + + except Exception as e: + raise utils.DataErr(file_pdf.show(), f'Error generating PDF file: {e}') + +############################ map ############################################## +def buildOutputPath(dataset1Name :str, dataset2Name = "rest", *, details = "", ext :utils.FileFormat) -> utils.FilePath: + """ + Builds a FilePath instance from the names of confronted datasets ready to point to a location in the + "result/" folder, used by this tool for output files in collections. + + Args: + dataset1Name : _description_ + dataset2Name : _description_. Defaults to "rest". + details : _description_ + ext : _description_ + + Returns: + utils.FilePath : _description_ + """ + # This function returns a util data structure but is extremely specific to this module. + # RAS also uses collections as output and as such might benefit from a method like this, but I'd wait + # TODO: until a third tool with multiple outputs appears before porting this to utils. + return utils.FilePath( + f"{dataset1Name}_vs_{dataset2Name}" + (f" ({details})" if details else ""), + # ^^^ yes this string is built every time even if the form is the same for the same 2 datasets in + # all output files: I don't care, this was never the performance bottleneck of the tool and + # there is no other net gain in saving and re-using the built string. + ext, + prefix = "result") + +FIELD_NOT_AVAILABLE = '/' +def writeToCsv(rows: List[list], fieldNames :List[str], outPath :utils.FilePath) -> None: + fieldsAmt = len(fieldNames) + with open(outPath.show(), "w", newline = "") as fd: + writer = csv.DictWriter(fd, fieldnames = fieldNames, delimiter = '\t') + writer.writeheader() + + for row in rows: + sizeMismatch = fieldsAmt - len(row) + if sizeMismatch > 0: row.extend([FIELD_NOT_AVAILABLE] * sizeMismatch) + writer.writerow({ field : data for field, data in zip(fieldNames, row) }) + +OldEnrichedScores = Dict[str, List[Union[float, FoldChange]]] #TODO: try to use Tuple whenever possible +def writeTabularResult(enrichedScores : OldEnrichedScores, ras_enrichment: bool, outPath :utils.FilePath) -> None: + fieldNames = ["ids", "P_Value", "Log2(fold change)"] + if not ras_enrichment: fieldNames.extend(["average_1", "average_2"]) + + writeToCsv([ [reactId] + values for reactId, values in enrichedScores.items() ], fieldNames, outPath) + +def temp_thingsInCommon(tmp :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, max_F_C :float, dataset1Name :str, dataset2Name = "rest", ras_enrichment = True) -> None: + # this function compiles the things always in common between comparison modes after enrichment. + # TODO: organize, name better. + writeTabularResult(tmp, ras_enrichment, buildOutputPath(dataset1Name, dataset2Name, details = "Tabular Result", ext = utils.FileFormat.TSV)) + + if ras_enrichment: + fix_map(tmp, core_map, ARGS.pValue, ARGS.fChange, max_F_C) + return + + for reactId, enrichData in tmp.items(): tmp[reactId] = tuple(enrichData) + applyRpsEnrichmentToMap(tmp, core_map, max_F_C) + +def computePValue(dataset1Data :List[float], dataset2Data :List[float]) -> float: + """ + Computes the statistical significance score (P-value) of the comparison between coherent data + from two datasets. The data is supposed to, in both datasets: + - be related to the same reaction ID; + - be ordered by sample, such that the item at position i in both lists is related to the + same sample or cell line. + + Args: + dataset1Data : data from the 1st dataset. + dataset2Data : data from the 2nd dataset. + + Returns: + float: P-value from a Kolmogorov-Smirnov test on the provided data. + """ + return st.ks_2samp(dataset1Data, dataset2Data)[1] + +def compareDatasetPair(dataset1Data :List[List[float]], dataset2Data :List[List[float]], ids :List[str]) -> Tuple[Dict[str, List[Union[float, FoldChange]]], float]: + #TODO: the following code still suffers from "dumbvarnames-osis" + tmp :Dict[str, List[Union[float, FoldChange]]] = {} + count = 0 + max_F_C = 0 + + for l1, l2 in zip(dataset1Data, dataset2Data): + reactId = ids[count] + count += 1 + if not reactId: continue # we skip ids that have already been processed + + try: #TODO: identify the source of these errors and minimize code in the try block + reactDir = ReactionDirection.fromReactionId(reactId) + # Net score is computed only for reversible reactions when user wants it on arrow tips or when RAS datasets aren't used + if (ARGS.net or not ARGS.using_RAS) and reactDir is not ReactionDirection.Unknown: + try: position = ids.index(reactId[:-1] + ('B' if reactDir is ReactionDirection.Direct else 'F')) + except ValueError: continue # we look for the complementary id, if not found we skip + + nets1 = np.subtract(l1, dataset1Data[position]) + nets2 = np.subtract(l2, dataset2Data[position]) + + p_value = computePValue(nets1, nets2) + avg1 = sum(nets1) / len(nets1) + avg2 = sum(nets2) / len(nets2) + net = (avg1 - avg2) / abs(avg2) + + if math.isnan(net): continue + tmp[reactId[:-1] + "RV"] = [p_value, net, avg1, avg2] + + # vvv complementary directional ids are set to None once processed if net is to be applied to tips + if ARGS.net: + ids[position] = None + continue + + # fallthrough is intended, regular scores need to be computed when tips aren't net but RAS datasets aren't used + p_value = computePValue(l1, l2) + avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2)) + if not isinstance(avg, str) and max_F_C < abs(avg): max_F_C = abs(avg) + tmp[reactId] = [float(p_value), avg] + + except (TypeError, ZeroDivisionError): continue + + return tmp, max_F_C + +def computeEnrichment(metabMap :ET.ElementTree, class_pat :Dict[str, List[List[float]]], ids :List[str], *, fromRAS = True) -> None: + """ + Compares clustered data based on a given comparison mode and applies enrichment-based styling on the + provided metabolic map. + + Args: + metabMap : SVG map to modify. + class_pat : the clustered data. + ids : ids for data association. + fromRAS : whether the data to enrich consists of RAS scores. + + Returns: + None + + Raises: + sys.exit : if there are less than 2 classes for comparison + + Side effects: + metabMap : mut + ids : mut + """ + class_pat = { k.strip() : v for k, v in class_pat.items() } + #TODO: simplfy this stuff vvv and stop using sys.exit (raise the correct utils error) + if (not class_pat) or (len(class_pat.keys()) < 2): sys.exit('Execution aborted: classes provided for comparisons are less than two\n') + + if ARGS.comparison == "manyvsmany": + for i, j in it.combinations(class_pat.keys(), 2): + #TODO: these 2 functions are always called in pair and in this order and need common data, + # some clever refactoring would be appreciated. + comparisonDict, max_F_C = compareDatasetPair(class_pat.get(i), class_pat.get(j), ids) + temp_thingsInCommon(comparisonDict, metabMap, max_F_C, i, j, fromRAS) + + elif ARGS.comparison == "onevsrest": + for single_cluster in class_pat.keys(): + t :List[List[List[float]]] = [] + for k in class_pat.keys(): + if k != single_cluster: + t.append(class_pat.get(k)) + + rest :List[List[float]] = [] + for i in t: + rest = rest + i + + comparisonDict, max_F_C = compareDatasetPair(class_pat.get(single_cluster), rest, ids) + temp_thingsInCommon(comparisonDict, metabMap, max_F_C, single_cluster, fromRAS) + + elif ARGS.comparison == "onevsmany": + controlItems = class_pat.get(ARGS.control) + for otherDataset in class_pat.keys(): + if otherDataset == ARGS.control: continue + + comparisonDict, max_F_C = compareDatasetPair(controlItems, class_pat.get(otherDataset), ids) + temp_thingsInCommon(comparisonDict, metabMap, max_F_C, ARGS.control, otherDataset, fromRAS) + +def createOutputMaps(dataset1Name :str, dataset2Name :str, core_map :ET.ElementTree) -> None: + svgFilePath = buildOutputPath(dataset1Name, dataset2Name, details = "SVG Map", ext = utils.FileFormat.SVG) + utils.writeSvg(svgFilePath, core_map) + + if ARGS.generate_pdf: + pngPath = buildOutputPath(dataset1Name, dataset2Name, details = "PNG Map", ext = utils.FileFormat.PNG) + pdfPath = buildOutputPath(dataset1Name, dataset2Name, details = "PDF Map", ext = utils.FileFormat.PDF) + convert_to_pdf(svgFilePath, pngPath, pdfPath) + + if not ARGS.generate_svg: os.remove(svgFilePath.show()) + +ClassPat = Dict[str, List[List[float]]] +def getClassesAndIdsFromDatasets(datasetsPaths :List[str], datasetPath :str, classPath :str, names :List[str]) -> Tuple[List[str], ClassPat]: + # TODO: I suggest creating dicts with ids as keys instead of keeping class_pat and ids separate, + # for the sake of everyone's sanity. + class_pat :ClassPat = {} + if ARGS.option == 'datasets': + num = 1 #TODO: the dataset naming function could be a generator + for path, name in zip(datasetsPaths, names): + name = name_dataset(name, num) + resolve_rules_float, ids = getDatasetValues(path, name) + if resolve_rules_float != None: + class_pat[name] = list(map(list, zip(*resolve_rules_float.values()))) + + num += 1 + + elif ARGS.option == "dataset_class": + classes = read_dataset(classPath, "class") + classes = classes.astype(str) + + resolve_rules_float, ids = getDatasetValues(datasetPath, "Dataset Class (not actual name)") + if resolve_rules_float != None: class_pat = split_class(classes, resolve_rules_float) + + return ids, class_pat + #^^^ TODO: this could be a match statement over an enum, make it happen future marea dev with python 3.12! (it's why I kept the ifs) + +#TODO: create these damn args as FilePath objects +def getDatasetValues(datasetPath :str, datasetName :str) -> Tuple[ClassPat, List[str]]: + """ + Opens the dataset at the given path and extracts the values (expected nullable numerics) and the IDs. + + Args: + datasetPath : path to the dataset + datasetName (str): dataset name, used in error reporting + + Returns: + Tuple[ClassPat, List[str]]: values and IDs extracted from the dataset + """ + dataset = read_dataset(datasetPath, datasetName) + IDs = pd.Series.tolist(dataset.iloc[:, 0].astype(str)) + + dataset = dataset.drop(dataset.columns[0], axis = "columns").to_dict("list") + return { id : list(map(utils.Float("Dataset values, not an argument"), values)) for id, values in dataset.items() }, IDs + +############################ MAIN ############################################# +def main() -> None: + """ + Initializes everything and sets the program in motion based on the fronted input arguments. + + Returns: + None + + Raises: + sys.exit : if a user-provided custom map is in the wrong format (ET.XMLSyntaxError, ET.XMLSchemaParseError) + """ + + print('PROVA') + global ARGS + ARGS = process_args() + + if os.path.isdir('result') == False: os.makedirs('result') + + core_map :ET.ElementTree = ARGS.choice_map.getMap( + ARGS.tool_dir, + utils.FilePath.fromStrPath(ARGS.custom_map) if ARGS.custom_map else None) + # TODO: ^^^ ugly but fine for now, the argument is None if the model isn't custom because no file was given. + # getMap will None-check the customPath and panic when the model IS custom but there's no file (good). A cleaner + # solution can be derived from my comment in FilePath.fromStrPath + + if ARGS.using_RAS: + ids, class_pat = getClassesAndIdsFromDatasets(ARGS.input_datas, ARGS.input_data, ARGS.input_class, ARGS.names) + computeEnrichment(core_map, class_pat, ids) + + if ARGS.using_RPS: + ids, class_pat = getClassesAndIdsFromDatasets(ARGS.input_datas_rps, ARGS.input_data_rps, ARGS.input_class_rps, ARGS.names_rps) + computeEnrichment(core_map, class_pat, ids, fromRAS = False) + + # create output files: TODO: this is the same comparison happening in "maps", find a better way to organize this + if ARGS.comparison == "manyvsmany": + for i, j in it.combinations(class_pat.keys(), 2): createOutputMaps(i, j, core_map) + return + + if ARGS.comparison == "onevsrest": + for single_cluster in class_pat.keys(): createOutputMaps(single_cluster, "rest", core_map) + return + + for otherDataset in class_pat.keys(): + if otherDataset != ARGS.control: createOutputMaps(i, j, core_map) + + if not ERRORS: return + utils.logWarning( + f"The following reaction IDs were mentioned in the dataset but weren't found in the map: {ERRORS}", + ARGS.out_log) + + print('Execution succeded') + +############################################################################### +if __name__ == "__main__": main() \ No newline at end of file