comparison spring_roc.py @ 30:b0e195a47df7 draft

"planemo upload commit b306c715d8284c097220bc5c8461399fdc05eac7"

29:41353488926c

#! /usr/bin/env python
import argparse
import math
import random
from datetime import datetime

from matplotlib import pyplot as plt

    # process prediction file
    print("Loading prediction file...")
    prediction, _ = getReference(args.input, scoreCol=2)

    # estimate background noise
    print("Estimating background noise...")
    negative = set()
    filterAList = list(filterA)
    filterBList = list(filterB)
    negativeCount = positiveCount
    negativeRequired = negativeCount
    random.seed(datetime.now())
    while negativeRequired > 0:
        nameA = random.choice(filterAList)
        nameB = random.choice(filterBList)
        key = getKey(nameA, nameB)
        if key not in putative and key not in negative:
            negative.add(key)
            negativeRequired = negativeRequired - 1

    # create plot
    print("Producing plot data...")
    print("Total count in prediction file: %d." % len(prediction))
    print("Total count in positive file: %d." % len(positive))

    plt.savefig(args.output, format="png")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Create ROC plot.')
    parser.add_argument('-i', '--input', help='Input prediction file.',
                        required=True)
    parser.add_argument('-b', '--biogrid', help='BioGRID interaction ' +
                        'database file', required=True)
    parser.add_argument('-e', '--experiment', help='Type (physical/genetic)',
                        default="", required=False)
    parser.add_argument('-t', '--throughput', help='Throughput (low/high)',
                        default="", required=False)
    parser.add_argument('-m', '--method', help='Method e.g. Two-hybrid',
                        default="", required=False)
    parser.add_argument('-o', '--output', help='Output (png)', required=True)
    args = parser.parse_args()
    main(args)

30:b0e195a47df7

#! /usr/bin/env python
import argparse
import math
import random
from os.path import isfile
from datetime import datetime

from matplotlib import pyplot as plt

    # process prediction file
    print("Loading prediction file...")
    prediction, _ = getReference(args.input, scoreCol=2)

    # get subcellular locations from UniProt export
    locations = dict()
    if isfile(args.locations):
        with open(args.locations) as locFile:
            for line in locFile:
                searchKey = "SUBCELLULAR LOCATION"
                searchPos = line.find(searchKey)
                if searchPos != -1:
                    uniId = line.split()[0]
                    locStart = searchPos + len(searchKey) + 1
                    locId = line[locStart:].split()[0]
                    if locId in ["Nucleus", "Membrane", "Cytoplasm"]:
                        if uniId in filterA and uniId in filterB:
                            locations[uniId] = locId
        print("Found %d subcellular locations." % (len(list(locations.keys()))))

    # estimate background noise
    print("Estimating background noise...")
    negative = set()
    filterAList = sorted(list(filterA))
    filterBList = sorted(list(filterB))
    negativeRequired = positiveCount
    random.seed(0)
    totalAttempts = int(len(filterAList) * len(filterBList) / 2)
    while totalAttempts > 0:
        totalAttempts = totalAttempts - 1
        nameA = random.choice(filterAList)
        nameB = random.choice(filterBList)
        if locations:
            if nameA not in locations or nameB not in locations:
                continue
            if locations[nameA] == locations[nameB]:
                continue
        key = getKey(nameA, nameB)
        if key not in putative and key not in negative:
            negative.add(key)
            negativeRequired = negativeRequired - 1
            if negativeRequired == 0:
                break

    # create plot
    print("Producing plot data...")
    print("Total count in prediction file: %d." % len(prediction))
    print("Total count in positive file: %d." % len(positive))

    plt.savefig(args.output, format="png")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Create ROC plot.')
    parser.add_argument('-i', '--input', help='Input prediction file.', required=True)
    parser.add_argument('-b', '--biogrid', help='BioGRID interaction database file', required=True)
    parser.add_argument('-l', '--locations', help='UniProt export table with subcellular locations', required=False)
    parser.add_argument('-e', '--experiment', help='Type (physical/genetic)', default="", required=False)
    parser.add_argument('-t', '--throughput', help='Throughput (low/high)', default="", required=False)
    parser.add_argument('-m', '--method', help='Method e.g. Two-hybrid', default="", required=False)
    parser.add_argument('-o', '--output', help='Output (png)', required=True)
    args = parser.parse_args()
    main(args)