comparison spring_mcc.py @ 41:f316caf098a6 draft default tip

"planemo upload commit 685e1236afde7cf6bb0c9236de06998d2c211dd3"

40:06337927c198

#! /usr/bin/env python
import argparse
import math
from os.path import isfile
import re
from matplotlib import pyplot as plt


def getIds(rawIds):
    return rawIds.split("|")

                            elif (regionA not in locId and regionB in locId):
                                locations[regionB].append(uniId)
            filterAList = sorted(locations[regionA])
            filterBList = sorted(locations[regionB])
        else:
            filterAList = list(filterA)
            filterBList = list(filterB)
        for i, j in randomPairs(len(filterAList), len(filterBList), jSize):
            nameA = filterAList[i]
            nameB = filterBList[j]
            key = getKey(nameA, nameB)
            if key not in negative:

        filterB = filterSets[filterKeys[1]]
    else:
        filterB = filterA

    # identify biogrid filter options
    filterValues = list()
    filterValues.append([11, args.method])

    # process biogrid database
    print("Loading positive set from BioGRID file...")
    positive, positiveCount = getReference(args.biogrid, aCol=23, bCol=26,
                                           separator="\t", filterA=filterA,
                                           filterB=filterB, skipFirstLine=True,
                                           filterValues=filterValues)

    # estimate negative set
    negative = getNegativeSet(args, filterA, filterB, positiveCount)

    # get prediction results
    print("Loading prediction file...")
    prediction, _ = getReference(args.input, scoreCol=2, minScore=0.8)
    mcc = getMCC(prediction, positive, positiveCount, negative)
    yValues = [mcc]
    yTicks = ["SPRING"]

    # identify biogrid filter options
    for method in ["Affinity Capture-MS",
                   "Biochemical Activity",
                   "Co-crystal Structure",
                   "Co-fractionation",
                   "Co-localization",
                   "Co-purification",
                   "Far Western",
                   "FRET",
                   "PCA",
                   "Reconstituted Complex",
                   "Two-hybrid"]:
        if args.method != method:
            print("Method: %s" % method)
            filterValues = [[11, method]]
            prediction, _ = getReference(args.biogrid, aCol=23, bCol=26,
                                         separator="\t", filterA=filterA,
                                         filterB=filterB, skipFirstLine=True,
                                         filterValues=filterValues)
            mcc = getMCC(prediction, positive, positiveCount, negative)
            yValues.append(mcc)
            yTicks.append(method)

    # 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.xlabel("Matthews-Correlation Coefficient (MCC)")
    plt.title("Positive set: %s" % args.method)
    plt.barh(yTicks, yValues)
    plt.tight_layout()
    plt.savefig(args.output, format="png")


if __name__ == "__main__":

    parser.add_argument('-l', '--locations', help='UniProt export table with subcellular locations', required=False)
    parser.add_argument('-ra', '--region_a', help='First subcellular location', required=False)
    parser.add_argument('-rb', '--region_b', help='Second subcellular location', required=False)
    parser.add_argument('-n', '--negative', help='Negative set (2-columns)', required=False)
    parser.add_argument('-t', '--throughput', help='Throughput (low/high)', required=False)
    parser.add_argument('-m', '--method', help='Method e.g. Two-hybrid', required=False)
    parser.add_argument('-o', '--output', help='Output (png)', required=True)
    args = parser.parse_args()
    main(args)

41:f316caf098a6

#! /usr/bin/env python
import argparse
import math
import pandas as pd
from os.path import isfile
import re

METHODS = ["Biochemical Activity",
           "Co-fractionation",
           "Co-localization",
           "Far Western",
           "FRET",
           "PCA",
           "Co-crystal Structure",
           "Co-purification",
           "Two-hybrid",
           "Affinity Capture-MS"]


def getIds(rawIds):
    return rawIds.split("|")

                            elif (regionA not in locId and regionB in locId):
                                locations[regionB].append(uniId)
            filterAList = sorted(locations[regionA])
            filterBList = sorted(locations[regionB])
        else:
            filterAList = sorted(filterA)
            filterBList = sorted(filterB)
        for i, j in randomPairs(len(filterAList), len(filterBList), jSize):
            nameA = filterAList[i]
            nameB = filterBList[j]
            key = getKey(nameA, nameB)
            if key not in negative:

        filterB = filterSets[filterKeys[1]]
    else:
        filterB = filterA

    # identify biogrid filter options
    performance = dict()
    for methodReference in METHODS:

        # process biogrid database
        print("Loading positive set from BioGRID file (%s)..." % methodReference)
        filterValues = [[11, methodReference]]
        positive, positiveCount = getReference(args.biogrid, aCol=23, bCol=26,
                                               separator="\t", filterA=filterA,
                                               filterB=filterB, skipFirstLine=True,
                                               filterValues=filterValues)

        # estimate negative set
        negative = getNegativeSet(args, filterA, filterB, positiveCount)

        # evaluate other methods
        yValues = list()
        for method in METHODS:
            if methodReference != method:
                print("Method: %s" % method)
                filterValues = [[11, method]]
                prediction, _ = getReference(args.biogrid, aCol=23, bCol=26,
                                             separator="\t", filterA=filterA,
                                             filterB=filterB, skipFirstLine=True,
                                             filterValues=filterValues)
                mcc = getMCC(prediction, positive, positiveCount, negative)
                yValues.append(mcc)
            else:
                yValues.append(0.0)

        # add results to performance dication
        performance[methodReference] = yValues

        # get and append prediction results
        print("Loading prediction file...")
        prediction, _ = getReference(args.input, scoreCol=2, minScore=0.0)
        mcc = getMCC(prediction, positive, positiveCount, negative)
        performance[methodReference].append(mcc)

    # build yTicks
    yTicks = METHODS[:]
    yTicks.append("SPRING")

    # create plot
    print("Producing plot data...")
    print("Total count in prediction file: %d." % len(prediction))
    print("Total count in positive file: %d." % len(positive))
    df = pd.DataFrame(performance, index=yTicks)
    ax = df.plot.barh()
    ax.set_title(args.experiment)
    ax.set_xlabel("Matthews-Correlation Coefficient (MCC)")
    plt = ax.get_figure()
    plt.tight_layout()
    plt.savefig(args.output, format="png")


if __name__ == "__main__":

    parser.add_argument('-l', '--locations', help='UniProt export table with subcellular locations', required=False)
    parser.add_argument('-ra', '--region_a', help='First subcellular location', required=False)
    parser.add_argument('-rb', '--region_b', help='Second subcellular location', required=False)
    parser.add_argument('-n', '--negative', help='Negative set (2-columns)', required=False)
    parser.add_argument('-t', '--throughput', help='Throughput (low/high)', required=False)
    parser.add_argument('-e', '--experiment', help='Experiment Title', required=False, default="Results")
    parser.add_argument('-o', '--output', help='Output (png)', required=True)
    args = parser.parse_args()
    main(args)