Python GenomicRegionSet.subtract方法代码示例

# 需要导入模块: from rgt.GenomicRegionSet import GenomicRegionSet [as 别名]
# 或者: from rgt.GenomicRegionSet.GenomicRegionSet import subtract [as 别名]
    def create_file(self):
        # Expanding summits
        tfbs_summit_regions = GenomicRegionSet("TFBS Summit Regions")
        tfbs_summit_regions.read_bed(self.tfbs_summit_fname)

        for region in iter(tfbs_summit_regions):
            summit = int(region.data.split()[-1]) + region.initial
            region.initial = max(summit - (self.peak_ext / 2), 0)
            region.final = summit + (self.peak_ext / 2)

        # Calculating intersections
        mpbs_regions = GenomicRegionSet("MPBS Regions")
        mpbs_regions.read_bed(self.mpbs_fname)

        tfbs_summit_regions.sort()
        mpbs_regions.sort()

        with_overlap_regions = mpbs_regions.intersect(tfbs_summit_regions, mode=OverlapType.ORIGINAL)
        without_overlap_regions = mpbs_regions.subtract(tfbs_summit_regions, whole_region=True)
        tfbs_regions = GenomicRegionSet("TFBS Regions")

        for region in iter(with_overlap_regions):
            region.name = region.name.split(":")[0] + ":Y"
            tfbs_regions.add(region)

        for region in iter(without_overlap_regions):
            region.name = region.name.split(":")[0] + ":N"
            tfbs_regions.add(region)

        tfbs_regions.sort()

        tfbs_fname = os.path.join(self.output_location, "{}.bed".format(self.mpbs_name))
        tfbs_regions.write_bed(tfbs_fname)

# 需要导入模块: from rgt.GenomicRegionSet import GenomicRegionSet [as 别名]
# 或者: from rgt.GenomicRegionSet.GenomicRegionSet import subtract [as 别名]
    def subtract(self, x):
        """
        Subtract GenomicVariantSet.
        
        *Keyword arguments:*

        - x -- instance of GenomicVariantSet which is subtracted
        
        """
        tmp = GenomicRegionSet.subtract(self, x, whole_region=False)
        self.sequences = self._reconstruct_info(tmp)

# 需要导入模块: from rgt.GenomicRegionSet import GenomicRegionSet [as 别名]
# 或者: from rgt.GenomicRegionSet.GenomicRegionSet import subtract [as 别名]
    def test_subtract_exact(self):
        reference = GenomicRegionSet("reference")
        reference.read(os.path.join(os.path.dirname(__file__), "test_result.bed"))
        background = GenomicRegionSet("background")
        background.read(os.path.join(os.path.dirname(__file__), "test_background.bed"))
        target = GenomicRegionSet("target")
        target.read(os.path.join(os.path.dirname(__file__), "test_target.bed"))

        background_tmp = background.subtract(target, exact=True)

        reference.sort()

        self.assertEqual(len(background_tmp.sequences), len(reference.sequences))

        for region, region_ref in zip(background_tmp.sequences, reference.sequences):
            self.assertEqual(region.__cmp__(region_ref), 0)

# 需要导入模块: from rgt.GenomicRegionSet import GenomicRegionSet [as 别名]
# 或者: from rgt.GenomicRegionSet.GenomicRegionSet import subtract [as 别名]
    def chip_evaluate(self):
        """
        This evaluation methodology uses motif-predicted binding sites (MPBSs) together with TF ChIP-seq data
        to evaluate the footprint predictions.

        return:
        """

        # Evaluate Statistics
        fpr = dict()
        tpr = dict()
        roc_auc = dict()
        roc_auc_1 = dict()
        roc_auc_2 = dict()
        recall = dict()
        precision = dict()
        prc_auc = dict()

        if "SEG" in self.footprint_type:
            mpbs_regions = GenomicRegionSet("TFBS")
            mpbs_regions.read_bed(self.tfbs_file)
            mpbs_regions.sort()

            # Verifying the maximum score of the MPBS file
            max_score = -99999999
            for region in iter(mpbs_regions):
                score = int(region.data)
                if score > max_score:
                    max_score = score
            max_score += 1

        for i in range(len(self.footprint_file)):
            footprints_regions = GenomicRegionSet("Footprints Prediction")
            footprints_regions.read_bed(self.footprint_file[i])

            # Sort footprint prediction bed files
            footprints_regions.sort()

            if self.footprint_type[i] == "SEG":
                # Increasing the score of MPBS entry once if any overlaps found in the predicted footprints.
                increased_score_mpbs_regions = GenomicRegionSet("Increased Regions")
                intersect_regions = mpbs_regions.intersect(footprints_regions, mode=OverlapType.ORIGINAL)
                for region in iter(intersect_regions):
                    region.data = str(int(region.data) + max_score)
                    increased_score_mpbs_regions.add(region)


                # Keep the score of remained MPBS entry unchanged
                without_intersect_regions = mpbs_regions.subtract(footprints_regions, whole_region=True)
                for region in iter(without_intersect_regions):
                    increased_score_mpbs_regions.add(region)

                increased_score_mpbs_regions.sort_score()

                fpr[i], tpr[i], roc_auc[i], roc_auc_1[i], roc_auc_2[i] = self.roc_curve(increased_score_mpbs_regions)
                recall[i], precision[i], prc_auc[i] = self.precision_recall_curve(increased_score_mpbs_regions)
            elif self.footprint_type[i] == "SC":
                footprints_regions.sort_score()
                fpr[i], tpr[i], roc_auc[i], roc_auc_1[i], roc_auc_2[i] = self.roc_curve(footprints_regions)
                recall[i], precision[i], prc_auc[i] = self.precision_recall_curve(footprints_regions)

        # Output the statistics results into text
        stats_fname = self.output_location + self.tf_name + "_stats.txt"
        stats_header = ["METHOD", "AUC_100", "AUC_10", "AUC_1", "AUPR"]
        with open(stats_fname, "w") as stats_file:
            stats_file.write("\t".join(stats_header) + "\n")
            for i in range(len(self.footprint_name)):
                stats_file.write(self.footprint_name[i] + "\t" + str(roc_auc[i]) + "\t" + str(roc_auc_1[i]) + "\t"
                                 + str(roc_auc_2[i]) + "\t" + str(prc_auc[i]) + "\n")

        # Output the curves
        if self.print_roc_curve:
            label_x = "False Positive Rate"
            label_y = "True Positive Rate"
            curve_name = "ROC"
            self.plot_curve(fpr, tpr, roc_auc, label_x, label_y, self.tf_name, curve_name)
        if self.print_pr_curve:
            label_x = "Recall"
            label_y = "Precision"
            curve_name = "PRC"
            self.plot_curve(recall, precision, prc_auc, label_x, label_y, self.tf_name, curve_name)

        self.output_points(self.tf_name, fpr, tpr, recall, precision)

# 需要导入模块: from rgt.GenomicRegionSet import GenomicRegionSet [as 别名]
# 或者: from rgt.GenomicRegionSet.GenomicRegionSet import subtract [as 别名]
def chip_evaluate(args):
    # Evaluate Statistics
    fpr = dict()
    tpr = dict()
    roc_auc_1 = dict()
    roc_auc_10 = dict()
    roc_auc_50 = dict()
    roc_auc_100 = dict()
    recall = dict()
    precision = dict()
    prc_auc_1 = dict()
    prc_auc_10 = dict()
    prc_auc_50 = dict()
    prc_auc_100 = dict()

    footprint_file = args.footprint_file.split(",")
    footprint_name = args.footprint_name.split(",")
    footprint_type = args.footprint_type.split(",")

    max_score = 0
    if "SEG" in footprint_type:
        mpbs_regions = GenomicRegionSet("TFBS")
        mpbs_regions.read(args.tfbs_file)

        # Verifying the maximum score of the MPBS file
        for region in iter(mpbs_regions):
            score = int(region.data.split("\t")[0])
            if score > max_score:
                max_score = score
        max_score += 1

    max_points = []
    for i in range(len(footprint_file)):
        footprints_regions = GenomicRegionSet("Footprints Prediction")
        footprints_regions.read(footprint_file[i])
        footprints_regions.sort()

        if footprint_type[i] == "SEG":
            # Increasing the score of MPBS entry once if any overlaps found in the predicted footprints.
            increased_score_mpbs_regions = GenomicRegionSet("Increased Regions")
            intersect_regions = mpbs_regions.intersect(footprints_regions, mode=OverlapType.ORIGINAL)
            for region in iter(intersect_regions):
                region.data = str(int(region.data.split("\t")[0]) + max_score)
                increased_score_mpbs_regions.add(region)

            # Keep the score of remained MPBS entry unchanged
            without_intersect_regions = mpbs_regions.subtract(footprints_regions, whole_region=True)
            for region in iter(without_intersect_regions):
                increased_score_mpbs_regions.add(region)

            increased_score_mpbs_regions.sort_score()

            fpr[i], tpr[i], roc_auc_1[i], roc_auc_10[i], roc_auc_50[i], roc_auc_100[i] = \
                roc_curve(increased_score_mpbs_regions)
            recall[i], precision[i], prc_auc_1[i], prc_auc_10[i], prc_auc_50[i], prc_auc_100[i] = \
                precision_recall_curve(increased_score_mpbs_regions)

            max_points.append(len(intersect_regions))

        elif footprint_type[i] == "SC":
            footprints_regions.sort_score()
            fpr[i], tpr[i], roc_auc_1[i], roc_auc_10[i], roc_auc_50[i], roc_auc_100[i] = \
                roc_curve(footprints_regions)
            recall[i], precision[i], prc_auc_1[i], prc_auc_10[i], prc_auc_50[i], prc_auc_100[i] = \
                precision_recall_curve(footprints_regions)

            max_points.append(len(footprints_regions))

    # Output the statistics results into text
    stats_fname = os.path.join(args.output_location, "{}_stats.txt".format(args.output_prefix))
    stats_header = ["METHOD", "AUC_100", "AUC_50", "AUC_10", "AUC_1", "AUPR_100", "AUPR_50", "AUPR_10", "AUPR_1"]
    with open(stats_fname, "w") as stats_file:
        stats_file.write("\t".join(stats_header) + "\n")
        for i in range(len(footprint_name)):
            stats_file.write(footprint_name[i] + "\t" +
                             str(roc_auc_100[i]) + "\t" + str(roc_auc_50[i]) + "\t" + str(roc_auc_10[i]) + "\t" +
                             str(roc_auc_1[i]) + "\t" + str(prc_auc_100[i]) + "\t" + str(prc_auc_50[i]) + "\t" +
                             str(prc_auc_10[i]) + "\t" + str(prc_auc_1[i]) + "\n")

    # Output the curves
    if args.print_roc_curve:
        label_x = "False Positive Rate"
        label_y = "True Positive Rate"
        curve_name = "ROC"
        plot_curve(footprint_name, args.output_location, fpr, tpr, roc_auc_100, label_x, label_y, args.output_prefix,
                   curve_name, max_points=max_points)
    if args.print_pr_curve:
        label_x = "Recall"
        label_y = "Precision"
        curve_name = "PRC"
        plot_curve(footprint_name, args.output_location, recall, precision, prc_auc_100, label_x, label_y,
                   args.output_prefix, curve_name, max_points=max_points)

    output_points(footprint_name, args.output_location, args.output_prefix, fpr, tpr, recall, precision)

# 需要导入模块: from rgt.GenomicRegionSet import GenomicRegionSet [as 别名]
# 或者: from rgt.GenomicRegionSet.GenomicRegionSet import subtract [as 别名]
        print("output:\t" + args.o)
        
        if not args.t:
            print("Please define the file for target regions.")
            sys.exit(1)
        else:
            print("target:\t" + args.t)

        # with open(args.target) as f:
        t = GenomicRegionSet("targets")
        t.read_bed(args.t)

        # with open(args.i) as fi, open(args.o, "w") as fo:
        input_regions = GenomicRegionSet("input")
        input_regions.read_bed(args.i)
        output_regions = input_regions.subtract(t,whole_region=True)
        output_regions.write_bed(args.o)
        print("complete.")

    ############### BED add columns #############################################
    elif args.mode == "bed_add_columns":
        print("input:\t" + args.i)
        print("reference:\t" + args.ref)
        print("output:\t" + args.o)
        
        if not args.ref:
            print("Please define the file for reference.")
            sys.exit(1)

        with open(args.ref) as f:
            genes = {}

# 需要导入模块: from rgt.GenomicRegionSet import GenomicRegionSet [as 别名]
# 或者: from rgt.GenomicRegionSet.GenomicRegionSet import subtract [as 别名]
 def subtract(self, x):
     tmp = GenomicRegionSet.subtract(self, x, whole_region=False)
     self.sequences = self._reconstruct_info(tmp)