# HG changeset patch # User devteam # Date 1396357915 14400 # Node ID e11314245c2a47122d516a0f1a02849bbbc74a59 Imported from capsule None diff -r 000000000000 -r e11314245c2a WeightedAverage.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/WeightedAverage.py Tue Apr 01 09:11:55 2014 -0400 @@ -0,0 +1,94 @@ +#!/usr/bin/env python +""" +usage: %prog bed_file_1 bed_file_2 out_file + -1, --cols1=N,N,N,N: Columns for chr, start, end, strand in first file + -2, --cols2=N,N,N,N,N: Columns for chr, start, end, strand, name/value in second file +""" + +import collections +import sys +#import numpy +from galaxy import eggs +import pkg_resources +pkg_resources.require( "bx-python" ) +from galaxy.tools.util.galaxyops import * +from bx.cookbook import doc_optparse + + +#export PYTHONPATH=~/galaxy/lib/ +#running command python WeightedAverage.py interval_interpolate.bed value_interpolate.bed interpolate_result.bed + +def stop_err(msg): + sys.stderr.write(msg) + sys.exit() + + +def FindRate(chromosome, start_stop, dictType): + OverlapList = [] + for tempO in dictType[chromosome]: + DatabaseInterval = [tempO[0], tempO[1]] + Overlap = GetOverlap( start_stop, DatabaseInterval ) + if Overlap > 0: + OverlapList.append([Overlap, tempO[2]]) + + if len(OverlapList) > 0: + SumRecomb = 0 + SumOverlap = 0 + for member in OverlapList: + SumRecomb += member[0]*member[1] + SumOverlap += member[0] + averageRate = SumRecomb/SumOverlap + return averageRate + else: + return 'NA' + + +def GetOverlap(a, b): + return min(a[1], b[1])-max(a[0], b[0]) + + +options, args = doc_optparse.parse( __doc__ ) + +try: + chr_col_1, start_col_1, end_col_1, strand_col1 = parse_cols_arg( options.cols1 ) + chr_col_2, start_col_2, end_col_2, strand_col2, name_col_2 = parse_cols_arg( options.cols2 ) + input1, input2, input3 = args +except Exception, eee: + print eee + stop_err( "Data issue: click the pencil icon in the history item to correct the metadata attributes." ) + +fd2 = open(input2) +lines2 = fd2.readlines() +RecombChrDict = collections.defaultdict(list) + +skipped = 0 +for line in lines2: + temp = line.strip().split() + try: + assert float(temp[int(name_col_2)]) + except: + skipped += 1 + continue + tempIndex = [int(temp[int(start_col_2)]), int(temp[int(end_col_2)]), float(temp[int(name_col_2)])] + RecombChrDict[temp[int(chr_col_2)]].append(tempIndex) + +print "Skipped %d features with invalid values" % (skipped) + +fd1 = open(input1) +lines = fd1.readlines() +finalProduct = '' +for line in lines: + temp = line.strip().split('\t') + chromosome = temp[int(chr_col_1)] + start = int(temp[int(start_col_1)]) + stop = int(temp[int(end_col_1)]) + start_stop = [start, stop] + RecombRate = FindRate( chromosome, start_stop, RecombChrDict ) + try: + RecombRate = "%.4f" % (float(RecombRate)) + except: + RecombRate = RecombRate + finalProduct += line.strip()+'\t'+str(RecombRate)+'\n' +fdd = open(input3, 'w') +fdd.writelines(finalProduct) +fdd.close() diff -r 000000000000 -r e11314245c2a WeightedAverage.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/WeightedAverage.xml Tue Apr 01 09:11:55 2014 -0400 @@ -0,0 +1,71 @@ + + of the values of features overlapping an interval + WeightedAverage.py $genomic_interval $genomic_feature $out_file1 -1 ${genomic_interval.metadata.chromCol},${genomic_interval.metadata.startCol},${genomic_interval.metadata.endCol},${genomic_interval.metadata.strandCol} -2 ${genomic_feature.metadata.chromCol},${genomic_feature.metadata.startCol},${genomic_feature.metadata.endCol},${genomic_feature.metadata.strandCol},${genomic_feature.metadata.nameCol} + + + + + + + + + + + + + + + + + + + + +.. class:: infomark + +**What it does** + +For each interval in your first dataset, this tool calculates the weighted average value of the overlapping features in your second dataset. + +- When a genomic interval partially or totally overlaps a single genomic feature, the value of that genomic feature is assigned to the genomic interval. +- When a genomic interval partially or totally overlaps with more than one genomic features, the average of the values of the overlapping genomic features weighted by the corresponding number of overlapping bases is assigned to the genomic interval. +- When a genomic interval does not overlap with any genomic feature, 'NA' will be assigned as it's value. + +----- + +.. class:: warningmark + +**Note** + +The input datasets should be in **bed** or **interval** format. Please use "edit attributes"/pencil icon to specify the column containing the values for the features in the second dataset as **name/identifier** column. + +The output will contain all the columns in the first input plus a new column containing the assigned value for each interval. + +----- + +**Example** + +- Suppose our first dataset contains the following **genomic intervals**:: + + chr start stop + chr1 1000 2000 + chr1 3000 5000 + chr1 8000 9000 + +- and our second dataset contains the following **genomic features** each having an associated value (in fourth column) :: + + chr start stop name + chr1 900 1200 0.5 + chr1 2900 3100 0.2 + chr1 4800 5100 0.8 + +- For each **genomic interval** in our first dataset, this tool calculates the weighted average value of the overlapping **genomic features** in our second dataset :: + + chr1 1000 2000 0.5 + chr1 3000 5000 0.6 + chr1 8000 9000 NA + + + + + \ No newline at end of file diff -r 000000000000 -r e11314245c2a 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