def test_multiBigwigSummary(): outfile = "/tmp/result.bg" args = "bins -b {} {} --binSize 50 -o {}".format(BIGWIG_A, BIGWIG_B, outfile).split() bwCorr.main(args) resp = np.load(outfile) matrix = resp["matrix"] labels = resp["labels"] nt.assert_equal(matrix, np.array([[np.nan, np.nan], [np.nan, 1.0], [1.0, 1.0], [1.0, 2.0]])) nt.assert_equal(labels, ["testA_skipNAs.bw", "testB_skipNAs.bw"]) unlink(outfile)
def test_multiBigwigSummary_metagene(): outfile = '/tmp/_test.npz' args = "BED-file --metagene -b {0} {0} --BED {1}/test.gtf -o {2}".format(BIGWIG_C, ROOT, outfile).split() bwCorr.main(args) resp = np.load(outfile) matrix = resp['matrix'] labels = resp['labels'] nt.assert_equal(labels, ['test1.bw.bw', 'test1.bw.bw']) nt.assert_allclose(matrix, np.array([[20.28956028, 20.28956028], [22.1923501, 22.1923501]])) unlink(outfile)
def test_multiBigwigSummary_gtf(): outfile = '/tmp/_test.npz' args = "BED-file -b {0} {0} --BED {1}/test.gtf -o {2}".format(BIGWIG_C, ROOT, outfile).split() bwCorr.main(args) resp = np.load(outfile) matrix = resp['matrix'] labels = resp['labels'] nt.assert_equal(labels, ['test1.bw.bw', 'test1.bw.bw']) nt.assert_allclose(matrix, np.array([[27.475, 27.475], [27.31248719, 27.31248719]])) unlink(outfile)
def test_multiBigwigSummary(): outfile = '/tmp/result.bg' args = "bins -b {} {} --binSize 50 -o {}".format(BIGWIG_A, BIGWIG_B, outfile).split() bwCorr.main(args) resp = np.load(outfile) matrix = resp['matrix'] labels = resp['labels'] nt.assert_equal( matrix, np.array([[np.nan, np.nan], [np.nan, 1.], [1., 1.], [1., 2.]])) nt.assert_equal(labels, ['testA_skipNAs.bw', 'testB_skipNAs.bw']) unlink(outfile)
def test_multiBigwigSummary(): outfile = '/tmp/result.bg' args = "bins -b {} {} --binSize 50 -o {}".format(BIGWIG_A, BIGWIG_B, outfile).split() bwCorr.main(args) resp = np.load(outfile) matrix = resp['matrix'] labels = resp['labels'] nt.assert_equal(matrix, np.array([[np.nan, np.nan], [np.nan, 1.], [1., 1.], [1., 2.]])) nt.assert_equal(labels, ['testA_skipNAs.bw', 'testB_skipNAs.bw']) unlink(outfile)
def test_multiBigwigSummary_outrawcounts(): """ Test multiBigwigSummary raw counts output """ outfile = "/tmp/result.bg" args = "bins -b {} {} --binSize 50 -o /tmp/null --outRawCounts {} ".format(BIGWIG_A, BIGWIG_B, outfile).split() bwCorr.main(args) resp = open(outfile, "r").read() expected = """#'chr' 'start' 'end' 'testA_skipNAs.bw' 'testB_skipNAs.bw' 3R 0 50 nan nan 3R 50 100 nan 1.0 3R 100 150 1.0 1.0 3R 150 200 1.0 2.0 """ assert resp == expected, "{} != {}".format(resp, expected) unlink(outfile) unlink("/tmp/null")
def test_multiBigwigSummary_outrawcounts(): """ Test multiBigwigSummary raw counts output """ outfile = '/tmp/result.bg' args = "bins -b {} {} --binSize 50 -o /tmp/null --outRawCounts {} ".format( BIGWIG_A, BIGWIG_B, outfile).split() bwCorr.main(args) resp = open(outfile, 'r').read() expected = """#'chr' 'start' 'end' 'testA_skipNAs.bw' 'testB_skipNAs.bw' 3R 0 50 nan nan 3R 50 100 nan 1.0 3R 100 150 1.0 1.0 3R 150 200 1.0 2.0 """ assert resp == expected, "{} != {}".format(resp, expected) unlink(outfile) unlink("/tmp/null")
# output file as compressed numpy array out_file = bw + sample_name + '_replCorr' + '.npz' out_fileB = bw + sample_name + '_replCorr' + '.npz' # output figure in SVG format out_figP = bw + sample_name + '_replCorr_pear' + '.svg' out_figS = bw + sample_name + '_replCorr_spear' + '.svg' out_fig = bw + sample_name + '_replCorr' + '.svg' print repl1, repl2, out_file # --binSize 50 #args = "bins -b {} {} -o {}".format(repl1, repl2, out_file).split() args = "BED-file -b {} {} -o {} --binSize 500 --BED {}".format( repl1, repl2, out_fileB, bed_merged).split() bwCorr.main(args) #~ if not(os.path.exists(out_file)): #~ args = "bins -b {} {} -o {}".format(repl1, repl2, out_file).split() #~ #args = "BED-file -b {} {} -o {} --BED {}".format(repl1, repl2, out_file, bed_merged).split() #~ bwCorr.main(args) # Pearson correlation argsP = "-in {} --whatToPlot scatterplot --labels {} {} --corMethod {} -o {}".format( out_fileB, lb1, lb2, 'pearson', out_figP).split() pltCorr.main(argsP) # Spearman correlation argsS = "-in {} --whatToPlot scatterplot --labels {} {} --corMethod {} -o {}".format( out_fileB, lb1, lb2, 'spearman', out_figS).split() pltCorr.main(argsS)