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_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)
