def test_resampling_adaptive(self):
args = [
ctrl_data_txt, exp_data_txt, annotation, output, "-s", "1000", "-a"
]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
def test_resampling_ZINFNB(self):
args = [
ctrl_rep1, ctrl_rep2, small_annotation, output, "-s", "1000", "-n",
"zinfnb"
]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
def test_resampling_multistrain(self):
args = [ctrl_data_txt, exp_data_txt, ','.join([small_annotation, small_annotation]), output, "-h"]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
self.assertTrue(
os.path.isdir(hist_path),
"histpath expected: %s" % (hist_path))
def test_resampling_histogram(self):
args = [ctrl_data_txt, exp_data_txt, small_annotation, output, "-s", "1000", "-h"]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
self.assertTrue(
os.path.isdir(hist_path),
"histpath expected: %s" % (hist_path))
def test_resampling_TotReads(self):
args = [ctrl_rep1, ctrl_rep2, small_annotation, output, "-s", "1000", "-n", "totreads"]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
pvals, qvals = significant_pvals_qvals(output)
self.assertLessEqual(len(pvals), 5)
self.assertLessEqual(len(qvals), 1)
def test_resampling_TTR(self):
args = [
ctrl_rep1, ctrl_rep2, annotation, output, "-s", "1000", "-n", "TTR"
]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
hits = count_hits(output)
self.assertLessEqual(hits, 10)
def test_resampling_multistrain(self):
args = [
ctrl_data_txt, exp_data_txt,
','.join([small_annotation, small_annotation]), output, "-h"
]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
self.assertTrue(os.path.isdir(hist_path),
"histpath expected: %s" % (hist_path))
def test_resampling_histogram(self):
args = [
ctrl_data_txt, exp_data_txt, small_annotation, output, "-s",
"1000", "-h"
]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
self.assertTrue(os.path.isdir(hist_path),
"histpath expected: %s" % (hist_path))
def test_resampling_NZMean(self):
args = [
ctrl_rep1, ctrl_rep2, small_annotation, output, "-s", "1000", "-n",
"nzmean"
]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
pvals, qvals = significant_pvals_qvals(output)
self.assertLessEqual(len(pvals), 5)
self.assertLessEqual(len(qvals), 1)
def test_resampling(self):
args = [ctrl_data_txt, exp_data_txt, small_annotation, output, "-l"]
G = ResamplingMethod.fromargs(args)
G.Run()
(sig_pvals, sig_qvals) = (significant_pvals_qvals(output, pcol=-2, qcol=-1))
self.assertLessEqual(
abs(len(sig_pvals) - 37),
2,
"sig_pvals expected in range: %s, actual: %d" % ("[35, 39]", len(sig_qvals)))
self.assertLessEqual(
abs(len(sig_qvals) - 35),
2,
"sig_qvals expected in range: %s, actual: %d" % ("[33, 37]", len(sig_qvals)))
def test_resampling_adaptive(self):
args = [ctrl_data_txt, exp_data_txt, small_annotation, output, "-a"]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
(sig_pvals, sig_qvals) = (significant_pvals_qvals(output, pcol=-2, qcol=-1))
self.assertLessEqual(
abs(len(sig_pvals) - 37),
2,
"sig_pvals expected in range: %s, actual: %d" % ("[35, 39]", len(sig_qvals)))
self.assertLessEqual(
abs(len(sig_qvals) - 35),
1,
"sig_qvals expected in range: %s, actual: %d" % ("[34, 36]", len(sig_qvals)))
def test_resampling(self):
args = [ctrl_data_txt, exp_data_txt, small_annotation, output, "-l"]
G = ResamplingMethod.fromargs(args)
G.Run()
(sig_pvals, sig_qvals) = (significant_pvals_qvals(output,
pcol=-2,
qcol=-1))
self.assertLessEqual(
abs(len(sig_pvals) - 37), 2,
"sig_pvals expected in range: %s, actual: %d" %
("[35, 39]", len(sig_qvals)))
self.assertLessEqual(
abs(len(sig_qvals) - 35), 2,
"sig_qvals expected in range: %s, actual: %d" %
("[33, 37]", len(sig_qvals)))
def test_resampling_combined_wig(self):
# The conditions in the args should be matched case-insensitively.
args = ["-c", combined_wig, samples_metadata, "Glycerol", "cholesterol", small_annotation, output, "-a"]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
(sig_pvals, sig_qvals) = (significant_pvals_qvals(output, pcol=-2, qcol=-1))
print(len(sig_pvals))
print(len(sig_qvals))
self.assertLessEqual(
abs(len(sig_pvals) - 37),
2,
"sig_pvals expected in range: %s, actual: %d" % ("[35, 39]", len(sig_qvals)))
self.assertLessEqual(
abs(len(sig_qvals) - 35),
1,
"sig_qvals expected in range: %s, actual: %d" % ("[34, 36]", len(sig_qvals)))
def test_resampling_adaptive(self):
args = [
ctrl_data_txt, exp_data_txt, small_annotation, output, "-a",
"--ctrl_lib", "AA", "--exp_lib", "AAA"
]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
(sig_pvals, sig_qvals) = (significant_pvals_qvals(output,
pcol=-2,
qcol=-1))
self.assertLessEqual(
abs(len(sig_pvals) - 37), 2,
"sig_pvals expected in range: %s, actual: %d" %
("[35, 39]", len(sig_qvals)))
self.assertLessEqual(
abs(len(sig_qvals) - 35), 2,
"sig_qvals expected in range: %s, actual: %d" %
("[34, 36]", len(sig_qvals)))
def test_resampling_combined_wig(self):
# The conditions in the args should be matched case-insensitively.
args = [
"-c", combined_wig, samples_metadata, "Glycerol", "cholesterol",
small_annotation, output, "-a"
]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
(sig_pvals, sig_qvals) = (significant_pvals_qvals(output,
pcol=-2,
qcol=-1))
print(len(sig_pvals))
print(len(sig_qvals))
self.assertLessEqual(
abs(len(sig_pvals) - 37), 2,
"sig_pvals expected in range: %s, actual: %d" %
("[35, 39]", len(sig_qvals)))
self.assertLessEqual(
abs(len(sig_qvals) - 35), 1,
"sig_qvals expected in range: %s, actual: %d" %
("[34, 36]", len(sig_qvals)))
def test_resampling_ZINFNB(self):
args = [ctrl_rep1, ctrl_rep2, small_annotation, output, "-s", "1000", "-n", "zinfnb"]
G = ResamplingMethod.fromargs(args)
G.Run()
self.assertTrue(os.path.exists(output))
