def testOverextendingIsochoresFail(self):
'''
annotations: covering full workspace
isochores: covering full workspace
workspace: covering isochores partially
segments: uniformly distributed in isochores
'''
annotations = {}
ws_size = 1000000
nworkspaces = 20
for x in range(nworkspaces):
annotations["chr%i" % x] = gat.SegmentList(iter=[
(x * ws_size, (x + 1) * ws_size)
],
normalize=True)
workspacelist = gat.SegmentList(iter=[(0, nworkspaces * ws_size)],
normalize=True)
segmentlist = getRegularSegments(nworkspaces * ws_size, 1, 0.001)
ss, aa, ww = self.addSingleIsochore(segmentlist, annotations,
workspacelist)
self.check(ss,
aa,
ww,
"testChromosomalBiasFail",
fold_is_different=False)
def createSet(self):
'''create a test set.
Segments are constructed starting from the first residue in
the workspace and then added with one residue gap in-between
to prevent them from merging.
Segments are NOT randomly distributed within the workspace.
Segments can partially overlap the workspace, but gaps in
the workspace are accounted for properly.
Returns a list of segments and workspaces.
'''
workspace = self.createWorkspace()
assert len(workspace) > 0
workspace_idx = 0
start = max(0, workspace[workspace_idx][0] - (self.segment_length - 1))
segments = []
for x in range(self.nsegments):
end = start + self.segment_length
segments.append((start, end))
# add gap between segments
end += 1
if end > workspace[workspace_idx][1]:
# segment extending beyond current workspace segment.
# advance workspace
workspace_idx += 1
# continue advancing workspace until end of workspace is larger than end of segment
# accounts for segments straddling gaps in workspace or
# multiple workspace segments.
while workspace_idx < len(workspace) and workspace[workspace_idx][1] < end:
workspace_idx += 1
if workspace_idx == len(workspace):
# can't find any more workspaces, done
break
start = max(workspace[workspace_idx - 1][1] + 1,
workspace[workspace_idx][0] - (self.segment_length - 1))
else:
start = end
if len(segments) != self.nsegments:
# print segments
# print workspace
# E.warn( "not enough space in workspace for %i segments" % (self.nsegments) )
pass
_segments = gat.SegmentList(iter=segments, normalize=True)
_workspace = gat.SegmentList(iter=workspace, normalize=True)
return _segments, _workspace
def computeExpectedCoverage(self, samples):
# expected coverage
# number_of_samples * segment_length / workspace_length
# modifier:
# can be simplified
# number of segments
nsegments = float(len(self.segments))
# number of nucleotides in segments overlapping the workspace
tmp = gat.SegmentList(clone=self.workspace)
tmp.intersect(self.segments)
expected_overlap = tmp.sum()
# for computing expected overlap use complete segments
# as there is no truncation
segment_size = self.segments.sum()
# density of segment nucleotides in workspace
segment_density = float(expected_overlap) / self.workspace.sum()
# average length of segments within workspace
expected_segment_length = segment_size / nsegments
# expected coverage of segments
expected_coverage = len(samples) * \
expected_overlap / float(self.workspace.sum())
# float(workspace.sum()) / (workspace.sum() + segments.sum() * len(workspace) )
return (nsegments, segment_size, segment_density,
expected_overlap,
expected_segment_length,
expected_coverage)
def testLengthBiasFail(self):
annotations, start, end = getRegularAnnotations()
workspacelist = gat.SegmentList(iter=[(start - 100, end + 100)],
normalize=True)
segmentlist = getRegularSegments(workspacelist.sum(),
self.segment_size,
self.segment_density)
def testIntervalsPartialOverlap(self):
'''test with intervals with
increasing amount of overlap.
'''
return
workspaces, segments, annotations = \
gat.IntervalCollection("workspace"), \
gat.IntervalCollection("segment"), \
gat.IntervalCollection("annotation")
workspace_size = 1000
size = 100
# workspace of size 1000000
workspaces.add(
"default", "chr1",
gat.SegmentList(iter=[
(0, workspace_size),
], normalize=True))
workspace = workspaces["default"]
# segment of size 10
segments.add("default", "chr1",
gat.SegmentList(iter=[
(0, size),
], normalize=True))
# annotations: a collection of segments.
# overlap increases
annotations.add(
"full", "chr1",
gat.SegmentList(iter=[
(y, size + y),
], normalize=True))
self.check(workspace, annotations, segments)
def testDefault(self):
annotations, start, end = getRegularAnnotations()
workspacelist = gat.SegmentList(iter=[(start - 100, end + 100)],
normalize=True)
segmentlist = getRegularSegments(workspacelist.sum(),
self.segment_size,
self.segment_density)
ss, aa, ww = self.addSingleIsochore(segmentlist, annotations,
workspacelist)
self.check(ss, aa, ww, "testDefaultPass", fold_is_different=False)
def getRegularSegments(workspace_size, size, density):
'''return regular segments of size *size* and *density* in
workspace.
'''
nsegments = int(workspace_size * density / size)
rest = workspace_size * (1.0 - density)
distance = rest // (nsegments + 2)
assert distance > 0
start = int(distance)
s = []
for x in range(nsegments):
s.append((start, start + size))
start += distance + size
return gat.SegmentList(iter=s, normalize=True)
def testCompositionBiasFail(self):
isochores = gat.SegmentList()
for x in range(self.nisochores):
start = x * self.isochore_size * self.nisochores
i = start
for x in range(self.nisochores):
isochores.add(i, i + self.isochore_size)
i += self.isochore_size
# segments have increasing spacing within isochores
s = getIncrementallySpacedSegments(
self.nisochores * self.isochore_size, self.segment_size,
self.segment_density)
s.shift(start)
segmentlist.extend(s)
def testAscertainmentBiasFail2(self):
annotations, start, end = getRegularAnnotations()
workspacelist = gat.SegmentList(iter=[(start - 100, end + 100)],
normalize=True)
segmentlist = getRegularSegmentsInAnnotations(annotations,
self.segment_size,
self.segment_density)
ss, aa, ww = self.addSingleIsochore(segmentlist, annotations,
workspacelist)
self.check(ss,
aa,
ww,
"testAscertainmentBiasFail2",
fold_is_different=True)
def testChromosomalBiasPass(self):
annotations1, start, end = getRegularAnnotations()
annotations2, start, end = getRegularAnnotations()
workspacelist = gat.SegmentList(iter=[(start - 100, end + 100)],
normalize=True)
# segments only in first part of workspace
segmentlist = getRegularSegments(end - start + 200, self.segment_size,
self.segment_density)
ss, aa, ww = self.addIsochores((segmentlist, None),
(annotations1, annotations2),
(workspacelist, workspacelist))
self.check(ss,
aa,
ww,
"testChromosomalBiasPass",
fold_is_different=False)
def testAscertainmentBiasFail1(self):
segment_density = 0.1
segment_size = 100
annotations, start, end = getRegularAnnotations()
# workspace twice as large as needed
workspacelist = gat.SegmentList(iter=[(start - 100, 2 * end + 100)],
normalize=True)
segmentlist = getRegularSegments(workspacelist.sum(),
self.segment_size,
self.segment_density)
ss, aa, ww = self.addSingleIsochore(segmentlist, annotations,
workspacelist)
self.check(ss,
aa,
ww,
"testAscertainmentBiasFail1",
fold_is_different=True)
def computeExpectedCoverageOld(self, samples):
# expected coverage
# number_of_samples * segment_length / workspace_length
# modifier:
# can be simplified
# number of segments
nsegments = float(len(self.segments))
# number of nucleotides in segments overlapping the workspace
tmp = gat.SegmentList(clone=self.workspace)
tmp.intersect(self.segments)
segment_overlap = tmp.sum()
# density of segment nucleotides in workspace
segment_density = float(segment_overlap) / self.workspace.sum()
segment_length = segment_size / nsegments
expected = len(samples) * segment_overlap / float(self.workspace.sum())
# float(workspace.sum()) / (workspace.sum() + segments.sum() * len(workspace) )
return nsegments, segment_overlap, segment_density, segment_length, expected
def getIncrementallySpacedSegments(workspace_size, size, density):
'''return segments of size *size* and *density* in
workspace. The inter-segment distance increases over the range.
'''
nsegments = int(workspace_size * density / size)
rest = workspace_size * (1.0 - density)
ngaps = nsegments + 2
# sum of distances is total with increment.
increment = rest // sum(range(ngaps))
assert increment > 0
distance = increment
start = int(distance)
s = []
for x in range(nsegments):
s.append((start, start + size))
distance += increment
start += distance + size
return gat.SegmentList(iter=s, normalize=True)
def testChromosomalBiasFail(self):
annotations1, start, end = getRegularAnnotations()
annotations2, start, end = getRegularAnnotations()
# concatenate annotations
annotations2.shift(workspacelist.sum())
annotations1.extend(annotations2)
workspacelist = gat.SegmentList(iter=[(start - 100, 2 * end + 100)],
normalize=True)
# segments only in first part of workspace
segmentlist = getRegularSegments(end - start + 200, self.segment_size,
self.segment_density)
ss, aa, ww = self.addSingleIsochore(segmentlist, annotations,
workspacelist)
self.check(ss,
aa,
ww,
"testChromosomalBiasFail",
fold_is_different=True)
def getRegularAnnotations(
sizes=(100, 200, 400, 800, 1600, 3200, 6400, 128000, 256000, 512000),
nsegments=20,
distance=100):
'''return annotations of size *size*. Each annotation has
*nsegments* and segments are *distance* bases apart.
Returns a dictionary of segment lists and start/end of
the annotations.
'''
annos = [[] for x in range(len(sizes))]
start = distance
for x in range(nsegments):
for s, size in enumerate(sizes):
annos[s].append((start, start + size))
start += size + distance
annotations = {}
for x, size in enumerate(sizes):
annotations["size%06i" % size] = gat.SegmentList(iter=annos[x],
normalize=True)
return annotations, distance, start - distance
def validate(self, samples):
# filename = getPlotFilename()
# compute expected coverage
(nsegments, segment_size, segment_density,
expected_overlap,
expected_segment_length,
expected_coverage) = self.computeExpectedCoverage(samples)
# compute actual coverage counts
counts_within_workspace, segment_sizes, starts, ends = computeSegmentDensityProfile(self.workspace,
samples)
# plotCounts( None,
# counts_within_workspace,
# segment_sizes,
# starts, ends,
# self.workspace,
# expected_coverage = expected_coverage,
# density = segment_density )
##################################
# check if each sample has the correct number of nucleotides
nucleotide_ok = True
sums = [x.sum() for x in samples]
overlaps = []
for x, s in enumerate(samples):
tmp = gat.SegmentList(clone=self.workspace, normalize=True)
s.normalize()
tmp.intersect(s)
ovl = tmp.sum()
overlaps.append(ovl)
if ovl != expected_overlap:
nucleotide_ok = False
E.warn("incorrect number of nucleotides in sample %i, got %i, expected %i, %s" %
(x, ovl, expected_overlap, samples[x]))
##################################
# check if average overlap is ok
overlap_ok = True
average_overlap = numpy.mean(overlaps)
average_overlap_d = abs(
average_overlap - expected_overlap) / float(expected_overlap)
if average_overlap_d >= self.stringency_level:
overlap_ok = False
E.warn("expected_overlap counts (%f) != sampled counts (%f)" % (expected_overlap,
average_overlap))
##################################
# check average coverage
average_ok = True
average_coverage = counts_within_workspace.mean()
average_d = abs(average_coverage - expected_coverage) / \
float(expected_coverage)
if average_d >= self.stringency_level:
average_ok = False
E.warn("expected_coverage counts (%f) != sampled counts (%f)" % (expected_coverage,
counts_within_workspace.mean()))
# check for uniform coverage
uniform_ok = True
stddev = numpy.std(counts_within_workspace)
uniform_d = stddev / float(expected_coverage)
if uniform_d >= self.stringency_level:
uniform_ok = False
E.warn("coverage variation too large : stddev (%f) / %f = %f > %f" %
(stddev,
expected_coverage,
uniform_d,
self.stringency_level))
return "\t".join(("%i" % nucleotide_ok,
"%i" % average_ok,
"%i" % uniform_ok,
"%i" % overlap_ok,
"%6.4f" % average_d,
"%6.4f" % uniform_d,
"%6.4f" % segment_density,
"%6.4f" % average_overlap,
"%6.4f" % expected_overlap,
"%6.4f" % average_coverage,
"%6.4f" % expected_coverage))
def getRegularSegmentsInAnnotations(annotations, segment_size,
segment_density):
'''get a collection of segments overlapping with annotations.
Segments are uniformly distributed over annotations. Longer annotations
will have more segments.
Segments straddling annotations are truncated.
'''
merged = gat.SegmentList()
for x, i in annotations.items():
merged.extend(i)
merged.normalize()
workspace_size = merged.sum()
nsegments = int(workspace_size * segment_density / segment_size)
rest = workspace_size * (1.0 - segment_density)
distance = rest // (nsegments + 2)
# get regularly placed segments in a virtual workspace
segments = getRegularSegments(workspace_size, segment_size,
segment_density)
# size of annotations
n = 0
m = 0
s = []
# negative for inter-segment segments
overhang = distance
annotation_start, annotation_end = merged[m]
lannotation = annotation_end - annotation_start
for start, end in segments:
lsegment = end - start
# place intergap segment, split if necessary
while overhang > 0:
if overhang < lannotation:
annotation_start += overhang
overhang = 0
break
overhang -= lannotation
m += 1
annotation_start, annotation_end = merged[m]
lannotation = annotation_end - annotation_start
lannotation = annotation_end - annotation_start
# place segment - split if necessary
while lsegment > 0:
if lsegment < lannotation:
s.append((annotation_start, annotation_start + lsegment))
annotation_start += lsegment
break
s.append((annotation_start, annotation_end))
lsegment -= lannotation
m += 1
annotation_start, annotation_end = merged[m]
lannotation = annotation_end - annotation_start
lannotation = annotation_end - annotation_start
segments = gat.SegmentList(iter=s, normalize=True)
noverlap = segments.overlapWithSegments(merged)
return segments