def checkExactOverlap(bed1, bed2):
""" make sure two bed files cover same region exactly: a requirement for all
code based on the comparisons in this module."""
errorMessage = (
"Bed files %s and %s cannot be compared. xxx. "
" Input files must be both sorted, cover the exact same region,"
" and contain no self-overlaps.") % (bed1, bed2)
# empty file may break downstream comparisons
size1 = os.path.getsize(bed1)
size2 = os.path.getsize(bed2)
if size1 == 0 or size2 == 0:
raise RuntimeError(
errorMessage.replace("xxx", "one or both inputs empty"))
# test self-overlap and sorting
intervals1 = readBedIntervals(bed1, sort=False)
for i in xrange(1, len(intervals1)):
if intersectSize(intervals1[i - 1], intervals1[i]) != 0:
raise RuntimeError(
errorMessage.replace(
"xxx", "Overlapping intervals %s and %s found in input1" %
(intervals1[i - 1], intervals1[i])))
if intervals1[i - 1] > intervals1[i]:
raise RuntimeError(
errorMessage.replace(
"xxx", "Out of order intervals %s and %s found in input1" %
(intervals1[i - 1], intervals1[i])))
# test self-overlap and sorting
intervals2 = readBedIntervals(bed1, sort=False)
for i in xrange(1, len(intervals2)):
if intersectSize(intervals2[i - 1], intervals2[i]) != 0:
raise RuntimeError(
errorMessage.replace(
"xxx", "Overlapping intervals %s and %s found in input2" %
(intervals2[i - 1], intervals2[i])))
if intervals2[i - 1] > intervals2[i]:
raise RuntimeError(
errorMessage.replace(
"xxx", "Out of order intervals %s and %s found in input2" %
(intervals2[i - 1], intervals2[i])))
# test intersection size
tempFile = getLocalTempPath("Temp_test", ".bed")
runShellCommand("subtractBed -a %s -b %s > %s" % (bed1, bed2, tempFile))
if os.path.getsize(tempFile) != 0:
runShellCommand("rm -f %s" % tempFile)
raise RuntimeError(
errorMessage.replace("xxx",
"Input1 covers regions outside input2"))
runShellCommand("subtractBed -a %s -b %s > %s" % (bed2, bed1, tempFile))
if os.path.getsize(tempFile) != 0:
runShellCommand("rm -f %s" % tempFile)
raise RuntimeError(
errorMessage.replace("xxx",
"Input2 covers regions outside input1"))
runShellCommand("rm -f %s" % tempFile)
def checkExactOverlap(bed1, bed2):
""" make sure two bed files cover same region exactly: a requirement for all
code based on the comparisons in this module."""
errorMessage = ("Bed files %s and %s cannot be compared. xxx. "
" Input files must be both sorted, cover the exact same region,"
" and contain no self-overlaps.") % (bed1, bed2)
# empty file may break downstream comparisons
size1 = os.path.getsize(bed1)
size2 = os.path.getsize(bed2)
if size1 == 0 or size2 == 0:
raise RuntimeError(errorMessage.replace("xxx", "one or both inputs empty"))
# test self-overlap and sorting
intervals1 = readBedIntervals(bed1, sort=False)
for i in xrange(1, len(intervals1)):
if intersectSize(intervals1[i-1], intervals1[i]) != 0:
raise RuntimeError(errorMessage.replace(
"xxx", "Overlapping intervals %s and %s found in input1" % (
intervals1[i-1], intervals1[i])))
if intervals1[i-1] > intervals1[i]:
raise RuntimeError(errorMessage.replace(
"xxx", "Out of order intervals %s and %s found in input1" % (
intervals1[i-1], intervals1[i])))
# test self-overlap and sorting
intervals2 = readBedIntervals(bed1, sort=False)
for i in xrange(1, len(intervals2)):
if intersectSize(intervals2[i-1], intervals2[i]) != 0:
raise RuntimeError(errorMessage.replace(
"xxx", "Overlapping intervals %s and %s found in input2" % (
intervals2[i-1], intervals2[i])))
if intervals2[i-1] > intervals2[i]:
raise RuntimeError(errorMessage.replace(
"xxx", "Out of order intervals %s and %s found in input2" % (
intervals2[i-1], intervals2[i])))
# test intersection size
tempFile = getLocalTempPath("Temp_test", ".bed")
runShellCommand("subtractBed -a %s -b %s > %s" % (bed1, bed2, tempFile))
if os.path.getsize(tempFile) != 0:
runShellCommand("rm -f %s" % tempFile)
raise RuntimeError(errorMessage.replace(
"xxx", "Input1 covers regions outside input2"))
runShellCommand("subtractBed -a %s -b %s > %s" % (bed2, bed1, tempFile))
if os.path.getsize(tempFile) != 0:
runShellCommand("rm -f %s" % tempFile)
raise RuntimeError(errorMessage.replace(
"xxx", "Input2 covers regions outside input1"))
runShellCommand("rm -f %s" % tempFile)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Subselect some output of chunkBedRegions.py")
parser.add_argument("inBed", help="Input bed file (generated with chunkBedRegions.py)")
parser.add_argument("sampleSize", help="Desired sample size (in bases).", type=int)
args = parser.parse_args()
tempBedToolPath = initBedTool()
assert os.path.exists(args.inBed)
bedIntervals = readBedIntervals(args.inBed)
outIntervals = []
curSize = 0
# dumb n^2 alg should be enough for our current purposes
while curSize < args.sampleSize and len(bedIntervals) > 0:
idx = random.randint(0, len(bedIntervals)-1)
interval = bedIntervals[idx]
sampleLen = interval[2] - interval[1]
if sampleLen + curSize > args.sampleSize:
sampleLen = (sampleLen + curSize) - args.sampleSize
interval = (interval[0], interval[1], interval[1] + sampleLen)
outIntervals.append(interval)
curSize += sampleLen
del bedIntervals[idx]
for interval in sorted(outIntervals):
sys.stdout.write("%s\t%d\t%d\n" % interval)
cleanBedTool(tempBedToolPath)
def baserize(inBed, outBed):
outFile = open(outBed, "w")
for interval in readBedIntervals(inBed):
for i in xrange(interval[2] - interval[1]):
outFile.write("%s\t%d\t%d\n" %
(interval[0], interval[1] + i, interval[1] + i + 1))
outFile.close()
def fillGaps(inBed):
""" Make two interval sets from a given bed file:
filledIntervals: Set of intervals with intervals added between consecutive
intervals on same seq (ala addBedGaps.y)
mergedIntervals: Set of intervals spanning each continuous region from
above (ala getMergeItnervals)
probably reimplementing stuff but oh well """
filledIntervals = []
mergedIntervals = []
intervals = readBedIntervals(inBed, ncol=4, sort=True)
if len(intervals) == 0:
return [], []
prevInterval = None
for interval in intervals:
if prevInterval is not None and prevInterval[0] == interval[0] and\
prevInterval[2] != interval[1]:
# update fill for discontinuity
assert prevInterval[2] < interval[1]
filledIntervals.append(
(interval[0], prevInterval[2], interval[1], filTok))
if prevInterval is None or prevInterval[0] != interval[0]:
# update merge for new sequence
mergedIntervals.append(interval)
else:
# extend merge for same sequence
mergedIntervals[-1] = (mergedIntervals[-1][0],
mergedIntervals[-1][1], interval[2],
mergedIntervals[-1][3])
# update fill with current interval
filledIntervals.append(interval)
prevInterval = interval
return filledIntervals, mergedIntervals
def combineTrack(track, outPath, tempRegionPath, iter, args):
""" merge track with outPath """
# make sure track is of form chrom start end state
tempColPath = getLocalTempPath("Temp", "_col.bed")
tempColFile = open(tempColPath, "w")
vc = track.getValCol() + 1
if track.getDist() == "binary":
assert track.getName() != args.outside
vc = 3
bedIntervals = readBedIntervals(track.getPath(), vc,
sort = True)
for bedInterval in bedIntervals:
outStr = "\t".join([str(x) for x in bedInterval])
if track.getDist() == "binary":
# state name = track name for binary track
outStr += "\t%s" % track.getName()
outStr += "\n"
tempColFile.write(outStr)
tempColFile.close()
# intersect the target region
tempIntersectPath = getLocalTempPath("Temp", "_int.bed")
runShellCommand("intersectBed -a %s -b %s > %s" % (
tempColPath, tempRegionPath, tempIntersectPath))
# add the outside states
tempGappedPath = getLocalTempPath("Temp", "_gap.bed")
runShellCommand("addBedGaps.py --state %s %s %s %s" % (
args.outside, tempRegionPath, tempIntersectPath, tempGappedPath))
# fit the names with previous interations' result
tempFitPath = getLocalTempPath("Temp", "_fit.bed")
if iter == 0:
runShellCommand("cp %s %s" % (tempGappedPath, tempFitPath))
else:
runShellCommand("fitStateNames.py %s %s %s --qualThresh %f --ignoreTgt %s" % (
outPath, tempGappedPath, tempFitPath, args.fitThresh, args.outside))
# now merge into outPath
runShellCommand("cat %s >> %s" % (tempFitPath, outPath))
runShellCommand("removeBedOverlaps.py %s > %s" % (outPath, tempColPath))
runShellCommand("mv %s %s" % (tempColPath, outPath))
# clean crap (note tempCol should already be gone)
runShellCommand("rm -f %s" % tempColPath)
runShellCommand("rm -f %s" % tempIntersectPath)
runShellCommand("rm -f %s" % tempGappedPath)
runShellCommand("rm -f %s" % tempFitPath)
def makeNearnessBED(intervals, args):
""" for each interval, measure distance to nearest interval in args.nearness and
write as score """
compIntervals = readBedIntervals(args.nearness, ncol=4, sort=True)
if len(intervals) == 0:
return ""
# only correct if sorted non-overlapping
outBedString = ""
i = 0
for interval in intervals:
distI = sys.maxint
for j in xrange(i, len(compIntervals)):
distJ = distance(interval, compIntervals[j])
if distJ <= distI:
i, distI = j, distJ
else:
break
outBedString += "%s\t%d\t%d\t%s\t%d\n" % (
interval[0], interval[1], interval[2], interval[3], distI)
return outBedString
def makeNearnessBED(intervals, args):
""" for each interval, measure distance to nearest interval in args.nearness and
write as score """
compIntervals = readBedIntervals(args.nearness, ncol = 4, sort = True)
if len(intervals) == 0:
return ""
# only correct if sorted non-overlapping
outBedString = ""
i = 0
for interval in intervals:
distI = sys.maxint
for j in xrange(i, len(compIntervals)):
distJ = distance(interval, compIntervals[j])
if distJ <= distI:
i, distI = j, distJ
else:
break
outBedString += "%s\t%d\t%d\t%s\t%d\n" % (interval[0], interval[1], interval[2],
interval[3], distI)
return outBedString
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Subselect some output of chunkBedRegions.py")
parser.add_argument(
"inBed", help="Input bed file (generated with chunkBedRegions.py)")
parser.add_argument("sampleSize",
help="Desired sample size (in bases).",
type=int)
args = parser.parse_args()
tempBedToolPath = initBedTool()
assert os.path.exists(args.inBed)
bedIntervals = readBedIntervals(args.inBed)
outIntervals = []
curSize = 0
# dumb n^2 alg should be enough for our current purposes
while curSize < args.sampleSize and len(bedIntervals) > 0:
idx = random.randint(0, len(bedIntervals) - 1)
interval = bedIntervals[idx]
sampleLen = interval[2] - interval[1]
if sampleLen + curSize > args.sampleSize:
sampleLen = (sampleLen + curSize) - args.sampleSize
interval = (interval[0], interval[1], interval[1] + sampleLen)
outIntervals.append(interval)
curSize += sampleLen
del bedIntervals[idx]
for interval in sorted(outIntervals):
sys.stdout.write("%s\t%d\t%d\n" % interval)
cleanBedTool(tempBedToolPath)
def fillGaps(inBed):
""" Make two interval sets from a given bed file:
filledIntervals: Set of intervals with intervals added between consecutive
intervals on same seq (ala addBedGaps.y)
mergedIntervals: Set of intervals spanning each continuous region from
above (ala getMergeItnervals)
probably reimplementing stuff but oh well """
filledIntervals = []
mergedIntervals = []
intervals = readBedIntervals(inBed, ncol=4, sort=True)
if len(intervals) == 0:
return [], []
prevInterval = None
for interval in intervals:
if prevInterval is not None and prevInterval[0] == interval[0] and\
prevInterval[2] != interval[1]:
# update fill for discontinuity
assert prevInterval[2] < interval[1]
filledIntervals.append((interval[0], prevInterval[2], interval[1],
filTok))
if prevInterval is None or prevInterval[0] != interval[0]:
# update merge for new sequence
mergedIntervals.append(interval)
else:
# extend merge for same sequence
mergedIntervals[-1] = (mergedIntervals[-1][0],
mergedIntervals[-1][1],
interval[2],
mergedIntervals[-1][3])
# update fill with current interval
filledIntervals.append(interval)
prevInterval = interval
return filledIntervals, mergedIntervals
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Compare two bed files where Model states are represented"
" in a column. Used to determine sensitivity and specificity. NOTE"
" that both bed files must be sorted and cover the exact same regions"
" of the same genome.")
parser.add_argument("bed1", help="Bed file (TRUTH)")
parser.add_argument("bed2", help="Bed file covering same regions in same"
" order as bed1")
parser.add_argument("--col", help="Column of bed files to use for state"
" (currently only support 4(name) or 5(score))",
default = 4, type = int)
parser.add_argument("--thresh", help="Threshold to consider interval from"
" bed1 covered by bed2.",
type=float, default=0.8)
parser.add_argument("--plot", help="Path of file to write Precision/Recall"
" graphs to in PDF format", default=None)
parser.add_argument("--ignore", help="Comma-separated list of stateNames to"
" ignore", default=None)
parser.add_argument("--strictPrec", help="By default, precision is computed"
" in a manner strictly symmetric to recall. So calling"
" compareBedStates.py A.bed B.bed would give the exact"
" same output as compareBedStates.py B.bed A.bed except"
" precision and recall values would be swapped. With "
" this option, a predicted element only counts toward"
" precision if it overlaps with 80pct of the true"
" element, as opposed to only needing 80pct of itself"
" overlapping with the true element. ",
action="store_true", default = False)
parser.add_argument("--noBase", help="Skip base-level stats (and only show"
" interval stats). Runs faster", action="store_true",
default=False)
parser.add_argument("--noFrag", help="Do not allow fragmented matches in"
"interval predictions. ie if a single truth interval"
" is covered by a series of predicted intervals, only "
"the best match will be counted if this flag is used",
action="store_true", default=False)
parser.add_argument("--tl", help="Path to tracks XML file. Used to cut "
"out mask tracks so they are removed from comparison."
" (convenience option to not have to manually run "
"subtractBed everytime...)", default=None)
parser.add_argument("--delMask", help="Entirely remove intervals from "
"mask tracks that are > given length. Probably "
"only want to set to non-zero value K if using"
" with a prediction that was processed with "
"interpolateMaskedRegions.py --max K",
type=int, default=0)
parser.add_argument("--window", help="A comma-delimited 5-tuple of "
"windowSize,stateName,compType,score,outBed. "
"Where windowSize is the sliding window size "
"(overlap .5), stateName is target stateName,"
" compType is in {base,interval,weighted}, sore is"
" in {f1,precision,recall} and "
"outBed is the path of a bedFile to write positional"
" accuracy to. For example, --window 1000000,TE,base,f1"
",acc.bed will write base-level f1 for 1MB sliding windows"
" to acc.bed. These can be viewed on the browser by first"
" converting to BigWig.", default=None)
args = parser.parse_args()
tempBedToolPath = initBedTool()
if args.ignore is not None:
args.ignore = set(args.ignore.split(","))
else:
args.ignore = set()
assert args.col == 4 or args.col == 5
print "Commandline %s" % " ".join(sys.argv)
origArgs = copy.deepcopy(args)
tempFiles = []
if args.tl is not None:
cutBed1 = cutOutMaskIntervals(args.bed1, args.delMask,
sys.maxint, args.tl)
cutBed2 = cutOutMaskIntervals(args.bed2, args.delMask,
sys.maxint, args.tl)
if cutBed1 is not None:
assert cutBed2 is not None
tempFiles += [cutBed1, cutBed2]
args.bed1 = cutBed1
args.bed2 = cutBed2
checkExactOverlap(args.bed1, args.bed2)
if args.window is not None:
runPositionalComparison(argv, origArgs)
intervals1 = readBedIntervals(args.bed1, ncol = args.col)
intervals2 = readBedIntervals(args.bed2, ncol = args.col)
if args.noBase is False:
stats = compareBaseLevel(intervals1, intervals2, args.col - 1)[0]
totalRight, totalWrong, accMap = summarizeBaseComparision(stats, args.ignore)
print "Base counts [False Negatives, False Positives, True Positives]:"
print stats
totalBoth = totalRight + totalWrong
accuracy = float(totalRight) / float(totalBoth)
print "Accuaracy: %d / %d = %f" % (totalRight, totalBoth, accuracy)
print "State-by-state (Precision, Recall):"
print "Base-by-base Accuracy"
print accMap
trueStats = compareIntervalsOneSided(intervals1, intervals2, args.col -1,
args.thresh, False, not args.noFrag)[0]
predStats = compareIntervalsOneSided(intervals2, intervals1, args.col -1,
args.thresh, args.strictPrec,
not args.noFrag)[0]
intAccMap = summarizeIntervalComparison(trueStats, predStats, False,
args.ignore)
intAccMapWeighted = summarizeIntervalComparison(trueStats, predStats, True,
args.ignore)
print "\nInterval Accuracy"
print intAccMap
print ""
print "\nWeighted Interval Accuracy"
print intAccMapWeighted
print ""
# print some row data to be picked up by scrapeBenchmarkRow.py
if args.noBase is False:
header, row = summaryRow(accuracy, stats, accMap)
print " ".join(header)
print " ".join(row)
# make graph
if args.plot is not None:
if canPlot is False:
raise RuntimeError("Unable to write plots. Maybe matplotlib is "
"not installed?")
writeAccPlots(accuracy, accMap, intAccMap, intAccMapWeighted,
args.thresh, args.plot)
if len(tempFiles) > 0:
runShellCommand("rm -f %s" % " ".join(tempFiles))
cleanBedTool(tempBedToolPath)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Fill in masked intervals of an hmm prediction "
"(from teHmmEval.py) with state corresponding to surrounding"
" intervals.")
parser.add_argument("tracksXML",
help="XML track list (used to id masking"
" tracks")
parser.add_argument("allBed",
help="Target scope. Masked intervals outside"
" of these regions will not be included")
parser.add_argument(
"inBed",
help="TE prediction BED file. State labels"
" should probably be mapped (ie with fitStateNames.py)")
parser.add_argument("outBed",
help="Output BED. Will be equivalent to"
" the input bed except all gaps corresponding to "
"masked intervals will be filled")
parser.add_argument(
"--maxLen",
help="Maximum length of a masked interval"
" to fill (inclusive). Use --delMask option with same value"
"if running compareBedStates.py after.",
type=int,
default=sys.maxint)
parser.add_argument("--default",
help="Default label to give to masked "
"region if no label can be determined",
default="0")
parser.add_argument(
"--tgts",
help="Only relabel gaps that "
"are flanked on both sides by the same state, and this state"
" is in this comma- separated list. --default used for other"
" gaps. If not targetst specified then all states checked.",
default=None)
parser.add_argument(
"--oneSidedTgts",
help="Only relabel gaps that "
"are flanked on at least one side by a state in this comma-"
"separated list --default used for other gaps",
default=None)
parser.add_argument(
"--onlyDefault",
help="Add the default state (--default) no"
" no all masked gaps no matter what. ie ignoring all other "
"logic",
action="store_true",
default=False)
parser.add_argument(
"--cut",
help="Cut out gaps for masked tracks from the input."
" By default, the input is expected to come from the HMM "
"with mask intervals already absent, and will crash on with"
" an assertion error if an overlap is detected.",
action="store_true",
default=False)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
# make sets
tgtSet = set()
if args.tgts is not None:
tgtSet = set(args.tgts.split(","))
oneSidedTgtSet = set()
if args.oneSidedTgts is not None:
oneSidedTgtSet = set(args.oneSidedTgts.split(","))
assert len(tgtSet.intersection(oneSidedTgtSet)) == 0
# read the track list
trackList = TrackList(args.tracksXML)
maskTracks = trackList.getMaskTracks()
# read the input bed
inBed = args.inBed
if args.cut is True:
inBed = cutOutMaskIntervals(inBed, -1, args.maxLen + 1, args.tracksXML)
inputIntervals = readBedIntervals(inBed, ncol=4, sort=True)
if args.cut is True:
runShellCommand("rm -f %s" % inBed)
if len(maskTracks) == 0 or len(inputIntervals) == 0:
runShellCommand("cp %s %s" % (args.inBed, args.outBed))
logger.warning("No mask tracks located in %s or"
" %s empty" % (args.tracksXML, args.inBed))
return 0
# make a temporary, combined, merged masking bed file
tempMaskBed = getLocalTempPath("Temp_mb", ".bed")
for maskTrack in maskTracks:
assert os.path.isfile(maskTrack.getPath())
runShellCommand(
"cat %s | setBedCol.py 3 mask | awk \'{print $1\"\t\"$2\"\t\"$3}\'>> %s"
% (maskTrack.getPath(), tempMaskBed))
maskedIntervals = getMergedBedIntervals(tempMaskBed, sort=True)
resolvedMasks = 0
if len(inputIntervals) == 0:
logger.warning("No mask tracks located in %s" % args.tracksXML)
return
inputIdx = 0
rightFlank = inputIntervals[inputIdx]
tempOutMask = getLocalTempPath("Temp_om", ".bed")
tempOutMaskFile = open(tempOutMask, "w")
for maskIdx, maskInterval in enumerate(maskedIntervals):
if maskInterval[2] - maskInterval[1] > args.maxLen:
continue
# find candidate right flank
while rightFlank < maskInterval:
if inputIdx == len(inputIntervals) - 1:
rightFlank = None
break
else:
inputIdx += 1
rightFlank = inputIntervals[inputIdx]
# candidate left flank
leftFlank = None
if inputIdx > 0:
leftFlank = inputIntervals[inputIdx - 1]
# identify flanking states if the intervals perfectly abut
leftState = None
if leftFlank is not None:
if leftFlank[0] == maskInterval[0] and leftFlank[
2] == maskInterval[1]:
leftState = str(leftFlank[3])
else:
assert intersectSize(leftFlank, maskInterval) == 0
rightState = None
if rightFlank is not None:
if rightFlank[0] == maskInterval[0] and rightFlank[
1] == maskInterval[2]:
rightState = str(rightFlank[3])
else:
assert intersectSize(rightFlank, maskInterval) == 0
# choose a state for the mask interval
maskState = str(args.default)
if args.onlyDefault is True:
pass
elif leftState is not None and leftState == rightState:
if len(tgtSet) == 0 or leftState in tgtSet:
maskState = leftState
elif leftState in oneSidedTgtSet:
maskState = leftState
elif rightState in oneSidedTgtSet:
maskState = rightState
# write our mask interval
tempOutMaskFile.write(
"%s\t%d\t%d\t%s\n" %
(maskInterval[0], maskInterval[1], maskInterval[2], maskState))
tempOutMaskFile.close()
tempMergePath1 = getLocalTempPath("Temp_mp", ".bed")
tempMergePath2 = getLocalTempPath("Temp_mp", ".bed")
runShellCommand("cp %s %s ; cat %s >> %s" %
(args.inBed, tempMergePath1, tempOutMask, tempMergePath1))
runShellCommand("cat %s | sortBed > %s" % (tempMergePath1, tempMergePath2))
tempScopePath = getLocalTempPath("temp_all", ".bed")
runShellCommand("mergeBed -i %s |sortBed > %s" %
(args.allBed, tempScopePath))
runShellCommand("intersectBed -a %s -b %s > %s" %
(tempMergePath2, tempScopePath, args.outBed))
runShellCommand("rm -f %s" % " ".join([
tempMaskBed, tempOutMask, tempMergePath1, tempMergePath2, tempScopePath
]))
cleanBedTool(tempBedToolPath)
def baserize(inBed, outBed):
outFile = open(outBed, "w")
for interval in readBedIntervals(inBed):
for i in xrange(interval[2] - interval[1]):
outFile.write("%s\t%d\t%d\n" % (interval[0], interval[1] + i, interval[1] + i + 1))
outFile.close()
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=" Given two bed files: a prediction and a true (or target)"
" annotation, re-label the prediction's state names so that they "
" best match the true annotation. Usees same logic as "
" compareBedStates.py for determining accuracy")
parser.add_argument("tgtBed", help="Target bed file")
parser.add_argument("predBed", help="Predicted bed file to re-label. ")
parser.add_argument("outBed", help="Output bed (relabeling of predBed)")
parser.add_argument("--col",
help="Column of bed files to use for state"
" (currently only support 4(name) or 5(score))",
default=4,
type=int)
parser.add_argument(
"--intThresh",
help="Threshold to consider interval from"
" tgtBed covered by predBed. If not specified, then base"
" level statistics will be used. Value in range (0,1]",
type=float,
default=None)
parser.add_argument("--noFrag",
help="Dont allow fragmented interval matches ("
"see help for --frag in compareBedStates.py). Only"
" relevant with --intThresh",
action="store_true",
default=False)
parser.add_argument(
"--qualThresh",
help="Minimum match ratio between truth"
" and prediction to relabel prediction. Example, if"
" predicted state X overlaps target state LTR 25 pct of "
"the time, then qualThresh must be at least 0.25 to "
"label X as LTR in the output. Value in range (0, 1]",
type=float,
default=0.1)
parser.add_argument("--ignore",
help="Comma-separated list of stateNames to"
" ignore (in prediction)",
default=None)
parser.add_argument("--ignoreTgt",
help="Comma-separated list of stateNames to"
" ignore (int target)",
default=None)
parser.add_argument("--tgt",
help="Comma-separated list of stateNames to "
" consider (in target). All others will be ignored",
default=None)
parser.add_argument(
"--unique",
help="If more than one predicted state maps"
" to the same target state, add a unique id (numeric "
"suffix) to the output so that they can be distinguished",
action="store_true",
default=False)
parser.add_argument("--model",
help="Apply state name mapping to the model"
" in the specified path (it is strongly advised to"
" make a backup of the model first)",
default=None)
parser.add_argument("--noMerge",
help="By default, adjacent intervals"
" with the same state name in the output are "
"automatically merged into a single interval. This"
" flag disables this.",
action="store_true",
default=False)
parser.add_argument("--hm",
help="Write confusion matrix as heatmap in PDF"
" format to specified file",
default=None)
parser.add_argument("--old",
help="Use old name mapping logic which just "
"takes biggest overlap in forward confusion matrix. "
"faster than new default logic which does the greedy"
" f1 optimization",
action="store_true",
default=False)
parser.add_argument("--fdr",
help="Use FDR cutoff instead of (default)"
" greedy F1 optimization for state labeling",
type=float,
default=None)
parser.add_argument("--tl",
help="Path to tracks XML file. Used to cut "
"out mask tracks so they are removed from comparison."
" (convenience option to not have to manually run "
"subtractBed everytime...)",
default=None)
parser.add_argument(
"--colOrder",
help="List of states used to force"
" ordering in heatmap (otherwise alphabetical) columns. These"
" states will correspond to the tgtBed when --old used and"
" --predBed otherwise.",
default=None)
parser.add_argument(
"--hmCovRow",
help="Path to write 1-row heatmap of "
"state coverage (fraction of bases). only works with --hm",
default=None)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
if args.ignore is not None:
args.ignore = set(args.ignore.split(","))
else:
args.ignore = set()
if args.ignoreTgt is not None:
args.ignoreTgt = set(args.ignoreTgt.split(","))
else:
args.ignoreTgt = set()
if args.tgt is not None:
args.tgt = set(args.tgt.split(","))
if args.old is True:
raise RuntimeError("--tgt option not implemented for --old")
else:
args.tgt = set()
if args.old is True and args.fdr is not None:
raise RuntimeError("--old and --fdr options are exclusive")
assert args.col == 4 or args.col == 5
tempFiles = []
if args.tl is not None:
cutBedTgt = cutOutMaskIntervals(args.tgtBed, -1, sys.maxint, args.tl)
cutBedPred = cutOutMaskIntervals(args.predBed, -1, sys.maxint, args.tl)
if cutBedTgt is not None:
assert cutBedPred is not None
tempFiles += [cutBedTgt, cutBedPred]
args.tgtBed = cutBedTgt
args.predBed = cutBedPred
checkExactOverlap(args.tgtBed, args.predBed)
intervals1 = readBedIntervals(args.tgtBed, ncol=args.col)
intervals2 = readBedIntervals(args.predBed, ncol=args.col)
cfName = "reverse"
if args.old is True:
intervals1, intervals2 = intervals2, intervals1
cfName = "forward"
# generate confusion matrix based on accuracy comparison using
# base or interval stats as desired
if args.intThresh is not None:
logger.info("Computing interval %s confusion matrix" % cfName)
confMat = compareIntervalsOneSided(intervals2, intervals1,
args.col - 1, args.intThresh, False,
not args.noFrag)[1]
else:
logger.info("Computing base %s confusion matrix" % cfName)
confMat = compareBaseLevel(intervals2, intervals1, args.col - 1)[1]
logger.info("%s Confusion Matrix:\n%s" % (cfName, str(confMat)))
# find the best "true" match for each predicted state
if args.old is True:
intervals1, intervals2 = intervals2, intervals1
stateMap = getStateMapFromConfMatrix_simple(confMat)
else:
stateMap = getStateMapFromConfMatrix(confMat, args.tgt, args.ignoreTgt,
args.ignore, args.qualThresh,
args.fdr)
# filter the stateMap to take into account the command-line options
# notably --ignore, --ignoreTgt, --qualThresh, and --unique
filterStateMap(stateMap, args)
logger.info("State Map:\n%s", str(stateMap))
# write the model if spefied
if args.model is not None:
applyNamesToModel(stateMap, args.model)
# generate the output bed using the statemap
writeFittedBed(intervals2, stateMap, args.outBed, args.col - 1,
args.noMerge, args.ignoreTgt)
# write the confusion matrix as heatmap
if args.hm is not None:
if canPlot is False:
raise RuntimeError("Unable to write heatmap. Maybe matplotlib is "
"not installed?")
writeHeatMap(confMat, args.hm, args.colOrder, args.hmCovRow)
if len(tempFiles) > 0:
runShellCommand("rm -f %s" % " ".join(tempFiles))
cleanBedTool(tempBedToolPath)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Evaluate a given data set with a trained HMM. Display"
" the log probability of the input data given the model, and "
"optionally output the most likely sequence of hidden states.")
parser.add_argument("tracksInfo", help="Path of Tracks Info file "
"containing paths to genome annotation tracks")
parser.add_argument("inputModel", help="Path of hmm created with"
"teHmmTrain.py")
parser.add_argument("bedRegions", help="Intervals to process")
parser.add_argument("--bed", help="path of file to write viterbi "
"output to (most likely sequence of hidden states)",
default=None)
parser.add_argument("--numThreads", help="Number of threads to use (only"
" applies to CFG parser for the moment)",
type=int, default=1)
parser.add_argument("--slice", help="Make sure that regions are sliced"
" to a maximum length of the given value. Most "
"useful when model is a CFG to keep memory down. "
"When 0, no slicing is done",
type=int, default=0)
parser.add_argument("--segment", help="Use the intervals in bedRegions"
" as segments which each count as a single column"
" for evaluattion. Note the model should have been"
" trained with the --segment option pointing to this"
" same bed file.", action="store_true", default=False)
parser.add_argument("--segLen", help="Effective segment length used for"
" normalizing input segments (specifying 0 means no"
" normalization applied)", type=int, default=0)
parser.add_argument("--maxPost", help="Use maximum posterior decoding instead"
" of Viterbi for evaluation", action="store_true",
default=False)
parser.add_argument("--pd", help="Output BED file for posterior distribution. Must"
" be used in conjunction with --pdStates (View on the "
"browser via bedGraphToBigWig)", default=None)
parser.add_argument("--pdStates", help="comma-separated list of state names to use"
" for computing posterior distribution. For example: "
" --pdStates inside,LTR_left,LTR_right will compute the probability"
", for each observation, that the hidden state is inside OR LTR_left"
" OR LTR_right. Must be used with --pd to specify output "
"file.", default=None)
parser.add_argument("--bic", help="save Bayesian Information Criterion (BIC) score"
" in given file", default=None)
parser.add_argument("--ed", help="Output BED file for emission distribution. Must"
" be used in conjunction with --edStates (View on the "
"browser via bedGraphToBigWig)", default=None)
parser.add_argument("--edStates", help="comma-separated list of state names to use"
" for computing emission distribution. For example: "
" --edStates inside,LTR_left for each obsercation the probability "
" that inside emitted that observaiton plus the probabillity that"
" LTR_left emitted it. If more than one state is selected, this "
" is not a distribution, but a sum of distributions (and values"
" can exceed 1). Mostly for debugging purposes. Note output in LOG",
default=None)
parser.add_argument("--chroms", help="list of chromosomes, or regions, to run in parallel"
" (in BED format). input regions will be intersected with each line"
" in this file, and the result will correspsond to an individual job",
default=None)
parser.add_argument("--proc", help="number of processes (use in conjunction with --chroms)",
type=int, default=1)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
if args.slice <= 0:
args.slice = sys.maxint
elif args.segment is True:
raise RuntimeError("--slice and --segment options are not compatible at "
"this time")
if (args.pd is not None) ^ (args.pdStates is not None):
raise RuntimeError("--pd requires --pdStates and vice versa")
if (args.ed is not None) ^ (args.edStates is not None):
raise RuntimeError("--ed requires --edStates and vice versa")
if args.bed is None and (args.pd is not None or args.ed is not None):
raise RuntimeError("Both --ed and --pd only usable in conjunction with"
" --bed")
if args.chroms is not None:
# hack to allow chroms argument to chunk and rerun
parallelDispatch(argv, args)
cleanBedTool(tempBedToolPath)
return 0
# load model created with teHmmTrain.py
logger.info("loading model %s" % args.inputModel)
model = loadModel(args.inputModel)
if isinstance(model, MultitrackCfg):
if args.maxPost is True:
raise RuntimeErorr("--post not supported on CFG models")
# apply the effective segment length
if args.segLen > 0:
assert args.segment is True
model.getEmissionModel().effectiveSegmentLength = args.segLen
# read intervals from the bed file
logger.info("loading target intervals from %s" % args.bedRegions)
mergedIntervals = getMergedBedIntervals(args.bedRegions, ncol=4)
if mergedIntervals is None or len(mergedIntervals) < 1:
raise RuntimeError("Could not read any intervals from %s" %
args.bedRegions)
# slice if desired
choppedIntervals = [x for x in slicedIntervals(mergedIntervals, args.slice)]
# read segment intervals
segIntervals = None
if args.segment is True:
logger.info("loading segment intervals from %s" % args.bedRegions)
segIntervals = readBedIntervals(args.bedRegions, sort=True)
# load the input
# read the tracks, while intersecting them with the given interval
trackData = TrackData()
# note we pass in the trackList that was saved as part of the model
# because we do not want to generate a new one.
logger.info("loading tracks %s" % args.tracksInfo)
trackData.loadTrackData(args.tracksInfo, choppedIntervals,
model.getTrackList(),
segmentIntervals=segIntervals)
# do the viterbi algorithm
if isinstance(model, MultitrackHmm):
algname = "viterbi"
if args.maxPost is True:
algname = "posterior decoding"
logger.info("running %s algorithm" % algname)
elif isinstance(model, MultitrackCfg):
logger.info("running CYK algorithm")
vitOutFile = None
if args.bed is not None:
vitOutFile = open(args.bed, "w")
totalScore = 0
tableIndex = 0
totalDatapoints = 0
# Note: in general there's room to save on memory by only computing single
# track table at once (just need to add table by table interface to hmm...)
posteriors = [None] * trackData.getNumTrackTables()
posteriorsFile = None
posteriorsMask = None
if args.pd is not None:
posteriors = model.posteriorDistribution(trackData)
posteriorsFile = open(args.pd, "w")
posteriorsMask = getPosteriorsMask(args.pdStates, model)
assert len(posteriors[0][0]) == len(posteriorsMask)
emProbs = [None] * trackData.getNumTrackTables()
emissionsFile = None
emissionsMask = None
if args.ed is not None:
emProbs = model.emissionDistribution(trackData)
emissionsFile = open(args.ed, "w")
emissionsMask = getPosteriorsMask(args.edStates, model)
assert len(emProbs[0][0]) == len(emissionsMask)
decodeFunction = model.viterbi
if args.maxPost is True:
decodeFunction = model.posteriorDecode
for i, (vitLogProb, vitStates) in enumerate(decodeFunction(trackData,
numThreads=args.numThreads)):
totalScore += vitLogProb
if args.bed is not None or args.pd is not None:
if args.bed is not None:
vitOutFile.write("#Viterbi Score: %f\n" % (vitLogProb))
trackTable = trackData.getTrackTableList()[tableIndex]
tableIndex += 1
statesToBed(trackTable,
vitStates, vitOutFile, posteriors[i], posteriorsMask,
posteriorsFile, emProbs[i], emissionsMask, emissionsFile)
totalDatapoints += len(vitStates) * trackTable.getNumTracks()
print "Viterbi (log) score: %f" % totalScore
if isinstance(model, MultitrackHmm) and model.current_iteration is not None:
print "Number of EM iterations: %d" % model.current_iteration
if args.bed is not None:
vitOutFile.close()
if posteriorsFile is not None:
posteriorsFile.close()
if emissionsFile is not None:
emissionsFile.close()
if args.bic is not None:
bicFile = open(args.bic, "w")
# http://en.wikipedia.org/wiki/Bayesian_information_criterion
lnL = float(totalScore)
try:
k = float(model.getNumFreeParameters())
except:
# numFreeParameters still not done for semi-supervised
# just pass through a 0 instead of crashing for now
k = 0.0
n = float(totalDatapoints)
bic = -2.0 * lnL + k * (np.log(n) + np.log(2 * np.pi))
bicFile.write("%f\n" % bic)
bicFile.write("# = -2.0 * lnL + k * (lnN + ln(2 * np.pi))\n"
"# where lnL=%f k=%d (%d states) N=%d (%d obs * %d tracks) lnN=%f\n" % (
lnL, int(k), model.getEmissionModel().getNumStates(), int(totalDatapoints),
totalDatapoints / model.getEmissionModel().getNumTracks(),
model.getEmissionModel().getNumTracks(), np.log(n)))
bicFile.close()
cleanBedTool(tempBedToolPath)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=" Given two bed files: a prediction and a true (or target)"
" annotation, re-label the prediction's state names so that they "
" best match the true annotation. Usees same logic as "
" compareBedStates.py for determining accuracy")
parser.add_argument("tgtBed", help="Target bed file")
parser.add_argument("predBed", help="Predicted bed file to re-label. ")
parser.add_argument("outBed", help="Output bed (relabeling of predBed)")
parser.add_argument("--col", help="Column of bed files to use for state"
" (currently only support 4(name) or 5(score))",
default = 4, type = int)
parser.add_argument("--intThresh", help="Threshold to consider interval from"
" tgtBed covered by predBed. If not specified, then base"
" level statistics will be used. Value in range (0,1]",
type=float, default=None)
parser.add_argument("--noFrag", help="Dont allow fragmented interval matches ("
"see help for --frag in compareBedStates.py). Only"
" relevant with --intThresh", action="store_true",
default=False)
parser.add_argument("--qualThresh", help="Minimum match ratio between truth"
" and prediction to relabel prediction. Example, if"
" predicted state X overlaps target state LTR 25 pct of "
"the time, then qualThresh must be at least 0.25 to "
"label X as LTR in the output. Value in range (0, 1]",
type=float, default=0.1)
parser.add_argument("--ignore", help="Comma-separated list of stateNames to"
" ignore (in prediction)", default=None)
parser.add_argument("--ignoreTgt", help="Comma-separated list of stateNames to"
" ignore (int target)", default=None)
parser.add_argument("--tgt", help="Comma-separated list of stateNames to "
" consider (in target). All others will be ignored",
default=None)
parser.add_argument("--unique", help="If more than one predicted state maps"
" to the same target state, add a unique id (numeric "
"suffix) to the output so that they can be distinguished",
action="store_true", default=False)
parser.add_argument("--model", help="Apply state name mapping to the model"
" in the specified path (it is strongly advised to"
" make a backup of the model first)", default=None)
parser.add_argument("--noMerge", help="By default, adjacent intervals"
" with the same state name in the output are "
"automatically merged into a single interval. This"
" flag disables this.", action="store_true",
default=False)
parser.add_argument("--hm", help="Write confusion matrix as heatmap in PDF"
" format to specified file", default = None)
parser.add_argument("--old", help="Use old name mapping logic which just "
"takes biggest overlap in forward confusion matrix. "
"faster than new default logic which does the greedy"
" f1 optimization", action="store_true", default=False)
parser.add_argument("--fdr", help="Use FDR cutoff instead of (default)"
" greedy F1 optimization for state labeling",
type=float, default=None)
parser.add_argument("--tl", help="Path to tracks XML file. Used to cut "
"out mask tracks so they are removed from comparison."
" (convenience option to not have to manually run "
"subtractBed everytime...)", default=None)
parser.add_argument("--colOrder", help="List of states used to force"
" ordering in heatmap (otherwise alphabetical) columns. These"
" states will correspond to the tgtBed when --old used and"
" --predBed otherwise.", default=None)
parser.add_argument("--hmCovRow", help="Path to write 1-row heatmap of "
"state coverage (fraction of bases). only works with --hm",
default=None)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
if args.ignore is not None:
args.ignore = set(args.ignore.split(","))
else:
args.ignore = set()
if args.ignoreTgt is not None:
args.ignoreTgt = set(args.ignoreTgt.split(","))
else:
args.ignoreTgt = set()
if args.tgt is not None:
args.tgt = set(args.tgt.split(","))
if args.old is True:
raise RuntimeError("--tgt option not implemented for --old")
else:
args.tgt = set()
if args.old is True and args.fdr is not None:
raise RuntimeError("--old and --fdr options are exclusive")
assert args.col == 4 or args.col == 5
tempFiles = []
if args.tl is not None:
cutBedTgt = cutOutMaskIntervals(args.tgtBed, -1, sys.maxint, args.tl)
cutBedPred = cutOutMaskIntervals(args.predBed, -1, sys.maxint, args.tl)
if cutBedTgt is not None:
assert cutBedPred is not None
tempFiles += [cutBedTgt, cutBedPred]
args.tgtBed = cutBedTgt
args.predBed = cutBedPred
checkExactOverlap(args.tgtBed, args.predBed)
intervals1 = readBedIntervals(args.tgtBed, ncol = args.col)
intervals2 = readBedIntervals(args.predBed, ncol = args.col)
cfName = "reverse"
if args.old is True:
intervals1, intervals2 = intervals2, intervals1
cfName = "forward"
# generate confusion matrix based on accuracy comparison using
# base or interval stats as desired
if args.intThresh is not None:
logger.info("Computing interval %s confusion matrix" % cfName)
confMat = compareIntervalsOneSided(intervals2, intervals1, args.col -1,
args.intThresh, False,
not args.noFrag)[1]
else:
logger.info("Computing base %s confusion matrix" % cfName)
confMat = compareBaseLevel(intervals2, intervals1, args.col - 1)[1]
logger.info("%s Confusion Matrix:\n%s" % (cfName, str(confMat)))
# find the best "true" match for each predicted state
if args.old is True:
intervals1, intervals2 = intervals2, intervals1
stateMap = getStateMapFromConfMatrix_simple(confMat)
else:
stateMap = getStateMapFromConfMatrix(confMat, args.tgt, args.ignoreTgt,
args.ignore, args.qualThresh,
args.fdr)
# filter the stateMap to take into account the command-line options
# notably --ignore, --ignoreTgt, --qualThresh, and --unique
filterStateMap(stateMap, args)
logger.info("State Map:\n%s", str(stateMap))
# write the model if spefied
if args.model is not None:
applyNamesToModel(stateMap, args.model)
# generate the output bed using the statemap
writeFittedBed(intervals2, stateMap, args.outBed, args.col-1, args.noMerge,
args.ignoreTgt)
# write the confusion matrix as heatmap
if args.hm is not None:
if canPlot is False:
raise RuntimeError("Unable to write heatmap. Maybe matplotlib is "
"not installed?")
writeHeatMap(confMat, args.hm, args.colOrder, args.hmCovRow)
if len(tempFiles) > 0:
runShellCommand("rm -f %s" % " ".join(tempFiles))
cleanBedTool(tempBedToolPath)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Fill in masked intervals of an hmm prediction "
"(from teHmmEval.py) with state corresponding to surrounding"
" intervals.")
parser.add_argument("tracksXML", help="XML track list (used to id masking"
" tracks")
parser.add_argument("allBed", help="Target scope. Masked intervals outside"
" of these regions will not be included")
parser.add_argument("inBed", help="TE prediction BED file. State labels"
" should probably be mapped (ie with fitStateNames.py)")
parser.add_argument("outBed", help="Output BED. Will be equivalent to"
" the input bed except all gaps corresponding to "
"masked intervals will be filled")
parser.add_argument("--maxLen", help="Maximum length of a masked interval"
" to fill (inclusive). Use --delMask option with same value"
"if running compareBedStates.py after.",
type=int, default=sys.maxint)
parser.add_argument("--default", help="Default label to give to masked "
"region if no label can be determined", default="0")
parser.add_argument("--tgts", help="Only relabel gaps that "
"are flanked on both sides by the same state, and this state"
" is in this comma- separated list. --default used for other"
" gaps. If not targetst specified then all states checked.",
default=None)
parser.add_argument("--oneSidedTgts", help="Only relabel gaps that "
"are flanked on at least one side by a state in this comma-"
"separated list --default used for other gaps",
default=None)
parser.add_argument("--onlyDefault", help="Add the default state (--default) no"
" no all masked gaps no matter what. ie ignoring all other "
"logic", action="store_true", default=False)
parser.add_argument("--cut", help="Cut out gaps for masked tracks from the input."
" By default, the input is expected to come from the HMM "
"with mask intervals already absent, and will crash on with"
" an assertion error if an overlap is detected.",
action="store_true", default=False)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
# make sets
tgtSet = set()
if args.tgts is not None:
tgtSet = set(args.tgts.split(","))
oneSidedTgtSet = set()
if args.oneSidedTgts is not None:
oneSidedTgtSet = set(args.oneSidedTgts.split(","))
assert len(tgtSet.intersection(oneSidedTgtSet)) == 0
# read the track list
trackList = TrackList(args.tracksXML)
maskTracks = trackList.getMaskTracks()
# read the input bed
inBed = args.inBed
if args.cut is True:
inBed = cutOutMaskIntervals(inBed, -1, args.maxLen + 1, args.tracksXML)
inputIntervals = readBedIntervals(inBed, ncol = 4, sort = True)
if args.cut is True:
runShellCommand("rm -f %s" % inBed)
if len(maskTracks) == 0 or len(inputIntervals) == 0:
runShellCommand("cp %s %s" % (args.inBed, args.outBed))
logger.warning("No mask tracks located in %s or"
" %s empty" % (args.tracksXML, args.inBed))
return 0
# make a temporary, combined, merged masking bed file
tempMaskBed = getLocalTempPath("Temp_mb", ".bed")
for maskTrack in maskTracks:
assert os.path.isfile(maskTrack.getPath())
runShellCommand("cat %s | setBedCol.py 3 mask | awk \'{print $1\"\t\"$2\"\t\"$3}\'>> %s" % (
maskTrack.getPath(), tempMaskBed))
maskedIntervals = getMergedBedIntervals(tempMaskBed, sort = True)
resolvedMasks = 0
if len(inputIntervals) == 0:
logger.warning("No mask tracks located in %s" % args.tracksXML)
return
inputIdx = 0
rightFlank = inputIntervals[inputIdx]
tempOutMask = getLocalTempPath("Temp_om", ".bed")
tempOutMaskFile = open(tempOutMask, "w")
for maskIdx, maskInterval in enumerate(maskedIntervals):
if maskInterval[2] - maskInterval[1] > args.maxLen:
continue
# find candidate right flank
while rightFlank < maskInterval:
if inputIdx == len(inputIntervals) - 1:
rightFlank = None
break
else:
inputIdx += 1
rightFlank = inputIntervals[inputIdx]
# candidate left flank
leftFlank = None
if inputIdx > 0:
leftFlank = inputIntervals[inputIdx - 1]
# identify flanking states if the intervals perfectly abut
leftState = None
if leftFlank is not None:
if leftFlank[0] == maskInterval[0] and leftFlank[2] == maskInterval[1]:
leftState = str(leftFlank[3])
else:
assert intersectSize(leftFlank, maskInterval) == 0
rightState = None
if rightFlank is not None:
if rightFlank[0] == maskInterval[0] and rightFlank[1] == maskInterval[2]:
rightState = str(rightFlank[3])
else:
assert intersectSize(rightFlank, maskInterval) == 0
# choose a state for the mask interval
maskState = str(args.default)
if args.onlyDefault is True:
pass
elif leftState is not None and leftState == rightState:
if len(tgtSet) == 0 or leftState in tgtSet:
maskState = leftState
elif leftState in oneSidedTgtSet:
maskState = leftState
elif rightState in oneSidedTgtSet:
maskState = rightState
# write our mask interval
tempOutMaskFile.write("%s\t%d\t%d\t%s\n" % (maskInterval[0], maskInterval[1],
maskInterval[2], maskState))
tempOutMaskFile.close()
tempMergePath1 = getLocalTempPath("Temp_mp", ".bed")
tempMergePath2 = getLocalTempPath("Temp_mp", ".bed")
runShellCommand("cp %s %s ; cat %s >> %s" % (args.inBed, tempMergePath1,
tempOutMask, tempMergePath1))
runShellCommand("cat %s | sortBed > %s" % (tempMergePath1, tempMergePath2))
tempScopePath = getLocalTempPath("temp_all", ".bed")
runShellCommand("mergeBed -i %s |sortBed > %s" % (args.allBed, tempScopePath))
runShellCommand("intersectBed -a %s -b %s > %s" % (tempMergePath2, tempScopePath,
args.outBed))
runShellCommand("rm -f %s" % " ".join([tempMaskBed, tempOutMask, tempMergePath1,
tempMergePath2, tempScopePath]))
cleanBedTool(tempBedToolPath)
def parallelDispatch(argv, args):
""" chunk up input with chrom option. recursivlely launch eval. merge
results """
jobList = []
chromIntervals = readBedIntervals(args.chroms, sort=True)
chromFiles = []
regionFiles = []
segFiles = []
statsFiles = []
offset = args.co
for chrom in chromIntervals:
cmdToks = copy.deepcopy(argv)
cmdToks[cmdToks.index("--chrom") + 1] = ""
cmdToks[cmdToks.index("--chrom")] = ""
chromPath = getLocalTempPath("TempChromPath", ".bed")
cpFile = open(chromPath, "w")
cpFile.write("%s\t%d\t%d\t0\t0\t.\n" % (chrom[0], chrom[1], chrom[2]))
cpFile.close()
regionPath = getLocalTempPath("Temp", ".bed")
runShellCommand("intersectBed -a %s -b %s | sortBed > %s" % (args.allBed,
chromPath,
regionPath))
if os.path.getsize(regionPath) < 2:
continue
offset += int(chrom[2]) - int(chrom[1])
regionFiles.append(regionPath)
chromFiles.append(chromPath)
cmdToks[2] = regionPath
segPath = getLocalTempPath("Temp", ".bed")
cmdToks[3] = segPath
segFiles.append(segPath)
if "--co" in cmdToks:
cmdToks[cmdToks.index("--co")+1] = str(offset)
else:
cmdToks.append("--co")
cmdToks.append(str(offset))
if args.stats is not None:
statsPath = getLocalTempPath("Temp", ".bed")
cmdToks[cmdToks.index("--stats")+1] = statsPath
statsFiles.append(statsPath)
cmd = " ".join(cmdToks)
jobList.append(cmd)
runParallelShellCommands(jobList, args.proc)
for i in xrange(len(jobList)):
if i == 0:
ct = ">"
else:
ct = ">>"
runShellCommand("cat %s %s %s" % (segFiles[i], ct, args.outBed))
if len(statsFiles) > 0:
runShellCommand("cat %s %s %s" % (statsFiles[i], ct, args.stats))
for i in itertools.chain(chromFiles, regionFiles, segFiles, statsFiles):
runShellCommand("rm %s" % i)
def main(argv=None):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=" Thin wrapper of teHmmTrain.py and teHmmEval.py "
"to generate a table of Number-of-HMM-states VS BIC. Lower BIC"
" is better")
parser.add_argument("tracks", help="tracks xml used for training and eval")
parser.add_argument(
"trainingBeds",
help="comma-separated list of training regions"
" (training region size will be a variable in output table). "
"if segmentation is activated, these must also be the "
"segmented beds...")
parser.add_argument("evalBed", help="eval region")
parser.add_argument("trainOpts", help="all teHmmTrain options in quotes")
parser.add_argument("evalOpts", help="all teHmmEval options in quotes")
parser.add_argument("states",
help="comma separated-list of numbers of states"
" to try")
parser.add_argument("outDir", help="output directory")
parser.add_argument("--reps",
help="number of replicates",
type=int,
default=1)
parser.add_argument("--proc",
help="maximum number of processors to use"
" in parallel",
type=int,
default=1)
parser.add_argument("--resume",
help="try not to rewrite existing files",
action="store_true",
default=False)
parser.add_argument(
"--initTrans",
help="the states argument is overridden"
" to specify a list of transition initialization files "
"instead of state numbers",
action="store_true",
default=False)
parser.add_argument("--numReps",
help="the states argument is overridden"
" to specifiy a list of replicate numbers (--reps)"
" arguments",
action="store_true",
default=False)
parser.add_argument("--numIter",
help="the states argument is overridden"
" to specifiy a list of iteration counts (--iter)"
" arugments",
action="store_true",
default=False)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
if sum([int(i) for i in [args.initTrans, args.numReps, args.numIter]]) > 1:
raise RuntimeError("only one of {--initTrans, --numReps, --numIter} "
"can be used at a time")
if not os.path.isdir(args.outDir):
runShellCommand("mkdir %s" % args.outDir)
# get the sizes of the trianing beds
trainingSizes = []
trainingBeds = []
for tb in args.trainingBeds.split(","):
if len(tb) > 0:
trainingBeds.append(tb)
for bed in trainingBeds:
assert os.path.isfile(bed)
bedLen = 0
for interval in readBedIntervals(bed):
bedLen += interval[2] - interval[1]
trainingSizes.append(bedLen)
# make sure --bed not in teHmmEval options and --numStates not in train
# options
trainOpts = args.trainOpts.split()
if "--numStates" in args.trainOpts and not args.numReps and not args.numIter:
nsIdx = trainOpts.index("--numStates")
assert nsIdx < len(trainOpts) - 1
del trainOpts[nsIdx]
del trainOpts[nsIdx]
if "--initTransProbs" in args.trainOpts:
tpIdx = trainOpts.index("--initTransProbs")
assert tpIdx < len(trainOpts) - 1
del trainOpts[tpIdx]
del trianOpts[tpIdx]
trainProcs = 1
if "--numThreads" in args.trainOpts:
npIdx = trainOpts.index("--numThreads")
assert npIdx < len(trainOpts) - 1
trainProcs = int(trainOpts[npIdx + 1])
segOptIdx = -1
if "--segment" in args.trainOpts:
segIdx = trainOpts.index("--segment")
assert segIdx < len(trainOpts) - 1
segOptIdx = segIdx + 1
if args.numReps and "--reps" in args.trainOpts:
repsIdx = trainOpts.index("--reps")
assert repsIdx < len(trainOpts) - 1
del trainOpts[repsIdx]
del trainOpts[repsIdx]
if args.numIter and "--iter" in args.trainOpts:
iterIdx = trainOpts.index("--iter")
assert iterIdx < len(trainOpts) - 1
del trainOpts[iterIdx]
del trainOpts[iterIdx]
evalOpts = args.evalOpts.split()
if "--bed" in args.evalOpts:
bedIdx = evalOpts.index("--bed")
assert bedIdx < len(evalOpts) - 1
del evalOpts[bedIdx]
del evalOpts[bedIdx]
if "--bic" in args.evalOpts:
bicIdx = evalOpts.index("--bic")
assert bicIdx < len(evalOpts) - 1
del evalOpts[bicIdx]
del evalOpts[bicIdx]
# hack in support for --initTrans option by munging out model sizes
# from the text files
if args.initTrans is True:
transFiles = args.states.split(",")
states = []
for tf in transFiles:
stateSet = set()
with open(tf) as f:
for line in f:
toks = line.split()
print toks
if len(toks) > 1 and toks[0][0] != "#":
stateSet.add(toks[0])
stateSet.add(toks[1])
states.append(len(stateSet))
else:
states = args.states.split(",")
trainCmds = []
evalCmds = []
prevSize = -1
sameSizeCount = 0
for trainingSize, trainingBed in zip(trainingSizes, trainingBeds):
# hack to take into account we may have different inputs with same
# same size, so their corresponding results need unique filenames
if trainingSize == prevSize:
sameSizeCount += 1
else:
sameSizeCount = 0
prevSize = trainingSize
print prevSize, trainingSize, sameSizeCount
for numStates in states:
for rep in xrange(args.reps):
outMod = os.path.join(
args.outDir, "hmm_%d.%d.%d.%d.mod" %
(trainingSize, sameSizeCount, int(numStates), int(rep)))
if segOptIdx != -1:
trainOpts[segOptIdx] = trainingBed
if args.initTrans is True:
statesOpt = "--initTransProbs %s" % transFiles[
states.index(numStates)]
elif args.numIter is True:
# states argument overridden by iterations
statesOpt = "--iter %d" % int(numStates)
elif args.numReps is True:
# states argument overridden by reps
statesOpt = "--reps %d" % int(numStates)
else:
statesOpt = "--numStates %d" % int(numStates)
trainCmd = "teHmmTrain.py %s %s %s %s %s" % (
args.tracks, trainingBed, outMod, " ".join(trainOpts),
statesOpt)
if not args.resume or not os.path.isfile(outMod) or \
os.path.getsize(outMod) < 100:
trainCmds.append(trainCmd)
outBic = outMod.replace(".mod", ".bic")
outBed = outMod.replace(".mod", "_eval.bed")
evalCmd = "teHmmEval.py %s %s %s --bed %s --bic %s %s" % (
args.tracks, outMod, args.evalBed, outBed, outBic,
" ".join(evalOpts))
if not args.resume or not os.path.isfile(outBic) or \
os.path.getsize(outBic) < 2:
evalCmds.append(evalCmd)
# run the training
runParallelShellCommands(trainCmds, max(1, args.proc / trainProcs))
# run the eval
runParallelShellCommands(evalCmds, args.proc)
# make the table header
tableFile = open(os.path.join(args.outDir, "bictable.csv"), "w")
stateColName = "states"
if args.numIter is True:
statesColName = "iter"
elif args.numReps is True:
stateColName = "reps"
tableFile.write("trainFile, trainSize, %s, meanBic, minBic, maxBic" %
stateColName)
for i in xrange(args.reps):
tableFile.write(", bic.%d" % i)
tableFile.write("\n")
# make the table body
prevSize = -1
sameSizeCount = 0
for (trainingSize, trainingBed) in zip(trainingSizes, trainingBeds):
# hack to take into account we may have different inputs with same
# same size, so their corresponding results need unique filenames
if trainingSize == prevSize:
sameSizeCount += 1
else:
sameSizeCount = 0
prevSize = trainingSize
for numStates in states:
bics = []
printBics = []
for rep in xrange(args.reps):
outMod = os.path.join(
args.outDir, "hmm_%d.%d.%d.%d.mod" %
(trainingSize, sameSizeCount, int(numStates), int(rep)))
outBic = outMod.replace(".mod", ".bic")
try:
with open(outBic, "r") as obFile:
for line in obFile:
bic = float(line.split()[0])
break
bics.append(bic)
printBics.append(bic)
except:
logger.warning("Coudn't find bic %s" % outBic)
printBics.append("ERROR")
# write row
tableFile.write("%s, %d, %d" %
(trainingBed, int(trainingSize), int(numStates)))
if len(bics) > 0:
tableFile.write(", %f, %f, %f" %
(np.mean(bics), np.min(bics), np.max(bics)))
else:
tableFile.write(", ERROR, ERROR, ERROR")
for pb in printBics:
tableFile.write(", %s" % pb)
tableFile.write("\n")
tableFile.close()
cleanBedTool(tempBedToolPath)
def main(argv=None):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=" Thin wrapper of teHmmTrain.py and teHmmEval.py "
"to generate a table of Number-of-HMM-states VS BIC. Lower BIC"
" is better")
parser.add_argument("tracks", help="tracks xml used for training and eval")
parser.add_argument("trainingBeds", help="comma-separated list of training regions"
" (training region size will be a variable in output table). "
"if segmentation is activated, these must also be the "
"segmented beds...")
parser.add_argument("evalBed", help="eval region")
parser.add_argument("trainOpts", help="all teHmmTrain options in quotes")
parser.add_argument("evalOpts", help="all teHmmEval options in quotes")
parser.add_argument("states", help="comma separated-list of numbers of states"
" to try")
parser.add_argument("outDir", help="output directory")
parser.add_argument("--reps", help="number of replicates", type = int,
default=1)
parser.add_argument("--proc", help="maximum number of processors to use"
" in parallel", type = int, default = 1)
parser.add_argument("--resume", help="try not to rewrite existing files",
action="store_true", default=False)
parser.add_argument("--initTrans", help="the states argument is overridden"
" to specify a list of transition initialization files "
"instead of state numbers", action="store_true",
default=False)
parser.add_argument("--numReps", help="the states argument is overridden"
" to specifiy a list of replicate numbers (--reps)"
" arguments", action="store_true", default=False)
parser.add_argument("--numIter", help="the states argument is overridden"
" to specifiy a list of iteration counts (--iter)"
" arugments", action="store_true", default=False)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
if sum([int(i) for i in [args.initTrans, args.numReps, args.numIter]]) > 1:
raise RuntimeError("only one of {--initTrans, --numReps, --numIter} "
"can be used at a time")
if not os.path.isdir(args.outDir):
runShellCommand("mkdir %s" % args.outDir)
# get the sizes of the trianing beds
trainingSizes = []
trainingBeds = []
for tb in args.trainingBeds.split(","):
if len(tb) > 0:
trainingBeds.append(tb)
for bed in trainingBeds:
assert os.path.isfile(bed)
bedLen = 0
for interval in readBedIntervals(bed):
bedLen += interval[2] - interval[1]
trainingSizes.append(bedLen)
# make sure --bed not in teHmmEval options and --numStates not in train
# options
trainOpts = args.trainOpts.split()
if "--numStates" in args.trainOpts and not args.numReps and not args.numIter:
nsIdx = trainOpts.index("--numStates")
assert nsIdx < len(trainOpts) - 1
del trainOpts[nsIdx]
del trainOpts[nsIdx]
if "--initTransProbs" in args.trainOpts:
tpIdx = trainOpts.index("--initTransProbs")
assert tpIdx < len(trainOpts) - 1
del trainOpts[tpIdx]
del trianOpts[tpIdx]
trainProcs = 1
if "--numThreads" in args.trainOpts:
npIdx = trainOpts.index("--numThreads")
assert npIdx < len(trainOpts) - 1
trainProcs = int(trainOpts[npIdx + 1])
segOptIdx = -1
if "--segment" in args.trainOpts:
segIdx = trainOpts.index("--segment")
assert segIdx < len(trainOpts) - 1
segOptIdx = segIdx + 1
if args.numReps and "--reps" in args.trainOpts:
repsIdx = trainOpts.index("--reps")
assert repsIdx < len(trainOpts) - 1
del trainOpts[repsIdx]
del trainOpts[repsIdx]
if args.numIter and "--iter" in args.trainOpts:
iterIdx = trainOpts.index("--iter")
assert iterIdx < len(trainOpts) - 1
del trainOpts[iterIdx]
del trainOpts[iterIdx]
evalOpts = args.evalOpts.split()
if "--bed" in args.evalOpts:
bedIdx = evalOpts.index("--bed")
assert bedIdx < len(evalOpts) - 1
del evalOpts[bedIdx]
del evalOpts[bedIdx]
if "--bic" in args.evalOpts:
bicIdx = evalOpts.index("--bic")
assert bicIdx < len(evalOpts) - 1
del evalOpts[bicIdx]
del evalOpts[bicIdx]
# hack in support for --initTrans option by munging out model sizes
# from the text files
if args.initTrans is True:
transFiles = args.states.split(",")
states = []
for tf in transFiles:
stateSet = set()
with open(tf) as f:
for line in f:
toks = line.split()
print toks
if len(toks) > 1 and toks[0][0] != "#":
stateSet.add(toks[0])
stateSet.add(toks[1])
states.append(len(stateSet))
else:
states = args.states.split(",")
trainCmds = []
evalCmds = []
prevSize = -1
sameSizeCount = 0
for trainingSize, trainingBed in zip(trainingSizes, trainingBeds):
# hack to take into account we may have different inputs with same
# same size, so their corresponding results need unique filenames
if trainingSize == prevSize:
sameSizeCount += 1
else:
sameSizeCount = 0
prevSize = trainingSize
print prevSize, trainingSize, sameSizeCount
for numStates in states:
for rep in xrange(args.reps):
outMod = os.path.join(args.outDir, "hmm_%d.%d.%d.%d.mod" % (
trainingSize, sameSizeCount, int(numStates), int(rep)))
if segOptIdx != -1:
trainOpts[segOptIdx] = trainingBed
if args.initTrans is True:
statesOpt = "--initTransProbs %s" % transFiles[states.index(numStates)]
elif args.numIter is True:
# states argument overridden by iterations
statesOpt = "--iter %d" % int(numStates)
elif args.numReps is True:
# states argument overridden by reps
statesOpt = "--reps %d" % int(numStates)
else:
statesOpt = "--numStates %d" % int(numStates)
trainCmd = "teHmmTrain.py %s %s %s %s %s" % (
args.tracks, trainingBed, outMod, " ".join(trainOpts),
statesOpt)
if not args.resume or not os.path.isfile(outMod) or \
os.path.getsize(outMod) < 100:
trainCmds.append(trainCmd)
outBic = outMod.replace(".mod", ".bic")
outBed = outMod.replace(".mod", "_eval.bed")
evalCmd = "teHmmEval.py %s %s %s --bed %s --bic %s %s" % (
args.tracks, outMod, args.evalBed, outBed, outBic,
" ".join(evalOpts))
if not args.resume or not os.path.isfile(outBic) or \
os.path.getsize(outBic) < 2:
evalCmds.append(evalCmd)
# run the training
runParallelShellCommands(trainCmds, max(1, args.proc / trainProcs))
# run the eval
runParallelShellCommands(evalCmds, args.proc)
# make the table header
tableFile = open(os.path.join(args.outDir, "bictable.csv"), "w")
stateColName = "states"
if args.numIter is True:
statesColName = "iter"
elif args.numReps is True:
stateColName = "reps"
tableFile.write("trainFile, trainSize, %s, meanBic, minBic, maxBic" % stateColName)
for i in xrange(args.reps):
tableFile.write(", bic.%d" % i)
tableFile.write("\n")
# make the table body
prevSize = -1
sameSizeCount = 0
for (trainingSize,trainingBed) in zip(trainingSizes, trainingBeds):
# hack to take into account we may have different inputs with same
# same size, so their corresponding results need unique filenames
if trainingSize == prevSize:
sameSizeCount += 1
else:
sameSizeCount = 0
prevSize = trainingSize
for numStates in states:
bics = []
printBics = []
for rep in xrange(args.reps):
outMod = os.path.join(args.outDir, "hmm_%d.%d.%d.%d.mod" % (
trainingSize, sameSizeCount, int(numStates), int(rep)))
outBic = outMod.replace(".mod", ".bic")
try:
with open(outBic, "r") as obFile:
for line in obFile:
bic = float(line.split()[0])
break
bics.append(bic)
printBics.append(bic)
except:
logger.warning("Coudn't find bic %s" % outBic)
printBics.append("ERROR")
# write row
tableFile.write("%s, %d, %d" % (trainingBed, int(trainingSize), int(numStates)))
if len(bics) > 0:
tableFile.write(", %f, %f, %f" % (np.mean(bics), np.min(bics),
np.max(bics)))
else:
tableFile.write(", ERROR, ERROR, ERROR")
for pb in printBics:
tableFile.write(", %s" % pb)
tableFile.write("\n")
tableFile.close()
cleanBedTool(tempBedToolPath)
def testSupervisedLearn(self):
intervals = readBedIntervals(getTestDirPath("truth.bed"), ncol=4)
truthIntervals = []
for i in intervals:
truthIntervals.append((i[0], i[1], i[2], int(i[3])))
allIntervals = [(truthIntervals[0][0], truthIntervals[0][1],
truthIntervals[-1][2])]
trackData = TrackData()
trackData.loadTrackData(getTracksInfoPath(3), allIntervals)
assert len(trackData.getTrackTableList()) == 1
# set the fudge to 1 since when the test was written this was
# hardcoded default
em = IndependentMultinomialEmissionModel(
4, trackData.getNumSymbolsPerTrack(), fudge=1.0)
hmm = MultitrackHmm(em)
hmm.supervisedTrain(trackData, truthIntervals)
hmm.validate()
# check emissions, they should basically be binary.
trackList = hmm.getTrackList()
emp = np.exp(em.getLogProbs())
ltrTrack = trackList.getTrackByName("ltr")
track = ltrTrack.getNumber()
cmap = ltrTrack.getValueMap()
s0 = cmap.getMap(None)
s1 = cmap.getMap(0)
# we add 1 to all frequencies like emission trainer
assert_array_almost_equal(emp[track][0][s0], 36. / 37.)
assert_array_almost_equal(emp[track][0][s1], 1 - 36. / 37.)
assert_array_almost_equal(emp[track][1][s0], 1 - 6. / 7.)
assert_array_almost_equal(emp[track][1][s1], 6. / 7.)
assert_array_almost_equal(emp[track][2][s0], 26. / 27.)
assert_array_almost_equal(emp[track][2][s1], 1. - 26. / 27.)
assert_array_almost_equal(emp[track][3][s0], 1. - 6. / 7.)
assert_array_almost_equal(emp[track][3][s1], 6. / 7.)
insideTrack = trackList.getTrackByName("inside")
track = insideTrack.getNumber()
cmap = insideTrack.getValueMap()
s0 = cmap.getMap(None)
s1 = cmap.getMap("Inside")
assert_array_almost_equal(emp[track][0][s0], 36. / 37.)
assert_array_almost_equal(emp[track][0][s1], 1 - 36. / 37.)
assert_array_almost_equal(emp[track][1][s0], 6. / 7.)
assert_array_almost_equal(emp[track][1][s1], 1 - 6. / 7.)
assert_array_almost_equal(emp[track][2][s0], 1. - 26. / 27.)
assert_array_almost_equal(emp[track][2][s1], 26. / 27.)
assert_array_almost_equal(emp[track][3][s0], 6. / 7.)
assert_array_almost_equal(emp[track][3][s1], 1. - 6. / 7.)
# crappy check for start probs. need to test transition too!
freq = [0.0] * em.getNumStates()
total = 0.0
for interval in truthIntervals:
state = interval[3]
freq[state] += float(interval[2]) - float(interval[1])
total += float(interval[2]) - float(interval[1])
sprobs = hmm.getStartProbs()
assert len(sprobs) == em.getNumStates()
for state in xrange(em.getNumStates()):
assert_array_almost_equal(freq[state] / total, sprobs[state])
# transition probabilites
# from eyeball:
#c 0 5 0 0->0 +4 0->1 +1 0-> +5
#c 5 10 1 1->1 +4 1->2 +1 1-> +5
#c 10 35 2 2->2 +24 2->3 +1 2-> +25
#c 35 40 3 3->3 +4 3->0 +1 3-> +5
#c 40 70 0 0->0 +29 0-> +19
realTransProbs = np.array([[33. / 34., 1. / 34., 0., 0.],
[0., 4. / 5., 1. / 5., 0.],
[0., 0., 24. / 25., 1. / 25.],
[1. / 5., 0., 0., 4. / 5.]])
tprobs = hmm.getTransitionProbs()
assert tprobs.shape == (em.getNumStates(), em.getNumStates())
assert_array_almost_equal(tprobs, realTransProbs)
prob, states = hmm.viterbi(trackData)[0]
for truthInt in truthIntervals:
for i in xrange(truthInt[1], truthInt[2]):
assert states[i] == truthInt[3]
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Make some tables of statistics from a BED file. All"
" output will be written in one big CSV table to be viewed in a "
"spreadsheet.")
parser.add_argument("inBed", help="Input bed file")
parser.add_argument("outCsv", help="Path to write output in CSV format")
parser.add_argument("--ignore",
help="Comma-separated list of names"
" to ignore",
default="")
parser.add_argument("--numBins",
help="Number of (linear) bins for "
"histograms",
type=int,
default=10)
parser.add_argument("--logHist",
help="Apply log-transform to data for "
"histogram",
action="store_true",
default=False)
parser.add_argument("--histRange",
help="Histogram range as comma-"
"separated pair of numbers",
default=None)
parser.add_argument("--noHist",
help="skip hisograms",
action="store_true",
default=False)
parser.add_argument("--noScore",
help="Just do length stats",
action="store_true",
default=False)
parser.add_argument("--noLen",
help="Just do score stats",
action="store_true",
default=False)
parser.add_argument("--nearness",
help="Compute nearness stats (instead "
"of normal stats) of input bed with given BED. Output"
" will be a BED instead of CSV, with nearness in the "
"score position",
default=None)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
if args.histRange is not None:
args.histRange = args.histRange.split(",")
assert len(args.histRange) == 2
args.histRange = int(args.histRange[0]), int(args.histRange[1])
outFile = open(args.outCsv, "w")
args.ignoreSet = set(args.ignore.split(","))
intervals = readBedIntervals(args.inBed,
ncol=5,
sort=args.nearness is not None)
csvStats = ""
# nearness stats
if args.nearness is not None:
args.noScore = True
csvStats = makeNearnessBED(intervals, args)
# length stats
elif args.noLen is False:
csvStats = makeCSV(intervals, args, lambda x: int(x[2]) - int(x[1]),
"Length")
# score stats
try:
if args.noScore is False:
csvStats += "\n" + makeCSV(intervals, args, lambda x: float(x[4]),
"Score")
csvStats += "\n" + makeCSV(
intervals, args, lambda x: float(x[4]) *
(float(x[2]) - float(x[1])), "Score*Length")
except Exception as e:
logger.warning("Couldn't make score stats because %s" % str(e))
outFile.write(csvStats)
outFile.write("\n")
outFile.close()
cleanBedTool(tempBedToolPath)
def main(argv=None):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Write track data into ASCII dump. Row i corresponds"
" to the ith position found when scanning query BED IN SORTED ORDER."
"Column j corresponds to the jth track in the XML file. --map option"
" used to write internal integer format used by HMM. Unobserved values"
" written as \"None\" if default attribute not specified or track not"
" binary. Rounding can occur if scaling parameters present.\n\n"
"IMPORTANT: values stored in 8bit integers internally. Any track with"
" more than 256 different values will get clamped (with a warning)")
parser.add_argument("tracks", help="Path of Tracks Info file "
"containing paths to genome annotation tracks")
parser.add_argument("query", help="BED region(s) to dump. SCANNED IN"
" SORTED ORDER")
parser.add_argument("output", help="Path of file to write output to")
parser.add_argument("--map", help="Apply name mapping, including"
" transformation specified in scale, logScale"
", etc. attributes, that HMM uses internally"
". Important to note that resulting integers"
" are just unique IDs. ID_1 > ID_2 does not"
" mean anything", action="store_true",
default=False)
parser.add_argument("--segment", help="Treat each interval in query"
" as a single segment (ie with only one data point)"
". In this case, query should probably have been"
" generated with segmentTracks.py",
action="store_true",
default=False)
parser.add_argument("--noPos", help="Do not print genomic position"
" (first 2 columnts)", action="store_true",
default=False)
parser.add_argument("--noMask", help="Ignore mask tracks",
default=False, action="store_true")
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
# make sure output writeable
outFile = open(args.output, "w")
# need to remember to fix this, disable as precaution for now
assert args.noMask is True or args.segment is False
# read query intervals from the bed file
logger.info("loading query intervals from %s" % args.query)
mergedIntervals = getMergedBedIntervals(args.query, ncol=3)
if mergedIntervals is None or len(mergedIntervals) < 1:
raise RuntimeError("Could not read any intervals from %s" %
args.query)
# read the segment intervals from the (same) bed file
segIntervals = None
if args.segment is True:
logger.info("loading segment intervals from %s" % args.query)
segIntervals = readBedIntervals(args.query, sort=True)
# read all data from track xml
logger.info("loading tracks %s" % args.tracks)
trackData = TrackData()
trackData.loadTrackData(args.tracks, mergedIntervals,
segmentIntervals=segIntervals,
applyMasking = not args.noMask)
# dump the data to output
dumpTrackData(trackData, outFile, args.map, not args.noPos)
outFile.close()
def runPositionalComparison(argv, args):
""" hack to recursively exectute compareBedStates.py on a sliding window of the two
inputs and report accuracy in a BED file """
try:
windowToks = args.window.split(",")
assert len(windowToks) == 5
windowSize = int(windowToks[0])
stateName = windowToks[1]
compType = windowToks[2]
score = windowToks[3]
outBed = windowToks[4]
except:
raise RuntimeError("value passed to --window is not in valid format")
if compType == "base":
compIdx = 0
elif compType == "interval":
compIdx = 1
elif compType == "weighted":
compIdx = 2
else:
raise RuntimeError("invalid compType, %s, passed to --window" % compType)
if score != "f1" and score != "precision" and score != "recall":
raise RuntimeError("invalid score, %s, passed to --window" % score)
try:
outFile = open(outBed, "w")
except:
raise RuntimeError("invalid outBed, %s, passed to --window" % outBed)
tempBed = getLocalTempPath("Temp_region", ".bed")
runShellCommand("mergeBed -i %s > %s" % (args.bed1, tempBed))
chunkBed = getLocalTempPath("Temp_chunkBed", ".bed")
runShellCommand("chunkBedRegions.py %s %d --overlap .5 > %s" % (
tempBed, windowSize, chunkBed))
window = getLocalTempPath("Temp_window", ".bed")
slice1 = getLocalTempPath("Temp_slice1", ".bed")
slice2 = getLocalTempPath("Temp_slice2", ".bed")
compFile = getLocalTempPath("Temp_compFile", ".bed")
compOpts = ""
winIdx = argv.index("--window")
assert winIdx > 0 and winIdx < len(argv) -1 and argv[winIdx + 1] == args.window
for i in xrange(3, len(argv)):
if i != winIdx and i != winIdx + 1:
compOpts += " " + argv[i]
for chunk in readBedIntervals(chunkBed):
runShellCommand("echo \"%s\t%d\t%d\" > %s" % (chunk[0], chunk[1], chunk[2],
window))
runShellCommand("intersectBed -a %s -b %s | sortBed > %s" % (
args.bed1, window, slice1))
runShellCommand("intersectBed -a %s -b %s | sortBed > %s" % (
args.bed2, window, slice2))
runShellCommand("compareBedStates.py %s %s %s > %s" % (
slice1, slice2, compOpts, compFile))
stats = extractCompStatsFromFile(compFile)[compIdx]
if stateName not in stats:
stats[stateName] = (0,0)
f1 = 0.
prec, rec = stats[stateName]
if prec + rec > 0:
f1 = (2. * prec * rec) / (prec + rec)
val = f1
if score == "precision":
val = prec
elif score == "recall":
val = rec
outFile.write("%s\t%d\t%d\t%f\n" % (chunk[0], chunk[1], chunk[2], val))
runShellCommand("rm -f %s %s %s %s %s %s" % (tempBed, chunkBed, window,
slice1, slice2, compFile))
outFile.close()
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Make some tables of statistics from a BED file. All"
" output will be written in one big CSV table to be viewed in a "
"spreadsheet.")
parser.add_argument("inBed", help="Input bed file")
parser.add_argument("outCsv", help="Path to write output in CSV format")
parser.add_argument("--ignore", help="Comma-separated list of names"
" to ignore", default="")
parser.add_argument("--numBins", help="Number of (linear) bins for "
"histograms", type=int, default=10)
parser.add_argument("--logHist", help="Apply log-transform to data for "
"histogram", action="store_true", default=False)
parser.add_argument("--histRange", help="Histogram range as comma-"
"separated pair of numbers", default=None)
parser.add_argument("--noHist", help="skip hisograms", action="store_true",
default=False)
parser.add_argument("--noScore", help="Just do length stats",
action="store_true", default=False)
parser.add_argument("--noLen", help="Just do score stats",
action="store_true", default=False)
parser.add_argument("--nearness", help="Compute nearness stats (instead "
"of normal stats) of input bed with given BED. Output"
" will be a BED instead of CSV, with nearness in the "
"score position", default=None)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
if args.histRange is not None:
args.histRange = args.histRange.split(",")
assert len(args.histRange) == 2
args.histRange = int(args.histRange[0]), int(args.histRange[1])
outFile = open(args.outCsv, "w")
args.ignoreSet = set(args.ignore.split(","))
intervals = readBedIntervals(args.inBed, ncol = 5, sort = args.nearness is not None)
csvStats = ""
# nearness stats
if args.nearness is not None:
args.noScore = True
csvStats = makeNearnessBED(intervals, args)
# length stats
elif args.noLen is False:
csvStats = makeCSV(intervals, args, lambda x : int(x[2])-int(x[1]),
"Length")
# score stats
try:
if args.noScore is False:
csvStats += "\n" + makeCSV(intervals, args, lambda x : float(x[4]),
"Score")
csvStats += "\n" + makeCSV(intervals, args, lambda x : float(x[4]) * (
float(x[2]) - float(x[1])), "Score*Length")
except Exception as e:
logger.warning("Couldn't make score stats because %s" % str(e))
outFile.write(csvStats)
outFile.write("\n")
outFile.close()
cleanBedTool(tempBedToolPath)
def testHmmSupervisedLearn(self):
""" Pretty much copied from the HMM unit test. We try to recapitualte
all results with a CFG with no nest states, which should be same as
HMM"""
intervals = readBedIntervals(getTestDirPath("truth.bed"), ncol=4)
truthIntervals = []
for i in intervals:
truthIntervals.append((i[0], i[1], i[2], int(i[3])))
allIntervals = [(truthIntervals[0][0], truthIntervals[0][1],
truthIntervals[-1][2])]
trackData = TrackData()
trackData.loadTrackData(getTracksInfoPath(3), allIntervals)
assert len(trackData.getTrackTableList()) == 1
# set the fudge to 1 since when the test was written this was
# hardcoded default
em = IndependentMultinomialEmissionModel(
4, trackData.getNumSymbolsPerTrack(), fudge=1.0)
hmm = MultitrackHmm(em)
hmm.supervisedTrain(trackData, truthIntervals)
hmm.validate()
pairModel = PairEmissionModel(em, [1.0] * em.getNumStates())
# Test validates with neststate just for fun
cfg = MultitrackCfg(em, pairModel, nestStates=[1])
cfg.supervisedTrain(trackData, truthIntervals)
cfg.validate()
# Then reload as an hmm-equivalent
cfg = MultitrackCfg(em, pairModel, nestStates=[])
cfg.supervisedTrain(trackData, truthIntervals)
cfg.validate()
# check emissions, they should basically be binary.
trackList = cfg.getTrackList()
emp = np.exp(em.getLogProbs())
ltrTrack = trackList.getTrackByName("ltr")
track = ltrTrack.getNumber()
cmap = ltrTrack.getValueMap()
s0 = cmap.getMap(None)
s1 = cmap.getMap(0)
# we add 1 to all frequencies like emission trainer
assert_array_almost_equal(emp[track][0][s0], 36. / 37.)
assert_array_almost_equal(emp[track][0][s1], 1 - 36. / 37.)
assert_array_almost_equal(emp[track][1][s0], 1 - 6. / 7.)
assert_array_almost_equal(emp[track][1][s1], 6. / 7.)
assert_array_almost_equal(emp[track][2][s0], 26. / 27.)
assert_array_almost_equal(emp[track][2][s1], 1. - 26. / 27.)
assert_array_almost_equal(emp[track][3][s0], 1. - 6. / 7.)
assert_array_almost_equal(emp[track][3][s1], 6. / 7.)
insideTrack = trackList.getTrackByName("inside")
track = insideTrack.getNumber()
cmap = insideTrack.getValueMap()
s0 = cmap.getMap(None)
s1 = cmap.getMap("Inside")
assert_array_almost_equal(emp[track][0][s0], 36. / 37.)
assert_array_almost_equal(emp[track][0][s1], 1 - 36. / 37.)
assert_array_almost_equal(emp[track][1][s0], 6. / 7.)
assert_array_almost_equal(emp[track][1][s1], 1 - 6. / 7.)
assert_array_almost_equal(emp[track][2][s0], 1. - 26. / 27.)
assert_array_almost_equal(emp[track][2][s1], 26. / 27.)
assert_array_almost_equal(emp[track][3][s0], 6. / 7.)
assert_array_almost_equal(emp[track][3][s1], 1. - 6. / 7.)
# crappy check for start probs. need to test transition too!
freq = [0.0] * em.getNumStates()
total = 0.0
for interval in truthIntervals:
state = interval[3]
freq[state] += float(interval[2]) - float(interval[1])
total += float(interval[2]) - float(interval[1])
sprobs = cfg.getStartProbs()
assert len(sprobs) == em.getNumStates()
for state in xrange(em.getNumStates()):
assert_array_almost_equal(freq[state] / total, sprobs[state])
# transition probabilites
# from eyeball:
#c 0 5 0 0->0 +4 0->1 +1 0-> +5
#c 5 10 1 1->1 +4 1->2 +1 1-> +5
#c 10 35 2 2->2 +24 2->3 +1 2-> +25
#c 35 40 3 3->3 +4 3->0 +1 3-> +5
#c 40 70 0 0->0 +29 0-> +19
realTransProbs = np.array([[33. / 34., 1. / 34., 0., 0.],
[0., 4. / 5., 1. / 5., 0.],
[0., 0., 24. / 25., 1. / 25.],
[1. / 5., 0., 0., 4. / 5.]])
tprobs = np.exp(cfg.getLogProbTables()[0])
assert tprobs.shape == (em.getNumStates(), em.getNumStates(),
em.getNumStates())
for i in xrange(em.getNumStates()):
for j in xrange(em.getNumStates()):
fbTot = tprobs[i, i, j]
if i != j:
fbTot += tprobs[i, j, i]
assert_array_almost_equal(fbTot, realTransProbs[i, j])
prob, states = cfg.decode(trackData.getTrackTableList()[0])
for truthInt in truthIntervals:
for i in xrange(truthInt[1], truthInt[2]):
# gah, just realized that ltr track is binary, which means
# ltr states can be either 1 or 3. need to fix test properly
# but just relax comparison for now.
if truthInt[3] == 1 or truthInt[3] == 3:
assert states[i] == 1 or states[i] == 3
else:
assert states[i] == truthInt[3]
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Rename HMM states.")
parser.add_argument("inputModel", help="Path of teHMM model created with"
" teHmmTrain.py")
parser.add_argument("outputModel", help="Path of model with renamed states")
parser.add_argument("--newNames", help="comma-separated list of state names to"
" apply. This list must have exactly the same number of"
" states as the model. The ith name in the list will be "
"assigned to the ith name of the model...", default=None)
parser.add_argument("--teNumbers", help="comma-separated list of state numbers"
" that will be assigned TE states, with everything else"
" assigned Other. This is less flexible but maybe more"
" convenient at times than --newNames.", default=None)
parser.add_argument("--bed", help="apply naming to bed file and print "
"results to stdout", default=None)
parser.add_argument("--sizes", help="bedFile to use for computing state numbering"
" by using decreasing order in total coverage (only works"
" with --teNumbers)", default=None)
parser.add_argument("--noMerge", help="dont merge adjacent intervals with same"
" name with --bed option", action="store_true",default=False)
parser.add_argument("--changeTrackName", help="dont do anything else, just change"
" the name of one track. specified value should be of form"
" currentNAme, newName", default=None)
args = parser.parse_args()
assert args.inputModel != args.outputModel
# load model created with teHmmTrain.py
model = loadModel(args.inputModel)
# trackChangeName logic hacked in completely separate from everything else
if args.changeTrackName is not None:
oldName, newName = args.changeTrackName.split(",")
track = model.getTrackList().getTrackByName(oldName)
track.setName(newName)
saveModel(args.outputModel, model)
return 0
assert (args.newNames is None) != (args.teNumbers is None)
# names manually specified
if args.newNames is not None:
names = args.newNames.split(",")
# names computed using simple scheme from set of "TE" state numbers (as found from
# log output of fitStateNames.py)
elif args.teNumbers is not None:
teNos = set([int(x) for x in args.teNumbers.split(",")])
teCount, otherCount = 0, 0
numStates = model.getEmissionModel().getNumStates()
# re-order from sizing info
if args.sizes is not None:
bedIntervals = readBedIntervals(args.sizes, ncol=4)
sizeMap = defaultdict(int)
for interval in bedIntervals:
sizeMap[int(interval[3])] += interval[2] - interval[1]
stateNumbers = sorted([x for x in xrange(numStates)],
reverse=True, key = lambda x : sizeMap[x])
else:
stateNumbers = [x for x in xrange(numStates)]
names = [""] * numStates
for i in stateNumbers:
if i in teNos:
name = "TE-%.2d" % teCount
teCount += 1
else:
name = "Other-%.2d" % otherCount
otherCount += 1
names[i] = name
assert teCount == len(teNos) and teCount + otherCount == len(names)
assert len(names) == model.getEmissionModel().getNumStates()
# throw names in the mapping object and stick into model
catMap = CategoryMap(reserved=0)
for i, name in enumerate(names):
catMap.getMap(name, update=True)
model.stateNameMap = catMap
# save model
saveModel(args.outputModel, model)
# process optional bed file
if args.bed is not None:
prevInterval = None
bedIntervals = readBedIntervals(args.bed, ncol=4)
for interval in bedIntervals:
oldName = interval[3]
newName = names[int(oldName)]
newInterval = list(interval)
newInterval[3] = newName
if args.noMerge:
# write interval
print "\t".join(str(x) for x in newInterval)
else:
if prevInterval is None:
# update prev interval first time
prevInterval = newInterval
elif newInterval[3] == prevInterval[3] and\
newInterval[0] == prevInterval[0] and\
newInterval[1] == prevInterval[2]:
# glue onto prev interval
prevInterval[2] = newInterval[2]
else:
# write and update prev
print "\t".join(str(x) for x in prevInterval)
prevInterval = newInterval
if prevInterval is not None:
print "\t".join(str(x) for x in prevInterval)
def parallelDispatch(argv, args):
""" chunk up input with chrom option. recursivlely launch eval. merge
results """
jobList = []
chromIntervals = readBedIntervals(args.chroms, sort=True)
chromFiles = []
regionFiles = []
segFiles = []
statsFiles = []
offset = args.co
for chrom in chromIntervals:
cmdToks = copy.deepcopy(argv)
cmdToks[cmdToks.index("--chrom") + 1] = ""
cmdToks[cmdToks.index("--chrom")] = ""
chromPath = getLocalTempPath("TempChromPath", ".bed")
cpFile = open(chromPath, "w")
cpFile.write("%s\t%d\t%d\t0\t0\t.\n" % (chrom[0], chrom[1], chrom[2]))
cpFile.close()
regionPath = getLocalTempPath("Temp", ".bed")
runShellCommand("intersectBed -a %s -b %s | sortBed > %s" %
(args.allBed, chromPath, regionPath))
if os.path.getsize(regionPath) < 2:
continue
offset += int(chrom[2]) - int(chrom[1])
regionFiles.append(regionPath)
chromFiles.append(chromPath)
cmdToks[2] = regionPath
segPath = getLocalTempPath("Temp", ".bed")
cmdToks[3] = segPath
segFiles.append(segPath)
if "--co" in cmdToks:
cmdToks[cmdToks.index("--co") + 1] = str(offset)
else:
cmdToks.append("--co")
cmdToks.append(str(offset))
if args.stats is not None:
statsPath = getLocalTempPath("Temp", ".bed")
cmdToks[cmdToks.index("--stats") + 1] = statsPath
statsFiles.append(statsPath)
cmd = " ".join(cmdToks)
jobList.append(cmd)
runParallelShellCommands(jobList, args.proc)
for i in xrange(len(jobList)):
if i == 0:
ct = ">"
else:
ct = ">>"
runShellCommand("cat %s %s %s" % (segFiles[i], ct, args.outBed))
if len(statsFiles) > 0:
runShellCommand("cat %s %s %s" % (statsFiles[i], ct, args.stats))
for i in itertools.chain(chromFiles, regionFiles, segFiles, statsFiles):
runShellCommand("rm %s" % i)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Create starting transition and emission distributions "
"from a candidate BED annotation, which can"
" be used with teHmmTrain.py using the --initTransProbs and "
"--initEmProbs options, respectively. The distributions created here"
" are extremely simple, but this can be a good shortcut to at least "
"getting the state names into the init files, which can be further "
"tweeked by hand.")
parser.add_argument("tracksInfo", help="Path of Tracks Info file "
"containing paths to genome annotation tracks")
parser.add_argument("trackName", help="Name of Track to use as initial"
" annotation")
parser.add_argument("queryBed", help="Bed file with regions to query")
parser.add_argument("outTransProbs", help="File to write transition model"
" to")
parser.add_argument("outEmProbs", help="File to write emission model to")
parser.add_argument("--numOut", help="Number of \"outside\" states to add"
" to the model.", default=1, type=int)
parser.add_argument("--numTot", help="Add x \"outside\" states such "
"that total states is this. (overrieds --numOut)",
default=0, type=int)
parser.add_argument("--outName", help="Name of outside states (will have"
" numeric suffix if more than 1)", default="Outside")
parser.add_argument("--mode", help="Strategy for initializing the "
"transition graph: {\'star\': all states are connected"
" to the oustide state(s) but not each other; "
" \'data\': transitions estimated from input bed; "
" \'full\': dont write edges and let teHmmTrain.py "
"initialize as a clique}", default="star")
parser.add_argument("--selfTran", help="This script will always write all"
" the self-transition probabilities to the output file. "
"They will all be set to the specified value using this"
" option, or estimated from the data if -1", default=-1.,
type=float)
parser.add_argument("--em", help="Emission probability for input track ("
"ie probability that state emits itself)",
type=float, default=0.95)
parser.add_argument("--outEmNone", help="Add None emission probabilities"
" for target track for Outside states",
action="store_true", default=None)
addLoggingOptions(parser)
args = parser.parse_args()
if args.mode == "star" and args.numOut < 1:
raise RuntimeError("--numOut must be at least 1 if --mode star is used")
if args.mode != "star" and args.mode != "data" and args.mode != "full":
raise RuntimeError("--mode must be one of {star, data, full}")
if args.mode == "data":
raise RuntimeError("--data not implemented yet")
assert os.path.isfile(args.tracksInfo)
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
# Read the tracks info
trackList = TrackList(args.tracksInfo)
# Extract the track we want
track = trackList.getTrackByName(args.trackName)
if track is None:
raise RuntimeError("Track %s not found in tracksInfo" % args.trackName)
trackPath = track.getPath()
if track.getDist() != "multinomial" and track.getDist() != "gaussian":
raise RuntimeError("Track %s does not have multinomial or "
"gaussian distribution" % args.trackName)
if track.getScale() is not None or track.getLogScale() is not None:
raise RuntimeError("Track %s must not have scale" % args.trackName)
# read query intervals from the bed file
logger.info("loading query intervals from %s" % args.queryBed)
mergedIntervals = getMergedBedIntervals(args.queryBed, ncol=4)
if mergedIntervals is None or len(mergedIntervals) < 1:
raise RuntimeError("Could not read any intervals from %s" %
args.queryBed)
# read the track, while intersecting with query intervals
# (track is saved as temp XML file for sake not changing interface)
bedIntervals = []
for queryInterval in mergedIntervals:
bedIntervals += readBedIntervals(trackPath,
ncol = track.getValCol() + 1,
chrom=queryInterval[0],
start=queryInterval[1],
end=queryInterval[2])
# 1st pass to collect set of names
nameMap = CategoryMap(reserved = 0)
for interval in bedIntervals:
nameMap.update(interval[track.getValCol()])
outNameMap = CategoryMap(reserved = 0)
if args.numTot > 0:
args.numOut = max(0, args.numTot - len(nameMap))
for i in xrange(args.numOut):
outName = args.outName
if args.numOut > 1:
outName += str(i)
assert nameMap.has(outName) is False
outNameMap.update(outName)
# write the transition model for use with teHmmTrain.py --initTransProbs
writeTransitions(bedIntervals, nameMap, outNameMap, args)
# write the emission model for use with teHmmTrain.py --initEmProbs
writeEmissions(bedIntervals, nameMap, outNameMap, args)
cleanBedTool(tempBedToolPath)
def parallelDispatch(argv, args):
""" chunk up input with chrom option. recursivlely launch eval. merge
results """
jobList = []
chromIntervals = readBedIntervals(args.chroms, sort=True)
chromFiles = []
regionFiles = []
bedFiles = []
pdFiles = []
bicFiles = []
edFiles = []
for chrom in chromIntervals:
cmdToks = copy.deepcopy(argv)
cmdToks[cmdToks.index("--chrom") + 1] = ""
cmdToks[cmdToks.index("--chrom")] = ""
chromPath = getLocalTempPath("Temp", ".bed")
cpFile = open(chromPath, "w")
cpFile.write("%s\t%d\t%d\t0\t0\t.\n" % (chrom[0], chrom[1], chrom[2]))
cpFile.close()
regionPath = getLocalTempPath("Temp", ".bed")
runShellCommand("intersectBed -a %s -b %s | sortBed > %s" % (args.bedRegions,
chromPath,
regionPath))
if os.path.getsize(regionPath) < 2:
continue
regionFiles.append(regionPath)
chromFiles.append(chromPath)
cmdToks[3] = regionPath
if args.bed is not None:
bedPath = getLocalTempPath("Temp", ".bed")
cmdToks[cmdToks.index("--bed")+1] = bedPath
bedFiles.append(bedPath)
if args.pd is not None:
pdPath = getLocalTempPath("Temp", ".bed")
cmdToks[cmdToks.index("--pd")+1] = pdPath
pdFiles.append(pdPath)
if args.ed is not None:
edPath = getLocalTempPath("Temp", ".bed")
cmdToks[cmdToks.index("--ed")+1] = edPath
edFiles.append(edPath)
if args.bic is not None:
bicPath = getLocalTempPath("Temp", ".bic")
cmdToks[cmdToks.index("--bic")+1] = bicPath
bicFiles.append(bicPath)
cmd = " ".join(cmdToks)
jobList.append(cmd)
runParallelShellCommands(jobList, args.proc)
for i in xrange(len(jobList)):
if i == 0:
ct = ">"
else:
ct = ">>"
if len(bedFiles) > 0:
runShellCommand("cat %s %s %s" % (bedFiles[i], ct, args.bed))
if len(pdFiles) > 0:
runShellCommand("cat %s %s %s" % (pdFiles[i], ct, args.pd))
if len(edFiles) > 0:
runShellCommand("cat %s %s %s" % (edFiles[i], ct, args.ed))
if len(bicFiles) > 0:
runShellCommand("cat %s %s %s" % (bicFiles[i], ct, args.bic))
for i in itertools.chain(chromFiles, regionFiles, bedFiles, pdFiles, edFiles,
bicFiles):
runShellCommand("rm %s" % i)
def runPositionalComparison(argv, args):
""" hack to recursively exectute compareBedStates.py on a sliding window of the two
inputs and report accuracy in a BED file """
try:
windowToks = args.window.split(",")
assert len(windowToks) == 5
windowSize = int(windowToks[0])
stateName = windowToks[1]
compType = windowToks[2]
score = windowToks[3]
outBed = windowToks[4]
except:
raise RuntimeError("value passed to --window is not in valid format")
if compType == "base":
compIdx = 0
elif compType == "interval":
compIdx = 1
elif compType == "weighted":
compIdx = 2
else:
raise RuntimeError("invalid compType, %s, passed to --window" %
compType)
if score != "f1" and score != "precision" and score != "recall":
raise RuntimeError("invalid score, %s, passed to --window" % score)
try:
outFile = open(outBed, "w")
except:
raise RuntimeError("invalid outBed, %s, passed to --window" % outBed)
tempBed = getLocalTempPath("Temp_region", ".bed")
runShellCommand("mergeBed -i %s > %s" % (args.bed1, tempBed))
chunkBed = getLocalTempPath("Temp_chunkBed", ".bed")
runShellCommand("chunkBedRegions.py %s %d --overlap .5 > %s" %
(tempBed, windowSize, chunkBed))
window = getLocalTempPath("Temp_window", ".bed")
slice1 = getLocalTempPath("Temp_slice1", ".bed")
slice2 = getLocalTempPath("Temp_slice2", ".bed")
compFile = getLocalTempPath("Temp_compFile", ".bed")
compOpts = ""
winIdx = argv.index("--window")
assert winIdx > 0 and winIdx < len(argv) - 1 and argv[winIdx +
1] == args.window
for i in xrange(3, len(argv)):
if i != winIdx and i != winIdx + 1:
compOpts += " " + argv[i]
for chunk in readBedIntervals(chunkBed):
runShellCommand("echo \"%s\t%d\t%d\" > %s" %
(chunk[0], chunk[1], chunk[2], window))
runShellCommand("intersectBed -a %s -b %s | sortBed > %s" %
(args.bed1, window, slice1))
runShellCommand("intersectBed -a %s -b %s | sortBed > %s" %
(args.bed2, window, slice2))
runShellCommand("compareBedStates.py %s %s %s > %s" %
(slice1, slice2, compOpts, compFile))
stats = extractCompStatsFromFile(compFile)[compIdx]
if stateName not in stats:
stats[stateName] = (0, 0)
f1 = 0.
prec, rec = stats[stateName]
if prec + rec > 0:
f1 = (2. * prec * rec) / (prec + rec)
val = f1
if score == "precision":
val = prec
elif score == "recall":
val = rec
outFile.write("%s\t%d\t%d\t%f\n" % (chunk[0], chunk[1], chunk[2], val))
runShellCommand("rm -f %s %s %s %s %s %s" %
(tempBed, chunkBed, window, slice1, slice2, compFile))
outFile.close()
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Find candidate TSDs (exact forward matches) flanking given"
"BED intervals. Score is distance between TSD and bed interval.")
parser.add_argument("fastaSequence", help="DNA sequence in FASTA format")
parser.add_argument("inBed", help="BED file with TEs whose flanking regions "
"we wish to search")
parser.add_argument("outBed", help="BED file containing (only) output TSDs")
parser.add_argument("--min", help="Minimum length of a TSD",
default=4, type=int)
parser.add_argument("--max", help="Maximum length of a TSD",
default=6, type=int)
parser.add_argument("--all", help="Report all matches in region (as opposed"
" to only the nearest to the BED element which is the "
"default behaviour", action="store_true", default=False)
parser.add_argument("--maxScore", help="Only report matches with given "
"score or smaller. The score is definied as the "
"maximum distance between the (two) TSD intervals and "
"the query interval",
default=None, type=int)
parser.add_argument("--left", help="Number of bases immediately left of the "
"BED element to search for the left TSD",
default=7, type=int)
parser.add_argument("--right", help="Number of bases immediately right of "
"the BED element to search for the right TSD",
default=7, type=int)
parser.add_argument("--overlap", help="Number of bases overlapping the "
"BED element to include in search (so total space "
"on each side will be --left + overlap, and --right + "
"--overlap", default=3, type=int)
parser.add_argument("--leftName", help="Name of left TSDs in output Bed",
default="L_TSD")
parser.add_argument("--rightName", help="Name of right TSDs in output Bed",
default="R_TSD")
parser.add_argument("--id", help="Assign left/right pairs of TSDs a unique"
" matching ID", action="store_true", default=False)
parser.add_argument("--names", help="Only apply to bed interval whose "
"name is in (comma-separated) list. If not specified"
" then all intervals are processed", default=None)
parser.add_argument("--numProc", help="Number of jobs to run in parallel."
" (parallization done on different sequences in FASTA"
"file", type=int, default=1)
parser.add_argument("--sequences", help="Only process given sequences of input"
" FASTA file (comma-separated list).", default=None)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
assert os.path.exists(args.inBed)
assert os.path.exists(args.fastaSequence)
assert args.min <= args.max
args.nextId = 0
if args.sequences is not None:
args.sequences = set(args.sequences.split(","))
# read intervals from the bed file
logger.info("loading target intervals from %s" % args.inBed)
bedIntervals = readBedIntervals(args.inBed, ncol=4, sort=True)
if bedIntervals is None or len(bedIntervals) < 1:
raise RuntimeError("Could not read any intervals from %s" %
args.inBed)
if args.numProc > 1:
runParallel(args, bedIntervals)
return 0
tsds = findTsds(args, bedIntervals)
writeBedIntervals(tsds, args.outBed)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Rename HMM states.")
parser.add_argument("inputModel",
help="Path of teHMM model created with"
" teHmmTrain.py")
parser.add_argument("outputModel",
help="Path of model with renamed states")
parser.add_argument(
"--newNames",
help="comma-separated list of state names to"
" apply. This list must have exactly the same number of"
" states as the model. The ith name in the list will be "
"assigned to the ith name of the model...",
default=None)
parser.add_argument(
"--teNumbers",
help="comma-separated list of state numbers"
" that will be assigned TE states, with everything else"
" assigned Other. This is less flexible but maybe more"
" convenient at times than --newNames.",
default=None)
parser.add_argument("--bed",
help="apply naming to bed file and print "
"results to stdout",
default=None)
parser.add_argument(
"--sizes",
help="bedFile to use for computing state numbering"
" by using decreasing order in total coverage (only works"
" with --teNumbers)",
default=None)
parser.add_argument("--noMerge",
help="dont merge adjacent intervals with same"
" name with --bed option",
action="store_true",
default=False)
parser.add_argument(
"--changeTrackName",
help="dont do anything else, just change"
" the name of one track. specified value should be of form"
" currentNAme, newName",
default=None)
args = parser.parse_args()
assert args.inputModel != args.outputModel
# load model created with teHmmTrain.py
model = loadModel(args.inputModel)
# trackChangeName logic hacked in completely separate from everything else
if args.changeTrackName is not None:
oldName, newName = args.changeTrackName.split(",")
track = model.getTrackList().getTrackByName(oldName)
track.setName(newName)
saveModel(args.outputModel, model)
return 0
assert (args.newNames is None) != (args.teNumbers is None)
# names manually specified
if args.newNames is not None:
names = args.newNames.split(",")
# names computed using simple scheme from set of "TE" state numbers (as found from
# log output of fitStateNames.py)
elif args.teNumbers is not None:
teNos = set([int(x) for x in args.teNumbers.split(",")])
teCount, otherCount = 0, 0
numStates = model.getEmissionModel().getNumStates()
# re-order from sizing info
if args.sizes is not None:
bedIntervals = readBedIntervals(args.sizes, ncol=4)
sizeMap = defaultdict(int)
for interval in bedIntervals:
sizeMap[int(interval[3])] += interval[2] - interval[1]
stateNumbers = sorted([x for x in xrange(numStates)],
reverse=True,
key=lambda x: sizeMap[x])
else:
stateNumbers = [x for x in xrange(numStates)]
names = [""] * numStates
for i in stateNumbers:
if i in teNos:
name = "TE-%.2d" % teCount
teCount += 1
else:
name = "Other-%.2d" % otherCount
otherCount += 1
names[i] = name
assert teCount == len(teNos) and teCount + otherCount == len(names)
assert len(names) == model.getEmissionModel().getNumStates()
# throw names in the mapping object and stick into model
catMap = CategoryMap(reserved=0)
for i, name in enumerate(names):
catMap.getMap(name, update=True)
model.stateNameMap = catMap
# save model
saveModel(args.outputModel, model)
# process optional bed file
if args.bed is not None:
prevInterval = None
bedIntervals = readBedIntervals(args.bed, ncol=4)
for interval in bedIntervals:
oldName = interval[3]
newName = names[int(oldName)]
newInterval = list(interval)
newInterval[3] = newName
if args.noMerge:
# write interval
print "\t".join(str(x) for x in newInterval)
else:
if prevInterval is None:
# update prev interval first time
prevInterval = newInterval
elif newInterval[3] == prevInterval[3] and\
newInterval[0] == prevInterval[0] and\
newInterval[1] == prevInterval[2]:
# glue onto prev interval
prevInterval[2] = newInterval[2]
else:
# write and update prev
print "\t".join(str(x) for x in prevInterval)
prevInterval = newInterval
if prevInterval is not None:
print "\t".join(str(x) for x in prevInterval)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Compare two bed files where Model states are represented"
" in a column. Used to determine sensitivity and specificity. NOTE"
" that both bed files must be sorted and cover the exact same regions"
" of the same genome.")
parser.add_argument("bed1", help="Bed file (TRUTH)")
parser.add_argument("bed2",
help="Bed file covering same regions in same"
" order as bed1")
parser.add_argument("--col",
help="Column of bed files to use for state"
" (currently only support 4(name) or 5(score))",
default=4,
type=int)
parser.add_argument("--thresh",
help="Threshold to consider interval from"
" bed1 covered by bed2.",
type=float,
default=0.8)
parser.add_argument("--plot",
help="Path of file to write Precision/Recall"
" graphs to in PDF format",
default=None)
parser.add_argument("--ignore",
help="Comma-separated list of stateNames to"
" ignore",
default=None)
parser.add_argument(
"--strictPrec",
help="By default, precision is computed"
" in a manner strictly symmetric to recall. So calling"
" compareBedStates.py A.bed B.bed would give the exact"
" same output as compareBedStates.py B.bed A.bed except"
" precision and recall values would be swapped. With "
" this option, a predicted element only counts toward"
" precision if it overlaps with 80pct of the true"
" element, as opposed to only needing 80pct of itself"
" overlapping with the true element. ",
action="store_true",
default=False)
parser.add_argument("--noBase",
help="Skip base-level stats (and only show"
" interval stats). Runs faster",
action="store_true",
default=False)
parser.add_argument("--noFrag",
help="Do not allow fragmented matches in"
"interval predictions. ie if a single truth interval"
" is covered by a series of predicted intervals, only "
"the best match will be counted if this flag is used",
action="store_true",
default=False)
parser.add_argument("--tl",
help="Path to tracks XML file. Used to cut "
"out mask tracks so they are removed from comparison."
" (convenience option to not have to manually run "
"subtractBed everytime...)",
default=None)
parser.add_argument("--delMask",
help="Entirely remove intervals from "
"mask tracks that are > given length. Probably "
"only want to set to non-zero value K if using"
" with a prediction that was processed with "
"interpolateMaskedRegions.py --max K",
type=int,
default=0)
parser.add_argument(
"--window",
help="A comma-delimited 5-tuple of "
"windowSize,stateName,compType,score,outBed. "
"Where windowSize is the sliding window size "
"(overlap .5), stateName is target stateName,"
" compType is in {base,interval,weighted}, sore is"
" in {f1,precision,recall} and "
"outBed is the path of a bedFile to write positional"
" accuracy to. For example, --window 1000000,TE,base,f1"
",acc.bed will write base-level f1 for 1MB sliding windows"
" to acc.bed. These can be viewed on the browser by first"
" converting to BigWig.",
default=None)
args = parser.parse_args()
tempBedToolPath = initBedTool()
if args.ignore is not None:
args.ignore = set(args.ignore.split(","))
else:
args.ignore = set()
assert args.col == 4 or args.col == 5
print "Commandline %s" % " ".join(sys.argv)
origArgs = copy.deepcopy(args)
tempFiles = []
if args.tl is not None:
cutBed1 = cutOutMaskIntervals(args.bed1, args.delMask, sys.maxint,
args.tl)
cutBed2 = cutOutMaskIntervals(args.bed2, args.delMask, sys.maxint,
args.tl)
if cutBed1 is not None:
assert cutBed2 is not None
tempFiles += [cutBed1, cutBed2]
args.bed1 = cutBed1
args.bed2 = cutBed2
checkExactOverlap(args.bed1, args.bed2)
if args.window is not None:
runPositionalComparison(argv, origArgs)
intervals1 = readBedIntervals(args.bed1, ncol=args.col)
intervals2 = readBedIntervals(args.bed2, ncol=args.col)
if args.noBase is False:
stats = compareBaseLevel(intervals1, intervals2, args.col - 1)[0]
totalRight, totalWrong, accMap = summarizeBaseComparision(
stats, args.ignore)
print "Base counts [False Negatives, False Positives, True Positives]:"
print stats
totalBoth = totalRight + totalWrong
accuracy = float(totalRight) / float(totalBoth)
print "Accuaracy: %d / %d = %f" % (totalRight, totalBoth, accuracy)
print "State-by-state (Precision, Recall):"
print "Base-by-base Accuracy"
print accMap
trueStats = compareIntervalsOneSided(intervals1, intervals2, args.col - 1,
args.thresh, False,
not args.noFrag)[0]
predStats = compareIntervalsOneSided(intervals2, intervals1, args.col - 1,
args.thresh, args.strictPrec,
not args.noFrag)[0]
intAccMap = summarizeIntervalComparison(trueStats, predStats, False,
args.ignore)
intAccMapWeighted = summarizeIntervalComparison(trueStats, predStats, True,
args.ignore)
print "\nInterval Accuracy"
print intAccMap
print ""
print "\nWeighted Interval Accuracy"
print intAccMapWeighted
print ""
# print some row data to be picked up by scrapeBenchmarkRow.py
if args.noBase is False:
header, row = summaryRow(accuracy, stats, accMap)
print " ".join(header)
print " ".join(row)
# make graph
if args.plot is not None:
if canPlot is False:
raise RuntimeError("Unable to write plots. Maybe matplotlib is "
"not installed?")
writeAccPlots(accuracy, accMap, intAccMap, intAccMapWeighted,
args.thresh, args.plot)
if len(tempFiles) > 0:
runShellCommand("rm -f %s" % " ".join(tempFiles))
cleanBedTool(tempBedToolPath)
def testSupervisedLearn(self):
intervals = readBedIntervals(getTestDirPath("truth.bed"), ncol=4)
truthIntervals = []
for i in intervals:
truthIntervals.append((i[0], i[1], i[2], int(i[3])))
allIntervals = [(truthIntervals[0][0],
truthIntervals[0][1],
truthIntervals[-1][2])]
trackData = TrackData()
trackData.loadTrackData(getTracksInfoPath(3), allIntervals)
assert len(trackData.getTrackTableList()) == 1
# set the fudge to 1 since when the test was written this was
# hardcoded default
em = IndependentMultinomialEmissionModel(
4, trackData.getNumSymbolsPerTrack(),
fudge = 1.0)
hmm = MultitrackHmm(em)
hmm.supervisedTrain(trackData, truthIntervals)
hmm.validate()
# check emissions, they should basically be binary.
trackList = hmm.getTrackList()
emp = np.exp(em.getLogProbs())
ltrTrack = trackList.getTrackByName("ltr")
track = ltrTrack.getNumber()
cmap = ltrTrack.getValueMap()
s0 = cmap.getMap(None)
s1 = cmap.getMap(0)
# we add 1 to all frequencies like emission trainer
assert_array_almost_equal(emp[track][0][s0], 36. / 37.)
assert_array_almost_equal(emp[track][0][s1], 1 - 36. / 37.)
assert_array_almost_equal(emp[track][1][s0], 1 - 6. / 7.)
assert_array_almost_equal(emp[track][1][s1], 6. / 7.)
assert_array_almost_equal(emp[track][2][s0], 26. / 27.)
assert_array_almost_equal(emp[track][2][s1], 1. - 26. / 27.)
assert_array_almost_equal(emp[track][3][s0], 1. - 6. / 7.)
assert_array_almost_equal(emp[track][3][s1], 6. / 7.)
insideTrack = trackList.getTrackByName("inside")
track = insideTrack.getNumber()
cmap = insideTrack.getValueMap()
s0 = cmap.getMap(None)
s1 = cmap.getMap("Inside")
assert_array_almost_equal(emp[track][0][s0], 36. / 37.)
assert_array_almost_equal(emp[track][0][s1], 1 - 36. / 37.)
assert_array_almost_equal(emp[track][1][s0], 6. / 7.)
assert_array_almost_equal(emp[track][1][s1], 1 - 6. / 7.)
assert_array_almost_equal(emp[track][2][s0], 1. - 26. / 27.)
assert_array_almost_equal(emp[track][2][s1], 26. / 27.)
assert_array_almost_equal(emp[track][3][s0], 6. / 7.)
assert_array_almost_equal(emp[track][3][s1], 1. - 6. / 7.)
# crappy check for start probs. need to test transition too!
freq = [0.0] * em.getNumStates()
total = 0.0
for interval in truthIntervals:
state = interval[3]
freq[state] += float(interval[2]) - float(interval[1])
total += float(interval[2]) - float(interval[1])
sprobs = hmm.getStartProbs()
assert len(sprobs) == em.getNumStates()
for state in xrange(em.getNumStates()):
assert_array_almost_equal(freq[state] / total, sprobs[state])
# transition probabilites
# from eyeball:
#c 0 5 0 0->0 +4 0->1 +1 0-> +5
#c 5 10 1 1->1 +4 1->2 +1 1-> +5
#c 10 35 2 2->2 +24 2->3 +1 2-> +25
#c 35 40 3 3->3 +4 3->0 +1 3-> +5
#c 40 70 0 0->0 +29 0-> +19
realTransProbs = np.array([
[33. / 34., 1. / 34., 0., 0.],
[0., 4. / 5., 1. / 5., 0.],
[0., 0., 24. / 25., 1. / 25.],
[1. / 5., 0., 0., 4. / 5.]
])
tprobs = hmm.getTransitionProbs()
assert tprobs.shape == (em.getNumStates(), em.getNumStates())
assert_array_almost_equal(tprobs, realTransProbs)
prob, states = hmm.viterbi(trackData)[0]
for truthInt in truthIntervals:
for i in xrange(truthInt[1], truthInt[2]):
assert states[i] == truthInt[3]
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Create a teHMM")
parser.add_argument("tracksInfo", help="Path of Tracks Info file "
"containing paths to genome annotation tracks")
parser.add_argument("trainingBed", help="Path of BED file containing"
" genome regions to train model on. If --supervised "
"is used, the names in this bed file will be treated "
"as the true annotation (otherwise it is only used for "
"interval coordinates)")
parser.add_argument("outputModel", help="Path of output hmm")
parser.add_argument("--numStates", help="Number of states in model",
type = int, default=2)
parser.add_argument("--iter", help="Number of EM iterations",
type = int, default=100)
parser.add_argument("--supervised", help="Use name (4th) column of "
"<traingingBed> for the true hidden states of the"
" model. Transition parameters will be estimated"
" directly from this information rather than EM."
" NOTE: The number of states will be determined "
"from the bed.",
action = "store_true", default = False)
parser.add_argument("--cfg", help="Use Context Free Grammar insead of "
"HMM. Only works with --supervised for now",
action = "store_true", default = False)
parser.add_argument("--saPrior", help="Confidence in self alignment "
"track for CFG. Probability of pair emission "
"is multiplied by this number if the bases are aligned"
" and its complement if bases are not aligned. Must"
" be between [0,1].", default=0.95, type=float)
parser.add_argument("--pairStates", help="Comma-separated list of states"
" (from trainingBed) that are treated as pair-emitors"
" for the CFG", default=None)
parser.add_argument("--emFac", help="Normalization factor for weighting"
" emission probabilities because when there are "
"many tracks, the transition probabilities can get "
"totally lost. 0 = no normalization. 1 ="
" divide by number of tracks. k = divide by number "
"of tracks / k", type=int, default=0)
parser.add_argument("--initTransProbs", help="Path of text file where each "
"line has three entries: FromState ToState Probability"
". This file (all other transitions get probability 0)"
" is used to specifiy the initial transition model."
" The names and number of states will be initialized "
"according to this file (overriding --numStates)",
default = None)
parser.add_argument("--fixTrans", help="Do not learn transition parameters"
" (best used with --initTransProbs)",
action="store_true", default=False)
parser.add_argument("--initEmProbs", help="Path of text file where each "
"line has four entries: State Track Symbol Probability"
". This file (all other emissions get probability 0)"
" is used to specifiy the initial emission model. All "
"states specified in this file must appear in the file"
" specified with --initTransProbs (but not vice versa).",
default = None)
parser.add_argument("--fixEm", help="Do not learn emission parameters"
" (best used with --initEmProbs)",
action="store_true", default=False)
parser.add_argument("--initStartProbs", help="Path of text file where each "
"line has two entries: State Probability"
". This file (all other start probs get probability 0)"
" is used to specifiy the initial start dist. All "
"states specified in this file must appear in the file"
" specified with --initTransProbs (but not vice versa).",
default = None)
parser.add_argument("--fixStart", help="Do not learn start parameters"
" (best used with --initStartProbs)",
action="store_true", default=False)
parser.add_argument("--forceTransProbs",
help="Path of text file where each "
"line has three entries: FromState ToState Probability"
". These transition probabilities will override any "
" learned probabilities after each training iteration"
" (unspecified "
"will not be set to 0 in this case. the learned values"
" will be kept, but normalized as needed)" ,
default=None)
parser.add_argument("--forceEmProbs", help="Path of text file where each "
"line has four entries: State Track Symbol Probability"
". These "
"emission probabilities will override any learned"
" probabilities after each training iteration "
"(unspecified "
"will not be set to 0 in this case. the learned values"
" will be kept, but normalized as needed.)" ,
default = None)
parser.add_argument("--flatEm", help="Use a flat emission distribution as "
"a baseline. If not specified, the initial emission "
"distribution will be randomized by default. Emission"
" probabilities specified with --initEmpProbs or "
"--forceEmProbs will never be affected by randomizaiton"
". The randomization is important for Baum Welch "
"training, since if two states dont have at least one"
" different emission or transition probability to begin"
" with, they will never learn to be different.",
action="store_true", default=False)
parser.add_argument("--emRandRange", help="When randomly initialzing an"
" emission distribution, constrain"
" the values to the given range (pair of "
"comma-separated numbers). Overridden by "
"--initEmProbs and --forceEmProbs when applicable."
" Completely overridden by --flatEm (which is equivalent"
" to --emRandRange .5,.5.). Actual values used will"
" always be normalized.", default="0.2,0.8")
parser.add_argument("--segment", help="Bed file of segments to treat as "
"single columns for HMM (ie as created with "
"segmentTracks.py). IMPORTANT: this file must cover "
"the same regions as the traininBed file. Unless in "
"supervised mode, probably best to use same bed file "
" as both traingBed and --segment argument. Otherwise"
" use intersectBed to make sure the overlap is exact",
default=None)
parser.add_argument("--segLen", help="Effective segment length used for"
" normalizing input segments (specifying 0 means no"
" normalization applied)", type=int, default=0)
parser.add_argument("--seed", help="Seed for random number generator"
" which will be used to initialize emissions "
"(if --flatEM and --supervised not specified)",
default=None, type=int)
parser.add_argument("--reps", help="Number of replicates (with different"
" random initializations) to run. The replicate"
" with the highest likelihood will be chosen for the"
" output", default=1, type=int)
parser.add_argument("--numThreads", help="Number of threads to use when"
" running replicates (see --rep) in parallel.",
type=int, default=1)
parser.add_argument("--emThresh", help="Threshold used for convergence"
" in baum welch training. IE delta log likelihood"
" must be bigger than this number (which should be"
" positive) for convergence", type=float,
default=0.001)
parser.add_argument("--saveAllReps", help="Save all replicates (--reps)"
" models to disk, instead of just the best one"
". Format is <outputModel>.repN. There will be "
" --reps -1 such models saved as the best output"
" counts as a replicate",
action="store_true", default=False)
parser.add_argument("--maxProb", help="Gaussian distributions and/or"
" segment length corrections can cause probability"
" to *decrease* during BW iteration. Use this option"
" to remember the parameters with the highest probability"
" rather than returning the parameters after the final "
"iteration.", action="store_true", default=False)
parser.add_argument("--maxProbCut", help="Use with --maxProb option to stop"
" training if a given number of iterations go by without"
" hitting a new maxProb", default=None, type=int)
parser.add_argument("--transMatEpsilons", help="By default, epsilons are"
" added to all transition probabilities to prevent "
"converging on 0 due to rounding error only for fully"
" unsupervised training. Use this option to force this"
" behaviour for supervised and semisupervised modes",
action="store_true", default=False)
addLoggingOptions(parser)
args = parser.parse_args()
if args.cfg is True:
assert args.supervised is True
assert args.saPrior >= 0. and args.saPrior <= 1.
if args.pairStates is not None:
assert args.cfg is True
if args.initTransProbs is not None or args.fixTrans is True or\
args.initEmProbs is not None or args.fixEm is not None:
if args.cfg is True:
raise RuntimeError("--transProbs, --fixTrans, --emProbs, --fixEm "
"are not currently compatible with --cfg.")
if args.fixTrans is True and args.supervised is True:
raise RuntimeError("--fixTrans option not compatible with --supervised")
if args.fixEm is True and args.supervised is True:
raise RuntimeError("--fixEm option not compatible with --supervised")
if (args.forceTransProbs is not None or args.forceEmProbs is not None) \
and args.cfg is True:
raise RuntimeError("--forceTransProbs and --forceEmProbs are not "
"currently compatible with --cfg")
if args.flatEm is True and args.supervised is False and\
args.initEmProbs is None and args.initTransProbs is None:
raise RuntimeError("--flatEm must be used with --initEmProbs and or"
" --initTransProbs")
if args.initEmProbs is not None and args.initTransProbs is None:
raise RuntimeError("--initEmProbs can only be used in conjunction with"
" --initTransProbs")
if args.emRandRange is not None:
args.emRandRange = args.emRandRange.split(",")
try:
assert len(args.emRandRange) == 2
args.emRandRange = (float(args.emRandRange[0]),
float(args.emRandRange[1]))
except:
raise RuntimeError("Invalid --emRandRange specified")
if args.transMatEpsilons is False:
# old logic here. now overriden with above options
args.transMatEpsilons = (args.supervised is False and
args.initTransProbs is None and
args.forceTransProbs is None)
setLoggingFromOptions(args)
tempBedToolPath = initBedTool()
# read training intervals from the bed file
logger.info("loading training intervals from %s" % args.trainingBed)
mergedIntervals = getMergedBedIntervals(args.trainingBed, ncol=4)
if mergedIntervals is None or len(mergedIntervals) < 1:
raise RuntimeError("Could not read any intervals from %s" %
args.trainingBed)
# read segment intervals
segIntervals = None
if args.segment is not None:
logger.info("loading segment intervals from %s" % args.segment)
try:
checkExactOverlap(args.trainingBed, args.segment)
except:
raise RuntimeError("bed file passed with --segments option"
" must exactly overlap trainingBed")
segIntervals = readBedIntervals(args.segment, sort=True)
elif args.segLen > 0:
raise RuntimeError("--segLen can only be used with --segment")
if args.segLen <= 0:
args.segLen = None
if args.segLen > 0 and args.segLen != 1:
logger.warning("--segLen should be 0 (no correction) or 1 (base"
" correction). Values > 1 may cause bias.")
# read the tracks, while intersecting them with the training intervals
logger.info("loading tracks %s" % args.tracksInfo)
trackData = TrackData()
trackData.loadTrackData(args.tracksInfo, mergedIntervals,
segmentIntervals=segIntervals)
catMap = None
userTrans = None
if args.supervised is False and args.initTransProbs is not None:
logger.debug("initializing transition model with user data")
catMap = stateNamesFromUserTrans(args.initTransProbs)
# state number is overrided by the transProbs file
args.numStates = len(catMap)
truthIntervals = None
# state number is overrided by the input bed file in supervised mode
if args.supervised is True:
logger.info("processing supervised state names")
# we reload because we don't want to be merging them here
truthIntervals = readBedIntervals(args.trainingBed, ncol=4)
catMap = mapStateNames(truthIntervals)
args.numStates = len(catMap)
# train the model
seeds = [random.randint(0, 4294967294)]
if args.seed is not None:
seeds = [args.seed]
random.seed(args.seed)
seeds += [random.randint(0, sys.maxint) for x in xrange(1, args.reps)]
def trainClosure(randomSeed):
return trainModel(randomSeed, trackData=trackData, catMap=catMap,
userTrans=userTrans, truthIntervals=truthIntervals,
args=args)
modelList = runParallelShellCommands(argList=seeds, numProc = args.numThreads,
execFunction = trainClosure,
useThreads = True)
# select best model
logmsg = ""
bestModel = (-1, LOGZERO)
for i in xrange(len(modelList)):
curModel = (i, modelList[i].getLastLogProb())
if curModel[1] > bestModel[1]:
bestModel = curModel
if curModel[1] is not None:
logmsg += "Rep %i: TotalProb: %f\n" % curModel
if len(modelList) > 1:
logging.info("Training Replicates Statistics:\n%s" % logmsg)
logging.info("Selecting best replicate (%d, %f)" % bestModel)
model = modelList[bestModel[0]]
# write the model to a pickle
logger.info("saving trained model to %s" % args.outputModel)
saveModel(args.outputModel, model)
# write all replicates
writtenCount = 0
if args.saveAllReps is True:
for i, repModel in enumerate(modelList):
if i != bestModel[0]:
repPath = "%s.rep%d" % (args.outputModel, writtenCount)
logger.info("saving replicate model to %s" % repPath)
saveModel(repPath, repModel)
writtenCount += 1
cleanBedTool(tempBedToolPath)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Find candidate TSDs (exact forward matches) flanking given"
"BED intervals. Score is distance between TSD and bed interval.")
parser.add_argument("fastaSequence", help="DNA sequence in FASTA format")
parser.add_argument("inBed",
help="BED file with TEs whose flanking regions "
"we wish to search")
parser.add_argument("outBed",
help="BED file containing (only) output TSDs")
parser.add_argument("--min",
help="Minimum length of a TSD",
default=4,
type=int)
parser.add_argument("--max",
help="Maximum length of a TSD",
default=6,
type=int)
parser.add_argument("--all",
help="Report all matches in region (as opposed"
" to only the nearest to the BED element which is the "
"default behaviour",
action="store_true",
default=False)
parser.add_argument("--maxScore",
help="Only report matches with given "
"score or smaller. The score is definied as the "
"maximum distance between the (two) TSD intervals and "
"the query interval",
default=None,
type=int)
parser.add_argument("--left",
help="Number of bases immediately left of the "
"BED element to search for the left TSD",
default=7,
type=int)
parser.add_argument("--right",
help="Number of bases immediately right of "
"the BED element to search for the right TSD",
default=7,
type=int)
parser.add_argument("--overlap",
help="Number of bases overlapping the "
"BED element to include in search (so total space "
"on each side will be --left + overlap, and --right + "
"--overlap",
default=3,
type=int)
parser.add_argument("--leftName",
help="Name of left TSDs in output Bed",
default="L_TSD")
parser.add_argument("--rightName",
help="Name of right TSDs in output Bed",
default="R_TSD")
parser.add_argument("--id",
help="Assign left/right pairs of TSDs a unique"
" matching ID",
action="store_true",
default=False)
parser.add_argument("--names",
help="Only apply to bed interval whose "
"name is in (comma-separated) list. If not specified"
" then all intervals are processed",
default=None)
parser.add_argument("--numProc",
help="Number of jobs to run in parallel."
" (parallization done on different sequences in FASTA"
"file",
type=int,
default=1)
parser.add_argument("--sequences",
help="Only process given sequences of input"
" FASTA file (comma-separated list).",
default=None)
addLoggingOptions(parser)
args = parser.parse_args()
setLoggingFromOptions(args)
assert os.path.exists(args.inBed)
assert os.path.exists(args.fastaSequence)
assert args.min <= args.max
args.nextId = 0
if args.sequences is not None:
args.sequences = set(args.sequences.split(","))
# read intervals from the bed file
logger.info("loading target intervals from %s" % args.inBed)
bedIntervals = readBedIntervals(args.inBed, ncol=4, sort=True)
if bedIntervals is None or len(bedIntervals) < 1:
raise RuntimeError("Could not read any intervals from %s" % args.inBed)
if args.numProc > 1:
runParallel(args, bedIntervals)
return 0
tsds = findTsds(args, bedIntervals)
writeBedIntervals(tsds, args.outBed)
