def countHits(infile, **kwargs):
"""
Count hits from a hit table.
Calls edl.hits.parseM8FileIter with the following optional parameters:
hitStringMap (None): dictionary (or file) mapping hit IDs to
something else
format (GENE): hit table format
filter_top_pct (0): only consider hits within this % of top score for
each read
parseStyle (ACCS): how to process hit data into an identifying string
countMethod ('all'): how to resolve hits to multiple sequences
taxonomy (None): An edl.taxon.Taxonomy object or directory
conatining taxdmp
rank (None): Maximum rank to resolve hits
"""
# if taxonomy or hitStringMap are file names, parse them
taxonomy = kwargs.pop('taxonomy', None)
if isinstance(taxonomy, str):
taxonomy = readTaxonomy(taxonomy,
namesMap=kwargs.pop('namesMap', False))
hitStringMap = kwargs.pop('hitStringMap', None)
if isinstance(hitStringMap, str):
if taxonomy is not None:
# the mapped hit ids will need to be ints
valueType = kwargs.pop('valueType', int)
else:
valueType = kwargs.pop('valueType', None)
hitStringMap = parseMapFile(hitStringMap, valueType=valueType)
# if infile is name (and not handle), open as a handle
if isinstance(infile, str):
inhandle = open(infile)
else:
inhandle = infile
# get iterator over reads that will parse hits
hitIter = parseM8FileIter(inhandle,
hitStringMap,
FilterParams(
format=kwargs.pop('format', GENE),
top_pct=kwargs.pop('filter_top_pct', 0),
),
kwargs.pop('parseStyle', ACCS),
kwargs.pop('countMethod', 'all'),
taxonomy=taxonomy,
rank=kwargs.pop('rank', None))
# count the hits
(total, counts) = countIterHits(hitIter,
allMethod=kwargs.pop('allMethod', ALLEQ),
returnMap=False)
logger.info("Total hits: %s" % total)
if isinstance(infile, str):
inhandle.close()
return counts
def parseM8FileIter(
inhandle,
hitStringMap,
format,
scorePct,
parsingStyle,
countMethod,
taxonomy=None,
rank=None,
ignoreEmptyHits=True,
sortReads=False):
"""
Wrapper method that combines filterM8, parseHits, and process hits to:
filter hits using format and scorePct
map reads to hits using parseHits
translate hits using processHits
If taxonomy is not None, hits will be TaxNode objects
contMethod can only be LCA if taxonomy given
Return an iterator over (read,hits) tuples.
"""
# check filtering options
if countMethod == 'first':
scorePct = -1
# get map from reads to lists of hit strings
logger.info("Parsing hits")
options = FilterParams()
options.format = format
if scorePct >= 0 or sortReads:
# filter hits on score if requested
if scorePct >= 0:
logger.info(
"Filtering for scores within %s pct of best" %
scorePct)
options.topPct = scorePct
options.sort = 'score'
options.sortReads = sortReads
# filters and parses
options.parseStyle = parsingStyle
hitIter = filterM8Stream(inhandle, options, returnLines=False)
# apply org or acc translation
# apply map of hit names if given'
# look up taxon node
hitIter = processHits(
hitIter,
hitStringMap=hitStringMap,
parseStyle=parsingStyle,
taxonomy=taxonomy,
rank=rank)
# apply count method
hitIter = applyCountMethod(hitIter, countMethod, ignoreEmptyHits)
return hitIter
def plotHitStats(axes,
sequenceFile,
hitsFile,
referenceLengths=None,
sequenceFormat='fasta',
bins=20,
hlog=False,
lengthRange=None,
barcolor='b',
baredgecolor='k',
hcolor='r',
params=None,
**kwargs):
"""
Given two or three matplotlib.axes.AxesSubplot objects create plot in
each binned by sequence length:
* overlay a histogram of sequence lengths on the fraction of sequences
in each bin that have a hit
* same bins as above, but use total sequence bases on top of fraction
of bases covered by hits
* if fasta or lengths of reference hits given, plot (using same bins)
fraction of reference bases used in hits
Positional Arguments:
* axes: length 2 list or tuple of ax objects
* sequenceFile: fasta or similar file of sequence data
* hitsFile: text hit table
Parameters:
* hit parsing
* params=None edl.blatm8.FilterParams object to filter hits
* **kwargs used to create FilterParams object if params object not given
* sequence parsing
* sequenceFormat='fasta'. Can be anything supported by BioPython
* referenceLengths=None: if give, create 3rd plot using given
dictionary of hits. It can also just be the fasta of the reference
sequences and the code will look up the lengths.
* plotting:
* bins=20 Number of length bins to divide sequence data into
* barcolor='b' Color of data bars
* baredgecolor='k' Color of data bar edges
* hcolor='r' Color of histogram line and axis labels
* lengthRange=None Can be used to force the x axis to span a
specific range
* hlog=False If set to True, histogram data plotted in log scale
"""
# get sequence lengths
lengths = getSequenceLengths(sequenceFile, format=sequenceFormat)
# parse hit file
if params is None:
params = FilterParams(**kwargs)
hits = getSequenceHits(hitsFile, params)
# plot data
plotTranscriptHitRateByLengthBins(axes[0],
lengths,
hits,
bins=bins,
lengthRange=lengthRange,
barcolor=barcolor,
baredgecolor=baredgecolor,
hcolor=hcolor,
hlog=hlog)
plotTranscriptCoverageByLengthBins(axes[1],
lengths,
hits,
bins=bins,
lengthRange=lengthRange,
barcolor=barcolor,
baredgecolor=baredgecolor,
hcolor=hcolor,
hlog=hlog)
if referenceLengths is not None:
plotHitCoverageByLengthBins(axes[2],
lengths,
hits,
referenceLengths,
bins=bins,
lengthRange=lengthRange,
barcolor=barcolor,
baredgecolor=baredgecolor,
hcolor=hcolor,
hlog=hlog)
