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Trackster: enable tools that produce BED output to be used in visual analytics framework. Enable cluster tool in Trackster.

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This commit is contained in:
Jeremy Goecks
2011-09-19 11:59:09 -04:00
parent e9f369b296
commit fd66a8220f
3 changed files with 115 additions and 74 deletions
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lib/galaxy/visualization/tracks/data_providers.py
+108 -73
View File
@@ -135,6 +135,88 @@ class TracksDataProvider( object ):
{ 'name' : attrs[ 'name' ], 'type' : column_types[viz_col_index], \
'index' : attrs[ 'index' ] } )
return filters
class BedDataProvider( TracksDataProvider ):
"""
Abstract class that processes BED data from text format to payload format.
Payload format: [ uid (offset), start, end, name, strand, thick_start, thick_end, blocks ]
"""
def get_iterator( self, chrom, start, end ):
raise "Unimplemented Method"
def get_data( self, chrom, start, end, start_val=0, max_vals=None, **kwargs ):
iterator = self.get_iterator( chrom, start, end )
return self.process_data( iterator, start_val, max_vals, **kwargs )
def process_data( self, iterator, start_val=0, max_vals=None, **kwargs ):
"""
Provides
"""
# Build data to return. Payload format is:
# [ <guid/offset>, <start>, <end>, <name>, <score>, <strand>, <thick_start>,
# <thick_end>, <blocks> ]
#
# First three entries are mandatory, others are optional.
#
filter_cols = from_json_string( kwargs.get( "filter_cols", "[]" ) )
no_detail = ( "no_detail" in kwargs )
rval = []
message = None
for count, line in enumerate( iterator ):
if count < start_val:
continue
if max_vals and count-start_val >= max_vals:
message = ERROR_MAX_VALS % ( max_vals, "features" )
break
# TODO: can we use column metadata to fill out payload?
# TODO: use function to set payload data
feature = line.split()
length = len(feature)
# Unique id is just a hash of the line
payload = [ hash(line), int(feature[1]), int(feature[2]) ]
if no_detail:
rval.append( payload )
continue
# Simpler way to add stuff, but type casting is not done.
# Name, score, strand, thick start, thick end.
#end = min( len( feature ), 8 )
#payload.extend( feature[ 3:end ] )
# Name, strand, thick start, thick end.
if length >= 4:
payload.append(feature[3])
if length >= 6:
payload.append(feature[5])
if length >= 8:
payload.append(int(feature[6]))
payload.append(int(feature[7]))
# Blocks.
if length >= 12:
block_sizes = [ int(n) for n in feature[10].split(',') if n != '']
block_starts = [ int(n) for n in feature[11].split(',') if n != '' ]
blocks = zip( block_sizes, block_starts )
payload.append( [ ( int(feature[1]) + block[1], int(feature[1]) + block[1] + block[0] ) for block in blocks ] )
# Score (filter data)
if length >= 5 and filter_cols and filter_cols[0] == "Score":
payload.append( float(feature[4]) )
rval.append( payload )
return { 'data': rval, 'message': message }
def write_data_to_file( self, chrom, start, end, filename ):
iterator = self.get_iterator( chrom, start, end )
out = open( filename, "w" )
for line in iterator:
out.write( "%s\n" % line )
out.close()
class SummaryTreeDataProvider( TracksDataProvider ):
"""
@@ -573,78 +655,7 @@ class IntervalIndexDataProvider( FilterableMixin, TracksDataProvider ):
results.append( payload )
return { 'data': results, 'message': message }
class BedDataProvider( TabixDataProvider ):
"""
Payload format: [ uid (offset), start, end, name, strand, thick_start, thick_end, blocks ]
"""
def process_data( self, iterator, start_val=0, max_vals=sys.maxint, **kwargs ):
#
# Build data to return. Payload format is:
# [ <guid/offset>, <start>, <end>, <name>, <score>, <strand>, <thick_start>,
# <thick_end>, <blocks> ]
#
# First three entries are mandatory, others are optional.
#
filter_cols = from_json_string( kwargs.get( "filter_cols", "[]" ) )
no_detail = ( "no_detail" in kwargs )
rval = []
message = None
for count, line in enumerate( iterator ):
if count < start_val:
continue
if count-start_val >= max_vals:
message = ERROR_MAX_VALS % ( max_vals, "features" )
break
# TODO: can we use column metadata to fill out payload?
# TODO: use function to set payload data
feature = line.split()
length = len(feature)
# Unique id is just a hash of the line
payload = [ hash(line), int(feature[1]), int(feature[2]) ]
if no_detail:
rval.append( payload )
continue
# Simpler way to add stuff, but type casting is not done.
# Name, score, strand, thick start, thick end.
#end = min( len( feature ), 8 )
#payload.extend( feature[ 3:end ] )
# Name, strand, thick start, thick end.
if length >= 4:
payload.append(feature[3])
if length >= 6:
payload.append(feature[5])
if length >= 8:
payload.append(int(feature[6]))
payload.append(int(feature[7]))
# Blocks.
if length >= 12:
block_sizes = [ int(n) for n in feature[10].split(',') if n != '']
block_starts = [ int(n) for n in feature[11].split(',') if n != '' ]
blocks = zip( block_sizes, block_starts )
payload.append( [ ( int(feature[1]) + block[1], int(feature[1]) + block[1] + block[0] ) for block in blocks ] )
# Score (filter data)
if length >= 5 and filter_cols and filter_cols[0] == "Score":
payload.append( float(feature[4]) )
rval.append( payload )
return { 'data': rval, 'message': message }
def write_data_to_file( self, chrom, start, end, filename ):
iterator = self.get_iterator( chrom, start, end )
out = open( filename, "w" )
for line in iterator:
out.write( "%s\n" % line )
out.close()
class VcfDataProvider( TabixDataProvider ):
"""
VCF data provider for the Galaxy track browser.
@@ -685,7 +696,7 @@ class GFFDataProvider( TracksDataProvider ):
Provide data from GFF file.
NOTE: this data provider does not use indices, and hence will be very slow
for large datasets.
for large datasets.
"""
def get_data( self, chrom, start, end, start_val=0, max_vals=sys.maxint, **kwargs ):
start, end = int( start ), int( end )
@@ -710,6 +721,30 @@ class GFFDataProvider( TracksDataProvider ):
offset += feature.raw_size
return { 'data': results, 'message': message }
class BedTabixDataProvider( TabixDataProvider, BedDataProvider ):
"""
Provides data from a BED file indexed via tabix.
"""
pass
class RawBedDataProvider( BedDataProvider ):
"""
Provide data from BED file.
NOTE: this data provider does not use indices, and hence will be very slow
for large datasets.
"""
def get_iterator( self, chrom, start, end ):
def line_filter_iter():
for line in open( self.original_dataset.file_name ):
feature = line.split()
feature_chrom, feature_start, feature_end = feature[ 0:3 ]
if feature_chrom != chrom or feature_start > end or feature_end < start:
continue
yield line
return line_filter_iter()
#
# Helper methods.
@@ -719,7 +754,7 @@ class GFFDataProvider( TracksDataProvider ):
# type. First key is converted dataset type; if result is another dict, second key
# is original dataset type. TODO: This needs to be more flexible.
dataset_type_name_to_data_provider = {
"tabix": { Vcf: VcfDataProvider, Bed: BedDataProvider, "default" : TabixDataProvider },
"tabix": { Vcf: VcfDataProvider, Bed: BedTabixDataProvider, "default" : TabixDataProvider },
"interval_index": IntervalIndexDataProvider,
"bai": BamDataProvider,
"summary_tree": SummaryTreeDataProvider,
lib/galaxy/web/controllers/tracks.py
+6 -1
View File
@@ -397,7 +397,7 @@ class TracksController( BaseController, UsesVisualization, UsesHistoryDatasetAss
def raw_data( self, trans, dataset_id, chrom, low, high, **kwargs ):
"""
Uses original (raw) dataset to return data. This method is useful
when the dataset is not yet indexed and hence using /data would
when the dataset is not yet indexed and hence using data would
be slow because indexes need to be created.
"""
@@ -409,10 +409,15 @@ class TracksController( BaseController, UsesVisualization, UsesHistoryDatasetAss
# Return data.
data = None
# TODO: for raw data requests, map dataset type to provider using dict in data_providers.py
if isinstance( dataset.datatype, Gff ):
data = GFFDataProvider( original_dataset=dataset ).get_data( chrom, low, high, **kwargs )
data[ 'dataset_type' ] = 'interval_index'
data[ 'extra_info' ] = None
if isinstance( dataset.datatype, Bed ):
data = RawBedDataProvider( original_dataset=dataset ).get_data( chrom, low, high, **kwargs )
data[ 'dataset_type' ] = 'interval_index'
data[ 'extra_info' ] = None
return data
@web.json
tools/new_operations/cluster.xml
+1
View File
@@ -1,5 +1,6 @@
<tool id="gops_cluster_1" name="Cluster">
<description>the intervals of a dataset</description>
<trackster_conf/>
<command interpreter="python">gops_cluster.py $input1 $output -1 ${input1.metadata.chromCol},${input1.metadata.startCol},${input1.metadata.endCol},${input1.metadata.strandCol} -d $distance -m $minregions -o $returntype</command>
<inputs>
<param format="interval" name="input1" type="data">