def findNeighbors(regiondb, genes):
"""Determine which genes in 'genes' are neighboring genes in the
chromosomes 'chroms'
returns a clustering of the genes in clusters of neighboring streaks
"""
geneset = set(genes)
neighbors = util.Dict(dim=2)
for gene in genes:
chrom = regiondb.species[gene.species][gene.seqname]
ind = regionlib.findRegion(chrom, gene)
# look for neighboring genes on same strand
if ind > 0:
left = chrom[ind-1]
if left in geneset and left.strand == gene.strand:
neighbors[gene][left] = 1
neighbors[left][gene] = 1
if ind < len(chrom)-1:
right = chrom[ind+1]
if right in geneset and right.strand == gene.strand:
neighbors[gene][right] = 1
neighbors[right][gene] = 1
comps = graph.connectedComponents(genes, lambda x: neighbors[x].keys())
# sort neighbors in order of appearance along strand
for comp in comps:
comp.sort(key=lambda x: x.start,
reverse=(comp[0].strand == -1))
return comps
def unionPart(* partsList):
"""Finds the union of several partitions"""
mat = part2graph(util.concat(* partsList))
parts = graph.connectedComponents(mat.keys(), lambda x: mat[x].keys())
# remove parition ids from partitioning
parts = map(lambda part: filter(lambda x: type(x) != int, part), parts)
return parts
def unionPart(*partsList):
"""Finds the union of several partitions"""
mat = part2graph(util.concat(*partsList))
parts = graph.connectedComponents(mat.keys(), lambda x: mat[x].keys())
# remove parition ids from partitioning
parts = map(lambda part: filter(lambda x: type(x) != int, part), parts)
return parts
