def sample_clonesize_stat(sample, samdir, freqs=[], numtop=50, args=None):
# calculate: number of clones/ counts that lie within each freq
# range. Note: freqs must be sorted, or the func will sort it
# <numtop>: number of top clones whose freqs will be report
stat = CloneSizeStat(sorted(freqs))
stat.set_sample_info(sample)
if args:
numtop = args[0]
clones = libsample.sample_all_clones(samdir)
sorted_clones = sorted(clones, reverse=True, key=lambda c: c.freq)
for index, clone in enumerate(sorted_clones):
if index < numtop:
stat.topfreqs.append(clone.freq)
for i, minfreq in enumerate(stat.freqs):
maxfreq = float('inf')
if i + 1 < len(stat.freqs):
maxfreq = stat.freqs[i + 1]
if minfreq <= clone.freq and clone.freq < maxfreq:
stat.numclones[i] += 1
stat.counts[i] += clone.count
# convert to frequencies:
stat.numclones = [libcommon.get_pc(c, stat.numclone) for c in
stat.numclones]
stat.counts = [libcommon.get_pc(c, stat.size) for c in stat.counts]
# get cumulative stats:
stat.numclones_cumul = libcommon.get_cumulative(stat.numclones)
stat.counts_cumul = libcommon.get_cumulative(stat.counts)
stat.topfreqs_cumul = libcommon.get_cumulative(stat.topfreqs, True)
return stat
def sample_lendist_stat(sample, samdir, args=None):
# lendist with counts and with number of clones
len2clones = {}
len2reads = {}
clones = libsample.sample_all_clones(samdir)
totalclone = 0
for clone in clones:
if clone.aa:
l = len(clone.aa)
if clone.vdel is not None:
totalclone += 1
if l not in len2clones:
len2clones[l] = 1
else:
len2clones[l] += 1
if l not in len2reads:
len2reads[l] = clone.freq
else:
len2reads[l] += clone.freq
# convert the number of clones into % total clones
for l, numclone in len2clones.iteritems():
len2clones[l] = float(numclone) / totalclone
stat = LenDistStat()
stat.set_sample_info(sample)
stat.set_stats(len2clones, len2reads)
return stat
def sample_geneusage_stat(sample, samdir, args=None):
# gene usage of a specific number
# initialize usage
types = ['v', 'd', 'j', 'vj', 'dj']
type2gene2clones = {}
type2gene2reads = {}
for t in types:
type2gene2clones[t] = {}
type2gene2reads[t] = {}
# get usage
clones = libsample.sample_all_clones(samdir)
for clone in clones:
# update each genetype usage
genetypes = ['v', 'd', 'j']
for type in genetypes:
gene2clones = type2gene2clones[type]
gene2reads = type2gene2reads[type]
gene = clone[type]
numclone = 1.0
freq = clone.freq
if gene not in gene2clones:
gene2clones[gene] = numclone
gene2reads[gene] = freq
else:
gene2clones[gene] += numclone
gene2reads[gene] += freq
# update gene combination usage
for type in ['vj', 'dj']:
gene2clones = type2gene2clones[type]
gene2reads = type2gene2reads[type]
g0 = clone[type[0]]
g1 = clone[type[1]]
numclone = 1.0
freq = clone.freq
combi = "|".join([g0, g1])
if combi not in gene2clones:
gene2clones[combi] = numclone
gene2reads[combi] = freq
else:
gene2clones[combi] += numclone
gene2reads[combi] += freq
# convert the number of clones into % total clones:
for type, gene2clones in type2gene2clones.iteritems():
for gene, numclone in gene2clones.iteritems():
gene2clones[gene] = float(numclone) / sample.numclone
# get the stat obj
stat = GeneUsageStat()
stat.set_sample_info(sample)
stat.set_stats(type2gene2clones, type2gene2reads)
return stat
