def phylogeny_js(out_file, dists):
"""Make a dendrogram using Jensen-Shannon divergence."""
tmp_input = 'tmp_data'
tmp_r = 'tmp_r' + str(random.randint(0,100))
tmp_labels = 'labels' + str(random.randint(0,100))
js_distances = defaultdict(dict)
for host1, host2 in itertools.combinations(dists, 2):
js_dis = utils.jensen_shannon_dists(dists[host1],
dists[host2])
js_distances[host1][host2] = js_dis
js_distances[host2][host1] = js_dis
with open(tmp_input, 'w') as f:
for host1 in dists:
line = ''
for host2 in dists:
if host1 == host2:
line += '0\t'
else:
line += str(js_distances[host1][host2]) + '\t'
f.write(line.strip('\t') + '\n')
with open(tmp_labels, 'w') as f:
f.write('\t'.join(dists.keys()) + '\n')
with open(tmp_r, 'w') as f:
f.write("source('funcs.R')\n")
f.write("library('MASS')\n")
f.write("d<-read.delim('"
+ tmp_input
+ "',header=FALSE,sep='\\t')\n")
f.write('dist.r<-as.dist(d)\n')
f.write("labels.d<-read.delim('"
+ tmp_labels
+ "',header=FALSE,sep='\\t')\n")
f.write('labels<-as.matrix(labels.d)\n')
f.write("h<-hclust(dist.r,method='average')\n")
f.write("png('" + out_file + "')\n")
f.write("plot(h,hang=-1,labels=labels[1,],main='Host Phylogeny')\n")
f.write('dev.off()\n')
os.system('R < ' + tmp_r + ' --no-save')
os.system('rm ' + ' '.join((tmp_r, tmp_labels, tmp_input)))
return True
results_dir = sys.argv[1]
suffix = sys.argv[2]
elmfile = sys.argv[3]
elms = {}
with open(elmfile) as f:
for line in f:
elm, exp = line.strip().split('\t')
elms[elm] = True
for elm in elms:
counts = utils.count_host_elmSeqs(global_settings.TEST_GENOMES,
False, {},
results_dir, {elm:True}, suffix)
all_elmSeqs = {}
for host in counts:
for elmSeq in counts[host]:
all_elmSeqs[elmSeq] = True
host_vecs = utils.mk_count_vecs(counts, all_elmSeqs)
host_dists = utils.mk_count_dists(host_vecs)
js = defaultdict(dict)
for host1, host2 in itertools.combinations(host_dists, 2):
dis = utils.jensen_shannon_dists(host_dists[host1],
host_dists[host2])
js[host1][host2] = dis
js[host2][host1] = dis
if check_phylogeny(js):
print elm
for line in f:
(elm, seq, count, fq) = line.strip().split('\t')
elmSeq = elm + ':' + seq
if elmSeq in host_flu_elmSeq_mapping:
for a_flu_elmSeq in host_flu_elmSeq_mapping[elmSeq]:
host_counts[host][a_flu_elmSeq] += int(count)
# else:
# host_counts[host][elmSeq] += int(count)
# all_elmSeqs[elmSeq] = True
flu_vecs = utils.mk_count_vecs(flu_counts, mapped)
flu_dists = utils.mk_count_dists(flu_vecs)
host_vecs = utils.mk_count_vecs(host_counts, mapped)
host_dists = utils.mk_count_dists(host_vecs)
js_distances = defaultdict(dict)
for host in hosts:
for flu in flus:
js_dis = utils.jensen_shannon_dists(host_dists[host],
flu_dists[flu])
js_distances[host][flu] = js_dis
print host, flu, js_dis
def print_it(name, vec):
print name, float(count_0s(vec))/float(len(vec))
print_it('chicken_flu', flu_vecs['chicken'])
print_it('human flu', flu_vecs['human'])
print_it('H sapiens', host_vecs['H_sapiens'])
print_it('Gallus gallus', host_vecs['Gallus_gallus'])
counts = utils.count_host_elmSeqs(('Gallus_gallus','H_sapiens'),
False, {},
'working/Jun29/', {use_elm:True},
'.init')
use_seqs = utils_graph.unionLists([use_seqs_pre, counts['Gallus_gallus'],
counts['H_sapiens']])
host_vecs = utils.mk_count_vecs(counts, use_seqs)
host_dists = utils.mk_count_dists(host_vecs)
flu_vecs = utils.mk_count_vecs(flu_counts, use_seqs)
flu_dists = utils.mk_count_dists(flu_vecs)
# for flu in flu_dists:
# print sum(flu_dists[flu])
# sys.exit(0)
for seq, hhuman, hchicken, hf, cf in zip(use_seqs, host_dists['Gallus_gallus'],
host_dists['H_sapiens'],
flu_dists['human'],
flu_dists['chicken']):
print seq + '\t' + str(hhuman) + '\t' + str(hchicken) + '\t' + str(hf) + '\t' + '\t' + str(cf)
for host in host_dists:
for flu in flu_dists:
print host, flu, utils.jensen_shannon_dists(host_dists[host],
flu_dists[flu])
# for flu in flu_counts:
# for elmSeq in flu_counts[flu]:
# elm, seq = elmSeq.split(':')
# if elm == use_elm:
# print flu, seq, flu_counts[flu][elmSeq]
# flu_elm_file = os.path.join('results',
# flu + '.H5N1.elms')
if "human" in flu:
flu_elm_file = os.path.join("working/Jul1_year", flu + ".H3N2.2008.elms")
else:
flu_elm_file = os.path.join("working/Jul1_year/", flu + ".H5N1.2006.elms")
utils.count_flu_sampled(flu, flu_elm_file, flu_counts, seen_seqs, {}, False)
for elmseq in seen_seqs[flu]:
elm, seq = elmseq.split(":")
elm2seqs[elm][elmseq] = True
counts = utils.count_host_elmSeqs(("Gallus_gallus", "H_sapiens"), False, {}, "working/Jun29/", working_elms, ".init")
for elm in working_elms:
use_seqs = elm2seqs[elm]
host_vecs = utils.mk_count_vecs(counts, use_seqs)
host_dists = utils.mk_count_dists(host_vecs)
flu_vecs = utils.mk_count_vecs(flu_counts, use_seqs)
flu_dists = utils.mk_count_dists(flu_vecs)
flu = flu_dists["human"]
human_score_H = utils.jensen_shannon_dists(host_dists["H_sapiens"], flu)
chicken_score_H = utils.jensen_shannon_dists(host_dists["Gallus_gallus"], flu)
flu = flu_dists["chicken"]
human_score_C = utils.jensen_shannon_dists(host_dists["H_sapiens"], flu)
chicken_score_C = utils.jensen_shannon_dists(host_dists["Gallus_gallus"], flu)
if human_score_C > chicken_score_C and human_score_H < chicken_score_H:
print elm
""" What is the Jensen-shannon distance
between 2 flu groups?
"""
import utils, sys, utils_graph
flu_group_1_file = sys.argv[1]
flu_group_2_file = sys.argv[2]
flu_counts = {}
seen_elmSeqs = {}
seen_seqs_ls = []
for name, file in (('g1', flu_group_1_file),
('g2', flu_group_2_file)):
utils.count_flu_sampled(name, file,
flu_counts,
seen_elmSeqs, {}, False)
seen_seqs_ls.append(seen_elmSeqs[name])
use_elmSeqs = utils_graph.unionLists(seen_seqs_ls)
flu_vecs = utils.mk_count_vecs(flu_counts, use_elmSeqs)
flu_dists = utils.mk_count_dists(flu_vecs)
js_dis = utils.jensen_shannon_dists(flu_dists['g1'],
flu_dists['g2'])
print js_dis
