def rolling_tree_apply(tup):
group_series, seq_series, kwargs = tup
fname = '/home/will/SubCData/Trees/Tree-%(sub)s-%(Prot)s-%(Start)i-%(WinSize)i.newick' % kwargs
if os.path.exists(fname):
return True
alpha = generic_dna if kwargs['Prot'] == 'LTR' else generic_protein
seq_series = seq_series.dropna(thresh = 5)
vseq, vgroup = seq_series.align(group_series.dropna(), join = 'inner', axis = 0)
nseq_ser = vseq.apply(append_seq, axis = 1)
nseqs = sorted(nseq_ser.to_dict().items())
trop_dict = vgroup.to_dict()
#print nseqs
#try:
# tree, dmat = TreeingTools.phylip_tree_collapse_unique(nseqs, alphabet=alpha, use_fast=True)
#except:
# return False
#print 'treeing', fname
tree = TreeingTools.run_FastTree(nseqs, alphabet=alpha, uniq_seqs=True)
with open(fname, 'w') as handle:
tree.write(handle, schema='newick')
return True
try:
tree, dmat = TreeingTools.phylip_tree_collapse_unique(nseqs, alphabet=alpha, use_fast=True)
benj_res = TreeingTools.check_distance_pvals(dmat, trop_dict, nreps = 50)
except:
return kwargs
benj_res.update(kwargs)
try:
out = TreeingTools.evaluate_association_index(tree, trop_dict)
benj_res['AI'], benj_res['AI-pval'], benj_res['AI-null'] = out
except:
benj_res['AI'], benj_res['AI-pval'], benj_res['AI-null'] = (None, None, None)
return benj_res
def test_fast_tree():
seqs = tree_seqs()
tree = TreeingTools.run_FastTree(seqs, alphabet=generic_dna)
for tst in check_tree(tree):
yield tst
wanted_pat = pat_data[cols.keys()].dropna()
wanted_scores = ltr_df_cp[score_cols+seq_cols].dropna()
wanted_scores['TFJoin'] = wanted_scores[seq_cols].apply(lambda x: ''.join(x), axis=1)
#wanted_scores = wanted_scores.drop(seq_cols, axis=1)
check_data = pd.concat(wanted_pat.align(wanted_scores, axis=0, join='inner'), axis=1).rename(columns = cols)
check_data = check_data.fillna(check_data[score_cols].min())
ncols = dict((col, col.replace('-', '_').replace('/', '_')) for col in check_data.columns)
check_data = check_data.rename(columns = ncols)
# <codecell>
import TreeingTools
tree = TreeingTools.run_FastTree(check_data['TFJoin'].to_dict().items(),
alphabet=TreeingTools.generic_dna)
# <codecell>
import networkx as nx
from itertools import combinations
import csv
with open('ltr_tree.nwk', 'w') as handle:
tree.write_to_stream(handle, schema = 'phylip', exclude_chars=True)
# <codecell>
# <codecell>
