def results(self,hid,*args,**kwargs) :
args = self.parse_kwargs(kwargs)
session_d = self.sessions.get(hid)
if session_d is None :
log.error('hid passed but no session found, aborting')
return 'Error occurred, please try submitting again'
thread = self.threads[hid]
session_d.update(thread.thread.session_d)
session_d.update(thread.thread.session_d['motif_enrich'])
templ_d = {}
templ_d.update(session_d)
# check for sortby argument from user
templ_d['sortby'] = str(args.get('sortby'))
top_template = self.template_env.get_template('adipo_site_results.html')
top_content = top_template.render(**templ_d)
return top_content
def processing(self,hid,*args,**kwargs) :
hid = str(hid)
cherrypy.log('processing hid %s (%s)'%(hid,type(hid)))
session_d = self.sessions.get(hid)
if session_d is None :
log.error('hid passed but no session found, aborting')
return 'Error occurred, please try submitting again'
# we use a thread to process in the bg
thread = self.threads.get(hid)
# processing hasn't begun yet
if thread is None :
gene_list = session_d.get('gene_list')
orig_gene_list = session_d.get('orig_gene_list')
self.gene_name_map = session_d.get('gene_name_map')
if gene_list is None :
log.error('hid is valid but no gene list was found, aborting')
return 'Error occurred, please try submitting again'
thread = AdipoThread()
enrich_thread = EnrichThread(session_d,self.db_session,self.args)
thread.run_thread(enrich_thread)
self.threads[hid] = thread
if thread.is_running() :
template = self.template_env.get_template('adipo_site.html')
top = ('Request is processing, please wait.<br/>This page refreshes automatically every 5 seconds.<br/>'
'<meta http-equiv="refresh" content="5; URL=processing?hid=%s"</meta>')%hid
return template.render(top_content=top,bottom_content='')
elif thread.is_done() :
raise cherrypy.HTTPRedirect("http://fraenkel.mit.edu/adipo_sight/results?hid=%s"%hid,status=303)
def run(self) :
session_d = self.session_d
if session_d is None :
log.error('hid passed but no session found, aborting')
return 'Error occurred, please try submitting again'
gene_list = session_d['gene_list']
d = {}
# get motif scores
cherrypy.log('getting motif scores')
# sqlite has a limitation of 999 SQL variables, need to split up
# this query
batch_size = 500
gene_list_batches = [gene_list[i:i+500] for i in xrange(0,len(gene_list),500)]
cherrypy.log('gene_list_batches len()s: %s'%str([len(b) for b in gene_list_batches]))
scores = []
for batch in gene_list_batches :
score_q = (self.db_session.query(db.Region,db.SeqData)
.join(db.RegionMembership)
.join(db.RegionSet)
.join(db.SeqData)
.filter(db.SeqData.seq_type.has(db.SeqType.name=='motif scores'))
.filter(db.RegionSet.name.in_(batch))
.filter(db.RegionMembership.dist_to_feature.between(
-int(self.args['upstream']),
int(self.args['downstream'])
))
)
scores.extend(score_q.all())
score_mat = []
gene_names = set()
condition_scores = defaultdict(list)
cherrypy.log('loading motif scores')
for region, seqdata in scores :
for region_membership in region.membership :
region_set = region_membership.region_set
gene_names.add(region_set.name)
condition = seqdata.condition.name
motif_scores = cPickle.loads(seqdata.value)
condition_scores[condition].append(motif_scores)
score_mat.append(motif_scores)
cherrypy.log('found conditions: %s'%condition_scores.keys())
score_mat = np.array(score_mat)
session_d['found'] = gene_names
session_d['missing'] = [g for g in gene_list if g not in gene_names]
# compare motif scores of requested genes to all hypersensitive regions
# in the dataset
cherrypy.log('loading motif background')
# pick n random background sequences, but seed so the same indices are picked for every
# different # of input DHS regions
random.seed("jo mama")
# walk through the conditions and compute scores
sig_scores = defaultdict(dict)
hs_regions = {}
enriched_motifs = {}
for c, scores in condition_scores.items() :
score_mat = np.array(scores)
hs_regions[c] = len(scores)
# get the background out for this condition
motif_bg_fn = resource_filename('adipo_sight','data/%s_hypersensitive_peaks_bg_motif_scores.npy'%c)
all_scores = np.load(motif_bg_fn).T
bg_inds = random.sample(xrange(all_scores.shape[0]),min(score_mat.shape[0],all_scores.shape[0]))
all_scores = all_scores[bg_inds,:]
cherrypy.log('score_mat.shape = %s'%str(score_mat.shape))
cherrypy.log('all_scores.shape = %s'%str(all_scores.shape))
cherrypy.log('done loading motif background')
# calculate MWW
pvals = mww_multiprocess(score_mat.T,all_scores.T,True)
log.debug('motif scores for condition: %s'%c)
log.debug('pvals: %s'%str(pvals.shape))
motif_name_fn = resource_filename('adipo_sight','data/motif_names.txt')
motif_names = np.array(open(motif_name_fn).readlines())
motif_cluster_fn = resource_filename('adipo_sight','data/motif_clusters.txt')
motif_cluster_map = dict((i,int(m)) for i,m in enumerate(open(motif_cluster_fn)))
thresh = pvals < self.args.get('diff_hyp_pval')
thresh_inds = np.where(thresh)[0]
thresh_names, thresh_pvals = motif_names[thresh], pvals[thresh]
thresh_imgs = np.array(['images/motif_logos/%03d_motif.png'%i for i in thresh_inds])
cluster_set = set()
for i,n,p in zip(thresh_inds,thresh_names,thresh_pvals) :
cluster_i = motif_cluster_map[i]
sig_scores[cluster_i].setdefault('name',set()).add(n.strip())
sig_scores[cluster_i][c] = min(sig_scores[cluster_i].get(c,1.),p)
cluster_set.add(cluster_i)
enriched_motifs[c] = len(cluster_set)
d['motifs'] = dict(sig_scores)
d['hs_regions'] = hs_regions
d['enriched_motifs'] = enriched_motifs
session_d['motif_enrich'] = d