def enrichment(exp, aspect, n=100):
aspect = set(aspect)
if not aspect.intersection('CFP'):
flash("That's not a valid aspect, try C, F, and/or P")
return redirect(url_for('experiment', exp=exp))
exp_data = es.experiment()
if exp_data is not None:
exp_data = exp_data[exp]
else:
flash("You need to implement <code>experiment()</code> first!")
return redirect(url_for('experiment', exp=exp))
go_list = [es.go_aspect(a) for a in aspect]
if None in go_list:
flash("You need to implement <code>go_aspect()</code> first!")
return redirect(url_for('experiment', exp=exp))
go_list = list(reduce(set.union, go_list, set()))
go_info = {goid:es.go_info(goid) for goid in go_list}
go_dict = {gene:es.go_to_gene(gene) for gene in go_list}
scores = hs.calculate_enrichment(exp_data, go_dict, n)
return render_template("enrichment.html", exp=exp, go_info=go_info,
e_scores=scores[0][:10], ne_scores=scores[1][:10])
def experiment(exp):
exp_data = es.experiment()
if exp_data is not None:
genes = [(es.gene_name(g) or 'Implement gene_name()!', v)
for g,v in exp_data[exp]]
fig_dict = hs.plot_experiment(genes)
else:
fig_dict = None
return render_template("experiment.html", exp=exp, fig_dict=fig_dict)
def homepage():
exp_data = es.experiment()
# exp_data = None
if exp_data is not None:
exps = np.random.choice(list(range(len(exp_data))), size=10, replace=False)
else:
exps = []
return render_template("index.html", experiments=exps)
def homepage():
exp_data = es.experiment()
if exp_data is not None:
exps = np.random.randint(0, len(exp_data), size=10)
fig_path = hs.plot_experiment_overview(exp_data)
else:
exps = []
fig_path = None
return render_template("index.html", experiments=exps, fig_path=fig_path)
def gene_list(exp, top_or_bottom, n=10):
exp_data = es.experiment()
if exp_data is not None:
exp_data = exp_data[exp]
else:
flash("You need to implement <code>experiment()</code> first!")
return redirect(url_for('experiment', exp=exp))
if top_or_bottom == 'top':
genes = sorted(exp_data, key=lambda g: g[1], reverse=True)[:n]
elif top_or_bottom == 'bottom':
genes = sorted(exp_data, key=lambda g: g[1])[:n]
else:
flash("That's not a valid URL")
return redirect(url_for('experiment', exp=exp))
return render_template("gene_list.html", label=top_or_bottom, n=n, exp=exp,
genes=[(g, es.gene_name(g), v) for g,v in genes])
def experiment(exp, n=10):
exp_data = es.experiment()
if exp_data is not None:
genes = [(es.gene_name(g) or 'Implement gene_name()!', v)
for g,v in exp_data[exp]]
fig_dict = hs.plot_experiment(genes)
if fig_dict is None:
flash("You could maybe implement <code>plot_experiment()</code>")
similar_exp = hs.similar_experiments(exp, exp_data, n)
if similar_exp is None:
flash("You could maybe implement <code>similar_experiments()</code>")
similar_exp = []
else:
flash("You should implement <code>experiment()</code> and <code>plot_experiment()</code>")
fig_dict = None
similar_exp = []
return render_template("experiment.html", exp=exp, n=n,
fig_dict=fig_dict, similar_exp=similar_exp)
def enrichment(exp, aspect, n=100):
aspect = set(aspect)
if not aspect.intersection('CFP'):
flash("That's not a valid aspect, try C, F, and/or P")
return redirect(url_for('experiment', exp=exp))
exp_data = es.experiment()
if exp_data is not None:
exp_data = exp_data[exp]
else:
flash("You need to implement <code>experiment()</code> first!")
return redirect(url_for('experiment', exp=exp))
go_list = list(reduce(set.union, (es.go_aspect(a) for a in aspect), set()))
go_info = {goid:es.go_info(goid) for goid in go_list}
go_dict = {gene:es.go_to_gene(gene) for gene in go_list}
scores = hs.calculate_enrichment(exp_data, go_dict, n)
return render_template("enrichment.html", exp=exp, go_info=go_info,
e_scores=scores[0][:10], ne_scores=scores[1][:10])
def calculate_enrichment(N=100):
# You need to replace this with something useful
experiment_dict = eas.experiment();
counttop100 = {}
countbot100 = {}
goid_prob_top = {}
goid_prob_bot = {}
#initialize dictionary of goids
with open("go_info.txt", 'r') as target:
target.readline();
for line in target:
lines = line.split();
counttop100[lines[0]] = np.zeros(32) #goid counts in top 100
countbot100[lines[0]] = np.zeros(32) #goid counts in top 100
goid_prob_top[lines[0]] = np.zeros(32) #goid prob
goid_prob_bot[lines[0]] = np.zeros(32) #goid prob
#add values for goid
for i in range(0, 33):
sorted_genes = experiment_dict[i].sort(key=lambda tup: tup[1])
top100 = sorted_genes[0:100]
bot100 = sorted_genes[-100:0]
#go hrough top 100, want list of goIds, count for top/bot100
for j in range(0, 100):
counttop100[ gene_to_go[ top100[j][0] ] ][i] += 1
countbot100[ gene_to_go[ top100[j][0] ] ][i] += 1
#hypergeom, what is the probability i got that many counts from top 100
#top
for j in counttop100:
gene_per_go = len(go_to_gene[j])
[ M, n, N] = [4767, gene_per_go, N];
rv = scistat.hypergeom(M, n, N)
x = np.arange(0, counttop100[j][i] + 1)
survival_exp = rv.sf(x)
goid_prob_top[j][i] = survival_exp
for j in countbot100:
gene_per_go = len(go_to_gene[j])
[ M, n, N] = [4767, gene_per_go, N];
rv = scistat.hypergeom(M, n, N)
x = np.arange(0, countbot100[j][i] + 1)
survival_exp = rv.sf(x)
goid_prob_bot[j][i] = survival_exp
#for j in experiment_dict[i]:
# mainstuff[ gene_to_go[j[0]] ][i] += j[1]
#sort by exp values
# mainstuff.sort(key=lambda tup: tup[1])
#take top 100
heatmaptop = plt.pcolor(goid_prob_top);
heatmapbot = plt.pcolor(goid_prob_bot);
plt.show()
positive_enrichment_scores = goid_prob_top;
negative_enrichment_scores = goid_prob_bot;
return positive_enrichment_scores,negative_enrichment_scores
