def to_json_file(conn, result_dir, output_dir):
ratios = read_ratios(result_dir)
cursor = conn.cursor()
cursor.execute("select max(iteration) from row_members")
iteration = cursor.fetchone()[0]
cursor.execute("select distinct cluster from row_members where iteration=?", [iteration])
clusters = [row[0] for row in cursor.fetchall()]
result = {}
for cluster in clusters:
cursor.execute(
"select name from row_names rn join row_members rm on rn.order_num=rm.order_num where cluster=? and iteration=?",
[cluster, iteration],
)
genes = [row[0] for row in cursor.fetchall()]
cursor.execute(
"select name from column_names cn join column_members cm on cn.order_num=cm.order_num where cluster=? and iteration=?",
[cluster, iteration],
)
cluster_conds = [row[0] for row in cursor.fetchall()]
cluster_data = ratios.loc[genes, cluster_conds]
values = [
{"gene": gene, "condition": cond, "value": cluster_data.values[rindex, cindex]}
for rindex, gene in enumerate(genes)
for cindex, cond in enumerate(cluster_conds)
]
result[str(cluster)] = values
buffer = json.dumps(result)
with open(os.path.join(output_dir, "cluster_expressions.json"), "w") as out:
out.write(buffer)
def generate_plots(session, result_dir, output_dir):
ratios = read_ratios(result_dir)
iteration = session.query(func.max(cm2db.RowMember.iteration))
clusters = [r[0] for r in session.query(cm2db.RowMember.cluster).distinct().filter(
cm2db.RowMember.iteration == iteration)]
figure = plt.figure(figsize=(6,3))
for cluster in clusters:
plt.clf()
plt.cla()
genes = [r.row_name.name for r in session.query(cm2db.RowMember).filter(
and_(cm2db.RowMember.cluster == cluster, cm2db.RowMember.iteration == iteration))]
cluster_conds = [c.column_name.name for c in session.query(cm2db.ColumnMember).filter(
and_(cm2db.ColumnMember.cluster == cluster, cm2db.ColumnMember.iteration == iteration))]
all_conds = [c[0] for c in session.query(cm2db.ColumnName.name).distinct()]
non_cluster_conds = [cond for cond in all_conds if not cond in set(cluster_conds)]
cluster_data = ratios.loc[genes, cluster_conds]
non_cluster_data = ratios.loc[genes, non_cluster_conds]
min_value = ratios.min()
max_value = ratios.max()
for gene in genes:
values = [normalize_js(val) for val in cluster_data.loc[gene,:].values]
values += [normalize_js(val) for val in non_cluster_data.loc[gene,:].values]
plt.plot(values)
# plot the "in"/"out" separator line
cut_line = len(cluster_conds)
plt.plot([cut_line, cut_line], [min_value, max_value], color='red',
linestyle='--', linewidth=1)
plt.savefig(os.path.join(output_dir, "exp-%d" % cluster))
plt.close(figure)
def to_json_file(conn, result_dir, output_dir):
ratios = read_ratios(result_dir)
cursor = conn.cursor()
cursor.execute('select max(iteration) from row_members')
iteration = cursor.fetchone()[0]
cursor.execute(
'select distinct cluster from row_members where iteration=?',
[iteration])
clusters = [row[0] for row in cursor.fetchall()]
result = {}
for cluster in clusters:
cursor.execute(
'select name from row_names rn join row_members rm on rn.order_num=rm.order_num where cluster=? and iteration=?',
[cluster, iteration])
genes = [row[0] for row in cursor.fetchall()]
cursor.execute(
'select name from column_names cn join column_members cm on cn.order_num=cm.order_num where cluster=? and iteration=?',
[cluster, iteration])
cluster_conds = [row[0] for row in cursor.fetchall()]
cluster_data = ratios.loc[genes, cluster_conds]
values = [{
'gene': gene,
'condition': cond,
'value': cluster_data.values[rindex, cindex]
} for rindex, gene in enumerate(genes)
for cindex, cond in enumerate(cluster_conds)]
result[str(cluster)] = values
buffer = json.dumps(result)
with open(os.path.join(output_dir, 'cluster_expressions.json'),
'w') as out:
out.write(buffer)
def cluster_expressions_to_json_file(session, result_dir, output_dir):
ratios = read_ratios(result_dir)
iteration = session.query(func.max(cm2db.RowMember.iteration))
clusters = [
r[0] for r in session.query(cm2db.RowMember.cluster).distinct().filter(
cm2db.RowMember.iteration == iteration)
]
result = {}
for cluster in clusters:
genes = [
r.row_name.name for r in session.query(cm2db.RowMember).filter(
and_(cm2db.RowMember.cluster == cluster,
cm2db.RowMember.iteration == iteration))
]
cluster_conds = [
c.column_name.name
for c in session.query(cm2db.ColumnMember).filter(
and_(cm2db.ColumnMember.cluster == cluster,
cm2db.ColumnMember.iteration == iteration))
]
cluster_data = ratios.loc[genes, cluster_conds]
values = [{
'gene': gene,
'condition': cond,
'value': cluster_data.values[rindex, cindex]
} for rindex, gene in enumerate(genes)
for cindex, cond in enumerate(cluster_conds)]
result[str(cluster)] = values
buffer = json.dumps(result)
with open(os.path.join(output_dir, 'cluster_expressions.json'),
'w') as out:
out.write(buffer)
def generate_plots(conn, result_dir, output_dir):
ratios = read_ratios(result_dir)
cursor = conn.cursor()
cursor.execute('select max(iteration) from row_members')
iteration = cursor.fetchone()[0]
cursor.execute(
'select distinct cluster from row_members where iteration=?',
[iteration])
clusters = [row[0] for row in cursor.fetchall()]
figure = plt.figure(figsize=(6, 3))
for cluster in clusters:
plt.clf()
plt.cla()
cursor.execute(
'select distinct name from row_members rm join row_names rn on rm.order_num=rn.order_num where cluster=? and iteration=?',
[cluster, iteration])
genes = [row[0] for row in cursor.fetchall()]
cursor.execute(
'select distinct name from column_members cm join column_names cn on cm.order_num=cn.order_num where cluster=? and iteration=?',
[cluster, iteration])
cluster_conds = [row[0] for row in cursor.fetchall()]
cursor.execute('select distinct name from column_names')
all_conds = [row[0] for row in cursor.fetchall()]
non_cluster_conds = [
cond for cond in all_conds if not cond in set(cluster_conds)
]
cluster_data = ratios.loc[genes, cluster_conds]
non_cluster_data = ratios.loc[genes, non_cluster_conds]
min_value = ratios.min()
max_value = ratios.max()
for gene in genes:
values = [
normalize_js(val) for val in cluster_data.loc[gene, :].values
]
values += [
normalize_js(val)
for val in non_cluster_data.loc[gene, :].values
]
plt.plot(values)
# plot the "in"/"out" separator line
cut_line = len(cluster_conds)
plt.plot([cut_line, cut_line], [min_value, max_value],
color='red',
linestyle='--',
linewidth=1)
plt.savefig(os.path.join(output_dir, "exp-%d" % cluster))
plt.close(figure)
def generate_plots(session, result_dir, output_dir):
ratios = read_ratios(result_dir)
iteration = session.query(func.max(cm2db.RowMember.iteration))
clusters = [
r[0] for r in session.query(cm2db.RowMember.cluster).distinct().filter(
cm2db.RowMember.iteration == iteration)
]
figure = plt.figure(figsize=(6, 3))
for cluster in clusters:
plt.clf()
plt.cla()
genes = [
r.row_name.name for r in session.query(cm2db.RowMember).filter(
and_(cm2db.RowMember.cluster == cluster,
cm2db.RowMember.iteration == iteration))
]
cluster_conds = [
c.column_name.name
for c in session.query(cm2db.ColumnMember).filter(
and_(cm2db.ColumnMember.cluster == cluster,
cm2db.ColumnMember.iteration == iteration))
]
all_conds = [
c[0] for c in session.query(cm2db.ColumnName.name).distinct()
]
non_cluster_conds = [
cond for cond in all_conds if not cond in set(cluster_conds)
]
cluster_data = ratios.loc[genes, cluster_conds]
non_cluster_data = ratios.loc[genes, non_cluster_conds]
min_value = ratios.min()
max_value = ratios.max()
for gene in genes:
values = [
normalize_js(val) for val in cluster_data.loc[gene, :].values
]
values += [
normalize_js(val)
for val in non_cluster_data.loc[gene, :].values
]
plt.plot(values)
# plot the "in"/"out" separator line
cut_line = len(cluster_conds)
plt.plot([cut_line, cut_line], [min_value, max_value],
color='red',
linestyle='--',
linewidth=1)
plt.savefig(os.path.join(output_dir, "exp-%d" % cluster))
plt.close(figure)
def cluster_expressions_to_json_file(session, result_dir, output_dir):
ratios = read_ratios(result_dir)
iteration = session.query(func.max(cm2db.RowMember.iteration))
clusters = [r[0] for r in session.query(cm2db.RowMember.cluster).distinct().filter(
cm2db.RowMember.iteration == iteration)]
result = {}
for cluster in clusters:
genes = [r.row_name.name for r in session.query(cm2db.RowMember).filter(
and_(cm2db.RowMember.cluster == cluster, cm2db.RowMember.iteration == iteration))]
cluster_conds = [c.column_name.name for c in session.query(cm2db.ColumnMember).filter(
and_(cm2db.ColumnMember.cluster == cluster, cm2db.ColumnMember.iteration == iteration))]
cluster_data = ratios.loc[genes, cluster_conds]
values = [{'gene': gene, 'condition': cond, 'value': cluster_data.values[rindex, cindex]}
for rindex, gene in enumerate(genes) for cindex, cond in enumerate(cluster_conds)]
result[str(cluster)] = values
buffer = json.dumps(result)
with open(os.path.join(output_dir, 'cluster_expressions.json'), 'w') as out:
out.write(buffer)
def generate_plots(conn, result_dir, output_dir):
ratios = read_ratios(result_dir)
cursor = conn.cursor()
cursor.execute('select max(iteration) from row_members')
iteration = cursor.fetchone()[0]
cursor.execute('select distinct cluster from row_members where iteration=?', [iteration])
clusters = [row[0] for row in cursor.fetchall()]
figure = plt.figure(figsize=(6,3))
for cluster in clusters:
plt.clf()
plt.cla()
cursor.execute('select distinct name from row_members rm join row_names rn on rm.order_num=rn.order_num where cluster=? and iteration=?', [cluster, iteration])
genes = [row[0] for row in cursor.fetchall()]
cursor.execute('select distinct name from column_members cm join column_names cn on cm.order_num=cn.order_num where cluster=? and iteration=?', [cluster, iteration])
cluster_conds = [row[0] for row in cursor.fetchall()]
cursor.execute('select distinct name from column_names')
all_conds = [row[0] for row in cursor.fetchall()]
non_cluster_conds = [cond for cond in all_conds if not cond in set(cluster_conds)]
cluster_data = ratios.loc[genes, cluster_conds]
non_cluster_data = ratios.loc[genes, non_cluster_conds]
min_value = ratios.min()
max_value = ratios.max()
for gene in genes:
values = [normalize_js(val) for val in cluster_data.loc[gene,:].values]
values += [normalize_js(val) for val in non_cluster_data.loc[gene,:].values]
plt.plot(values)
# plot the "in"/"out" separator line
cut_line = len(cluster_conds)
plt.plot([cut_line, cut_line], [min_value, max_value], color='red',
linestyle='--', linewidth=1)
plt.savefig(os.path.join(output_dir, "exp-%d" % cluster))
plt.close(figure)