def main(net, inst_dm, filter_sim):
from __init__ import Network
from copy import deepcopy
import calc_clust
sim_dict = {}
for inst_rc in ['row','col']:
sim_dict[inst_rc] = dm_to_sim(inst_dm[inst_rc], make_squareform=True,
filter_sim=filter_sim)
sim_net = {}
for inst_rc in ['row','col']:
sim_net[inst_rc] = deepcopy(Network())
sim_net[inst_rc].dat['mat'] = sim_dict[inst_rc]
sim_net[inst_rc].dat['nodes']['row'] = net.dat['nodes'][inst_rc]
sim_net[inst_rc].dat['nodes']['col'] = net.dat['nodes'][inst_rc]
sim_net[inst_rc].dat['node_info']['row'] = net.dat['node_info'][inst_rc]
sim_net[inst_rc].dat['node_info']['col'] = net.dat['node_info'][inst_rc]
calc_clust.cluster_row_and_col(sim_net[inst_rc])
return sim_net
def main(net, inst_dm, filter_sim):
from __init__ import Network
from copy import deepcopy
import calc_clust
sim_dict = {}
for inst_rc in ['row', 'col']:
sim_dict[inst_rc] = dm_to_sim(inst_dm[inst_rc],
make_squareform=True,
filter_sim=filter_sim)
sim_net = {}
for inst_rc in ['row', 'col']:
sim_net[inst_rc] = deepcopy(Network())
sim_net[inst_rc].dat['mat'] = sim_dict[inst_rc]
sim_net[inst_rc].dat['nodes']['row'] = net.dat['nodes'][inst_rc]
sim_net[inst_rc].dat['nodes']['col'] = net.dat['nodes'][inst_rc]
sim_net[inst_rc].dat['node_info']['row'] = net.dat['node_info'][
inst_rc]
sim_net[inst_rc].dat['node_info']['col'] = net.dat['node_info'][
inst_rc]
calc_clust.cluster_row_and_col(sim_net[inst_rc])
return sim_net
def N_rows(net, df, all_views, dist_type='cosine', rank_type='sum'):
from copy import deepcopy
from __init__ import Network
import calc_clust, run_filter
keep_top = ['all', 500, 250, 100, 50, 20, 10]
rows_sorted = run_filter.get_sorted_rows(df['mat'], rank_type)
for inst_keep in keep_top:
tmp_df = deepcopy(df)
if inst_keep < len(rows_sorted) or inst_keep == 'all':
tmp_net = deepcopy(Network())
if inst_keep != 'all':
keep_rows = rows_sorted[0:inst_keep]
tmp_df['mat'] = tmp_df['mat'].ix[keep_rows]
if 'mat_up' in tmp_df:
tmp_df['mat_up'] = tmp_df['mat_up'].ix[keep_rows]
tmp_df['mat_dn'] = tmp_df['mat_dn'].ix[keep_rows]
if 'mat_orig' in tmp_df:
tmp_df['mat_orig'] = tmp_df['mat_orig'].ix[keep_rows]
tmp_df = run_filter.df_filter_col_sum(tmp_df, 0.001)
tmp_net.df_to_dat(tmp_df)
else:
tmp_net.df_to_dat(tmp_df)
try:
try:
calc_clust.cluster_row_and_col(tmp_net,
dist_type,
run_clustering=True)
except:
calc_clust.cluster_row_and_col(tmp_net,
dist_type,
run_clustering=False)
# add view
inst_view = {}
inst_view['N_row_' + rank_type] = inst_keep
inst_view['dist'] = 'cos'
inst_view['nodes'] = {}
inst_view['nodes']['row_nodes'] = tmp_net.viz['row_nodes']
inst_view['nodes']['col_nodes'] = tmp_net.viz['col_nodes']
all_views.append(inst_view)
except:
# print('\t*** did not cluster N filtered view')
pass
return all_views
def pct_rows(net, df, all_views, dist_type, rank_type):
from __init__ import Network
from copy import deepcopy
import numpy as np
import calc_clust, run_filter
copy_net = deepcopy(net)
if len(net.dat['node_info']['col']['cat']) > 0:
cat_key_col = {}
for i in range(len(net.dat['nodes']['col'])):
cat_key_col[net.dat['nodes']['col'][i]] = \
net.dat['node_info']['col']['cat'][i]
all_filt = range(10)
all_filt = [i / float(10) for i in all_filt]
mat = deepcopy(df['mat'])
sum_row = np.sum(mat, axis=1)
max_sum = max(sum_row)
for inst_filt in all_filt:
cutoff = inst_filt * max_sum
copy_net = deepcopy(net)
inst_df = deepcopy(df)
inst_df = run_filter.df_filter_row(inst_df, cutoff, take_abs=False)
tmp_net = deepcopy(Network())
tmp_net.df_to_dat(inst_df)
try:
try:
calc_clust.cluster_row_and_col(tmp_net,
dist_type=dist_type,
run_clustering=True)
except:
calc_clust.cluster_row_and_col(tmp_net,
dist_type=dist_type,
run_clustering=False)
inst_view = {}
inst_view['pct_row_' + rank_type] = inst_filt
inst_view['dist'] = 'cos'
inst_view['nodes'] = {}
inst_view['nodes']['row_nodes'] = tmp_net.viz['row_nodes']
inst_view['nodes']['col_nodes'] = tmp_net.viz['col_nodes']
all_views.append(inst_view)
except:
pass
return all_views
def pct_rows(net, df, all_views, dist_type, rank_type):
from __init__ import Network
from copy import deepcopy
import numpy as np
import calc_clust, run_filter
copy_net = deepcopy(net)
if len(net.dat['node_info']['col']['cat']) > 0:
cat_key_col = {}
for i in range(len(net.dat['nodes']['col'])):
cat_key_col[net.dat['nodes']['col'][i]] = \
net.dat['node_info']['col']['cat'][i]
all_filt = range(10)
all_filt = [i / float(10) for i in all_filt]
mat = deepcopy(df['mat'])
sum_row = np.sum(mat, axis=1)
max_sum = max(sum_row)
for inst_filt in all_filt:
cutoff = inst_filt * max_sum
copy_net = deepcopy(net)
inst_df = deepcopy(df)
inst_df = run_filter.df_filter_row(inst_df, cutoff, take_abs=False)
tmp_net = deepcopy(Network())
tmp_net.df_to_dat(inst_df)
try:
try:
calc_clust.cluster_row_and_col(tmp_net, dist_type=dist_type,
run_clustering=True)
except:
calc_clust.cluster_row_and_col(tmp_net, dist_type=dist_type,
run_clustering=False)
inst_view = {}
inst_view['pct_row_' + rank_type] = inst_filt
inst_view['dist'] = 'cos'
inst_view['nodes'] = {}
inst_view['nodes']['row_nodes'] = tmp_net.viz['row_nodes']
inst_view['nodes']['col_nodes'] = tmp_net.viz['col_nodes']
all_views.append(inst_view)
except:
pass
return all_views
def N_rows(net, df, all_views, dist_type='cosine', rank_type='sum'):
from copy import deepcopy
from __init__ import Network
import calc_clust, run_filter
keep_top = ['all', 500, 250, 100, 50, 20, 10]
rows_sorted = run_filter.get_sorted_rows(df['mat'], rank_type)
for inst_keep in keep_top:
tmp_df = deepcopy(df)
if inst_keep < len(rows_sorted) or inst_keep == 'all':
tmp_net = deepcopy(Network())
if inst_keep != 'all':
keep_rows = rows_sorted[0:inst_keep]
tmp_df['mat'] = tmp_df['mat'].ix[keep_rows]
if 'mat_up' in tmp_df:
tmp_df['mat_up'] = tmp_df['mat_up'].ix[keep_rows]
tmp_df['mat_dn'] = tmp_df['mat_dn'].ix[keep_rows]
if 'mat_orig' in tmp_df:
tmp_df['mat_orig'] = tmp_df['mat_orig'].ix[keep_rows]
tmp_df = run_filter.df_filter_col_sum(tmp_df, 0.001)
tmp_net.df_to_dat(tmp_df)
else:
tmp_net.df_to_dat(tmp_df)
try:
try:
calc_clust.cluster_row_and_col(tmp_net, dist_type, run_clustering=True)
except:
calc_clust.cluster_row_and_col(tmp_net, dist_type, run_clustering=False)
# add view
inst_view = {}
inst_view['N_row_' + rank_type] = inst_keep
inst_view['dist'] = 'cos'
inst_view['nodes'] = {}
inst_view['nodes']['row_nodes'] = tmp_net.viz['row_nodes']
inst_view['nodes']['col_nodes'] = tmp_net.viz['col_nodes']
all_views.append(inst_view)
except:
# print('\t*** did not cluster N filtered view')
pass
return all_views
def make_clust(net, dist_type='cosine', run_clustering=True, dendro=True,
requested_views=['pct_row_sum', 'N_row_sum'],
linkage_type='average', sim_mat=False, filter_sim=0.1,
calc_cat_pval=False):
'''
This will calculate multiple views of a clustergram by filtering the
data and clustering after each filtering. This filtering will keep the top
N rows based on some quantity (sum, num-non-zero, etc).
'''
from copy import deepcopy
import calc_clust, run_filter, make_views, make_sim_mat, cat_pval
import scipy
df = net.dat_to_df()
threshold = 0.0001
df = run_filter.df_filter_row_sum(df, threshold)
df = run_filter.df_filter_col_sum(df, threshold)
# calculate initial view with no row filtering
net.df_to_dat(df)
inst_dm = calc_clust.cluster_row_and_col(net, dist_type=dist_type,
linkage_type=linkage_type,
run_clustering=run_clustering,
dendro=dendro, ignore_cat=False,
calc_cat_pval=calc_cat_pval)
all_views = []
send_df = deepcopy(df)
if 'N_row_sum' in requested_views:
all_views = make_views.N_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='sum')
if 'N_row_var' in requested_views:
all_views = make_views.N_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='var')
if 'pct_row_sum' in requested_views:
all_views = make_views.pct_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='sum')
if 'pct_row_var' in requested_views:
all_views = make_views.pct_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='var')
if sim_mat is True:
print('make similarity matrices of rows and columns, add to viz data structure')
sim_net = make_sim_mat.main(net, inst_dm, filter_sim)
net.sim = {}
net.sim['row'] = sim_net['row'].viz
net.sim['col'] = sim_net['col'].viz
net.viz['views'] = all_views
def main(net, inst_dm, filter_sim, sim_mat_views=['N_row_sum']):
from __init__ import Network
from copy import deepcopy
import calc_clust, make_views
sim_dict = {}
for inst_rc in ['row','col']:
sim_dict[inst_rc] = dm_to_sim(inst_dm[inst_rc], make_squareform=True,
filter_sim=filter_sim)
sim_net = {}
for inst_rc in ['row','col']:
sim_net[inst_rc] = deepcopy(Network())
sim_net[inst_rc].dat['mat'] = sim_dict[inst_rc]
sim_net[inst_rc].dat['nodes']['row'] = net.dat['nodes'][inst_rc]
sim_net[inst_rc].dat['nodes']['col'] = net.dat['nodes'][inst_rc]
sim_net[inst_rc].dat['node_info']['row'] = net.dat['node_info'][inst_rc]
sim_net[inst_rc].dat['node_info']['col'] = net.dat['node_info'][inst_rc]
calc_clust.cluster_row_and_col(sim_net[inst_rc])
all_views = []
df = sim_net[inst_rc].dat_to_df()
send_df = deepcopy(df)
if 'N_row_sum' in sim_mat_views:
all_views = make_views.N_rows(net, send_df, all_views,
dist_type='cos', rank_type='sum')
sim_net[inst_rc].viz['views'] = all_views
return sim_net
def make_clust(net,
dist_type='cosine',
run_clustering=True,
dendro=True,
requested_views=['pct_row_sum', 'N_row_sum'],
linkage_type='average',
sim_mat=False):
''' This will calculate multiple views of a clustergram by filtering the
data and clustering after each filtering. This filtering will keep the top
N rows based on some quantity (sum, num-non-zero, etc). '''
from copy import deepcopy
import calc_clust
import run_filter
import make_views
import scipy
df = net.dat_to_df()
threshold = 0.0001
df = run_filter.df_filter_row(df, threshold)
df = run_filter.df_filter_col(df, threshold)
# calculate initial view with no row filtering
net.df_to_dat(df)
# preparing to make similarity matrices of rows and cols
###########################################################
# tmp_dist_mat = calc_clust.calc_distance_matrix(net.dat['mat'], 'col',
# get_sim=True,
# make_squareform=True,
# filter_sim_below=0.1)
#
# print(tmp_dist_mat)
# print(net.dat['node_info']['row'])
# print('\n')
# print(net.dat['node_info']['col'])
calc_clust.cluster_row_and_col(net,
dist_type=dist_type,
linkage_type=linkage_type,
run_clustering=run_clustering,
dendro=dendro,
ignore_cat=False)
all_views = []
send_df = deepcopy(df)
if 'N_row_sum' in requested_views:
all_views = make_views.N_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='sum')
if 'N_row_var' in requested_views:
all_views = make_views.N_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='var')
if 'pct_row_sum' in requested_views:
all_views = make_views.pct_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='sum')
if 'pct_row_var' in requested_views:
all_views = make_views.pct_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='var')
if sim_mat is True:
print(
'make similarity matrices of rows and columns, add to viz data structure'
)
net.viz['views'] = all_views
def make_clust(net,
dist_type='cosine',
run_clustering=True,
dendro=True,
requested_views=['pct_row_sum', 'N_row_sum'],
linkage_type='average',
sim_mat=False,
filter_sim=0.1,
calc_cat_pval=False,
sim_mat_views=['N_row_sum'],
run_enrichr=None):
'''
This will calculate multiple views of a clustergram by filtering the
data and clustering after each filtering. This filtering will keep the top
N rows based on some quantity (sum, num-non-zero, etc).
'''
from copy import deepcopy
import scipy
import calc_clust, run_filter, make_views, make_sim_mat, cat_pval
import enrichr_functions as enr_fun
df = net.dat_to_df()
threshold = 0.0001
df = run_filter.df_filter_row_sum(df, threshold)
df = run_filter.df_filter_col_sum(df, threshold)
if run_enrichr is not None:
df = enr_fun.add_enrichr_cats(df, 'row', run_enrichr)
# calculate initial view with no row filtering
net.df_to_dat(df)
inst_dm = calc_clust.cluster_row_and_col(net,
dist_type=dist_type,
linkage_type=linkage_type,
run_clustering=run_clustering,
dendro=dendro,
ignore_cat=False,
calc_cat_pval=calc_cat_pval)
all_views = []
send_df = deepcopy(df)
if 'N_row_sum' in requested_views:
all_views = make_views.N_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='sum')
if 'N_row_var' in requested_views:
all_views = make_views.N_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='var')
if 'pct_row_sum' in requested_views:
all_views = make_views.pct_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='sum')
if 'pct_row_var' in requested_views:
all_views = make_views.pct_rows(net,
send_df,
all_views,
dist_type=dist_type,
rank_type='var')
if sim_mat is True:
print(
'make similarity matrices of rows and columns, add to viz data structure'
)
sim_net = make_sim_mat.main(net, inst_dm, filter_sim, sim_mat_views)
net.sim = {}
net.sim['row'] = sim_net['row'].viz
net.sim['col'] = sim_net['col'].viz
net.viz['views'] = all_views
def calc_cat_clust_order(net, inst_rc):
from __init__ import Network
from copy import deepcopy
import calc_clust
inst_keys = net.dat['node_info'][inst_rc].keys()
all_cats = [x for x in inst_keys if 'cat-' in x]
if len(all_cats) > 0:
for inst_name_cat in all_cats:
tmp_name = 'dict_' + inst_name_cat.replace('-', '_')
dict_cat = net.dat['node_info'][inst_rc][tmp_name]
all_cats = sorted(dict_cat.keys())
# this is the ordering of the columns based on their category, not
# including their clustering ordering within category
all_cat_orders = []
tmp_names_list = []
for inst_cat in all_cats:
inst_nodes = dict_cat[inst_cat]
tmp_names_list.extend(inst_nodes)
cat_net = deepcopy(Network())
cat_net.dat['mat'] = deepcopy(net.dat['mat'])
cat_net.dat['nodes'] = deepcopy(net.dat['nodes'])
cat_df = cat_net.dat_to_df()
sub_df = {}
if inst_rc == 'col':
sub_df['mat'] = cat_df['mat'][inst_nodes]
elif inst_rc == 'row':
# need to transpose df
cat_df['mat'] = cat_df['mat'].transpose()
sub_df['mat'] = cat_df['mat'][inst_nodes]
sub_df['mat'] = sub_df['mat'].transpose()
# load back to dat
cat_net.df_to_dat(sub_df)
try:
calc_clust.cluster_row_and_col(cat_net, 'cos')
# inst_cat_order = cat_net.dat['node_info'][inst_rc]
inst_cat_order = range(len(cat_net.dat['nodes'][inst_rc]))
except:
inst_cat_order = range(len(cat_net.dat['nodes'][inst_rc]))
prev_order_len = len(all_cat_orders)
# add prev order length to the current order number
inst_cat_order = [i + prev_order_len for i in inst_cat_order]
all_cat_orders.extend(inst_cat_order)
names_clust_list = [x for (y, x) in sorted(zip(all_cat_orders,
tmp_names_list))]
# calc category-cluster order
final_order = []
for i in range(len(net.dat['nodes'][inst_rc])):
inst_node_name = net.dat['nodes'][inst_rc][i]
inst_node_num = names_clust_list.index(inst_node_name)
final_order.append(inst_node_num)
net.dat['node_info'][inst_rc][inst_name_cat.replace('-', '_') +
'_index'] = final_order
def make_clust(net, dist_type='cosine', run_clustering=True,
dendro=True, requested_views=['pct_row_sum', 'N_row_sum'],
linkage_type='average', sim_mat=False):
''' This will calculate multiple views of a clustergram by filtering the
data and clustering after each filtering. This filtering will keep the top
N rows based on some quantity (sum, num-non-zero, etc). '''
from copy import deepcopy
import calc_clust
import run_filter
import make_views
import scipy
df = net.dat_to_df()
threshold = 0.0001
df = run_filter.df_filter_row(df, threshold)
df = run_filter.df_filter_col(df, threshold)
# calculate initial view with no row filtering
net.df_to_dat(df)
# preparing to make similarity matrices of rows and cols
###########################################################
# tmp_dist_mat = calc_clust.calc_distance_matrix(net.dat['mat'], 'col',
# get_sim=True,
# make_squareform=True,
# filter_sim_below=0.1)
#
# print(tmp_dist_mat)
# print(net.dat['node_info']['row'])
# print('\n')
# print(net.dat['node_info']['col'])
calc_clust.cluster_row_and_col(net, dist_type=dist_type,
linkage_type=linkage_type,
run_clustering=run_clustering,
dendro=dendro, ignore_cat=False)
all_views = []
send_df = deepcopy(df)
if 'N_row_sum' in requested_views:
all_views = make_views.N_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='sum')
if 'N_row_var' in requested_views:
all_views = make_views.N_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='var')
if 'pct_row_sum' in requested_views:
all_views = make_views.pct_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='sum')
if 'pct_row_var' in requested_views:
all_views = make_views.pct_rows(net, send_df, all_views,
dist_type=dist_type, rank_type='var')
if sim_mat is True:
print('make similarity matrices of rows and columns, add to viz data structure')
net.viz['views'] = all_views
def N_rows(net, df, all_views, dist_type='cosine', rank_type='sum'):
from copy import deepcopy
from __init__ import Network
import calc_clust, run_filter
keep_top = ['all', 500, 400, 300, 200, 100, 80, 60, 40, 20, 10]
df_abs = deepcopy(df['mat'])
df_abs = df_abs.transpose()
if rank_type == 'sum':
tmp_sum = df_abs.sum(axis=0)
elif rank_type == 'var':
tmp_sum = df_abs.var(axis=0)
tmp_sum = tmp_sum.abs()
tmp_sum.sort_values(inplace=True, ascending=False)
rows_sorted = tmp_sum.index.values.tolist()
for inst_keep in keep_top:
tmp_df = deepcopy(df)
if inst_keep < len(rows_sorted) or inst_keep == 'all':
tmp_net = deepcopy(Network())
if inst_keep != 'all':
keep_rows = rows_sorted[0:inst_keep]
tmp_df['mat'] = tmp_df['mat'].ix[keep_rows]
if 'mat_up' in tmp_df:
tmp_df['mat_up'] = tmp_df['mat_up'].ix[keep_rows]
tmp_df['mat_dn'] = tmp_df['mat_dn'].ix[keep_rows]
tmp_df = run_filter.df_filter_col(tmp_df, 0.001)
tmp_net.df_to_dat(tmp_df)
else:
tmp_net.df_to_dat(tmp_df)
try:
try:
calc_clust.cluster_row_and_col(tmp_net, dist_type, run_clustering=True)
except:
calc_clust.cluster_row_and_col(tmp_net, dist_type, run_clustering=False)
# add view
inst_view = {}
inst_view['N_row_' + rank_type] = inst_keep
inst_view['dist'] = 'cos'
inst_view['nodes'] = {}
inst_view['nodes']['row_nodes'] = tmp_net.viz['row_nodes']
inst_view['nodes']['col_nodes'] = tmp_net.viz['col_nodes']
all_views.append(inst_view)
except:
# print('\t*** did not cluster N filtered view')
pass
return all_views
def N_rows(net, df, all_views, dist_type='cosine', rank_type='sum'):
from copy import deepcopy
from __init__ import Network
import calc_clust, run_filter
keep_top = ['all', 500, 400, 300, 200, 100, 80, 60, 40, 20, 10]
df_abs = deepcopy(df['mat'])
df_abs = df_abs.transpose()
if rank_type == 'sum':
tmp_sum = df_abs.sum(axis=0)
elif rank_type == 'var':
tmp_sum = df_abs.var(axis=0)
tmp_sum = tmp_sum.abs()
tmp_sum.sort_values(inplace=True, ascending=False)
rows_sorted = tmp_sum.index.values.tolist()
for inst_keep in keep_top:
tmp_df = deepcopy(df)
if inst_keep < len(rows_sorted) or inst_keep == 'all':
tmp_net = deepcopy(Network())
if inst_keep != 'all':
keep_rows = rows_sorted[0:inst_keep]
tmp_df['mat'] = tmp_df['mat'].ix[keep_rows]
if 'mat_up' in tmp_df:
tmp_df['mat_up'] = tmp_df['mat_up'].ix[keep_rows]
tmp_df['mat_dn'] = tmp_df['mat_dn'].ix[keep_rows]
tmp_df = run_filter.df_filter_col(tmp_df, 0.001)
tmp_net.df_to_dat(tmp_df)
else:
tmp_net.df_to_dat(tmp_df)
try:
try:
calc_clust.cluster_row_and_col(tmp_net,
dist_type,
run_clustering=True)
except:
calc_clust.cluster_row_and_col(tmp_net,
dist_type,
run_clustering=False)
# add view
inst_view = {}
inst_view['N_row_' + rank_type] = inst_keep
inst_view['dist'] = 'cos'
inst_view['nodes'] = {}
inst_view['nodes']['row_nodes'] = tmp_net.viz['row_nodes']
inst_view['nodes']['col_nodes'] = tmp_net.viz['col_nodes']
all_views.append(inst_view)
except:
# print('\t*** did not cluster N filtered view')
pass
return all_views
