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 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 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
