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analysis.py
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analysis.py
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from io_functions import *
import numpy as np
import copy
import matplotlib as mpl
mpl.use('Agg')
from matplotlib import pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib.backends.backend_pdf import PdfPages
from functools import partial
import multiprocessing as mp
import mdtraj as md
import csv
import operator
from msmbuilder.utils import verbosedump, verboseload
from sklearn.metrics import mutual_info_score
from scipy.stats import pearsonr
from scipy import stats
import pandas as pd
def function_mapper(fxn, worker_pool, parallel, var_list):
if worker_pool is not None:
results = worker_pool.map_sync(fxn, var_list)
elif parallel:
pool = mp.Pool(mp.cpu_count())
results = pool.map(fxn, var_list)
pool.terminate
else:
results = [fxn(var) for var in var_list]
return(results)
def calc_mean_and_stdev(rmsd_map):
stats_map = {}
for key in list(rmsd_map.keys()):
rmsds = np.array(rmsd_map[key])
mean = np.mean(rmsds, axis = 0)
stdev = np.std(rmsds, axis = 0)
stats_map[key] = (mean, stdev)
return stats_map
def calc_mean(rmsd_map):
stats_map = {}
for key in list(rmsd_map.keys()):
rmsds = np.array(rmsd_map[key])
mean = np.mean(rmsds, axis = 0)
stats_map[key] = [mean]
return stats_map
def find_cos(index, k_mean, features):
traj = index[0]
frame = index[1]
conformation = features[traj][frame]
a = conformation
b = k_mean
return (traj, frame, np.dot(a,b) / (np.linalg.norm(a) * np.linalg.norm(b)))
def rmsd_connector(traj, inactive, residues=[], residues_map = None):
if residues_map is not None:
residues = map_residues(residues_map, residues)
nonsymmetric = ["CG2", "CG1", "CD1", "CD2", "CE1", "CE2"]
connector_atoms = []
for residue in residues:
connector_atoms += [(a.index, str(a)) for a in traj.topology.atoms if residue.is_mdtraj_res_equivalent(a.residue) and "hydrogen" not in a.element and not any(substring in str(a) for substring in nonsymmetric)]
connector_atoms = sorted(connector_atoms, key=operator.itemgetter(1), reverse = True)
connector_atoms = [a[0] for a in connector_atoms]
traj_stripped = traj.atom_slice(connector_atoms)
connector_atoms_target = []
for residue in residues:
connector_atoms_target += [(a.index, str(a)) for a in inactive.topology.atoms if residue.is_mdtraj_res_equivalent(a.residue) and "hydrogen" not in a.element and not any(substring in str(a) for substring in nonsymmetric)]
connector_atoms_target = sorted(connector_atoms_target, key=operator.itemgetter(1), reverse = True)
connector_atoms_target = [a[0] for a in connector_atoms_target]
inactive_stripped = inactive.atom_slice(connector_atoms_target)
try:
traj_stripped_aligned = traj_stripped.superpose(inactive_stripped)
rmsds = md.rmsd(traj_stripped, inactive_stripped) * 10.0
except:
rmsds = np.empty(traj_stripped.n_frames)
return rmsds
def rmsd_npxxy(traj, inactive, residues=[], residues_map = None):
if residues_map is not None:
residues = map_residues(residues_map, residues)
npxxy_atoms = []
for residue in residues:
npxxy_atoms += [(a.index,str(a)) for a in traj.topology.atoms if residue.is_mdtraj_res_equivalent(a.residue) and a.is_backbone and a.residue.is_protein]
npxxy_atoms = sorted(npxxy_atoms, key=operator.itemgetter(1), reverse = True)
npxxy_atoms = [a[0] for a in npxxy_atoms]
#print npxxy_atoms
traj_stripped = traj.atom_slice(npxxy_atoms)
npxxy_atoms_target = []
for residue in residues:
npxxy_atoms_target += [(a.index,str(a)) for a in inactive.topology.atoms if residue.is_mdtraj_res_equivalent(a.residue) and a.is_backbone and a.residue.is_protein]
npxxy_atoms_target = sorted(npxxy_atoms_target, key=operator.itemgetter(1), reverse = True)
npxxy_atoms_target = [a[0] for a in npxxy_atoms_target]
#print npxxy_atoms_target
inactive_stripped = inactive.atom_slice(npxxy_atoms_target)
try:
traj_stripped_aligned = traj_stripped.superpose(inactive_stripped)
rmsds = md.rmsd(traj_stripped, inactive_stripped) * 10.0
except:
rmsds = np.empty(traj_stripped.n_frames)
return rmsds
def compute_distance(traj, inactive, residues=[], residues_map = None):
if residues_map is not None:
residues = map_residues(residues_map, residues)
motif_atoms = []
for residue in residues:
motif_atoms += [(a.index,str(a)) for a in traj.topology.atoms if residue.is_mdtraj_res_equivalent(a.residue) and "H" not in a.name]
motif_atoms = sorted(motif_atoms, key=operator.itemgetter(1), reverse = True)
motif_atoms = [a[0] for a in motif_atoms]
#print motif_atoms
traj_stripped = traj.atom_slice(motif_atoms)
motif_atoms_target = []
for residue in residues:
motif_atoms_target += [(a.index,str(a)) for a in inactive.topology.atoms if residue.is_mdtraj_res_equivalent(a.residue) and "H" not in a.name]
motif_atoms_target = sorted(motif_atoms_target, key=operator.itemgetter(1), reverse = True)
motif_atoms_target = [a[0] for a in motif_atoms_target]
#print motif_atoms_target
inactive_stripped = inactive.atom_slice(motif_atoms_target)
traj_stripped_aligned = traj_stripped.superpose(inactive_stripped)
rmsds = md.rmsd(traj_stripped, inactive_stripped) * 10.0
return rmsds
def helix6_helix3_dist(traj, residues=[], residues_map = None):
if residues_map is not None:
residues = map_residues(residues_map, residues)
atom_3 = [a.index for a in traj.topology.atoms if residues[0].is_mdtraj_res_equivalent(a.residue) and a.name == "CA"][0]
atom_6 = [a.index for a in traj.topology.atoms if residues[1].is_mdtraj_res_equivalent(a.residue) and a.name == "CA"][0]
indices = np.empty([1,2])
indices[0] = [atom_3, atom_6]
dist = md.compute_distances(traj, indices) * 10.0
return np.concatenate(dist)
def compute_closest_heavy(traj, residues=[], residues_map = None):
if residues_map is not None:
residues = map_residues(residues_map, residues)
atoms_i = [a.index for a in traj.topology.atoms if residues[0].is_mdtraj_res_equivalent(a.residue) and "H" not in a.name]
atoms_j = [a.index for a in traj.topology.atoms if residues[1].is_mdtraj_res_equivalent(a.residue) and "H" not in a.name]
pairs = []
for i in range(0, len(atoms_i)):
for j in range(i, len(atoms_j)):
pairs.append([atoms_i[i], atoms_j[j]])
pairs = np.array(pairs)
dist = md.compute_distances(traj, pairs) * 10.0
dist = np.amin(dist, axis=1)
return dist
def plot_pnas_vs_docking(docking_dir, pnas_dir, save_dir, selected = False):
dock_scores = convert_csv_to_map_nocombine(docking_dir)
pnas_vectors = convert_csv_to_map_nocombine(pnas_dir)
x = []
y = []
c = []
for key in list(dock_scores.keys()):
if selected is not False:
if key not in selected: continue
#print "PNAS"
#print key
#print pnas_vectors[key]
if key in list(pnas_vectors.keys()):
#print pnas_vectors[key]
x.append(pnas_vectors[key][0])
y.append(pnas_vectors[key][1])
c.append(abs(dock_scores[key][0]))
#plt.annotate("%s" %key, xy=(pnas_vectors[key][0], pnas_vectors[key][1]), xytext=(pnas_vectors[key][0], pnas_vectors[key][1]),size=6)
#print dock_scores.keys()
plt.scatter(x, y, c=c, s=50, cmap = mpl.cm.RdYlBu_r)
pp = PdfPages(save_dir)
pp.savefig()
pp.close()
def pnas_distance(traj_file, inactive_file, active_file):
traj = md.load(traj_file)
inactive = md.load(inactive_file)
active = md.load(active_file)
scale = 7.14
inactive_tuple = np.array([helix6_helix3_dist(inactive) / scale, rmsd_npxxy(inactive, inactive)])
active_tuple = np.array([helix6_helix3_dist(active) / scale, rmsd_npxxy(active, inactive)])
traj_coords = [helix6_helix3_dist(traj, traj_file = traj_file) / scale, rmsd_npxxy(traj, inactive, traj_file = traj_file)]
traj_tuple = np.array(traj_coords)
active_dist = np.linalg.norm(traj_tuple - active_tuple)
inactive_dist = np.linalg.norm(traj_tuple - inactive_tuple)
distances = [inactive_dist, active_dist]
print(distances[1])
return [traj_coords, distances]
def pnas_distances(traj_dir, inactive_file, active_file):
scale = 7.14
save_file_i = "%s/inactive_pnas_distances.csv" %traj_dir
save_file_a = "%s/active_pnas_distances.csv" %traj_dir
coord_file = "%s/pnas_coords.csv" %traj_dir
distances_i = open(save_file_i, "wb")
distances_i.write("conformation, inactive_pnas_distance, a \n")
distances_a = open(save_file_a, "wb")
distances_a.write("conformation, active_pnas_distance, a \n")
coordinates = open(coord_file, "wb")
coordinates.write("cluster, tm6_tm3_dist, npxxy_rmsd_inactive \n")
pnas_partial = partial(pnas_distance, inactive_file = inactive_file, active_file = active_file)
trajs = get_trajectory_files(traj_dir, ext = ".pdb")
pool = mp.Pool(mp.cpu_count())
distances = pool.map(pnas_partial, trajs)
pool.terminate()
for i in range(0, len(trajs)):
traj = trajs[i]
pnas_coord = distances[i][0]
pnas_dist = distances[i][1]
traj_name = traj.split("/")[len(traj.split("/"))-1].split(".")[0]
distances_i.write("%s, %f \n" %(traj_name, pnas_dist[0]))
distances_a.write("%s, %f \n" %(traj_name, pnas_dist[1]))
coordinates.write("%s, %f, %f \n" %(traj_name, (pnas_coord[0]*scale), pnas_coord[1]))
distances_i.close()
distances_a.close()
coordinates.close()
return [save_file_i, save_file_a]
def plot_pnas_vs_tics(pnas_dir, tic_dir, pnas_names, directory, scale = 7.14, refcoords_file = None):
pnas = np.concatenate(load_file(pnas_dir))
pnas[:,0] *= scale
print((np.shape(pnas)))
print((len(pnas_names)))
if("ktICA" in tic_dir):
tics = load_dataset(tic_dir)
else:
tics = verboseload(tic_dir)
print((np.shape(tics)))
tics = np.concatenate(tics)
print((np.shape(tics)))
if len(pnas_names) != np.shape(pnas)[1]:
print("Invalid pnas names")
return
for i in range(0,np.shape(pnas)[1]):
for j in range(0,np.shape(tics)[1]):
tic = tics[:,j]
pnas_coord = pnas[:,i]
plt.hexbin(tic, pnas_coord, bins = 'log', mincnt=1, cmap=plt.cm.RdYlBu_r)
coord_name = pnas_names[i]
tic_name = "tIC.%d" %(j+1)
plt.xlabel(tic_name)
plt.ylabel(coord_name)
pp = PdfPages("%s/%s_%s_hexbin.pdf" %(directory, tic_name, coord_name))
pp.savefig()
pp.close()
plt.clf()
return
def plot_hex(transformed_data_file, figure_directory, colors = None, scale = 1.0):
transformed_data = verboseload(transformed_data_file)
trajs = np.concatenate(transformed_data)
print(trajs)
plt.hexbin(trajs[:,0] * scale, trajs[:,1], bins='log', mincnt=1, cmap=plt.cm.RdYlBu_r)
pp = PdfPages(figure_directory)
pp.savefig()
pp.close()
return
def plot_col(transformed_data_file, figure_directory, colors_file):
transformed_data = verboseload(transformed_data_file)
trajs = np.concatenate(transformed_data)
colors = np.concatenate(verboseload(colors_file))
sc = plt.scatter(trajs[:,0], trajs[:,1], c=colors, s=50, cmap = mpl.cm.RdYlBu_r)
plt.colorbar(sc)
plt.show()
pp = PdfPages(figure_directory)
pp.savefig()
pp.close()
return
def save_pdb(traj_dir, clusterer, i):
location = clusterer.cluster_ids_[i,:]
traj = get_trajectory_files(traj_dir)[location[0]]
print(("traj = %s, frame = %d" %(traj, location[1])))
conformation = md.load_frame(traj, location[1])
conformation.save_pdb("/scratch/users/enf/b2ar_analysis/clusters_1000_allprot/%d.pdb" %i)
return None
def get_cluster_centers(clusterer_dir, traj_dir):
clusterer = verboseload(clusterer_dir)
centers = clusterer.cluster_centers_
save_pdb_partial = partial(save_pdb, traj_dir = traj_dir, clusterer = clusterer)
indices = list(range(0, np.shape(centers)[0]))
pool = mp.Pool(mp.cpu_count())
pool.map(save_pdb_partial, indices)
pool.terminate()
return
def plot_tica(transformed_data_dir, lag_time):
transformed_data = verboseload(transformed_data_dir)
trajs = np.concatenate(transformed_data)
plt.hexbin(trajs[:,0], trajs[:,1], bins='log', mincnt=1, cmap=plt.cm.RdYlBu_r)
pp = PdfPages("/scratch/users/enf/b2ar_analysis/tica_phi_psi_chi2_t%d.pdf" %lag_time)
pp.savefig()
pp.close()
def plot_tica_and_clusters(component_j, transformed_data, lag_time, component_i, n_clusters, main="", label = "dot", active_cluster_ids = [], intermediate_cluster_ids = [], inactive_cluster_ids = [], inactive_subsample=5, intermediate_subsample=5, tica_dir = "", center_i=None, center_j=None, centers=None, concatenate=True, axes=None):
print((component_i, component_j))
if concatenate:
trajs = np.concatenate(transformed_data)
else:
trajs = transformed_data
plt.hexbin(trajs[:,component_i], trajs[:,component_j], bins='log', mincnt=1, cmap=plt.cm.RdYlBu_r)
if axes is not None:
plt.axis((axes[component_i][0], axes[component_i][1], axes[component_j][0], axes[component_j][1]))
plt.xlabel("tIC %d" %(component_i + 1))
plt.ylabel('tIC %d' %(component_j+1))
indices = [j for j in range(0,len(active_cluster_ids),1)]
if center_i is None:
center_i = component_i
if center_j is None:
center_j = component_j
for i in [active_cluster_ids[j] for j in indices]:
center = centers[int(i),:]
if label == "dot":
plt.scatter([center[center_i]],[center[center_j]], marker='v', c='k', s=10)
else:
plt.annotate('%d' %i, xy=(center[center_i],center[center_j]), xytext=(center[center_i], center[center_j]),size=6)
indices = [j for j in range(0,len(intermediate_cluster_ids),intermediate_subsample)]
for i in [intermediate_cluster_ids[j] for j in indices]:
center = centers[int(i),:]
if label == "dot":
plt.scatter([center[center_i]],[center[center_j]], marker='8', c='m', s=10)
else:
plt.annotate('%d' %i, xy=(center[center_i],center[center_j]), xytext=(center[center_i], center[center_j]),size=6)
indices = [j for j in range(0,len(inactive_cluster_ids),inactive_subsample)]
for i in [inactive_cluster_ids[j] for j in indices]:
center = centers[int(i),:]
if label == "dot":
plt.scatter([center[center_i]],[center[center_j]], marker='s', c='w', s=10)
else:
plt.annotate('%d' %i, xy=(center[center_i],center[center_j]), xytext=(center[center_i], center[center_j]),size=6)
pp = PdfPages("%s/%s_c%d_c%d_clusters%d.pdf" %(tica_dir, main, (component_i+1), (component_j+1), n_clusters))
pp.savefig()
pp.close()
plt.clf()
def plot_all_tics_and_clusters(tica_dir, transformed_data_dir, clusterer_dir, lag_time, tic_range=None, main="", label = "dot", active_cluster_ids = [], intermediate_cluster_ids = [], inactive_cluster_ids = [], inactive_subsample=5, intermediate_subsample=5, custom_cluster_centers=None, concatenate=True, axes=None):
try:
transformed_data = verboseload(transformed_data_dir)
except:
transformed_data = load_dataset(transformed_data_dir)
if custom_cluster_centers is None:
clusterer = verboseload(clusterer_dir)
centers = clusterer.cluster_centers_
#print centers
if not concatenate:
num_tics = np.shape(transformed_data)[1]
else:
num_tics = np.shape(transformed_data[0])[1]
if tic_range == None:
tic_range = range(0,num_tics)
for i in tic_range:
js = [j for j in tic_range if j > i]
plot_partial = partial(plot_tica_and_clusters, n_clusters = len(centers), tica_dir = tica_dir, main=main, transformed_data = transformed_data, lag_time = lag_time, label = label, active_cluster_ids = active_cluster_ids, intermediate_cluster_ids = intermediate_cluster_ids, inactive_cluster_ids = inactive_cluster_ids, inactive_subsample=inactive_subsample, intermediate_subsample=intermediate_subsample, component_i = i, centers=centers, concatenate=concatenate, axes=axes)
#for j in js:
# plot_partial(j)
pool = mp.Pool(mp.cpu_count())
pool.map(plot_partial, js)
pool.terminate()
#plot_tica_and_clusters(tica_dir = tica_dir, transformed_data = transformed_data, clusterer = clusterer, lag_time = lag_time, label = "dot", active_cluster_ids = active_cluster_ids, intermediate_cluster_ids = intermediate_cluster_ids, inactive_cluster_ids = inactive_cluster_ids, component_i = i, component_j = j)
print("Printed all tICA coords and all requested clusters")
def plot_tica_component_i_j(tica_dir, transformed_data_dir, lag_time, component_i = 0, component_j = 1):
transformed_data = verboseload(transformed_data_dir)
trajs = np.concatenate(transformed_data)
plt.hexbin(trajs[:,component_i], trajs[:,component_j], bins='log', mincnt=1)
pp = PdfPages("%s/c%d_c%d.pdf" %(tica_dir, component_i, component_j))
pp.savefig()
pp.close()
plt.clf()
def plot_column_pair(i, num_columns, save_dir, titles, data, refcoords, main, axes=None):
for j in range(i+1, num_columns):
plt.hexbin(data[:,i], data[:,j], bins = 'log', mincnt=1,cmap=plt.cm.RdYlBu_r)
if axes is not None:
x1 = axes[i][0]
x2 = axes[i][1]
y1 = axes[j][0]
y2 = axes[j][1]
plt.axis((x1, x2, y1, y2))
if refcoords is not None:
print([refcoords[0,i], refcoords[0,j]])
plt.scatter([refcoords[0,i]], [refcoords[0,j]], marker = 's', c='g',s=15)
plt.scatter([refcoords[1,i]], [refcoords[1,j]], marker = 'v', c='k',s=15)
if titles is not None:
pp = PdfPages("%s/%s_%s_%s.pdf" %(save_dir, main, titles[i], titles[j]))
plt.xlabel(titles[i])
plt.ylabel(titles[j])
plt.title(main)
pp.savefig()
pp.close()
plt.clf()
else:
pp = PdfPages("%s/tIC.%d_tIC.%d.pdf" %(save_dir, i+1, j+1))
plt.xlabel("tIC.%d" %(i+1))
plt.ylabel("tIC.%d" %(j+1))
pp.savefig()
pp.close()
plt.clf()
def plot_columns(save_dir, data_file, titles = None, main = "", tICA = False, scale = 1.0, refcoords_file = None, axes=None, concatenate=True, reshape=True):
data = verboseload(data_file)
if concatenate:
data = np.concatenate(data)
data[:,0] *= scale
if(refcoords_file is not None):
refcoords = load_file(refcoords_file)
if reshape:
refcoords = np.transpose(np.vstack(refcoords))
else:
refcoords = None
print((np.shape(refcoords)))
print(refcoords)
num_columns = np.shape(data)[1]
plot_column_pair_partial = partial(plot_column_pair, main = main, num_columns = num_columns, save_dir = save_dir, titles = titles, data = data, refcoords = refcoords, axes=axes)
pool = mp.Pool(mp.cpu_count())
pool.map(plot_column_pair_partial, range(0,num_columns))
pool.terminate()
#for i in range(0,num_columns):
# plot_column_pair_partial(i)
print("Done plotting columns")
return
def calc_kde(data, kde):
return kde(data.T)
def plot_all_tics(tica_dir, transformed_data_dir, lag_time):
transformed_data = verboseload(transformed_data_dir)
num_tics = np.shape(transformed_data[0])[1]
print("Looking at %d tICS" %num_tics)
for i in range(0,num_tics):
for j in range(i+1,num_tics):
plot_tica_component_i_j(tica_dir, transformed_data_dir, lag_time, component_i = i, component_j = j)
print("Printed all tICA coords")
#Add way to plot location of specific clusters as well
def plot_all_tics_samples(tica_coords_csv, save_dir, docking_csv = False, specific_clusters = False):
tica_coords_map = convert_csv_to_map_nocombine(tica_coords_csv)
n_samples = len(list(tica_coords_map.keys()))
if docking_csv is not False: docking_map = convert_csv_to_map_nocombine(docking_csv)
num_tics = len(tica_coords_map[list(tica_coords_map.keys())[0]])
for i in range(0, 1):
for j in range(i + 1, num_tics):
print(("plotting tICS %d %d" %(i, j)))
x = []
y = []
if docking_csv is not False: c = []
for sample in list(tica_coords_map.keys()):
x.append(tica_coords_map[sample][i])
y.append(tica_coords_map[sample][j])
if docking_csv is not False:
sample_id = "cluster%s" %sample
print(docking_map[sample_id])
c.append(abs(docking_map[sample_id][0]))
if docking_csv is not False:
plt.scatter(x, y, c=c, s=50, cmap = mpl.cm.RdYlBu_r)
else:
plt.scatter(x, y, s=50, color = 'red')
if specific_clusters is not False:
for cluster in specific_clusters:
cluster = str(cluster)
cluster_x = float(tica_coords_map[cluster][i])
cluster_y = float(tica_coords_map[cluster][j])
plt.annotate('%s' %cluster, xy=(cluster_x,cluster_y), xytext=(cluster_x, cluster_y),size=15)
plot_dir = "%s/samples_%d_tICS_%d_%d.pdf" %(save_dir, n_samples, i, j)
pp = PdfPages(plot_dir)
pp.savefig()
pp.close()
plt.clf()
def plot_timescales(clusterer_dir, n_clusters, lag_time):
clusterer = verboseload(clusterer_dir)
sequences = clusterer.labels_
lag_times = list(np.arange(1,150,5))
n_timescales = 5
msm_timescales = implied_timescales(sequences, lag_times, n_timescales=n_timescales, msm=MarkovStateModel(verbose=False))
print(msm_timescales)
for i in range(n_timescales):
plt.plot(lag_times, msm_timescales[:,i])
plt.semilogy()
pp = PdfPages("/scratch/users/enf/b2ar_analysis/kmeans_%d_%d_implied_timescales.pdf" %(n_clusters, lag_time))
pp.savefig()
pp.close()
plt.clf()
def rmsd_to_structure(clusters_dir, ref_dir, text):
pdbs = get_trajectory_files(clusters_dir)
ref = md.load_frame(ref_dir, index=0)
rmsds = np.zeros(shape=(len(pdbs),2))
for i in range(0,len(pdbs)):
print(i)
pdb_file = pdbs[i]
pdb = md.load_frame(pdb_file, index=0)
rmsd = md.rmsd(pdb, ref, 0)
rmsds[i,0] = i
rmsds[i,1] = rmsd[0]
rmsd_file = "%s/%s_rmsds.csv" %(clusters_dir, text)
np.savetxt(rmsd_file, rmsds, delimiter=",")
def rmsd_pymol(pdb_dir, ref_dir, script_dir, rmsd_dir):
script = open(script_dir, "rb")
lines = script.readlines()
new_script = open(script_dir, "wb")
for line in lines:
if line[0:7] == "pdb_dir":
#print ("found pdb line")
line = "pdb_dir = '%s'\n" %pdb_dir
elif line[0:7] == "ref_dir": line = "ref_dir = '%s'\n" %ref_dir
elif line[0:8] == "rmsd_dir": line = "rmsd_dir = '%s'\n" %rmsd_dir
new_script.write(line)
new_script.close()
command = "/scratch/users/enf/pymol/pymol %s" %script_dir
print(command)
os.system(command)
def analyze_rmsds(inactive_rmsd_file, active_rmsd_file, pnas_i, pnas_a, combined_file, analysis_file):
inactive_rmsd_map = convert_csv_to_map(inactive_rmsd_file)
inactive_stats_map = calc_mean_and_stdev(inactive_rmsd_map)
active_rmsd_map = convert_csv_to_map(active_rmsd_file)
active_stats_map = calc_mean_and_stdev(active_rmsd_map)
pnas_map_i = convert_csv_to_map(pnas_i)
pnas_stats_i = calc_mean_and_stdev(pnas_map_i)
pnas_map_a = convert_csv_to_map(pnas_a)
pnas_stats_a = calc_mean_and_stdev(pnas_map_a)
rmsd_i_map = convert_csv_to_map_nocombine(inactive_rmsd_file)
rmsd_a_map = convert_csv_to_map_nocombine(active_rmsd_file)
dist_i_map = convert_csv_to_map_nocombine(pnas_i)
dist_a_map = convert_csv_to_map_nocombine(pnas_a)
new_file = open(analysis_file, "wb")
new_file.write("cluster, inactive_rmsd, inactive_stdev, inactive_pnas_dist, inactive_pnas_stdev, active_rmsd, active_stdev, active_pnas_dist, active_pnas_stdev \n")
combined = open(combined_file, "wb")
combined.write("cluster, inactive_rmsd, inactive_pnas_dist, active_rmsd, active_pnas_dist \n")
for key in sorted(inactive_rmsd_map.keys()):
new_file.write("%s, %f, %f, %f, %f, %f, %f, %f, %f \n" %(key, inactive_stats_map[key][0], inactive_stats_map[key][1], pnas_stats_i[key][0], pnas_stats_i[key][1], active_stats_map[key][0], active_stats_map[key][1], pnas_stats_a[key][0], pnas_stats_a[key][1]))
for key in sorted(rmsd_i_map.keys()):
combined.write("%s, %f, %f, %f, %f \n" %(key, rmsd_i_map[key][0], dist_i_map[key][0], rmsd_a_map[key][0], dist_a_map[key][0]))
new_file.close()
combined.close()
return [inactive_stats_map, active_stats_map]
def merge_samples(results_map):
merged_results = {}
for key in list(results_map.keys()):
cluster = key.split("_")[0]
if cluster not in list(merged_results.keys()):
merged_results[cluster] = [results_map[key]]
else:
merged_results[cluster].append(results_map[key])
stats_map = calc_mean_and_stdev(merged_results)
return stats_map
def print_stats_map(merged_results, directory):
mapfile = "%s/stats_map.csv" %directory
mapcsv = open(mapfile, "wb")
mapcsv.write("cluster, mean_score, stdev \n")
for key in sorted(merged_results.keys()):
mapcsv.write("%s, %f, %f \n" %(key, merged_results[key][0], merged_results[key][1]))
mapcsv.close()
return
def analyze_log_file(log_file):
log = open(log_file, "rb")
conformation = log_file.rsplit(".", 1)[0]
conformation = conformation.split("/")[len(conformation.split("/"))-1 ]
score = 0.0
xp_score = None
lines = log.readlines()
current_pose = 0
best_pose = 0
for line in lines:
line = [w.decode("utf-8") for w in line.split()]
if len(line) >= 3:
if (line[0] == "Best" and line[1] == "XP" and line[2] == "pose:"):
current_pose += 1
xp_score = float(line[6])
#print "%f, %f" %(xp_score, score)
if xp_score < score:
score = xp_score
best_pose = current_pose
elif (line[0] == "Best" and line[1] == "Emodel="):
current_pose += 1
xp_score = float(line[8])
#print("%f, %f" %(xp_score, score))
if xp_score < score:
score = xp_score
best_pose = current_pose
score = -1.0*score
log.close()
return (conformation, score, best_pose)
def analyze_docking_results(docking_dir, ligand, precision, docking_summary, reread=True, write_to_disk=False):
try:
results_file = docking_summary
if os.path.exists(results_file):
if reread:
return pd.read_csv(results_file)
logs = get_trajectory_files(docking_dir, ext = ".log")
scores_list = []
for log in logs:
scores_list.append(analyze_log_file(log))
scores_df = pd.DataFrame(scores_list, columns=["sample", "%s" %("%s_%s_score" %(ligand, precision)), "best_pose_id"])
if write_to_disk:
scores_df.to_csv(results_file)
#scores_map = {score[0] : score[1] for score in scores_list}
#titles = ["sample", "%s" %("%s_%s_score" %(ligand, precision))]
#write_map_to_csv(results_file, scores_map, titles)
#merged_results = merge_samples(scores_map)
return scores_df
except:
return None
def analyze_docking_results_wrapper(args):
return analyze_docking_results(*args)
def get_lig_names(docking_dir):
subdirs = [x[0] for x in os.walk(docking_dir)]
lig_names = []
for subdir in subdirs:
lig_name = subdir.split("/")[len(subdir.split("/"))-1]
lig_names.append(lig_name)
return lig_names
def listdirs(folder):
#return [
# d for d in (os.path.join(folder, d1) for d1 in os.listdir(folder))
# if os.path.isdir(d)
#]
#from glob import glob
return(["%s/%s" %(folder, f) for f in os.listdir(folder)])
#return([d.path for d in os.scandir(folder) if d.is_dir()])
def parse_log_file(result):
keep_columns = [c for c in result.columns.values.tolist() if "score" in c]
result.index = result['sample'].values
docking_pose = result["best_pose_id"]
result = result[keep_columns]
return (docking_pose, result)
def get_arg_tuple(subdir, ligands=None, precision="SP", redo=False, reread=True, write_to_disk=False):
lig_name = subdir.split("/")[len(subdir.split("/"))-1]
if ligands is not None:
if lig_name not in ligands:
return None
docking_summary = "%s/docking_summary.csv" %subdir
return([subdir, lig_name, precision, docking_summary, reread, write_to_disk])
def analyze_docking_results_multiple(docking_dir, precision, summary,
ligands=None, poses_summary=None, redo=False, reread=True,
write_to_disk=False, parallel=False, worker_pool=None):
if os.path.exists(summary) and not redo:
with open(summary, "rb") as f:
df = pickle.load(f)
return(df, None)
#df = pd.read_csv(summary, index_col=0).transpose()
return df, None
print("Analyzing docking results")
print(docking_dir)
subdirs = listdirs(docking_dir)
#print subdirs
results_list = []
lig_names = []
arg_tuples = []
print("Obtaining docking scores now...")
get_arg_tuple_partial = partial(get_arg_tuple, ligands=ligands, precision=precision, redo=redo, reread=reread, write_to_disk=write_to_disk)
arg_tuples = function_mapper(get_arg_tuple_partial, worker_pool, parallel, subdirs)
arg_tuples = [t for t in arg_tuples if t is not None]
lig_names = [t[1] for t in arg_tuples]
print("Obtained ligand arguments.")
results_list = function_mapper(analyze_docking_results_wrapper, worker_pool, parallel, arg_tuples)
result_ids = [idx for idx in range(0, len(results_list)) if results_list[idx] is not None]
results_list = [results_list[idx] for idx in result_ids]
lig_names = [lig_names[idx] for idx in result_ids]
print("Examined all ligands.")
results = function_mapper(parse_log_file, worker_pool, parallel, results_list)
print("Parsed all log files.")
all_docking_results = [t[1] for t in results]
all_docking_poses = [t[0] for t in results]
all_docking_poses = pd.concat(all_docking_poses, axis=1)
all_docking_poses.columns = lig_names
if poses_summary is not None:
all_docking_poses.to_csv(poses_summary)
all_docking_results = pd.concat(all_docking_results, axis=1)
all_docking_results.columns = lig_names
all_docking_results = all_docking_results.transpose()
with open(summary, "wb") as f:
pickle.dump(all_docking_results, f, protocol=2)
#all_docking_results.to_csv(summary)
return all_docking_results, all_docking_poses
#combined_map = combine_maps(results_list)
#combined_filename = summary
#write_map_to_csv(combined_filename, combined_map, ["sample"] + lig_names)
def compute_means(docking_csv, joined_csv, means_csv):
print("analyzing %s" %docking_csv)
titles = get_titles(docking_csv)
docking_scores = convert_csv_to_joined_map(docking_csv, joined_csv)[0]
docking_averages = calc_mean(docking_scores)
write_map_to_csv(means_csv, docking_averages, titles)
return docking_averages
def compute_means_ligands(docking_dir, pnas_means, ligands):
subdirs = [x[0] for x in os.walk(docking_dir)]
subdirs = subdirs[1:len(subdirs)]
docking_csv_files = []
docking_means_files = []
docking_joined_files = []
for subdir in subdirs:
lig = subdir.split("/")[len(subdir.split("/"))-1]
if lig not in ligands: continue
docking_csv = "%s/docking_summary.csv" %subdir
docking_means = "%s/docking_means.csv" %subdir
docking_joined = "%s/docking_joined.csv" %subdir
compute_means(docking_csv, docking_joined, docking_means)
pnas_joined_csv = "%s/docking_means_pnas_means.csv" %subdir
combine_csv_list([docking_means, pnas_means], pnas_joined_csv)
return
def compute_z(value, mean, stdev):
return (value - mean) / stdev
def compute_aggregate_scores(docking_csv, inverse_agonists = [], summary = "", z_scores_csv = ""):
scores_map = convert_csv_to_map_nocombine(docking_csv)
docking_titles = get_titles(docking_csv)
lig_names = docking_titles[1:len(docking_titles)]
scores_per_ligand = {}
for lig_name in lig_names:
scores_per_ligand[lig_name] = []
for receptor in list(scores_map.keys()):
receptor_scores = scores_map[receptor]
for i in range(0, len(receptor_scores)):
lig_name = lig_names[i]
lig_score = receptor_scores[i]
scores_per_ligand[lig_name].append(lig_score)
ligand_stats = calc_mean_and_stdev(scores_per_ligand)
z_scores_per_receptor = {}
for receptor in list(scores_map.keys()):
receptor_scores = scores_map[receptor]
z_scores_per_receptor[receptor] = []
for i in range(0, len(receptor_scores)):
lig_score = receptor_scores[i]
lig_name = lig_names[i]
lig_mean = ligand_stats[lig_name][0]
lig_stdev = ligand_stats[lig_name][1]
z_score = compute_z(lig_score, lig_mean, lig_stdev)
if abs(lig_score - 0.0) < 0.001: z_score = -3.0
if lig_name in inverse_agonists:
#print lig_name
z_score = -1.0 * z_score
z_scores_per_receptor[receptor].append(z_score)
aggregate_scores = calc_mean(z_scores_per_receptor)
titles = ["sample", "average_z_score"]
write_map_to_csv(z_scores_csv, z_scores_per_receptor, docking_titles)
write_map_to_csv(summary, aggregate_scores, titles)
def combine_docking_distances(docking_csv, distances_csv, docking_dir):
docking_map = convert_csv_to_map_nocombine(docking_csv)
distances_map = convert_csv_to_map_nocombine(distances_csv)
combined_map = copy.deepcopy(distances_map)
for key in list(distances_map.keys()):
if key in list(docking_map.keys()):
combined_map[key].append(-1.0 * docking_map[key][0])
else:
combined_map.pop(key, None)
firstline = "cluster, inactive_rmsd, inactive_pnas, active_rmsd, active_pnas, docking \n"
filename = "%s/distances_docking.csv" %docking_dir
write_map_to_csv(filename, combined_map, firstline)
def top_n_scoring_clusters(docking_csv, score_type = 1, n = 50):
docking_list = convert_csv_to_list(docking_csv)
docking_list_sorted = sorted(docking_list, key=operator.itemgetter(score_type), reverse = True)
top_n = []
for i in range(0, n):
top_n.append(docking_list_sorted[i][0])
return top_n
def top_n_scoring_samples(docking_csv, score_type = "mean_docking_score", n = 100, n_samples = 10):
docking_list = convert_csv_to_list(docking_csv)
titles = docking_list[0]
for i in range(0, len(titles)):
if titles[i] == score_type: break
#print i
#print titles[i]
docking_list_sorted = sorted(docking_list, key=operator.itemgetter(i), reverse = True)
#print docking_list_sorted
top_n = []
for i in range(0, n):
top_n.append(docking_list_sorted[i][0])
if "sample" not in top_n[0]:
top_n_new = []
for cluster in top_n:
for i in range(0,n_samples):
top_n_new.append("%s_sample%d" %(cluster, i))
top_n = top_n_new
return top_n
def combine_docking_mmgbsa(combined_csv, mmgbsa_csv, mmgbsa_dir, filename):
combined_map = convert_csv_to_map_nocombine(combined_csv)
mmgbsa_map = convert_csv_to_map_nocombine(mmgbsa_csv)
new_map = copy.deepcopy(combined_map)
for key in list(mmgbsa_map.keys()):
if key in list(combined_map.keys()):
new_map[key].append(-1.0 * mmgbsa_map[key][0])
else:
new_map.pop(key, None)
firstline = "sample, inactive_rmsd, inactive_pnas, active_rmsd, active_pnas, docking, mmgbsa \n"
write_map_to_csv(filename, new_map, firstline)
def analyze_mmgbsa_results(mmgbsa_dir, ligand, chosen_receptors):
analysis_csv = "%s/mmgbsa_summary.csv" %mmgbsa_dir
csvfile = open(analysis_csv, "wb")
csvfile.write("sample, %s_mmgbsa \n" %ligand)
outputs = get_trajectory_files(mmgbsa_dir, ".csv")
analyzed_samples = set()
for output in outputs:
output_name = output.split("/")[len(output.split("/"))-1]
sample = output_name.split("-out.csv")[0]
if chosen_receptors is not False:
if sample not in chosen_receptors: continue
analyzed_samples.add(sample)
results = open(output, "rt")
reader = csv.reader(results)
score = 10000.0
i = 0
for row in reader:
if i == 0:
i += 1
continue
print(row[0])
temp = float(row[1])
if temp < score:
score = temp
score = -1.0 * score
csvfile.write("%s, %f \n" %(sample, score))
results.close()
not_analyzed_samples = set(chosen_receptors) - analyzed_samples
for sample in not_analyzed_samples:
if sample not in analyzed_samples:
csvfile.write("%s, %f \n" %(sample, 0.00))
csvfile.close()
results_map = convert_csv_to_map_nocombine(analysis_csv)
return results_map
def analyze_mmgbsa_results_multiple(mmgbsa_dir, summary, ligands, chosen_receptors):
subdirs = [x[0] for x in os.walk(mmgbsa_dir)]
subdirs = subdirs[1:len(subdirs)]
#print subdirs
results_list = []
lig_names = []
for subdir in subdirs:
lig_name = subdir.split("/")[len(subdir.split("/"))-1]
if lig_name not in ligands: continue
lig_names.append(lig_name)
mmgbsa_summary = "%s/mmgbsa_summary.csv" %subdir
results = analyze_mmgbsa_results(subdir, ligand = lig_name, chosen_receptors = chosen_receptors)
#print results
#print subdir
results_list.append(results)
combined_map = combine_maps(results_list)
combined_filename = summary
write_map_to_csv(combined_filename, combined_map, ["sample"] + lig_names)
def keep_only_sample0(original_csv, output_csv):
original = open(original_csv, "rb")
output = open(output_csv, "wb")
lines = original.readlines()
i = 0
for line in lines:
if i == 0:
output.write(line)
i += 1
else:
if "sample0" in line:
output.write(line)
original.close()
output.close()
def combine_rmsd_docking_maps(rmsd_csv, docking_csv):
rmsd_map = {}
docking_map = {}
rmsd_file = open(rmsd_csv, "rb")
docking_file = open(docking_csv, "rb")
rmsd_lines = rmsd_file.readlines()