def main(args):
feat, case_ids = load_features(args.src)
lab = load_labels(args.labsrc)
feat = drop_high_cor(feat, cor_thresh=0.8)
print('Features after high cor drop')
print(feat.head())
run_tsne(feat, lab)
def main(args):
scores = pd.read_csv(args.scores_src, index_col=None, header=0)
scores.drop(scores.columns[-1], inplace=True, axis=1)
scores_caseids = scores['Surgical Number']
scores_caseids = np.array([translate_sn2hash(x) for x in scores_caseids])
drop_rows = np.squeeze(scores.index.values[scores_caseids == 'drop_me'])
print('Dropping: ', drop_rows)
scores['case_id'] = scores_caseids
scores.drop(drop_rows, inplace=True)
print(scores.head())
print(scores.shape)
features = pd.read_csv(args.feature_src, index_col=0)
print('Features')
print(features.head())
print(features.shape)
caseids = features['case_id'].values
features.drop('case_id', axis=1, inplace=True)
features = drop_high_cor(features, 0.8)
remaining_features = features.columns
indices = []
feature_case_mean = []
for cid in np.unique(caseids):
cid_idx = caseids == cid
f = features.loc[cid_idx, :].values
fmean = np.mean(f, axis=0)
print('{}:'.format(cid), fmean.shape)
feature_case_mean.append(np.expand_dims(fmean, axis=0))
indices.append(cid)
features = pd.DataFrame(np.concatenate(feature_case_mean, axis=0), columns=remaining_features)
features['case_id'] = indices
print('Features grouped by case')
print(features.head())
print(features.shape)
matching_indices = np.intersect1d(features['case_id'], scores['case_id'])
print('Matched indices:', matching_indices, len(matching_indices))
# Drop rows from features and scores -- then sort them
drop_rows = [x for x,c in \
zip(features.index.values, features['case_id']) if c not in matching_indices]
features.drop(drop_rows, axis=0, inplace=True)
print('FEATURES BEFORE SORTING\n', features.head())
features.sort_values(by='case_id', inplace=True)
sorted_caseids_features = features['case_id'].values
features.drop('case_id', axis=1, inplace=True)
print('FEATURES AFTER SORTING\n', features.head())
features = features.transform(lambda x: (x - np.mean(x)) / np.std(x))
print(features.shape)
drop_rows = [x for x,c in \
zip(scores.index.values, scores['case_id'].values) if c not in matching_indices]
scores.drop(drop_rows, axis=0, inplace=True)
# shuffle columns
print('SCORES BEFORE SORTING\n', scores.head())
scores.sort_values(by='case_id', inplace=True)
sorted_caseids_scores = scores['case_id'].values
to_drop = ['case_id', 'caseid', 'Disease Stage', 'sample name', 'Surgical Number']
scores.drop(to_drop, axis=1, inplace=True)
print('SCORES AFTER SORTING\n', scores.head())
print(scores.shape)
for fid, sid in zip(sorted_caseids_features, sorted_caseids_scores):
print(fid, sid)
assert fid == sid
fig = plt.figure(figsize=(2,2), dpi=300)
logfile = os.path.join(args.dst, 'qvalues.csv')
comparison_ids = []
pvalues = []
for c in features.columns:
cx = features[c].values
for s in scores.columns:
sy = scores[s].values
try:
corr = spearmanr(cx, sy)
pcorr = pearsonr(cx, sy)
except:
print('Failed at {} x {}'.format(c, s))
print('cx: {} sy: {}'.format(cx.shape, sy.shape))
comparison_ids.append('{}_{}'.format(c, s))
pvalues.append(corr.pvalue)
if corr.pvalue < 0.001:
outstr = '*{}\t{}\tr={:3.3f}\tp={:3.3f}\tpr={:3.3f}\tpp={:3.3f}'.format(
c, s, corr.correlation, corr.pvalue, pcorr[0], pcorr[1])
plt.clf()
plt.scatter(cx, sy)
plt.title('sr={:3.3f} sp={:3.3f}\npr={:3.3f} pp={:3.3f}'.format(
corr.correlation, corr.pvalue,
pcorr[0], pcorr[1],
))
plt.xlabel(c)
plt.ylabel(s)
plt.savefig(os.path.join(args.dst, '{}_{}.png'.format(c, s)), bbox_inches='tight')
else:
outstr = ' {}\t{}\tr={:3.3f}\tp={:3.3f}\tpr={:3.3f}\tpp={:3.3f}'.format(
c, s, corr.correlation, corr.pvalue, pcorr[0], pcorr[1])
print(outstr)
_, qvalues, _, _ = multipletests(pvalues, alpha=0.01, method='fdr_bh')
qdf = pd.DataFrame({'q': qvalues, 'p': pvalues}, index=comparison_ids)
qdf.sort_values('q', inplace=True)
qdf.to_csv(logfile)
def main(args):
feat = pd.read_csv(args.feature_src, index_col=None)
case_ids = feat['case_id']
tile_ids = feat['tile_id']
stages = feat['stage_str']
feat.drop(['case_id', 'tile_id', 'stage_str'], axis=1, inplace=True)
feat.drop([c for c in feat.columns if 'Unnamed' in c],
axis=1,
inplace=True)
feat = feat.sample(frac=args.pct)
case_ids = case_ids.loc[feat.index]
tile_ids = tile_ids.loc[feat.index]
stages = stages.loc[feat.index]
print(feat.shape)
print(case_ids.shape)
print(tile_ids.shape)
feat = feat.loc[:, usecols]
print('Dropping nan, inf and high corr')
feat = drop_high_cor(feat, 0.8)
feat = feat.transform(lambda x: (x - np.mean(x)) / np.std(x))
feat = drop_nan_inf(feat)
feat = drop_var(feat, 0.5)
print(feat.shape)
print(feat.head())
if args.average == 'tile':
print('Average by tile')
feat = feat.groupby(by=tile_ids).mean()
stages = stages.groupby(by=tile_ids).max()
print(feat.shape)
elif args.average == 'case':
print('Average by case')
feat = feat.groupby(by=case_ids).mean()
stages = stages.groupby(by=case_ids).max()
print(feat.shape)
col_p = sns.color_palette('deep', 2)
col_colors = [col_p[int('ae' in x)] for x in feat.columns]
row_p = sns.color_palette('muted', 4)
row_colors = []
print(np.unique(stages.values))
for s in stages.values:
if s in m0_strs:
row_colors.append(row_p[0])
elif s in m1_strs:
row_colors.append(row_p[1])
elif 'NEPC' in s:
row_colors.append(row_p[2])
else:
row_colors.append(row_p[3])
print('col_colors', len(col_colors))
print('row_colors', len(row_colors))
# projected = TruncatedSVD(n_components=10).fit_transform(feat.values)
# projected = PCA(n_components=10).fit_transform(feat.values)
sns.clustermap(feat.values,
metric=args.metric,
standard_scale=1,
col_colors=col_colors,
row_colors=row_colors)
plt.show()
def main(args):
feat = pd.read_csv(args.src, index_col=0, header=0)
labels = pd.read_csv(args.labsrc, index_col=0, header=0, sep='\t')
# print(feat.head())
# print(labels.head())
case_ids = labels['case_id'].values
tile_ids = labels.index.values
stages = labels['stage_str'].values
feat = drop_high_cor(feat, 0.8)
print('Features after high cor drop')
# print(feat.shape)
# print(feat.head())
feat = feat.transform(lambda x: (x - np.mean(x)) / np.std(x))
print('Features after zscore')
# print(feat.shape)
# print(feat.head())
feat = feat.fillna(value=0)
# feat = drop_nan_inf(feat)
# print('Features after dropping nan and infs')
# print(feat.shape)
# print(feat.head())
((nepc_f, nepc_lab), (m0_f, m0_lab), (m0p_f, m0p_lab),
(m1_f, m1_lab)) = split_sets(feat, labels)
del feat
if args.filter_stats:
remove_cols = filter_stats(nepc_f, m0_f)
nepc_f.drop(remove_cols, inplace=True, axis=1)
m0_f.drop(remove_cols, inplace=True, axis=1)
m0p_f.drop(remove_cols, inplace=True, axis=1)
m1_f.drop(remove_cols, inplace=True, axis=1)
train_x, train_y = make_training(m0_f, nepc_f)
print('train_x', train_x.shape)
print('train_y', train_y.shape)
print('m1_f', m1_f.shape)
# model = ElasticNet(alpha=1e-3, max_iter=10000).fit(train_x, train_y)
# model = ElasticNetCV(cv=25).fit(train_x, train_y)
# model = ElasticNetCV(alphas=np.arange(1e-5, 1e-1, 20),
# cv=10, max_iter=20000, n_jobs=-1).fit(train_x, train_y)
model = RandomForestRegressor(oob_score=True,
max_features='sqrt',
max_depth=20,
n_estimators=50,
n_jobs=-1).fit(train_x, train_y)
with open('feature_importance.txt', 'w+') as f:
for v, coef in zip(train_x.columns, model.feature_importances_):
f.write('{}\t{}\n'.format(v, coef))
if args.aggr_fn == 'max':
aggr_fn = np.max
elif args.aggr_fn == 'mean':
aggr_fn = np.mean
""" Predict the M1 cases and gather by max and mean """
yhat_m1 = model.predict(m1_f)
case_aggr = []
m1_case_numbers = []
m1_case_vect = m1_lab['case_id'].values
for uc in np.unique(m1_case_vect):
yx = yhat_m1[m1_case_vect == uc]
case_aggr.append(aggr_fn(yx))
case_num = int(uc.split('-')[1])
m1_case_numbers.append(case_num)
m1_case_aggr = np.array(case_aggr)
m1_case_numbers = np.array(m1_case_numbers)
""" Predict M0P cases """
yhat_m0p = model.predict(m0p_f)
case_aggr = []
m0p_case_numbers = []
m0p_case_vect = m0p_lab['case_id'].values
for uc in np.unique(m0p_case_vect):
yx = yhat_m0p[m0p_case_vect == uc]
case_aggr.append(aggr_fn(yx))
case_num = int(uc.split('-')[1])
m0p_case_numbers.append(case_num)
m0p_case_aggr = np.array(case_aggr)
m0p_case_numbers = np.array(m0p_case_numbers)
""" Check on the training data """
# yhat_train = []
# # Just do m0 and nepc separately
# for cid in np.unique(m0_lab['case_id'].values):
# feat_case, feat_other = split_case(m0_f, m0_lab, cid)
# feat_split = pd.concat([feat_other, nepc_f])
# y_split = [0]*feat_other.shape[0] + [1]*nepc_f.shape[0]
# model = RandomForestRegressor(n_estimators=100, n_jobs=-1).fit(feat_split, y_split)
# yh = model.predict(feat_case)
# print(cid, yh)
# yhat_train += list(yh)
# for cid in np.unique(nepc_lab['case_id'].values):
# feat_case, feat_other = split_case(nepc_f, nepc_lab, cid)
# feat_split = pd.concat([m0_f, feat_other])
# y_split = [0]*m0_f.shape[0] + [1]*feat_other.shape[0]
# model = RandomForestRegressor(n_estimators=100, n_jobs=-1).fit(feat_split, y_split)
# yh = model.predict(feat_case)
# print(cid, yh)
# yhat_train += list(yh)
# yhat_train = np.asarray(yhat_train)
# print(yhat_train.shape)
m0_cases = m0_lab['case_id'].values
nepc_cases = nepc_lab['case_id'].values
train_case_vect = np.concatenate([m0_cases, nepc_cases])
# yhat_train = model.predict(train_x)
yhat_train = model.oob_prediction_
train_aggr, train_case_y = [], []
for uc in np.unique(train_case_vect):
idx = train_case_vect == uc
train_aggr.append(aggr_fn(yhat_train[idx]))
train_case_y.append(train_y[idx][0])
train_aggr = np.array(train_aggr)
train_case_y = np.array(train_case_y)
""" Do some statistical tests """
dotest = mannwhitneyu
# test_args = {'equal_var': True}
test_args = {}
test_m0_m1 = dotest(yhat_train[train_y == 0], yhat_m1, **test_args)
test_m0_m0p = dotest(yhat_train[train_y == 0], yhat_m0p, **test_args)
test_m0_nepc = dotest(yhat_train[train_y == 0], yhat_train[train_y == 1],
**test_args)
test_nepc_m1 = dotest(yhat_train[train_y == 1], yhat_m1, **test_args)
print('Tiles M0 vs M1', test_m0_m1)
print('Tiles M0 vs M0P', test_m0_m0p)
print('Tiles M0 vs NPEC', test_m0_nepc)
print('Tiles NEPC vs M1', test_nepc_m1)
test_m0_m1 = dotest(train_aggr[train_case_y == 0], m1_case_aggr,
**test_args)
test_m0_m0p = dotest(train_aggr[train_case_y == 0], m0p_case_aggr,
**test_args)
test_m0_nepc = dotest(train_aggr[train_case_y == 0],
train_aggr[train_case_y == 1], **test_args)
test_nepc_m1 = dotest(train_aggr[train_case_y == 1], m1_case_aggr,
**test_args)
print('aggr M0 vs M1', test_m0_m1)
print('aggr M0 vs M0P', test_m0_m0p)
print('aggr M0 vs NPEC', test_m0_nepc)
print('aggr NEPC vs M1', test_nepc_m1)
print(
'------------------------------------------------------------------------------------'
)
gene_scores = pd.read_csv('../data/signature_scores_beltram.csv',
index_col=None,
header=0,
sep=',')
gene_score_caseid = []
drop_rows = []
matching_scores = []
matching_indices = []
for i, (idx, sn) in enumerate(
zip(gene_scores.index.values,
gene_scores['Surgical Number'].values)):
try:
x = int(sn.split(' ')[-1])
if x in m1_case_numbers:
gene_score_caseid.append(x)
matching_indices.append(idx)
matching_scores.append(m1_case_aggr[m1_case_numbers == x][0])
elif x in m0p_case_numbers:
gene_score_caseid.append(x)
matching_indices.append(idx)
matching_scores.append(m0p_case_aggr[m0p_case_numbers == x][0])
else:
drop_rows.append(idx)
except:
drop_rows.append(idx)
gene_scores.drop(drop_rows, inplace=True)
gene_scores['NEPC Score'] = pd.Series(matching_scores,
index=matching_indices)
# if args.save_scores:
# gene_scores.to_csv('../signature_scores_nepc_scores_nuclei_mean.csv')
label_cols = ['caseid', 'Disease Stage', 'sample name', 'Surgical Number']
gene_scores.drop(label_cols, inplace=True, axis=1)
plt.figure(figsize=(5, 5), dpi=300)
sns.pairplot(gene_scores, kind='reg')
if args.dry_run:
pass
else:
plt.savefig('gene_scores_nepc_score_{}_tile.png'.format(args.aggr_fn),
bbox_inches='tight')
test_cols = [x for x in gene_scores.columns if x != 'NEPC Score']
scores = gene_scores['NEPC Score'].values
print(
'------------------------------------------------------------------------------------'
)
for c in test_cols:
ctest = spearmanr(scores, gene_scores[c].values)
print('spearman {:40}: {:3.5f} p={:3.5f}'.format(
c, ctest.correlation, ctest.pvalue))
ctest = pearsonr(scores, gene_scores[c].values)
print('pearson {:40}: {:3.5f} p={:3.5f}'.format(
c, ctest[0], ctest[1]))
print(
'------------------------------------------------------------------------------------'
)
if args.boxplot:
f, (ax_box,
ax_hist) = plt.subplots(2,
sharex=True,
gridspec_kw={"height_ratios": (.35, .65)})
plt_m0 = train_aggr[train_case_y == 0]
plt_nepc = train_aggr[train_case_y == 1]
plt_m1 = m1_case_aggr
plt_m0p = m0p_case_aggr
sns.distplot(
plt_m0,
bins=25,
norm_hist=True,
kde=True,
label='M0',
ax=ax_hist,
)
sns.distplot(
plt_nepc,
bins=25,
norm_hist=True,
kde=True,
label='NEPC',
ax=ax_hist,
)
sns.distplot(
plt_m1,
kde=True,
norm_hist=True,
bins=25,
label='M1',
ax=ax_hist,
)
sns.distplot(
plt_m0p,
kde=True,
norm_hist=True,
bins=25,
label='M0-P',
ax=ax_hist,
)
ax_hist.set_xlabel('Score')
ax_hist.set_ylabel('Frequency')
concat_scores = np.concatenate([plt_m0, plt_nepc, plt_m1, plt_m0p])
concat_labels = np.array(['M0'] * len(plt_m0) +
['NEPC'] * len(plt_nepc) +
['M1'] * len(plt_m1) + ['M0P'] * len(plt_m0p))
plt_df = pd.DataFrame({'Set': concat_labels, 'Score': concat_scores})
# fig = plt.figure(figsize=(2,2), dpi=300)
sns.boxplot(y='Set', x='Score', data=plt_df, ax=ax_box)
sns.stripplot(y='Set',
x='Score',
data=plt_df,
size=2.5,
jitter=True,
linewidth=0.5,
ax=ax_box)
# ax_box.set_ylabel('')
# ax_box.set_xlabel('')
# plt.show()
if args.dry_run:
pass
else:
plt.savefig('NEPC_score_{}_tile.png'.format(args.aggr_fn),
bbox_inches='tight')
def main(args):
data = pd.read_csv(args.src, index_col=0, memory_map=True)
lab = pd.read_csv(args.lab, index_col=0)
print('DATA')
print(data.shape)
print('LAB')
print(lab.shape)
print(lab.head())
data = data.sample(frac=args.pct)
print(data.shape)
# print(data.head())
# Grab the id columns
case_ids = data['case_id'].values
tile_ids = data['tile_id'].values
data.drop(['case_id', 'tile_id'], inplace=True, axis=1)
print(data.shape)
# print(data.head())
data = drop_high_cor(data, cor_thresh=0.7)
print('Features after high cor drop')
print(data.head())
lab_case_uid = np.array(
[hashlib.md5(x.encode()).hexdigest() for x in lab['case_id'].values])
is_nepc = np.zeros_like(case_ids, dtype=np.bool)
not_nepc = np.zeros_like(case_ids, dtype=np.bool)
for t_id in np.unique(case_ids):
t_idx = case_ids == t_id
print('{}: {} {}'.format(t_id, t_idx.shape, t_idx.sum()))
assert t_id in lab_case_uid
t_label = lab.loc[lab_case_uid == t_id].values
t_label = t_label[0, -3]
if t_label == 'NEPC':
is_nepc[t_idx] = 1
else:
not_nepc[t_idx] = 1
nepc_case_feat = data.loc[is_nepc, :].values
adeno_case_feat = data.loc[not_nepc, :].values
# nepc_case_feat = nepc_case_feat.sample(n=args.nsample).values
# adeno_case_feat = adeno_case_feat.sample(n=args.nsample).values
print('NEPC features:')
print(nepc_case_feat.shape)
print('Adeno features:')
print(adeno_case_feat.shape)
for c in range(nepc_case_feat.shape[1]):
nepc_ = nepc_case_feat[:, c]
adeno_ = adeno_case_feat[:, c]
tt = ttest_ind(nepc_, adeno_)
print('{}\t{:3.3f}\t{:3.3f}'.format(c, tt[0], tt[1]))
if tt[1] < args.thresh:
plt.clf()
# df = pd.DataFrame({'NEPC': nepc_,
# 'Adeno': adeno_})
sns.distplot(nepc_, label='NEPC')
sns.distplot(adeno_, label='Adeno')
plt.legend(frameon=True)
plt.title('{}\np={}'.format(c, tt[1]))
saveto = os.path.join(args.dst, '{}.png'.format(c))
plt.savefig(saveto, bbox_inches='tight')
def main(args):
feat, case_ids = load_features(args.src, zscore=True)
lab = load_labels(args.labsrc)
feat = drop_high_cor(feat, cor_thresh=0.8)
print('Features after high cor drop')
# train_x, train_y, test_x, test_y = holdout_cases(feat, lab)
((nepc_f, nepc_lab), (m0_f, m0_lab), (m0p_f, m0p_lab),
(m1_f, m1_lab)) = split_sets(feat, lab)
del feat
# Split out non-small-cell-NEPC:
nepc_is_sc = np.array([x in scnepc for x in nepc_lab['case_id'].values])
nepc_not_sc = np.array(
[x not in scnepc for x in nepc_lab['case_id'].values])
nepc_f_sc = nepc_f.loc[nepc_is_sc, :]
nepc_lab_sc = nepc_lab.loc[nepc_is_sc, :]
nepc_f_not_sc = nepc_f.loc[nepc_not_sc, :]
nepc_lab_not_sc = nepc_lab.loc[nepc_not_sc, :]
del nepc_f, nepc_lab
print('NEPC SC lab')
print(nepc_lab_sc.head())
print(nepc_lab_sc.shape)
print('NEPC not SC lab')
print(nepc_lab_not_sc.head())
print(nepc_lab_not_sc.shape)
if args.filter_stats:
remove_cols = filter_stats(nepc_f_sc, m0_f)
nepc_f_sc.drop(remove_cols, inplace=True, axis=1)
nepc_f_not_sc.drop(remove_cols, inplace=True, axis=1)
m0_f.drop(remove_cols, inplace=True, axis=1)
m0p_f.drop(remove_cols, inplace=True, axis=1)
m1_f.drop(remove_cols, inplace=True, axis=1)
train_x, train_y = make_training(m0_f, nepc_f_sc)
train_lab = pd.concat([m0_lab, nepc_lab_sc], axis=0)
print('train lab')
print(train_lab.head())
print(train_lab.shape)
# model = ElasticNet(alpha=1e-3, max_iter=50000).fit(train_x, train_y)
# model = ElasticNetCV(cv=25).fit(train_x, train_y)
# model = ElasticNetCV(alphas=np.arange(1e-5, 1e-1, 20),
# cv=10, max_iter=10000, n_jobs=-1).fit(train_x, train_y)
model = RandomForestRegressor(oob_score=True,
max_depth=25,
n_estimators=100,
n_jobs=-1).fit(train_x, train_y)
with open('feature_importance.txt', 'w+') as f:
for v, coef in zip(train_x.columns, model.feature_importances_):
f.write('{}\t{}\n'.format(v, coef))
if args.aggr_fn == 'max':
aggr_fn = np.max
elif args.aggr_fn == 'mean':
aggr_fn = np.mean
# """ Get M0 case numbers """
# m0_case_numbers = []
# m0_case_vect = m1_lab['case_id'].values
# print('M0 Cases:')
# for uc in np.unique(m0_case_vect):
# case_num = int(uc.plist('-')[1])
# m0_case_numbers.append(case_num)
""" Predict the M1 cases and gather by mean """
yhat_m1 = model.predict(m1_f)
case_aggr = []
m1_case_numbers = []
m1_case_vect = m1_lab['case_id'].values
for uc in np.unique(m1_case_vect):
yx = yhat_m1[m1_case_vect == uc]
case_aggr.append(aggr_fn(yx))
case_num = int(uc.split('-')[1])
m1_case_numbers.append(case_num)
m1_case_aggr = np.array(case_aggr)
m1_case_numbers = np.array(m1_case_numbers)
# Print out
m1_lab['NEPC_score'] = yhat_m1
print('m1 lab')
print(m1_lab.head())
""" Predict M0P cases """
yhat_m0p = model.predict(m0p_f)
case_aggr = []
m0p_case_numbers = []
m0p_case_vect = m0p_lab['case_id'].values
for uc in np.unique(m0p_case_vect):
yx = yhat_m0p[m0p_case_vect == uc]
case_aggr.append(aggr_fn(yx))
case_num = int(uc.split('-')[1])
m0p_case_numbers.append(case_num)
m0p_case_aggr = np.array(case_aggr)
m0p_case_numbers = np.array(m0p_case_numbers)
# Print out
m0p_lab['NEPC_score'] = yhat_m0p
print('m0p lab')
print(m0p_lab.head())
""" Predict NEPC not SC cases """
yhat_nepc_not_sc = model.predict(nepc_f_not_sc)
case_aggr = []
nepc_not_sc_case_numbers = []
nepc_not_sc_case_vect = nepc_lab_not_sc['case_id'].values
for uc in np.unique(nepc_not_sc_case_vect):
yx = yhat_nepc_not_sc[nepc_not_sc_case_vect == uc]
case_aggr.append(aggr_fn(yx))
nepc_not_sc_case_aggr = np.array(case_aggr)
nepc_not_sc_case_numbers = np.array(nepc_not_sc_case_numbers)
# Print out
nepc_lab_not_sc['NEPC_score'] = yhat_nepc_not_sc
print('NEPC not sc lab')
print(nepc_lab_not_sc.head())
""" Check on training data
Run a LOOCV on the training data """
# yhat_train = []
# # Just do m0 and nepc separately
# for cid in np.unique(m0_lab['case_id'].values):
# feat_case, feat_other = split_case(m0_f, m0_lab, cid)
# feat_split = pd.concat([feat_other, nepc_f])
# y_split = [0]*feat_other.shape[0] + [1]*nepc_f.shape[0]
# model = RandomForestRegressor(n_estimators=100, n_jobs=-1).fit(feat_split, y_split)
# yh = model.predict(feat_case)
# print(cid, yh)
# yhat_train += list(yh)
# for cid in np.unique(nepc_lab['case_id'].values):
# feat_case, feat_other = split_case(nepc_f, nepc_lab, cid)
# feat_split = pd.concat([m0_f, feat_other])
# y_split = [0]*m0_f.shape[0] + [1]*feat_other.shape[0]
# model = RandomForestRegressor(n_estimators=100, n_jobs=-1).fit(feat_split, y_split)
# yh = model.predict(feat_case)
# print(cid, yh)
# yhat_train += list(yh)
# yhat_train = np.asarray(yhat_train)
# print(yhat_train.shape)
m0_cases = m0_lab['case_id'].values
nepc_cases = nepc_lab_sc['case_id'].values
train_case_vect = np.concatenate([m0_cases, nepc_cases])
# yhat_train = model.predict(train_x)
yhat_train = model.oob_prediction_
train_aggr, train_case_y = [], []
for uc in np.unique(train_case_vect):
idx = train_case_vect == uc
train_aggr.append(aggr_fn(yhat_train[idx]))
train_case_y.append(train_y[idx][0])
train_aggr = np.array(train_aggr)
train_case_y = np.array(train_case_y)
# Print out
train_lab['NEPC_score'] = yhat_train
print('train lab')
print(train_lab.head())
score_lab = pd.concat([m1_lab, m0p_lab, train_lab], axis=0)
print(score_lab.shape)
score_lab.to_csv('tile_paths_with_NEPC_score.csv')
""" write out scores """
with open('nepc_case_scores.txt', 'w+') as f:
for mop, mop_score in zip(np.unique(m0p_case_vect), m0p_case_aggr):
s = '{}\t{}\n'.format(mop, mop_score)
f.write(s)
for mop, mop_score in zip(np.unique(m1_case_vect), m1_case_aggr):
s = '{}\t{}\n'.format(mop, mop_score)
f.write(s)
for mop, mop_score in zip(np.unique(train_case_vect), train_aggr):
s = '{}\t{}\n'.format(mop, mop_score)
f.write(s)
""" Do some statistical tests """
dotest = mannwhitneyu
# test_args = {'equal_var': True}
test_args = {}
test_m0_m1 = dotest(yhat_train[train_y == 0], yhat_m1, **test_args)
test_m0_m0p = dotest(yhat_train[train_y == 0], yhat_m0p, **test_args)
test_m0_nepc = dotest(yhat_train[train_y == 0], yhat_train[train_y == 1],
**test_args)
test_nepc_m1 = dotest(yhat_train[train_y == 1], yhat_m1, **test_args)
test_m0_nepc_not_sc = dotest(yhat_train[train_y == 0], yhat_nepc_not_sc,
**test_args)
test_nepc_sc_nepc_not_sc = dotest(yhat_train[train_y == 1],
yhat_nepc_not_sc, **test_args)
print('Tiles M0 vs M1', test_m0_m1)
print('Tiles M0 vs M0P', test_m0_m0p)
print('Tiles M0 vs NPEC SC', test_m0_nepc)
print('Tiles M0 vs NPEC NOT SC', test_m0_nepc_not_sc)
print('Tiles NEPC vs M1', test_nepc_m1)
print('Tiles NEPC SC vs NEPC NOT SC', test_nepc_sc_nepc_not_sc)
test_m0_m1 = dotest(train_aggr[train_case_y == 0], m1_case_aggr,
**test_args)
test_m0_m0p = dotest(train_aggr[train_case_y == 0], m0p_case_aggr,
**test_args)
test_m0_nepc = dotest(train_aggr[train_case_y == 0],
train_aggr[train_case_y == 1], **test_args)
test_nepc_m1 = dotest(train_aggr[train_case_y == 1], m1_case_aggr,
**test_args)
test_m0_nepc_not_sc = dotest(train_aggr[train_case_y == 0],
nepc_not_sc_case_aggr, **test_args)
test_nepc_sc_nepc_not_sc = dotest(train_aggr[train_case_y == 1],
nepc_not_sc_case_aggr, **test_args)
print('aggr M0 vs M1', test_m0_m1)
print('aggr M0 vs M0P', test_m0_m0p)
print('aggr M0 vs NPEC SC', test_m0_nepc)
print('aggr M0 vs NPEC NOT SC', test_m0_nepc_not_sc)
print('aggr NEPC vs M1', test_nepc_m1)
print('aggr NPEC SC vs NEPC NOT SC', test_nepc_sc_nepc_not_sc)
print(
'------------------------------------------------------------------------------------'
)
if args.genescore:
gene_scores = pd.read_csv('../data/signature_scores_beltram.csv',
index_col=None,
header=0,
sep=',')
gene_score_caseid = []
drop_rows = []
matching_scores = []
matching_indices = []
for i, (idx, sn) in enumerate(
zip(gene_scores.index.values,
gene_scores['Surgical Number'].values)):
try:
x = int(sn.split(' ')[-1])
if x in m1_case_numbers:
# print('M1 matched SN {}'.format(x))
gene_score_caseid.append(x)
matching_indices.append(idx)
matching_scores.append(
m1_case_aggr[m1_case_numbers == x][0])
# if x in m0_case_numbers:
# print('M0 matched SN {}'.format(x))
# gene_score_caseid.append(x)
# matching_indices.append(idx)
# matching_scores.append(m1_case_mean[m1_case_numbers==x][0])
elif x in m0p_case_numbers:
# print('M0P matched SN {}'.format(x))
gene_score_caseid.append(x)
matching_indices.append(idx)
matching_scores.append(
m0p_case_aggr[m0p_case_numbers == x][0])
else:
drop_rows.append(idx)
except:
drop_rows.append(idx)
print(sn)
gene_scores.drop(drop_rows, inplace=True)
print(gene_scores.shape)
gene_scores['NEPC Score'] = pd.Series(matching_scores,
index=matching_indices)
# if args.save_scores:
# gene_scores.to_csv('../data/signature_scores_nepc_scores_mean.csv')
label_cols = [
'caseid', 'Disease Stage', 'sample name', 'Surgical Number'
]
gene_scores.drop(label_cols, inplace=True, axis=1)
# plt.figure(figsize=(5,5), dpi=300)
# sns.pairplot(gene_scores, kind='reg')
# plt.savefig('gene_scores_nepc_score_{}.png'.format(args.aggr_fn), bbox_inches='tight')
test_cols = [x for x in gene_scores.columns if x != 'NEPC Score']
scores = gene_scores['NEPC Score'].values
for c in test_cols:
try:
ctest = spearmanr(scores, gene_scores[c].values)
print('spearman {:40}: {:3.5f} p={:3.5f}'.format(
c, ctest.correlation, ctest.pvalue))
ctest = pearsonr(scores, gene_scores[c].values)
print('pearson {:40}: {:3.5f} p={:3.5f}'.format(
c, ctest[0], ctest[1]))
except:
print('Test column {} failed'.format(c))
print(
'------------------------------------------------------------------------------------'
)
if args.boxplot:
f, (ax_box,
ax_hist) = plt.subplots(2,
sharex=True,
gridspec_kw={"height_ratios": (.35, .65)})
plt_m0 = train_aggr[train_case_y == 0]
plt_nepc_sc = train_aggr[train_case_y == 1]
plt_nepc_not_sc = nepc_not_sc_case_aggr
plt_m1 = m1_case_aggr
plt_m0p = m0p_case_aggr
auc_ = roc_auc_score(y_true=train_case_y, y_score=train_aggr)
print('M0 NEPC SC AUC = ', auc_)
m0m1 = np.concatenate([plt_m0, plt_m1])
m0m1_y = np.array([0] * len(plt_m0) + [1] * len(plt_m1))
auc_ = roc_auc_score(y_true=m0m1_y, y_score=m0m1)
print('M0 M1 AUC = ', auc_)
m0m0p = np.concatenate([plt_m0, plt_m0p])
m0m0p_y = np.array([0] * len(plt_m0) + [1] * len(plt_m0p))
auc_ = roc_auc_score(y_true=m0m0p_y, y_score=m0m0p)
print('M0 M0P AUC = ', auc_)
m0nepc_not_sc = np.concatenate([plt_m0, plt_nepc_not_sc])
m0nepc_not_sc_y = np.array([0] * len(plt_m0) +
[1] * len(plt_nepc_not_sc))
auc_ = roc_auc_score(y_true=m0nepc_not_sc_y, y_score=m0nepc_not_sc)
print('M0 NEPC not SC AUC = ', auc_)
sns.distplot(
plt_m0,
bins=25,
norm_hist=True,
kde=True,
label='M0',
ax=ax_hist,
)
sns.distplot(
plt_nepc_sc,
bins=25,
norm_hist=True,
kde=True,
label='NEPC SC',
ax=ax_hist,
)
sns.distplot(
plt_m1,
kde=True,
norm_hist=True,
bins=25,
label='M1',
ax=ax_hist,
)
sns.distplot(
plt_m0p,
kde=True,
norm_hist=True,
bins=25,
label='M0-P',
ax=ax_hist,
)
sns.distplot(
plt_nepc_not_sc,
kde=True,
norm_hist=True,
bins=25,
label='NEPC not SC',
ax=ax_hist,
)
ax_hist.set_xlabel('Score')
ax_hist.set_ylabel('Frequency')
concat_scores = np.concatenate(
[plt_m0, plt_nepc_sc, plt_m1, plt_m0p, plt_nepc_not_sc])
concat_labels = np.array(['M0'] * len(plt_m0) +
['NEPC SC'] * len(plt_nepc_sc) +
['M1'] * len(plt_m1) +
['M0P'] * len(plt_m0p) +
['NEPC not SC'] * len(plt_nepc_not_sc))
plt_df = pd.DataFrame({'Set': concat_labels, 'Score': concat_scores})
# fig = plt.figure(figsize=(2,2), dpi=300)
sns.boxplot(y='Set', x='Score', data=plt_df, ax=ax_box)
sns.stripplot(y='Set',
x='Score',
data=plt_df,
size=2.5,
jitter=True,
linewidth=0.5,
ax=ax_box)
# ax_box.set_ylabel('')
# ax_box.set_xlabel('')
# plt.show()
plt.savefig('NEPC_score_{}.png'.format(args.aggr_fn),
bbox_inches='tight')
def main(args):
data = pd.read_csv(args.src, index_col=0, memory_map=True)
lab = pd.read_csv(args.lab)
print(data.shape)
print(lab.shape)
print(lab.head())
data = data.sample(frac=args.pct)
print(data.shape)
print(data.head())
# Grab the id columns
case_id = data['case_id']
tile_id = data['tile_id']
data.drop(['case_id', 'tile_id'], inplace=True, axis=1)
print(data.shape)
print(data.head())
if args.ae_only:
to_drop = [x for x in data.columns if 'ae' not in x]
data.drop(to_drop, axis=1, inplace=True)
if args.hc_only:
to_drop = [x for x in data.columns if 'hc' not in x]
data.drop(to_drop, axis=1, inplace=True)
data = data.transform(lambda x: (x - np.mean(x)) / np.std(x))
isinfs = np.sum(np.isinf(data.values), axis=0)
print('isinfs', isinfs.shape)
isnans = np.sum(np.isnan(data.values), axis=0)
print('isnans', isnans.shape)
print(np.argwhere(isinfs))
print(np.argwhere(isnans))
# data = data.dropna(axis='index')
inf_cols = data.columns.values[np.squeeze(np.argwhere(isinfs))]
nan_cols = data.columns.values[np.squeeze(np.argwhere(isnans))]
print('inf_cols', inf_cols)
print('nan_cols', nan_cols)
data.drop(inf_cols, axis=1, inplace=True)
data.drop(nan_cols, axis=1, inplace=True)
print(data.shape)
# Drop correlated columns
data = drop_high_cor(data, 0.7)
if args.average:
print('Averaging features')
if args.average_by == 'case':
print('by: case')
data = data.groupby(by=case_id, group_keys=True).mean()
lab = lab.groupby('case_id').max()
elif args.average_by == 'tile':
print('by: tile')
data = data.groupby(by=tile_id, group_keys=True).mean()
lab = lab.groupby('tile_id').max()
else:
pass
print(data.shape)
print(data.head())
print(lab.head())
is_nepc = []
for x, t in zip(lab['stage_str'].values, lab.index.values):
if t in data.index:
is_nepc.append(x == 'NEPC')
is_nepc = np.array(is_nepc)
print(is_nepc.shape)
else:
pass
emb = MulticoreTSNE(n_jobs=-1).fit_transform(data)
# emb = umap.UMAP().fit_transform(data)
plot_embedded(emb, is_nepc)
def main(args):
feat = pd.read_csv(args.feature_src, index_col=0)
lab = pd.read_csv(args.label_src)
case_ids = feat['case_id']
tile_ids = feat.index
stages = lab['stage_str']
feat.drop(['case_id'], axis=1, inplace=True)
# feat.drop([c for c in feat.columns if 'Unnamed' in c], axis=1, inplace=True)
# case_ids = case_ids.loc[feat.index]
# tile_ids = tile_ids.loc[feat.index]
# stages = stages.loc[feat.index]
print(feat.shape)
print(case_ids.shape)
print(tile_ids.shape)
print(stages.shape)
print('Dropping nan, inf and high corr')
feat = drop_high_cor(feat, 0.8)
feat = feat.transform(lambda x: (x - np.mean(x)) / np.std(x))
if os.path.exists(args.reject_feats):
usecols = np.invert(np.load(args.reject_feats))
print('Rejecting features', args.reject_feats, usecols.shape, np.sum(usecols))
feat = feat.loc[:, usecols]
else:
feat = feat.loc[:, usecols]
feat = drop_nan_inf(feat)
feat = drop_var(feat, 0.5)
print(feat.shape)
print(feat.head())
if args.average:
print('Average by case')
feat = feat.groupby(by=case_ids.values).mean()
stages = stages.groupby(by=case_ids.values).max()
print(feat.shape)
print(stages.shape)
row_p = sns.color_palette('muted', 3)
row_colors = []
print(np.unique(stages.values))
for s in stages.values:
if s in m0_strs:
row_colors.append(row_p[0])
elif s in m1_strs:
row_colors.append(row_p[1])
elif 'NEPC' in s:
row_colors.append(row_p[2])
else:
row_colors.append(row_p[1])
print('row_colors', len(row_colors))
# projected = TruncatedSVD(n_components=10).fit_transform(feat.values)
# projected = PCA(n_components=10).fit_transform(feat.values)
sns.clustermap(feat.values,
metric=args.metric,
standard_scale=1,
row_colors=row_colors)
plt.savefig(args.dst)
def train(args):
feat = pd.read_csv(args.src, index_col=0, header=0)
print(feat.head())
print(feat.shape)
labels = pd.read_csv(args.labsrc, sep='\t')
print(labels.shape)
yvect = get_y(feat['case_id'], labels)
print(yvect.shape)
# Drop rows that come from cases we want to exclude
usable_data = yvect != 4
yvect = yvect[usable_data]
print(yvect.shape)
feat = feat.loc[usable_data, :]
nuclei_case_ids = feat['case_id']
nuclei_tile_ids = feat['tile_id']
feat.drop(['case_id', 'tile_id'], axis=1, inplace=True)
print('dropped label cols', feat.shape)
# drop_cols = [x for x in feat.columns if 'hc' not in x]
# feat.drop(drop_cols, inplace=True, axis=1)
# print('dropped chosen cols', feat.shape)
# Drop columns of features
feat = drop_var(feat)
print('dropped low var', feat.shape)
feat = drop_high_cor(feat, 0.8)
print('dropped corr', feat.shape)
feat = drop_nan_inf(feat)
print('dropped nan inf', feat.shape)
feat = feat.transform(lambda x: (x - np.mean(x)) / np.std(x))
print(feat.head())
print(feat.shape)
# Split off M1
m1rows = yvect == 2
nepc_not_sc_rows = yvect == 5
m0nepc_rows = yvect < 2
yvect_m0nepc = yvect[m0nepc_rows]
feat_m0nepc = feat.loc[m0nepc_rows, :]
feat_m1 = feat.loc[m1rows, :]
feat_nepc_not_sc = feat.loc[nepc_not_sc_rows, :]
del feat, yvect
train_idx, test_idx = train_test_split(np.arange(len(yvect_m0nepc)))
train_x = feat_m0nepc.iloc[train_idx, :]
train_y = yvect_m0nepc[train_idx]
test_x = feat_m0nepc.iloc[test_idx, :]
test_y = yvect_m0nepc[test_idx]
print(train_x.shape)
print(test_x.shape)
model = RandomForestRegressor(max_depth=35,
max_features='sqrt',
n_estimators=200,
n_jobs=-1).fit(train_x, train_y)
#ypred = model.predict(test_x)
#print(ypred.shape)
#print(ypred.mean())
#print(ypred)
#m1pred = model.predict(feat_m1)
#nepc_not_sc_pred = model.predict(feat_nepc_not_sc)
#plt_m0 = ypred[test_y == 0]
#plt_nepc = ypred[test_y == 1]
#plt_m1 = m1pred
#plt_nepc_not_sc = nepc_not_sc_pred
#do_boxplot(plt_m0, plt_nepc, plt_m1, plt_m0p, plt_nepc_not_sc, args.figout)
dump(model, args.save)
np.save('nucleus_classifier_features.npy', train_x.columns.values)
def main(args):
feat = pd.read_csv(args.src, index_col=0, header=0)
labels = pd.read_csv(args.labsrc, index_col=0, header=0, sep='\t')
feat.drop('case_id', axis=1, inplace=True)
use_rows = feat['n_score'].values != 0
feat = feat.iloc[use_rows, :]
labels = labels.iloc[use_rows, :]
print('using tables:', feat.shape, labels.shape)
case_ids = labels['case_id'].values
tile_ids = labels.index.values
stages = labels['stage_str'].values
feat = drop_high_cor(feat, 0.8)
print('Features after high cor drop')
feat = feat.transform(lambda x: (x - np.mean(x)) / np.std(x))
print('Features after zscore')
feat = drop_nan_inf(feat)
print('Features after dropping nan and infs')
((nepc_f, nepc_lab), (m0_f, m0_lab), (m0p_f, m0p_lab),
(m1_f, m1_lab)) = split_sets(feat, labels)
del feat
if args.filter_stats:
remove_cols = filter_stats(nepc_f, m0_f)
nepc_f.drop(remove_cols, inplace=True, axis=1)
m0_f.drop(remove_cols, inplace=True, axis=1)
m0p_f.drop(remove_cols, inplace=True, axis=1)
m1_f.drop(remove_cols, inplace=True, axis=1)
train_x, train_y = make_training(m0_f, nepc_f)
print('train_x', train_x.shape)
print('train_y', train_y.shape)
print('m1_f', m1_f.shape)
model = RandomForestRegressor(oob_score=True,
max_depth=20,
max_features='sqrt',
n_estimators=150,
n_jobs=-1).fit(train_x, train_y)
with open('feature_importance.txt', 'w+') as f:
for v, coef in zip(train_x.columns, model.feature_importances_):
f.write('{}\t{}\n'.format(v, coef))
if args.aggr_fn == 'max':
aggr_fn = np.max
elif args.aggr_fn == 'mean':
aggr_fn = np.mean
""" Predict the M1 cases and gather by max and mean """
yhat_m1 = model.predict(m1_f)
case_aggr = []
m1_case_numbers = []
m1_case_vect = m1_lab['case_id'].values
for uc in np.unique(m1_case_vect):
yx = yhat_m1[m1_case_vect == uc]
case_aggr.append(aggr_fn(yx))
case_num = int(uc.split('-')[1])
m1_case_numbers.append(case_num)
m1_case_aggr = np.array(case_aggr)
m1_case_numbers = np.array(m1_case_numbers)
""" Predict M0P cases """
yhat_m0p = model.predict(m0p_f)
case_aggr = []
m0p_case_numbers = []
m0p_case_vect = m0p_lab['case_id'].values
for uc in np.unique(m0p_case_vect):
yx = yhat_m0p[m0p_case_vect == uc]
case_aggr.append(aggr_fn(yx))
case_num = int(uc.split('-')[1])
m0p_case_numbers.append(case_num)
m0p_case_aggr = np.array(case_aggr)
m0p_case_numbers = np.array(m0p_case_numbers)
""" Check on the training data """
m0_cases = m0_lab['case_id'].values
nepc_cases = nepc_lab['case_id'].values
train_case_vect = np.concatenate([m0_cases, nepc_cases])
# yhat_train = model.predict(train_x)
yhat_train = model.oob_prediction_
train_aggr, train_case_y = [], []
for uc in np.unique(train_case_vect):
idx = train_case_vect == uc
train_aggr.append(aggr_fn(yhat_train[idx]))
train_case_y.append(train_y[idx][0])
train_aggr = np.array(train_aggr)
train_case_y = np.array(train_case_y)
""" write out scores """
with open('nepc_case_scores.txt', 'w+') as f:
for mop, mop_score in zip(np.unique(m0p_case_vect), m0p_case_aggr):
s = '{}\t{}\n'.format(mop, mop_score)
f.write(s)
for mop, mop_score in zip(np.unique(m1_case_vect), m1_case_aggr):
s = '{}\t{}\n'.format(mop, mop_score)
f.write(s)
for mop, mop_score in zip(np.unique(train_case_vect), train_aggr):
s = '{}\t{}\n'.format(mop, mop_score)
f.write(s)
""" Do some statistical tests """
dotest = mannwhitneyu
# test_args = {'equal_var': True}
test_args = {}
test_m0_m1 = dotest(yhat_train[train_y == 0], yhat_m1, **test_args)
test_m0_m0p = dotest(yhat_train[train_y == 0], yhat_m0p, **test_args)
test_m0_nepc = dotest(yhat_train[train_y == 0], yhat_train[train_y == 1],
**test_args)
test_nepc_m1 = dotest(yhat_train[train_y == 1], yhat_m1, **test_args)
print('Tiles M0 vs M1', test_m0_m1)
print('Tiles M0 vs M0P', test_m0_m0p)
print('Tiles M0 vs NPEC', test_m0_nepc)
print('Tiles NEPC vs M1', test_nepc_m1)
test_m0_m1 = dotest(train_aggr[train_case_y == 0], m1_case_aggr,
**test_args)
test_m0_m0p = dotest(train_aggr[train_case_y == 0], m0p_case_aggr,
**test_args)
test_m0_nepc = dotest(train_aggr[train_case_y == 0],
train_aggr[train_case_y == 1], **test_args)
test_nepc_m1 = dotest(train_aggr[train_case_y == 1], m1_case_aggr,
**test_args)
print('aggr M0 vs M1', test_m0_m1)
print('aggr M0 vs M0P', test_m0_m0p)
print('aggr M0 vs NPEC', test_m0_nepc)
print('aggr NEPC vs M1', test_nepc_m1)
""" ROC - AUC """
print(
'------------------------------------------------------------------------------------'
)
m0nepc_ypred = np.concatenate(
[train_aggr[train_case_y == 0], train_aggr[train_case_y == 1]])
m0nepc_ytrue = np.array([0] * np.sum(train_case_y == 0) +
[1] * np.sum(train_case_y == 1))
m0m1_ypred = np.concatenate([train_aggr[train_case_y == 0], m1_case_aggr])
m0m1_ytrue = np.array([0] * np.sum(train_case_y == 0) +
[1] * len(m1_case_aggr))
m0m0p_ypred = np.concatenate(
[train_aggr[train_case_y == 0], m0p_case_aggr])
m0m0p_ytrue = np.array([0] * np.sum(train_case_y == 0) +
[1] * len(m0p_case_aggr))
print('m0nepc_ypred', m0nepc_ypred.shape, m0nepc_ytrue.shape)
print('m0m1_ypred', m0m1_ypred.shape, m0m1_ypred.shape)
print('m0m0p_ypred', m0m0p_ypred.shape, m0m0p_ypred.shape)
auc_ = roc_auc_score(y_true=m0nepc_ytrue, y_score=m0nepc_ypred)
print('M0 - NEPC AUC = ', auc_)
auc_ = roc_auc_score(y_true=m0m1_ytrue, y_score=m0m1_ypred)
print('M0 - M1 AUC = ', auc_)
auc_ = roc_auc_score(y_true=m0m0p_ytrue, y_score=m0m0p_ypred)
print('M0 - M0P AUC = ', auc_)
print(
'------------------------------------------------------------------------------------'
)
gene_scores = pd.read_csv('../data/signature_scores_beltram.csv',
index_col=None,
header=0,
sep=',')
gene_score_caseid = []
drop_rows = []
matching_scores = []
matching_indices = []
for i, (idx, sn) in enumerate(
zip(gene_scores.index.values,
gene_scores['Surgical Number'].values)):
try:
x = int(sn.split(' ')[-1])
if x in m1_case_numbers:
gene_score_caseid.append(x)
matching_indices.append(idx)
matching_scores.append(m1_case_aggr[m1_case_numbers == x][0])
elif x in m0p_case_numbers:
gene_score_caseid.append(x)
matching_indices.append(idx)
matching_scores.append(m0p_case_aggr[m0p_case_numbers == x][0])
else:
drop_rows.append(idx)
except:
drop_rows.append(idx)
gene_scores.drop(drop_rows, inplace=True)
label_cols = ['caseid', 'Disease Stage', 'sample name', 'Surgical Number']
gene_scores.drop(label_cols, inplace=True, axis=1)
gene_scores['NEPC Score'] = pd.Series(matching_scores,
index=matching_indices)
plt.figure(figsize=(5, 5), dpi=300)
sns.pairplot(gene_scores, kind='reg')
plt.savefig('gene_scores_nepc_score_{}_tile.png'.format(args.aggr_fn),
bbox_inches='tight')
test_cols = [x for x in gene_scores.columns if x != 'NEPC Score']
scores = gene_scores['NEPC Score'].values
print(
'------------------------------------------------------------------------------------'
)
for c in test_cols:
ctest = spearmanr(scores, gene_scores[c].values)
print('spearman {:40}: {:3.5f} p={:3.5f}'.format(
c, ctest.correlation, ctest.pvalue))
ctest = pearsonr(scores, gene_scores[c].values)
print('pearson {:40}: {:3.5f} p={:3.5f}'.format(
c, ctest[0], ctest[1]))
print(
'------------------------------------------------------------------------------------'
)
if args.boxplot:
f, (ax_box,
ax_hist) = plt.subplots(2,
sharex=True,
gridspec_kw={"height_ratios": (.35, .65)})
plt_m0 = train_aggr[train_case_y == 0]
plt_nepc = train_aggr[train_case_y == 1]
plt_m1 = m1_case_aggr
plt_m0p = m0p_case_aggr
sns.distplot(
plt_m0,
bins=25,
norm_hist=True,
kde=True,
label='M0',
ax=ax_hist,
)
sns.distplot(
plt_nepc,
bins=25,
norm_hist=True,
kde=True,
label='NEPC',
ax=ax_hist,
)
sns.distplot(
plt_m1,
kde=True,
norm_hist=True,
bins=25,
label='M1',
ax=ax_hist,
)
sns.distplot(
plt_m0p,
kde=True,
norm_hist=True,
bins=25,
label='M0-P',
ax=ax_hist,
)
ax_hist.set_xlabel('Score')
ax_hist.set_ylabel('Frequency')
concat_scores = np.concatenate([plt_m0, plt_nepc, plt_m1, plt_m0p])
concat_labels = np.array(['M0'] * len(plt_m0) +
['NEPC'] * len(plt_nepc) +
['M1'] * len(plt_m1) + ['M0P'] * len(plt_m0p))
plt_df = pd.DataFrame({'Set': concat_labels, 'Score': concat_scores})
sns.boxplot(y='Set', x='Score', data=plt_df, ax=ax_box)
sns.stripplot(y='Set',
x='Score',
data=plt_df,
size=2.5,
jitter=True,
linewidth=0.5,
ax=ax_box)
plt.savefig('NEPC_score_{}_tile.png'.format(args.aggr_fn),
bbox_inches='tight')
def main(args):
feat, case_ids = load_features(args.src, zscore=True)
lab = load_labels(args.labsrc)
feat = drop_high_cor(feat, cor_thresh=0.8)
print('Features after high cor drop')
print(feat.head())
is_nepc = np.array([x in nepc_strs for x in lab['stage_str']])
is_adeno = np.array([x in adeno_strs for x in lab['stage_str']])
is_m0 = np.array([x in m0_strs for x in lab['stage_str']])
is_m0p = np.array([x in m0p_strs for x in lab['stage_str']])
is_m1 = np.array([x in m1_strs for x in lab['stage_str']])
nepc_case_feat = feat.loc[is_nepc, :]
nepc_lab = lab.loc[is_nepc, :]
adeno_case_feat = feat.loc[is_adeno, :]
adeno_lab = lab.loc[is_adeno, :]
m0_case_feat = feat.loc[is_m0, :]
m0_lab = lab.loc[is_m0, :]
m0p_case_feat = feat.loc[is_m0p, :]
m0p_lab = lab.loc[is_m0p, :]
m1_case_feat = feat.loc[is_m1, :]
m1_lab = lab.loc[is_m1, :]
if args.reduce_case:
nepc_case_feat = nepc_case_feat.groupby(nepc_lab['case_id']).mean()
adeno_case_feat = adeno_case_feat.groupby(adeno_lab['case_id']).mean()
m0_case_feat = m0_case_feat.groupby(m0_lab['case_id']).mean()
m0p_case_feat = m0p_case_feat.groupby(m0p_lab['case_id']).mean()
m1_case_feat = m1_case_feat.groupby(m1_lab['case_id']).mean()
print('NEPC features:', nepc_case_feat.shape)
print('Adeno features:', adeno_case_feat.shape)
print('M0 features:', m0_case_feat.shape)
print('M0p features:', m0p_case_feat.shape)
print('M1 features:', m1_case_feat.shape)
nepc_adeno_p = []
m0_m1_p = []
m0_m0p_p = []
for c in nepc_case_feat.columns:
nepc_ = nepc_case_feat[c].values
adeno_ = adeno_case_feat[c].values
m0_ = m0_case_feat[c].values
m0p_ = m0p_case_feat[c].values
m1_ = m1_case_feat[c].values
tt_nepc_adeno = ttest_ind(nepc_, adeno_)
tt_m0_m1 = ttest_ind(m0_, m1_)
tt_m0_m0p = ttest_ind(m0_, m0p_)
nepc_adeno_p.append(tt_nepc_adeno[1])
m0_m1_p.append(tt_m0_m1[1])
m0_m0p_p.append(tt_m0_m0p[1])
nepc_adeno_reject, nepc_adeno_q, _, _ = multipletests(nepc_adeno_p,
alpha=0.01,
method='fdr_bh')
m0_m1_reject, m0_m1_q, _, _ = multipletests(m0_m1_p,
alpha=0.01,
method='fdr_bh')
m0_m0p_reject, m0_m0p_q, _, _ = multipletests(m0_m0p_p,
alpha=0.01,
method='fdr_bh')
print('Rejecting {} '.format(np.sum(nepc_adeno_reject)))
print('Rejecting {} '.format(np.sum(m0_m1_reject)))
print('Rejecting {} '.format(np.sum(m0_m0p_reject)))
np.save('nepc_adeno_reject.npy', np.array(nepc_adeno_reject))
np.save('m0_m1_reject.npy', np.array(m0_m1_reject))
np.save('m0_m0p_reject.npy', np.array(m0_m0p_reject))
for i, c in enumerate(nepc_case_feat.columns):
print('plotting feature ', c)
if not nepc_adeno_reject[i]:
tt = True
elif not m0_m1_reject[i]:
tt = True
elif not m0_m0p_reject[i]:
tt = True
else:
tt = False
if tt:
nepc_ = nepc_case_feat[c].values
adeno_ = adeno_case_feat[c].values
m0_ = m0_case_feat[c].values
m0p_ = m0p_case_feat[c].values
m1_ = m1_case_feat[c].values
plt.clf()
sns.distplot(nepc_, label='NEPC')
sns.distplot(adeno_, label='Adeno')
sns.distplot(m0_, label='M0')
sns.distplot(m0p_, label='M0-P')
sns.distplot(m1_, label='M1')
plt.legend(frameon=True)
plt.title('{}\nnepc q={:.3E}\nm1 q={:.3E}\nm0p q={:.3E}'.format(
c, nepc_adeno_q[i], m0_m1_q[i], m0_m0p_q[i]))
saveto = os.path.join(args.dst, '{}.png'.format(c))
plt.savefig(saveto, bbox_inches='tight')
else:
print('skipping feature ', c)
