def analysis_go(self, s_go, S_hit, N_total=0, SRC_GENE=None, min_overlap=3):
c={'GO':s_go, '#TotalGeneInLibrary':N_total, '#GeneInGO':0, '#GeneInHitList':0, '#GeneInGOAndHitList':0, 'LogP':0.0, 'Enrichment':0}
#if SRC_GENE is not None:
# print "SRC_GENE: "+str(len(SRC_GENE))
S_gene=self.GO_GENE[s_go]
if not N_total:
N_total=len(self.ALL_GENE)
if SRC_GENE is not None:
S_gene=S_gene.intersection(SRC_GENE)
S_hit=set(S_hit).intersection(SRC_GENE)
else:
S_hit=set(S_hit)
c['#GeneInGO']=len(S_gene)
c['#GeneInHitList']=len(S_hit)
if c['#GeneInGO']<min_overlap or c['#GeneInHitList']<min_overlap:
return None
S_both=S_gene.intersection(S_hit)
c['#GeneInGOAndHitList']=len(S_both)
if c['#GeneInGOAndHitList']<min_overlap:
return None
c['%InGO']=c['#GeneInGOAndHitList']*100.0/c['#GeneInHitList']
q=min(max(c['%InGO']/100, 1.0/c['#GeneInHitList']), 1-1.0/c['#GeneInHitList'])
c['STDV %InGO']=np.sqrt(q*(1-q)/c['#GeneInHitList'])*100
c['Enrichment']=c['%InGO']/100.0*N_total/c['#GeneInGO']
S=[int(x) for x in S_both]
S.sort()
c['GeneID']='|'.join([str(x) for x in S])
if c['#GeneInGOAndHitList']<min_overlap: return c
c['LogP'] = hypergeom.logsf(c['#GeneInGOAndHitList']-1, N_total, c['#GeneInGO'], c['#GeneInHitList'])
c['LogP'] = c['LogP'] / TO_LOG10
return c
def get_single_enrichment(self, gene_list, term, gene_universe=15000, gene_list_size=None):
term_df = self.gene_term_df[self.gene_term_df[self.go_column_label] == term]
n = len(term_df)
x = sum(term_df[self.gene_column_label].isin(gene_list)) - 1 # matched genes
N = gene_list_size if gene_list_size else len(gene_list)
return hypergeom.logsf(x, gene_universe, n, N)
def _prob_hypergeo_fast(y_compute, name, X, M, n, N):
"""Compute hypergeometric Pvalue.
Description
-----------
Suppose you have a lot of 100 floppy disks (M), and you know that 20 of them are defective (n).
What is the prbability of drawing zero to 2 floppy disks (N=2), if you select 10 at random (N).
P=hypergeom.sf(2,100,20,10)
"""
P = np.nan
logP = np.nan
# M = len(yc) # Population size: Total number of samples, eg total number of genes; 10000
# n = np.sum(datac) # Number of successes in population, known in pathway, eg 2000
# N = np.sum(yc) # sample size: Random variate, eg clustersize or groupsize, over expressed genes, eg 300
# X = np.sum(np.logical_and(yc, datac.values)) - 1 # Let op, de -1 is belangrijk omdatje P<X wilt weten ipv P<=X. Als je P<=X doet dan kan je vele false positives krijgen als bijvoorbeeld X=1 en n=1 oid
# Do the hypergeo-test
if y_compute and (X > 0):
P = hypergeom.sf(X, M, n, N)
logP = hypergeom.logsf(X, M, n, N)
# Store
out = {}
out['category_label'] = name
out['P'] = P
out['logP'] = logP
out['overlap_X'] = X
out['popsize_M'] = M
out['nr_succes_pop_n'] = n
out['samplesize_N'] = N
out['dtype'] = 'categorical'
return (out)
def calculate_enrichment(gene_data, go_to_genes, n=100):
# get top n gene list
top_sorted_genes = sorted(gene_data, key=lambda tup: tup[1], reverse=True)
top_genes=top_sorted_genes[:n]
top_gene_names= list(zip(*top_genes)[0])
# get bottom n gene list
bot_sorted_genes = sorted(gene_data, key=lambda tup: tup[1])
bot_genes=bot_sorted_genes[:n]
bot_gene_names= list(zip(*bot_genes)[0])
tot_genes = len(gene_data)
# [+ top + goid (k), total genes (M), top genes (n), goid genes (N), score ]
# create score dictionary
top_score_list = dict((k,[0, tot_genes, n, len(go_to_genes[k]), 0]) for k in go_to_genes.keys())
bot_score_list = dict((k,[0, tot_genes, n, len(go_to_genes[k]), 0]) for k in go_to_genes.keys())
# calculate top hits
for g in top_gene_names:
for goid in top_score_list:
if g in go_to_genes[goid]:
top_score_list[goid][0] += 1
# calculate bottom hits
for g in bot_gene_names:
for goid in bot_score_list:
if g in go_to_genes[goid]:
bot_score_list[goid][0] += 1
positive_enrichment_scores = []
negative_enrichment_scores = []
# calculate scores
for goid in top_score_list:
top_score_list[goid][4] = hypergeom.logsf(top_score_list[goid][0]-1, top_score_list[goid][1], top_score_list[goid][2], top_score_list[goid][3])
positive_enrichment_scores.append((goid, top_score_list[goid][4]))
bot_score_list[goid][4] = hypergeom.logsf(bot_score_list[goid][0]-1, bot_score_list[goid][1], bot_score_list[goid][2], bot_score_list[goid][3])
negative_enrichment_scores.append((goid, bot_score_list[goid][4]))
#
positive_enrichment_scores_sorted = sorted(positive_enrichment_scores, key=lambda tup: tup[1])
negative_enrichment_scores_sorted = sorted(negative_enrichment_scores, key=lambda tup: tup[1])
return positive_enrichment_scores_sorted,negative_enrichment_scores_sorted
def calc_enrich(self, path_file, all_sigs=True):
#print('calc_enrich')
hw_temp = self.hw_genes_all
if (not all_sigs):
hw_temp = self.hw_genes_all
kegg = pandas.read_csv(path_file, header=None, sep='\t')
temp_kegg_en = [1] * hw_temp.shape[1]
for i in range(hw_temp.shape[1]):
#print(i)
path_en = []
sig_genes = self.compendium.index[np.where(hw_temp[:, i])]
for j in range(kegg.shape[0]):
path_genes = kegg[2][j].split(';')
x = len(list(set(sig_genes) & set(path_genes))) - 1
n = self.weights.shape[0]
k = len(sig_genes)
m = len(path_genes)
p = hypergeom.logsf(x, n, k, m)
path_en.append(-p)
#path_en_c = multipletests(path_en)[1]
temp_kegg_en[i] = path_en
kegg_df = pandas.DataFrame(temp_kegg_en, columns=kegg[0])
return (kegg_df.replace([np.inf, -np.inf, 'nan'], 0))
def main(datamatrix_path, test_index, response_variable_name, valid_index,
valid_fraction, feature_fraction, regularization_type,
inverse_regularization_strength, intercept_scaling,
pos_neg_weight_ratio, evaluation_statistic, save_weights, save_folder,
datamatrix):
print('loading datamatrix...', flush=False)
if datamatrix == None or type(datamatrix) == str:
dm = datasetIO.load_datamatrix(datamatrix_path)
else:
dm = datamatrix
print('setting random seed with test_index {0!s}...'.format(test_index),
flush=False)
np.random.seed(test_index)
print('getting bootstrap sample...', flush=False)
all_indices = np.arange(dm.shape[0])
boot_indices = np.random.choice(dm.shape[0], dm.shape[0], replace=True)
test_indices = all_indices[~np.in1d(all_indices, boot_indices)]
print('reserving out-of-bag samples as test set...', flush=False)
Y = {
'test': dm.rowmeta[response_variable_name][test_indices].astype('bool')
}
X = {'test': dm.matrix[test_indices, :]}
print('setting random seed with valid_index {0!s}...'.format(valid_index),
flush=False)
np.random.seed(valid_index)
print('splitting bootstrap sample into training and validation sets...',
flush=False)
if type(valid_fraction) == str and (valid_fraction.lower() == 'loo'
or valid_fraction.lower() == 'loocv'):
valid_fraction = 'loo'
valid_indices = all_indices
train_indices = all_indices
else:
valid_indices = np.random.choice(dm.shape[0],
round(valid_fraction * dm.shape[0]),
replace=False)
train_indices = all_indices[~np.in1d(all_indices, valid_indices)]
Y['train'] = dm.rowmeta[response_variable_name][boot_indices][
train_indices].astype('bool')
Y['valid'] = dm.rowmeta[response_variable_name][boot_indices][
valid_indices].astype('bool')
X['train'] = dm.matrix[boot_indices, :][train_indices, :]
X['valid'] = dm.matrix[boot_indices, :][valid_indices, :]
print('fitting and evaluating models...', flush=False)
stages = ['validation', 'testing']
data_subsets = ['fit', 'predict']
performance_stats = [
'auroc', 'auprc', 'brier', 'nll', 'tp', 'fn', 'tn', 'fp', 'ap', 'an',
'pp', 'pn', 'n', 'tpr', 'fnr', 'tnr', 'fpr', 'ppv', 'fdr', 'npv',
'fomr', 'acc', 'mcr', 'prev', 'plr', 'nlr', 'dor', 'drr', 'darr',
'mrr', 'marr', 'mcc', 'fnlp', 'f1', 'f1_100', 'f1_50', 'f1_25',
'f1_10', 'f1_5', 'f1_3', 'f1_2', 'f2', 'f3', 'f5', 'f10', 'f25', 'f50',
'f100'
]
if valid_fraction == 'loo':
X.update({
'validation': {
'fit': X['train'],
'predict': X['valid']
},
'testing': {
'fit': X['train'],
'predict': X['test']
}
})
Y.update({
'validation': {
'fit': Y['train'],
'predict': Y['valid']
},
'testing': {
'fit': Y['train'],
'predict': Y['test']
}
})
else:
X.update({
'validation': {
'fit': X['train'],
'predict': X['valid']
},
'testing': {
'fit': np.append(X['train'], X['valid'], 0),
'predict': X['test']
}
})
Y.update({
'validation': {
'fit': Y['train'],
'predict': Y['valid']
},
'testing': {
'fit': np.append(Y['train'], Y['valid']),
'predict': Y['test']
}
})
stat_subset = {}
for stage in stages:
print('working on {0} stage...'.format(stage), flush=False)
if feature_fraction < 1:
print('performing univariate feature selection...', flush=False)
num_features = round(feature_fraction * dm.shape[1])
test_stats, p_values = ttest_ind(
X[stage]['fit'][Y[stage]['fit'], :],
X[stage]['fit'][~Y[stage]['fit'], :],
axis=0,
equal_var=False,
nan_policy='propagate')
ranks = np.argsort(p_values)
selected_indices = ranks[:num_features]
selected_features = dm.columnlabels[selected_indices]
if stage == 'testing':
print('plotting univariate test statistics...', flush=False)
plt.figure()
plt.hist(test_stats, 50)
plt.savefig(
'{0}/univariate_test_statistics.png'.format(save_folder),
transparent=True,
pad_inches=0,
dpi=100)
plt.figure()
plt.hist(p_values, 50)
plt.savefig('{0}/univariate_pvalues.png'.format(save_folder),
transparent=True,
pad_inches=0,
dpi=100)
plt.figure()
plt.hist(-np.log10(p_values), 50)
plt.savefig('{0}/univariate_nlps.png'.format(save_folder),
transparent=True,
pad_inches=0,
dpi=100)
else:
print('skipping univariate feature selection...', flush=False)
selected_indices = np.arange(dm.shape[1], dtype='int64')
selected_features = dm.columnlabels.copy()
print('selected {0!s} features...'.format(selected_features.size),
flush=False)
print('calculating class weights...', flush=False)
pos_weight = np.sqrt(pos_neg_weight_ratio) * (
(Y[stage]['fit'].size) / 2 / (Y[stage]['fit'].sum())
) # (assign weight to class)*(adjust for unbalanced classes)
neg_weight = (1 / pos_weight) * (
(Y[stage]['fit'].size) / 2 / ((~Y[stage]['fit']).sum())
) # (assign weight to class)*(adjust for unbalanced classes)
class_weight = {True: pos_weight, False: neg_weight}
print('fitting model...', flush=False)
logistic_regression_model = LogisticRegression(
penalty=regularization_type,
C=inverse_regularization_strength,
intercept_scaling=intercept_scaling,
class_weight=class_weight).fit(
X[stage]['fit'][:, selected_indices], Y[stage]['fit'])
if stage == 'testing':
print('plotting feature weights...', flush=False)
iter_feature = DataMatrix(
rowname='iteration',
rowlabels=np.array(
['test{0!s}_valid{1!s}'.format(test_index, valid_index)],
dtype='object'),
rowmeta={
'intercept': logistic_regression_model.intercept_,
'test_index': np.array([test_index], dtype='int64'),
'valid_index': np.array([valid_index], dtype='int64')
},
columnname=dm.columnname,
columnlabels=dm.columnlabels.copy(),
columnmeta=copy.deepcopy(dm.columnmeta),
matrixname='feature_weights',
matrix=np.zeros((1, dm.shape[1]), dtype='float64'))
feature_idx = {f: i for i, f in enumerate(dm.columnlabels)}
for feature, weight in zip(selected_features,
logistic_regression_model.coef_[0, :]):
iter_feature.matrix[0, feature_idx[feature]] = weight
plt.figure()
plt.hist(iter_feature.matrix[0, :], 50)
plt.savefig('{0}/feature_weights.png'.format(save_folder),
transparent=True,
pad_inches=0,
dpi=100)
if feature_fraction < 1:
plt.figure()
plt.hist(iter_feature.matrix[0, selected_indices], 50)
plt.savefig(
'{0}/feature_weights_selected.png'.format(save_folder),
transparent=True,
pad_inches=0,
dpi=100)
if save_weights:
print('saving feature weights...', flush=False)
datasetIO.save_datamatrix(
'{0}/iter_feature_datamatrix.txt.gz'.format(save_folder),
iter_feature)
print('creating datamatrix for performance statistics...', flush=False)
stat_subset[stage] = DataMatrix(
rowname='performance_statistic',
rowlabels=np.array(performance_stats, dtype='object'),
rowmeta={},
columnname='data_subset',
columnlabels=np.array(data_subsets, dtype='object'),
columnmeta={},
matrixname='classifier_performance_on_data_subsets',
matrix=np.zeros((len(performance_stats), len(data_subsets)),
dtype='float64'))
for j, subset in enumerate(stat_subset[stage].columnlabels):
print('evaluating performance on {0} subset...'.format(subset),
flush=False)
if valid_fraction == 'loo' and stage == 'validation' and subset == 'predict':
P_pred = np.zeros(X[stage][subset].shape[0], dtype='float64')
for train_index, test_index in LeaveOneOut().split(
X[stage][subset]):
logistic_regression_model = LogisticRegression(
penalty=regularization_type,
C=inverse_regularization_strength,
intercept_scaling=intercept_scaling,
class_weight=class_weight).fit(
X[stage]['fit'][train_index, :][:,
selected_indices],
Y[stage]['fit'][train_index])
P_pred[
test_index] = logistic_regression_model.predict_proba(
X[stage][subset][test_index, :][:,
selected_indices]
)[:, logistic_regression_model.classes_ == 1][0][0]
else:
P_pred = logistic_regression_model.predict_proba(
X[stage][subset][:, selected_indices]
)[:, logistic_regression_model.classes_ == 1]
Y_pred = P_pred > 0.5
auroc = roc_auc_score(Y[stage][subset], P_pred)
auprc = average_precision_score(Y[stage][subset], P_pred)
brier = brier_score_loss(Y[stage][subset], P_pred)
nll = log_loss(Y[stage][subset], P_pred)
tn, fp, fn, tp = confusion_matrix(Y[stage][subset], Y_pred).ravel()
# incorporate a prior with effective sample size = n_eff, where prior represents random predictions
n_eff = 1
prevalence = (tp + fn) / (tn + fp + fn + tp)
tp += n_eff * prevalence / 2
fn += n_eff * prevalence / 2
tn += n_eff * (1 - prevalence) / 2
fp += n_eff * (1 - prevalence) / 2
ap = tp + fn
an = fp + tn
pp = tp + fp
pn = tn + fn
n = tn + fp + fn + tp
tpr = tp / ap # sensitivity, recall
fnr = fn / ap # 1-tpr, 1-sensitivity, 1-recall
tnr = tn / an # specificity
fpr = fp / an # 1-tnr, 1-specificity
ppv = tp / pp # precision
fdr = fp / pp # 1-ppv, 1-precision
npv = tn / pn
fomr = fn / pn # 1-npv
acc = (tp + tn) / n
mcr = (fp + fn) / n # 1-acc
prev = ap / n
plr = (tp / fp) / (
ap / an
) # tpr/fpr, sensitivity/(1-specificity), ratio of positives to negatives in positive predictions relative to ratio in whole sample, higher is better
nlr = (fn / tn) / (
ap / an
) # fnr/tnr, (1-sensitivity)/specificity, ratio of positives to negatives in negative predictions relative to ratio in whole sample, lower is better
dor = (tp / fp) / (
fn / tn
) # plr/nlr, ratio of positives to negatives in positive predictions, divided by ratio of positives to negatives in negative predictions
drr = (tp / pp) / (
fn / pn
) # ppv/fomr, relative risk or risk ratio, fraction of positives in positive predictions divided by fraction of positives in negative predictions
darr = (tp / pp) - (
fn / pn
) # ppv - fomr, absolute risk reduction, fraction of positives in positive predictions minus fraction of positives in negative predictions
mrr = (tp / pp) / (
ap / n
) # ppv/prev, modified (by me) relative risk or risk ratio, fraction of positives in positive predictions divided by fraction of positives in whole sample
marr = (tp / pp) - (
ap / n
) # ppv - prev, modified (by me) absolute risk reduction, fraction of positives in positive predictions minus fraction of positives in whole sample
mcc = (tp * tn - fp * fn) / np.sqrt(
(tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
fnlp = -hypergeom.logsf(tp, n, ap, pp, loc=1) / np.log(10)
precision = ppv
recall = tpr
f1 = (1 +
(1**2)) * precision * recall / ((1**2) * precision + recall)
f1_100 = (1 + (1 / 100**2)) * precision * recall / (
(1 / 100**2) * precision + recall)
f1_50 = (1 + (1 / 50**2)) * precision * recall / (
(1 / 50**2) * precision + recall)
f1_25 = (1 + (1 / 25**2)) * precision * recall / (
(1 / 25**2) * precision + recall)
f1_10 = (1 + (1 / 10**2)) * precision * recall / (
(1 / 10**2) * precision + recall)
f1_5 = (1 + (1 / 5**2)) * precision * recall / (
(1 / 5**2) * precision + recall)
f1_3 = (1 + (1 / 3**2)) * precision * recall / (
(1 / 3**2) * precision + recall)
f1_2 = (1 + (1 / 2**2)) * precision * recall / (
(1 / 2**2) * precision + recall)
f2 = (1 +
(2**2)) * precision * recall / ((2**2) * precision + recall)
f3 = (1 +
(3**2)) * precision * recall / ((3**2) * precision + recall)
f5 = (1 +
(5**2)) * precision * recall / ((5**2) * precision + recall)
f10 = (1 + (10**2)) * precision * recall / (
(10**2) * precision + recall)
f25 = (1 + (25**2)) * precision * recall / (
(25**2) * precision + recall)
f50 = (1 + (50**2)) * precision * recall / (
(50**2) * precision + recall)
f100 = (1 + (100**2)) * precision * recall / (
(100**2) * precision + recall)
stat_subset[stage].matrix[:, j] = [
auroc, auprc, brier, nll, tp, fn, tn, fp, ap, an, pp, pn, n,
tpr, fnr, tnr, fpr, ppv, fdr, npv, fomr, acc, mcr, prev, plr,
nlr, dor, drr, darr, mrr, marr, mcc, fnlp, f1, f1_100, f1_50,
f1_25, f1_10, f1_5, f1_3, f1_2, f2, f3, f5, f10, f25, f50, f100
]
print('saving performance statistics...', flush=False)
datasetIO.save_datamatrix(
'{0}/stat_subset_datamatrix_{1}.txt.gz'.format(save_folder, stage),
stat_subset[stage])
print('printing performance statistics...', flush=False)
print('\t'.join(['stage', stat_subset[stage].rowname] +
stat_subset[stage].columnlabels.tolist()),
flush=False)
for stat, vals in zip(stat_subset[stage].rowlabels,
stat_subset[stage].matrix):
print('\t'.join([stage, stat] +
['{0:1.3g}'.format(v) for v in vals]),
flush=False)
print('saving evaluation statistic...', flush=False)
objective = stat_subset['validation'].select(evaluation_statistic,
'predict')
with open('{0}/output.json'.format(save_folder),
mode='wt',
encoding='utf-8',
errors='surrogateescape') as fw:
json.dump(objective, fw, indent=2)
print('done logistic_regression.py', flush=False)
def calc_f_table(x, M, n, N):
return hypergeom.logsf(x, M, n, N) / math.log(10)
def main(dictionaries, year, datestamp, min_score, universe, n_prior,
min_count):
print('begin calc_term-term_stats_from_termite.py')
print('dictionaries: {0}, {1}'.format(dictionaries[0], dictionaries[1]))
print('year: {0}'.format(year))
print('datestamp: {0}'.format(datestamp))
print('min_score: {0!s}'.format(min_score))
print('universe: {0}'.format(universe))
print('n_prior: {0!s}'.format(n_prior))
print('min_count: {0!s}'.format(min_count))
# load counts datamatrix
# this file is generated by count_term-term_pmids_from_termite.py
print('loading counts datamatrix...')
row_dictionary = dictionaries[
0] # 'HUCELL', 'ANAT', 'INDICATION', 'HUCELLANAT', 'HUCELLANATINDICATION'
column_dictionary = dictionaries[
1] # 'HUCELL', 'ANAT', 'INDICATION', 'HUCELLANAT', 'HUCELLANATINDICATION'
counts_datamatrix_path = '{0}_{1}_datamatrix_pmidcounts_year_{2}_datestamp_{3}_minscore_{4!s}.pickle'.format(
row_dictionary, column_dictionary, year, datestamp, min_score)
term_term = datasetIO.load_datamatrix(counts_datamatrix_path)
print('counts_datamatrix_path: {0}'.format(counts_datamatrix_path))
print(term_term)
# find term-term pairs with sufficient counts
print('finding term-term pairs with sufficient counts...')
I, J = (term_term.matrix >= min_count).nonzero()
num_sufficient = I.size
print('term-term pairs with at least {0!s} counts: {1!s}'.format(
min_count, num_sufficient))
# convert counts to float
print('converting counts to float...')
term_term.matrix = np.float64(term_term.matrix)
term_term.updatedtypeattribute()
for field, values in term_term.rowmeta.items():
if values.dtype == np.int64:
term_term.rowmeta[field] = np.float64(values)
for field, values in term_term.columnmeta.items():
if values.dtype == np.int64:
term_term.columnmeta[field] = np.float64(values)
# set universe size
print('setting universe size...')
if universe == 'intersectionunion' or universe == 'union':
universe_size = term_term.rowmeta['all_count_{0}'.format(universe)][0]
elif universe == 'medline':
universe_size = 1e8 # 3e7
term_term.rowmeta['term_count_medline'] = term_term.rowmeta[
'term_count_union'].copy()
term_term.columnmeta['term_count_medline'] = term_term.columnmeta[
'term_count_union'].copy()
elif universe == 'infinity':
universe_size = 1e16
term_term.rowmeta['term_count_infinity'] = term_term.rowmeta[
'term_count_union'].copy()
term_term.columnmeta['term_count_infinity'] = term_term.columnmeta[
'term_count_union'].copy()
else:
raise ValueError('invalid universe')
# create matrices for select association statistics
print('creating matrices for select association statistics...')
selstats = ['mcc', 'mmcc', 'cos', 'mi', 'nmi', 'iqr']
statmats = {}
for selstat in selstats:
statmats[selstat] = np.zeros(term_term.shape, dtype='float64')
# calculate association statistics and write to dataframe
print('calculating association statistics and writing to dataframe...')
dataframe_path = '{0}_{1}_dataframe_yr_{2}_ds_{3}_ms_{4!s}_uv_{5}_np_{6!s}_mc_{7!s}.txt.gz'.format(
row_dictionary, column_dictionary, year, datestamp, min_score,
universe, n_prior, min_count)
rowmetalabels = ['term_id', 'term_name']
rowmetaheaders = [
'{0}_id'.format(row_dictionary), '{0}_name'.format(row_dictionary)
]
columnmetalabels = ['term_id', 'term_name']
columnmetaheaders = [
'{0}_id'.format(column_dictionary),
'{0}_name'.format(column_dictionary)
]
statheaders = [
'tp', 'fn', 'tn', 'fp', 'ap', 'an', 'pp', 'pn', 'n', 'tpr', 'fnr',
'tnr', 'fpr', 'ppv', 'fdr', 'npv', 'fomr', 'acc', 'mcr', 'prev', 'plr',
'nlr', 'dor', 'drr', 'darr', 'mrr', 'marr', 'f1', 'mcc', 'mmcc', 'cos',
'fnlp', 'sig', 'lrr', 'lrr_se', 'lrr_lb95', 'lrr_ub95', 'drr_lb95',
'drr_ub95', 'lor', 'lor_se', 'lor_lb95', 'lor_ub95', 'dor_lb95',
'dor_ub95', 'mi', 'nmi', 'iqr'
]
with gzip.open(dataframe_path,
mode='wt',
encoding='utf-8',
errors='surrogateescape') as fw:
writelist = ['{0}_dictidname'.format(row_dictionary)
] + rowmetaheaders + [
'{0}_dictidname'.format(column_dictionary)
] + columnmetaheaders + statheaders
fw.write('\t'.join(writelist) + '\n')
for k, (i, j) in enumerate(zip(I, J)):
if np.mod(k, 1000) == 0 or k + 1 == num_sufficient:
print('working on term-term pair {0!s} of {1!s}...'.format(
k + 1, num_sufficient))
# confusion matrix
tp = term_term.matrix[i, j]
fp = term_term.rowmeta['term_count_{0}'.format(universe)][i] - tp
fn = term_term.columnmeta['term_count_{0}'.format(
universe)][j] - tp
tn = universe_size - (tp + fp + fn)
# incorporate a random prior with effective sample size = n_prior,
# where prior distribution conforms to empirical marginal distributions
Rr = (tp + fp) / (fn + tn) # ratio of rows of confusion matrix
Rc = (tp + fn) / (fp + tn) # ratio of columns of confusion matrix
tp_prior = n_prior * Rc * Rr / (
Rc * Rr + Rr + Rc + 1
) # solve for tp given constraints tp/fn=Rr, fp/tn=Rr, tp/fp=Rc, fn/tn=Rc, tp+fp+fn+tn=n_eff
fp_prior = tp_prior / Rc
fn_prior = tp_prior / Rr
tn_prior = tp_prior / Rc / Rr
tp += tp_prior
fp += fp_prior
fn += fn_prior
tn += tn_prior
ap = tp + fn
an = fp + tn
pp = tp + fp
pn = tn + fn
n = tn + fp + fn + tp
tpr = tp / ap # sensitivity, recall
fnr = fn / ap # 1-tpr, 1-sensitivity, 1-recall
tnr = tn / an # specificity
fpr = fp / an # 1-tnr, 1-specificity
ppv = tp / pp # precision
fdr = fp / pp # 1-ppv, 1-precision
npv = tn / pn
fomr = fn / pn # 1-npv
acc = (tp + tn) / n
mcr = (fp + fn) / n # 1-acc
prev = ap / n
plr = (tp / fp) / (
ap / an
) # tpr/fpr, sensitivity/(1-specificity), ratio of positives to negatives in positive predictions relative to ratio in whole sample, higher is better
nlr = (fn / tn) / (
ap / an
) # fnr/tnr, (1-sensitivity)/specificity, ratio of positives to negatives in negative predictions relative to ratio in whole sample, lower is better
dor = (tp / fp) / (
fn / tn
) # plr/nlr, ratio of positives to negatives in positive predictions, divided by ratio of positives to negatives in negative predictions
drr = (tp / pp) / (
fn / pn
) # ppv/fomr, relative risk or risk ratio, fraction of positives in positive predictions divided by fraction of positives in negative predictions
darr = (tp / pp) - (
fn / pn
) # ppv - fomr, absolute risk reduction, fraction of positives in positive predictions minus fraction of positives in negative predictions
mrr = (tp / pp) / (
ap / n
) # ppv/prev, modified (by me) relative risk or risk ratio, fraction of positives in positive predictions divided by fraction of positives in whole sample
marr = (tp / pp) - (
ap / n
) # ppv - prev, modified (by me) absolute risk reduction, fraction of positives in positive predictions minus fraction of positives in whole sample
f1 = (1 + (1**2)) * ppv * tpr / ((1**2) * ppv + tpr)
mcc = (tp * tn - fp * fn) / np.sqrt(
(tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
mmcc = 1 - np.sqrt(
(fp * fn) / ((tp + fp) * (tp + fn))
) # modified (by me), equivalent to 1 + mcc with tn forced to 0
cos = tp / np.sqrt((tp + fp) * (tp + fn)) # ochiai
fnlp = -hypergeom.logsf(tp, n, ap, pp, loc=1) / np.log(10)
sig = fnlp > np.log10(term_term.size) - np.log10(0.05)
lrr = np.log10(tp) - np.log10(tp + fp) - np.log10(fn) + np.log10(
fn + tn) # log10 of relative risk
lrr_se = np.sqrt(
fp / tp / (tp + fp) + tn / fn / (fn + tn)) / np.log(
10) # standard error of log10 of relative risk
lrr_lb95 = lrr - 1.96 * lrr_se
lrr_ub95 = lrr + 1.96 * lrr_se
drr_lb95 = 10**lrr_lb95
drr_ub95 = 10**lrr_ub95
lor = np.log10(tp) - np.log10(fp) - np.log10(fn) + np.log10(
tn) # log10 of odds ratio
lor_se = np.sqrt(1 / tp + 1 / fp + 1 / fn + 1 / tn) / np.log(
10) # standard error of log10 of odds ratio
lor_lb95 = lor - 1.96 * lor_se
lor_ub95 = lor + 1.96 * lor_se
dor_lb95 = 10**lor_lb95
dor_ub95 = 10**lor_ub95
mi, nmi, iqr = mutualinformation(
tp, fp, fn, tn
) # mutual information, normalized mutual information, information quality ratio
count_stats = [tp, fn, tn, fp, ap, an, pp, pn, n]
other_stats = [
tpr, fnr, tnr, fpr, ppv, fdr, npv, fomr, acc, mcr, prev, plr,
nlr, dor, drr, darr, mrr, marr, f1, mcc, mmcc, cos, fnlp, sig,
lrr, lrr_se, lrr_lb95, lrr_ub95, drr_lb95, drr_ub95, lor,
lor_se, lor_lb95, lor_ub95, dor_lb95, dor_ub95, mi, nmi, iqr
]
rowwritelist = [term_term.rowlabels[i]] + [
term_term.rowmeta[l][i] if term_term.rowmeta[l].dtype
== 'object' else str(term_term.rowmeta[l][i])
for l in rowmetalabels
]
columnwritelist = [term_term.columnlabels[j]] + [
term_term.columnmeta[l][j] if term_term.columnmeta[l].dtype
== 'object' else str(term_term.columnmeta[l][j])
for l in columnmetalabels
]
writelist = rowwritelist + columnwritelist + [
str(s) for s in count_stats
] + ['{0:1.5g}'.format(s) for s in other_stats]
fw.write('\t'.join(writelist) + '\n')
statmats['mcc'][i, j] = mcc
statmats['mmcc'][i, j] = mmcc
statmats['cos'][i, j] = cos
statmats['mi'][i, j] = mi
statmats['nmi'][i, j] = nmi
statmats['iqr'][i, j] = iqr
# save matrices for select association statistics
print('saving matrices for select association statistics...')
for selstat in selstats:
term_term.matrix = statmats[selstat]
datasetIO.save_datamatrix(
'{0}_{1}_datamatrix_{2}_yr_{3}_ds_{4}_ms_{5!s}_uv_{6}_np_{7!s}_mc_{8!s}.txt.gz'
.format(row_dictionary, column_dictionary, selstat, year,
datestamp, min_score, universe, n_prior, min_count),
term_term)
datasetIO.save_datamatrix(
'{0}_{1}_datamatrix_{2}_yr_{3}_ds_{4}_ms_{5!s}_uv_{6}_np_{7!s}_mc_{8!s}.pickle'
.format(row_dictionary, column_dictionary, selstat, year,
datestamp, min_score, universe, n_prior, min_count),
term_term)
print('done calc_term-term_stats_from_termite.py')
def test_enrichment(self, gene_ontology):
features_of_interest = gene_ontology.all_genes[:10]
test_enrichment_df = gene_ontology.enrichment(features_of_interest)
p_value_cutoff = 1000000
min_feature_size = 3
min_background_size = 5
cross_reference = {}
domains = gene_ontology.domains
background = gene_ontology.all_genes
n_all_genes = len(background)
n_features_of_interest = len(features_of_interest)
enrichment = defaultdict(dict)
for go_term, go_genes in gene_ontology.ontology.items():
if go_genes['domain'] not in domains:
continue
features_in_go = go_genes['genes'].intersection(
features_of_interest)
background_in_go = go_genes['genes'].intersection(background)
too_few_features = len(features_in_go) < min_feature_size
too_few_background = len(background_in_go) < min_background_size
if too_few_features or too_few_background:
continue
# TODO D.R.Y. this
# Survival function is more accurate on small p-values
log_p_value = hypergeom.logsf(len(features_in_go), n_all_genes,
len(background_in_go),
n_features_of_interest)
# p_value = 0 if p_value < 0 else p_value
symbols = [cross_reference[f] if f in cross_reference else f for f
in features_in_go]
enrichment['negative_log_p_value'][go_term] = -log_p_value
enrichment['n_features_of_interest_in_go_term'][go_term] = len(
features_in_go)
enrichment['n_background_in_go_term'][go_term] = len(
background_in_go)
enrichment['n_features_total_in_go_term'][go_term] = len(
go_genes['genes'])
enrichment['features_of_interest_in_go_term'][
go_term] = ','.join(features_in_go)
enrichment['features_of_interest_in_go_term_gene_symbols'][
go_term] = ','.join(symbols)
enrichment['go_domain'][go_term] = go_genes['domain']
enrichment['go_name'][go_term] = go_genes['name']
enrichment_df = pd.DataFrame(enrichment)
# TODO D.R.Y. this
# Bonferonni correction
enrichment_df['bonferonni_corrected_negative_log_p_value'] = \
enrichment_df['negative_log_p_value'] \
- np.log(enrichment_df.shape[0])
ind = enrichment_df['bonferonni_corrected_negative_log_p_value'
] < np.log(p_value_cutoff)
enrichment_df = enrichment_df.ix[ind]
true_enrichment_df = enrichment_df.sort(
columns=['negative_log_p_value'], ascending=False)
pdt.assert_frame_equal(test_enrichment_df, true_enrichment_df)
def _one_fit(self):
if self.verbose:
print("\nCreating synthetic doublets...")
self._createDoublets()
# Normalize combined augmented set
if self.verbose:
print("Normalizing...")
if self.normalizer is not None:
aug_counts = self.normalizer(
sp_sparse.vstack((self._raw_counts, self._raw_synthetics)))
else:
# Follows doubletdetection.plot.normalize_counts, but uses memoized normed raw_counts
synth_lib_size = np.sum(self._raw_synthetics, axis=1).A1
aug_lib_size = np.concatenate([self._lib_size, synth_lib_size])
normed_synths = self._raw_synthetics.copy()
inplace_csr_row_normalize_l1(normed_synths)
aug_counts = sp_sparse.vstack(
(self._normed_raw_counts, normed_synths))
aug_counts = np.log(aug_counts.A * np.median(aug_lib_size) + 0.1)
self._norm_counts = aug_counts[:self._num_cells]
self._synthetics = aug_counts[self._num_cells:]
aug_counts = anndata.AnnData(aug_counts)
aug_counts.obs["n_counts"] = aug_lib_size
if self.standard_scaling is True:
sc.pp.scale(aug_counts, max_value=15)
if self.verbose:
print("Running PCA...")
sc.tl.pca(aug_counts,
n_comps=self.n_components,
random_state=self.random_state)
if self.verbose:
print("Clustering augmented data set...\n")
if self.use_phenograph:
f = io.StringIO()
with redirect_stdout(f):
fullcommunities, _, _ = phenograph.cluster(
aug_counts.obsm["X_pca"], **self.phenograph_parameters)
out = f.getvalue()
if self.verbose:
print(out)
else:
sc.pp.neighbors(
aug_counts,
random_state=self.random_state,
method="umap",
n_neighbors=10,
)
sc.tl.louvain(aug_counts,
random_state=self.random_state,
resolution=4,
directed=False)
fullcommunities = np.array(aug_counts.obs["louvain"], dtype=int)
min_ID = min(fullcommunities)
self.communities_ = fullcommunities[:self._num_cells]
self.synth_communities_ = fullcommunities[self._num_cells:]
community_sizes = [
np.count_nonzero(fullcommunities == i)
for i in np.unique(fullcommunities)
]
if self.verbose:
print("Found clusters [{0}, ... {2}], with sizes: {1}\n".format(
min(fullcommunities), community_sizes, max(fullcommunities)))
# Count number of fake doublets in each community and assign score
# Number of synth/orig cells in each cluster.
synth_cells_per_comm = collections.Counter(self.synth_communities_)
orig_cells_per_comm = collections.Counter(self.communities_)
community_IDs = orig_cells_per_comm.keys()
community_scores = {
i: float(synth_cells_per_comm[i]) /
(synth_cells_per_comm[i] + orig_cells_per_comm[i])
for i in community_IDs
}
scores = np.array([community_scores[i] for i in self.communities_])
community_log_p_values = {
i: hypergeom.logsf(
synth_cells_per_comm[i],
aug_counts.shape[0],
self._synthetics.shape[0],
synth_cells_per_comm[i] + orig_cells_per_comm[i],
)
for i in community_IDs
}
log_p_values = np.array(
[community_log_p_values[i] for i in self.communities_])
if min_ID < 0:
scores[self.communities_ == -1] = np.nan
log_p_values[self.communities_ == -1] = np.nan
return scores, log_p_values
def test_enrichment(self, gene_ontology):
features_of_interest = gene_ontology.all_genes[:10]
test_enrichment_df = gene_ontology.enrichment(features_of_interest)
p_value_cutoff = 1000000
min_feature_size = 3
min_background_size = 5
cross_reference = {}
domains = gene_ontology.domains
background = gene_ontology.all_genes
n_all_genes = len(background)
n_features_of_interest = len(features_of_interest)
enrichment = defaultdict(dict)
for go_term, go_genes in gene_ontology.ontology.items():
if go_genes['domain'] not in domains:
continue
features_in_go = go_genes['genes'].intersection(
features_of_interest)
background_in_go = go_genes['genes'].intersection(background)
too_few_features = len(features_in_go) < min_feature_size
too_few_background = len(background_in_go) < min_background_size
if too_few_features or too_few_background:
continue
# TODO D.R.Y. this
# Survival function is more accurate on small p-values
log_p_value = hypergeom.logsf(len(features_in_go), n_all_genes,
len(background_in_go),
n_features_of_interest)
# p_value = 0 if p_value < 0 else p_value
symbols = [
cross_reference[f] if f in cross_reference else f
for f in features_in_go
]
enrichment['negative_log_p_value'][go_term] = -log_p_value
enrichment['n_features_of_interest_in_go_term'][go_term] = len(
features_in_go)
enrichment['n_background_in_go_term'][go_term] = len(
background_in_go)
enrichment['n_features_total_in_go_term'][go_term] = len(
go_genes['genes'])
enrichment['features_of_interest_in_go_term'][go_term] = ','.join(
features_in_go)
enrichment['features_of_interest_in_go_term_gene_symbols'][
go_term] = ','.join(symbols)
enrichment['go_domain'][go_term] = go_genes['domain']
enrichment['go_name'][go_term] = go_genes['name']
enrichment_df = pd.DataFrame(enrichment)
# TODO D.R.Y. this
# Bonferonni correction
enrichment_df['bonferonni_corrected_negative_log_p_value'] = \
enrichment_df['negative_log_p_value'] \
- np.log(enrichment_df.shape[0])
ind = enrichment_df[
'bonferonni_corrected_negative_log_p_value'] < np.log(
p_value_cutoff)
enrichment_df = enrichment_df.ix[ind]
true_enrichment_df = enrichment_df.sort(
columns=['negative_log_p_value'], ascending=False)
pdt.assert_frame_equal(test_enrichment_df, true_enrichment_df)
def main(dictionaries, year, datestamp, min_score, universe, n_prior,
min_count, association_statistic, reference_datamatrix_path,
save_predictions):
print('begin benchmark_term-term_stats_from_termite.py')
print('dictionaries: {0}, {1}'.format(dictionaries[0], dictionaries[1]))
print('year: {0}'.format(year))
print('datestamp: {0}'.format(datestamp))
print('min_score: {0!s}'.format(min_score))
print('universe: {0}'.format(universe))
print('n_prior: {0!s}'.format(n_prior))
print('min_count: {0!s}'.format(min_count))
print('association_statistic: {0}'.format(association_statistic))
print('reference_datamatrix_path: {0}'.format(reference_datamatrix_path))
print('save_predictions: {0!s}'.format(save_predictions))
# create figures folder
print('creating figures folder...')
figures_folder = 'benchmark_figures'
if not os.path.exists(figures_folder):
os.mkdir(figures_folder)
# load counts datamatrix
# this file is generated by count_term-term_pmids_from_termite.py
print('loading counts datamatrix...')
row_dictionary = dictionaries[
0] # 'HUCELL', 'ANAT', 'INDICATION', 'HUCELLANAT', 'HUCELLANATINDICATION'
column_dictionary = dictionaries[
1] # 'HUCELL', 'ANAT', 'INDICATION', 'HUCELLANAT', 'HUCELLANATINDICATION'
counts_datamatrix_path = '{0}_{1}_datamatrix_pmidcounts_year_{2}_datestamp_{3}_minscore_{4!s}.pickle'.format(
row_dictionary, column_dictionary, year, datestamp, min_score)
term_term_counts_all = datasetIO.load_datamatrix(counts_datamatrix_path)
print('counts_datamatrix_path: {0}'.format(counts_datamatrix_path))
print(term_term_counts_all)
# load association statistic datamatrix
# this file is generated by calc_term-term_stats_from_termite.py
print('loading association statistic datamatrix...')
stats_datamatrix_path = '{0}_{1}_datamatrix_{2}_yr_{3}_ds_{4}_ms_{5!s}_uv_{6}_np_{7!s}_mc_{8!s}.pickle'.format(
row_dictionary, column_dictionary, association_statistic, year,
datestamp, min_score, universe, n_prior, min_count)
term_term_stats_all = datasetIO.load_datamatrix(stats_datamatrix_path)
print('stats_datamatrix_path: {0}'.format(stats_datamatrix_path))
print(term_term_stats_all)
# load reference datamatrix of positive and negative examples
print('loading reference datamatrix of positive and negative examples...')
term_term_ref = datasetIO.load_datamatrix(reference_datamatrix_path)
print('reference_datamatrix_path: {0}'.format(reference_datamatrix_path))
print(term_term_ref)
# align datamatrices to reference
print('aligning datamatrices to reference...')
term_term_counts = term_term_counts_all.tolabels(
rowlabels=term_term_ref.rowlabels.copy(),
columnlabels=term_term_ref.columnlabels.copy())
term_term_stats = term_term_stats_all.tolabels(
rowlabels=term_term_ref.rowlabels.copy(),
columnlabels=term_term_ref.columnlabels.copy())
# find term-term pairs with sufficient counts
print('finding term-term pairs with sufficient counts...')
I, J = (term_term_counts.matrix >= min_count).nonzero()
num_sufficient = I.size
print('term-term pairs with at least {0!s} counts: {1!s}'.format(
min_count, num_sufficient))
# find row_term_dicts and column_term_dicts
print('finding row_term_dicts and column_term_dicts')
row_term_dicts = np.unique(term_term_stats.rowmeta['term_dict'])
column_term_dicts = np.unique(term_term_stats.columnmeta['term_dict'])
# calculate performance on reference examples and write to dataframe
print(
'calculating performance on reference examples and writing to dataframe...'
)
dataframe_path = 'benchmark_term-term_stats_dataframe.txt'
metaheaders = [
'row_dictionary', 'column_dictionary', 'year', 'datestamp',
'min_score', 'universe', 'n_prior', 'min_count',
'association_statistic', 'reference_datamatrix_path', 'row_term_dict',
'column_term_dict'
]
statheaders = [
'tp', 'fn', 'tn', 'fp', 'ap', 'an', 'pp', 'pn', 'n', 'auroc', 'auprc',
'tpr', 'fnr', 'tnr', 'fpr', 'ppv', 'fdr', 'npv', 'fomr', 'acc', 'mcr',
'prev', 'plr', 'nlr', 'dor', 'drr', 'darr', 'mrr', 'marr', 'f1', 'mcc',
'cos', 'fnlp', 'lrr', 'lrr_se', 'lrr_lb95', 'lrr_ub95', 'drr_lb95',
'drr_ub95', 'lor', 'lor_se', 'lor_lb95', 'lor_ub95', 'dor_lb95',
'dor_ub95', 'mi', 'nmi', 'iqr', 'min_value_association_statistic'
]
with open(dataframe_path,
mode='at',
encoding='utf-8',
errors='surrogateescape') as fw:
writelist = metaheaders + statheaders
fw.write('\t'.join(writelist) + '\n')
for row_term_dict in row_term_dicts:
row_hidxs = (term_term_stats.rowmeta['term_dict'] == row_term_dict
).nonzero()[0]
for column_term_dict in column_term_dicts:
print('working on {0}-{1} associations...'.format(
row_term_dict, column_term_dict))
# get scores and labels
print('getting scores and labels...')
column_hidxs = (term_term_stats.columnmeta['term_dict'] ==
column_term_dict).nonzero()[0]
hit = np.logical_and(np.in1d(I, row_hidxs),
np.in1d(J, column_hidxs))
Y = term_term_ref.matrix[I[hit], J[hit]]
X = (term_term_stats.matrix[I[hit], J[hit]]).reshape(-1, 1)
X_prime = X.copy()
if association_statistic == 'mcc':
X_prime = (X_prime + 1) / 2
xpmin = (X_prime[X_prime > 0]).min() / 2
xpmax = 1 - (1 - (X_prime[X_prime < 1]).max()) / 2
X_prime[X_prime == 0] = xpmin
X_prime[X_prime == 1] = xpmax
logitX = np.log10(X_prime / (1 - X_prime))
# save score histograms
print('saving score histograms...')
values = X.reshape(-1)
title = 'uv_{0}_rd{1}_cd{2}, pos:{3:1.3g}, neg:{4:1.3g}'.format(
universe[:5], row_term_dict[:5], column_term_dict[:5],
np.median(values[Y]), np.median(values[~Y]))
save_path = '{0}/{1}_{2}_hist_{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
densities, edges = plot_step_density(
{
'positive': ('-r', values[Y]),
'negative': (':b', values[~Y])
}, association_statistic, title, save_path, 'auto',
(values.min(), values.max()), False)
save_path = '{0}/{1}_{2}_zoomhist_{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
densities, edges = plot_step_density(
{
'positive': ('-r', values[Y]),
'negative': (':b', values[~Y])
}, association_statistic, title, save_path, 'auto',
(values.min(), values.max()), False,
(np.percentile(values, 2.5), np.percentile(values, 97.5)))
values = logitX.reshape(-1)
title = 'uv_{0}_rd{1}_cd{2}, pos:{3:1.3g}, neg:{4:1.3g}'.format(
universe[:5], row_term_dict[:5], column_term_dict[:5],
np.median(values[Y]), np.median(values[~Y]))
save_path = '{0}/{1}_{2}_hist_LOGIT{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
densities, edges = plot_step_density(
{
'positive': ('-r', values[Y]),
'negative': (':b', values[~Y])
}, 'logit ' + association_statistic, title, save_path,
'auto', (values.min(), values.max()), False)
save_path = '{0}/{1}_{2}_zoomhist_LOGIT{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
densities, edges = plot_step_density(
{
'positive': ('-r', values[Y]),
'negative': (':b', values[~Y])
}, 'logit ' + association_statistic, title, save_path,
'auto', (values.min(), values.max()), False,
(np.percentile(values, 2.5), np.percentile(values, 97.5)))
# fit logistic regression classifier
print('fitting logistic regression classifier...')
robust_scaler = RobustScaler().fit(logitX)
Z = robust_scaler.transform(logitX)
logistic_regression_model = LogisticRegression(
penalty='l2',
C=1e3,
intercept_scaling=1.0,
class_weight='balanced').fit(Z, Y)
if logistic_regression_model.classes_[1] == 1:
decision_function = logistic_regression_model.decision_function(
Z)
else:
decision_function = -logistic_regression_model.decision_function(
Z)
Y_pred = decision_function > 0
min_value_association_statistic = (X.reshape(-1)[Y_pred]).min()
# save decision function and predicted probability histograms
print(
'saving decision function and predicted probability histograms...'
)
values = decision_function.reshape(-1)
title = 'uv_{0}_rd{1}_cd{2}, pos:{3:1.3g}, neg:{4:1.3g}'.format(
universe[:5], row_term_dict[:5], column_term_dict[:5],
np.median(values[Y]), np.median(values[~Y]))
save_path = '{0}/{1}_{2}_hist_DF{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
densities, edges = plot_step_density(
{
'positive': ('-r', values[Y]),
'negative': (':b', values[~Y])
}, 'decision fun ' + association_statistic, title,
save_path, 'auto', (values.min(), values.max()), False)
save_path = '{0}/{1}_{2}_zoomhist_DF{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
densities, edges = plot_step_density(
{
'positive': ('-r', values[Y]),
'negative': (':b', values[~Y])
}, 'decision fun ' + association_statistic, title,
save_path, 'auto', (values.min(), values.max()), False,
(np.percentile(values, 2.5), np.percentile(values, 97.5)))
values = (1 / (1 + np.exp(-decision_function))).reshape(-1)
title = 'uv_{0}_rd{1}_cd{2}, pos:{3:1.3g}, neg:{4:1.3g}'.format(
universe[:5], row_term_dict[:5], column_term_dict[:5],
np.median(values[Y]), np.median(values[~Y]))
save_path = '{0}/{1}_{2}_hist_PP{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
densities, edges = plot_step_density(
{
'positive': ('-r', values[Y]),
'negative': (':b', values[~Y])
}, 'pred prob ' + association_statistic, title, save_path,
'auto', (0, 1), False)
save_path = '{0}/{1}_{2}_zoomhist_PP{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
densities, edges = plot_step_density(
{
'positive': ('-r', values[Y]),
'negative': (':b', values[~Y])
}, 'pred prob ' + association_statistic, title, save_path,
'auto', (0, 1), False,
(np.percentile(values, 2.5), np.percentile(values, 97.5)))
# compute roc and pr curves
print('computing roc and pr curves...')
fpr, tpr, thresholds = roc_curve(Y, decision_function)
precision, recall, thresholds = precision_recall_curve(
Y, decision_function)
auroc = roc_auc_score(Y, decision_function)
auprc = average_precision_score(Y, decision_function)
# save roc and pr curves
print('saving roc and pr curves...')
title = 'uv_{0}_as_{1}_rd{2}_cd{3}, auc:{4:1.3g}'.format(
universe[:5], association_statistic, row_term_dict[:5],
column_term_dict[:5], auprc)
save_path = '{0}/{1}_{2}_prc_{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
fg, ax = plt.subplots(1, 1, figsize=(3, 2))
ax.plot(recall, precision, '-k', linewidth=1)
ax.set_position([0.55 / 3, 0.35 / 2, 2.1 / 3,
1.3 / 2]) # left, bottom, width, height
ax.set_title(title, fontsize=8)
ax.set_ylabel('Precision', fontsize=8, labelpad=4)
ax.set_xlabel('Recall', fontsize=8, labelpad=2)
ax.set_ylim((0, 1))
ax.set_xlim((0, 1))
ax.tick_params(axis='both',
which='major',
bottom=True,
top=False,
left=True,
right=False,
labelbottom=True,
labeltop=False,
labelleft=True,
labelright=False,
labelsize=8)
ax.ticklabel_format(axis='both',
style='sci',
scilimits=(-3, 3),
fontsize=8)
ax.yaxis.offsetText.set_fontsize(8)
ax.xaxis.offsetText.set_fontsize(8)
fg.savefig(save_path, transparent=True, pad_inches=0, dpi=300)
plt.close()
title = 'uv_{0}_as_{1}_rd{2}_cd{3}, auc:{4:1.3g}'.format(
universe[:5], association_statistic, row_term_dict[:5],
column_term_dict[:5], auroc)
save_path = '{0}/{1}_{2}_roc_{3}_yr_{4}_ds_{5}_ms_{6!s}_uv_{7}_np_{8!s}_mc_{9!s}_rd_{10}_cd_{11}.png'.format(
figures_folder, row_dictionary, column_dictionary,
association_statistic, year, datestamp, min_score,
universe, n_prior, min_count, row_term_dict,
column_term_dict)
fg, ax = plt.subplots(1, 1, figsize=(3, 2))
ax.plot(fpr, tpr, '-k', linewidth=1)
ax.set_position([0.55 / 3, 0.35 / 2, 2.1 / 3,
1.3 / 2]) # left, bottom, width, height
ax.set_title(title, fontsize=8)
ax.set_ylabel('Precision', fontsize=8, labelpad=4)
ax.set_xlabel('Recall', fontsize=8, labelpad=2)
ax.set_ylim((0, 1))
ax.set_xlim((0, 1))
ax.tick_params(axis='both',
which='major',
bottom=True,
top=False,
left=True,
right=False,
labelbottom=True,
labeltop=False,
labelleft=True,
labelright=False,
labelsize=8)
ax.ticklabel_format(axis='both',
style='sci',
scilimits=(-3, 3),
fontsize=8)
ax.yaxis.offsetText.set_fontsize(8)
ax.xaxis.offsetText.set_fontsize(8)
fg.savefig(save_path, transparent=True, pad_inches=0, dpi=300)
plt.close()
# save predictions for all term-term pairs
if save_predictions:
print('saving predictions for all term-term pairs...')
predictions = {}
X_all = term_term_stats_all.matrix.reshape(-1, 1)
if association_statistic == 'mcc':
X_all = (X_all + 1) / 2
xamin = (X_all[X_all > 0]).min() / 2
xamax = 1 - (1 - (X_all[X_all < 1]).max()) / 2
X_all[X_all == 0] = xamin
X_all[X_all == 1] = xamax
logitX_all = np.log10(X_all / (1 - X_all))
Z_all = robust_scaler.transform(logitX_all)
if logistic_regression_model.classes_[1] == 1:
predictions[
'decision_function'] = logistic_regression_model.decision_function(
Z_all)
else:
predictions[
'decision_function'] = -logistic_regression_model.decision_function(
Z_all)
predictions['probability_positive'] = 1 / (
1 + np.exp(-predictions['decision_function']))
if not np.all(np.diff(thresholds) > 0):
raise ValueError('thresholds not increasing')
predictions['precision'] = np.interp(
predictions['decision_function'], thresholds,
precision[:-1])
predictions['recall'] = np.interp(
predictions['decision_function'], thresholds,
recall[:-1])
I0, J0 = (term_term_counts_all.matrix <
min_count).nonzero()
IA, JA = (term_term_counts_all.matrix >=
min_count).nonzero()
new_stats = [
'{0}_dictidname'.format(row_dictionary),
'{0}_dictidname'.format(column_dictionary)
]
new_stat_mat = np.concatenate(
(term_term_counts_all.rowlabels[IA].reshape(-1, 1),
term_term_counts_all.columnlabels[JA].reshape(-1, 1)),
1)
for stat, values in predictions.items():
term_term_stats_all.matrix = values.reshape(
term_term_stats_all.shape[0],
term_term_stats_all.shape[1])
term_term_stats_all.matrix[I0, J0] = 0
datasetIO.save_datamatrix(
'{0}_{1}_datamatrix_{2}_yr_{3}_ds_{4}_ms_{5!s}_uv_{6}_np_{7!s}_mc_{8!s}_as_{9}_rd_{10}_cd_{11}.txt.gz'
.format(row_dictionary, column_dictionary, stat,
year, datestamp, min_score, universe,
n_prior, min_count, association_statistic,
row_term_dict, column_term_dict),
term_term_stats_all)
datasetIO.save_datamatrix(
'{0}_{1}_datamatrix_{2}_yr_{3}_ds_{4}_ms_{5!s}_uv_{6}_np_{7!s}_mc_{8!s}_as_{9}_rd_{10}_cd_{11}.pickle'
.format(row_dictionary, column_dictionary, stat,
year, datestamp, min_score, universe,
n_prior, min_count, association_statistic,
row_term_dict, column_term_dict),
term_term_stats_all)
new_stats.append(stat)
new_stat_mat = np.append(
new_stat_mat,
(term_term_stats_all.matrix[IA,
JA]).reshape(-1, 1), 1)
new_df = pd.DataFrame(data=new_stat_mat, columns=new_stats)
dataframe_path = '{0}_{1}_dataframe_yr_{2}_ds_{3}_ms_{4!s}_uv_{5}_np_{6!s}_mc_{7!s}.txt.gz'.format(
row_dictionary, column_dictionary, year, datestamp,
min_score, universe, n_prior, min_count)
joined_dataframe_path = '{0}_{1}_dataframe_yr_{2}_ds_{3}_ms_{4!s}_uv_{5}_np_{6!s}_mc_{7!s}_as_{8}_rd_{9}_cd_{10}.txt.gz'.format(
row_dictionary, column_dictionary, year, datestamp,
min_score, universe, n_prior, min_count,
association_statistic, row_term_dict, column_term_dict)
df = pd.read_table(dataframe_path,
compression='gzip',
index_col=False)
joined_df = df.set_index(new_stats[:2]).join(
new_df.set_index(new_stats[:2]))
joined_df.sort_values(by=association_statistic,
ascending=False,
inplace=True)
joined_df.to_csv(joined_dataframe_path,
sep='\t',
compression='gzip')
# compute classifier performance statistics
# note, these are in-sample statistics
# we are not worried about overfitting
# because we only have one feature
# and we are not trying to build a rigorous ML model
# we are simply trying to answer the question,
# given a reference set of positive and negative examples,
# which association statistic ranks term-term pairs the best?
print('computing classifier performance statistics...')
tn, fp, fn, tp = confusion_matrix(Y, Y_pred).ravel()
# incorporate a random prior with effective sample size = n_prior
prevalence = (tp + fn) / (tn + fp + fn + tp)
tp += n_prior * prevalence / 2
fn += n_prior * prevalence / 2
tn += n_prior * (1 - prevalence) / 2
fp += n_prior * (1 - prevalence) / 2
ap = tp + fn
an = fp + tn
pp = tp + fp
pn = tn + fn
n = tn + fp + fn + tp
tpr = tp / ap # sensitivity, recall
fnr = fn / ap # 1-tpr, 1-sensitivity, 1-recall
tnr = tn / an # specificity
fpr = fp / an # 1-tnr, 1-specificity
ppv = tp / pp # precision
fdr = fp / pp # 1-ppv, 1-precision
npv = tn / pn
fomr = fn / pn # 1-npv
acc = (tp + tn) / n
mcr = (fp + fn) / n # 1-acc
prev = ap / n
plr = (tp / fp) / (
ap / an
) # tpr/fpr, sensitivity/(1-specificity), ratio of positives to negatives in positive predictions relative to ratio in whole sample, higher is better
nlr = (fn / tn) / (
ap / an
) # fnr/tnr, (1-sensitivity)/specificity, ratio of positives to negatives in negative predictions relative to ratio in whole sample, lower is better
dor = (tp / fp) / (
fn / tn
) # plr/nlr, ratio of positives to negatives in positive predictions, divided by ratio of positives to negatives in negative predictions
drr = (tp / pp) / (
fn / pn
) # ppv/fomr, relative risk or risk ratio, fraction of positives in positive predictions divided by fraction of positives in negative predictions
darr = (tp / pp) - (
fn / pn
) # ppv - fomr, absolute risk reduction, fraction of positives in positive predictions minus fraction of positives in negative predictions
mrr = (tp / pp) / (
ap / n
) # ppv/prev, modified (by me) relative risk or risk ratio, fraction of positives in positive predictions divided by fraction of positives in whole sample
marr = (tp / pp) - (
ap / n
) # ppv - prev, modified (by me) absolute risk reduction, fraction of positives in positive predictions minus fraction of positives in whole sample
f1 = (1 + (1**2)) * ppv * tpr / ((1**2) * ppv + tpr)
mcc = (tp * tn - fp * fn) / np.sqrt(
(tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
cos = tp / np.sqrt((tp + fp) * (tp + fn)) # ochiai
fnlp = -hypergeom.logsf(tp, n, ap, pp, loc=1) / np.log(10)
lrr = np.log10(tp) - np.log10(tp + fp) - np.log10(
fn) + np.log10(fn + tn) # log10 of relative risk
lrr_se = np.sqrt(
fp / tp / (tp + fp) + tn / fn / (fn + tn)) / np.log(
10) # standard error of log10 of relative risk
lrr_lb95 = lrr - 1.96 * lrr_se
lrr_ub95 = lrr + 1.96 * lrr_se
drr_lb95 = 10**lrr_lb95
drr_ub95 = 10**lrr_ub95
lor = np.log10(tp) - np.log10(fp) - np.log10(fn) + np.log10(
tn) # log10 of odds ratio
lor_se = np.sqrt(1 / tp + 1 / fp + 1 / fn + 1 / tn) / np.log(
10) # standard error of log10 of odds ratio
lor_lb95 = lor - 1.96 * lor_se
lor_ub95 = lor + 1.96 * lor_se
dor_lb95 = 10**lor_lb95
dor_ub95 = 10**lor_ub95
mi, nmi, iqr = mutualinformation(
tp, fp, fn, tn
) # mutual information, normalized mutual information, information quality ratio
# write to dataframe
print('writing to dataframe...')
count_stats = [tp, fn, tn, fp, ap, an, pp, pn, n]
other_stats = [
auroc, auprc, tpr, fnr, tnr, fpr, ppv, fdr, npv, fomr, acc,
mcr, prev, plr, nlr, dor, drr, darr, mrr, marr, f1, mcc,
cos, fnlp, lrr, lrr_se, lrr_lb95, lrr_ub95, drr_lb95,
drr_ub95, lor, lor_se, lor_lb95, lor_ub95, dor_lb95,
dor_ub95, mi, nmi, iqr, min_value_association_statistic
]
writelist = [
row_dictionary, column_dictionary, year, datestamp,
str(min_score), universe,
str(n_prior),
str(min_count), association_statistic,
reference_datamatrix_path, row_term_dict, column_term_dict
]
writelist += [str(s) for s in count_stats]
writelist += ['{0:1.5g}'.format(s) for s in other_stats]
fw.write('\t'.join(writelist) + '\n')
print('done benchmark_term-term_stats_from_termite.py')
def enrichment(self, features_of_interest, background=None,
p_value_cutoff=1000000, cross_reference=None,
min_feature_size=3, min_background_size=5,
domain=None):
"""Bonferroni-corrected hypergeometric p-values of GO enrichment
Calculates hypergeometric enrichment of the features of interest,
in each GO category.
Parameters
----------
features_of_interest : list-like
List of features. Must match the identifiers in the ontology
database exactly, i.e. if your ontology database is ENSEMBL ids,
then you can only provide those and not common names like "RBFOX2"
background : list-like, optional
Background genes to use. It is best to use a relevant background
such as all expressed genes. If None, defaults to all genes.
p_value_cutoff : float, optional
Maximum accepted Bonferroni-corrected p-value
cross_reference : dict-like, optional
A mapping of gene ids to gene symbols, e.g. a pandas Series of
ENSEMBL genes e.g. ENSG00000139675 to gene symbols e.g HNRNPA1L2
min_feature_size : int, optional
Minimum number of features of interest overlapping in a GO Term,
to calculate enrichment
min_background_size : int, optional
Minimum number of features in the background overlapping a GO Term
domain : str or list, optional
Only calculate GO enrichment for a particular GO category or
subset of categories. Valid domains:
'biological_process', 'molecular_function', 'cellular_component'
Returns
-------
enrichment_df : pandas.DataFrame
A (n_go_categories, columns) DataFrame of the enrichment scores
Raises
------
ValueError
If features of interest and background do not overlap,
or invalid GO domains are given
"""
cross_reference = {} if cross_reference is None else cross_reference
background = self.all_genes if background is None else background
if len(set(background) & set(features_of_interest)) == 0:
raise ValueError('Features of interest and background do not '
'overlap! Not calculating GO enrichment')
if len(set(features_of_interest) & set(self.all_genes)) == 0:
raise ValueError('Features of interest do not overlap with GO term'
'gene ids. Not calculating GO enrichment.')
domains = self.domains
valid_domains = ",".join("'{}'".format(x) for x in self.domains)
# TODO more elegant type check
if isinstance(domain, str) or isinstance(domain, basestring):
if domain not in self.domains:
raise ValueError(
"'{}' is not a valid GO domain. "
"Only {} are acceptable".format(domain, valid_domains))
domains = frozenset([domain])
elif isinstance(domain, Iterable):
if len(set(domain) & self.domains) == 0:
raise ValueError(
"'{}' are not a valid GO domains. "
"Only {} are acceptable".format(
",".join("'{}'".format(x) for x in domain),
valid_domains))
domains = frozenset(domain)
n_all_genes = len(background)
n_features_of_interest = len(features_of_interest)
enrichment = defaultdict(dict)
for go_term, go_genes in self.ontology.items():
if go_genes['domain'] not in domains:
continue
features_in_go = go_genes['genes'].intersection(
features_of_interest)
background_in_go = go_genes['genes'].intersection(background)
too_few_features = len(features_in_go) < min_feature_size
too_few_background = len(background_in_go) < min_background_size
if too_few_features or too_few_background:
continue
# Survival function is more accurate on small p-values
log_p_value = hypergeom.logsf(len(features_in_go), n_all_genes,
len(background_in_go),
n_features_of_interest)
# p_value = 0 if p_value < 0 else p_value
symbols = [cross_reference[f] if f in cross_reference else f for f
in features_in_go]
enrichment['negative_log_p_value'][go_term] = -log_p_value
enrichment['n_features_of_interest_in_go_term'][go_term] = len(
features_in_go)
enrichment['n_background_in_go_term'][go_term] = len(
background_in_go)
enrichment['n_features_total_in_go_term'][go_term] = len(
go_genes['genes'])
enrichment['features_of_interest_in_go_term'][
go_term] = ','.join(features_in_go)
enrichment['features_of_interest_in_go_term_gene_symbols'][
go_term] = ','.join(symbols)
enrichment['go_domain'][go_term] = go_genes['domain']
enrichment['go_name'][go_term] = go_genes['name']
enrichment_df = pd.DataFrame(enrichment)
if enrichment_df.empty:
warnings.warn('No GO categories enriched in provided features')
return
# Bonferonni correction
enrichment_df['bonferonni_corrected_negative_log_p_value'] = \
enrichment_df['negative_log_p_value'] \
- np.log(enrichment_df.shape[0])
ind = enrichment_df['bonferonni_corrected_negative_log_p_value'
] < np.log(p_value_cutoff)
enrichment_df = enrichment_df.ix[ind]
enrichment_df = enrichment_df.sort(
columns=['negative_log_p_value'], ascending=False)
return enrichment_df
def enrichment_significance(term_row):
return LOG10_FACTOR * hypergeom.logsf(term_row['hit_count']-1, term_row['universe'],
term_row['term_count'], term_row['list_size'])
def _one_fit(self):
print("\nCreating synthetic doublets...")
self._createDoublets()
# Normalize combined augmented set
print("Normalizing...")
if self.normalizer is not None:
aug_counts = self.normalizer(
np.append(self._raw_counts, self._raw_synthetics, axis=0))
else:
# Follows doubletdetection.plot.normalize_counts, but uses memoized normed raw_counts
synth_lib_size = np.sum(self._raw_synthetics, axis=1)
aug_lib_size = np.concatenate([self._lib_size, synth_lib_size])
normed_synths = self._raw_synthetics / synth_lib_size[:,
np.newaxis]
aug_counts = np.concatenate(
[self._normed_raw_counts, normed_synths], axis=0)
aug_counts = np.log(aug_counts * np.median(aug_lib_size) + 0.1)
self._norm_counts = aug_counts[:self._num_cells]
self._synthetics = aug_counts[self._num_cells:]
print("Running PCA...")
# Get phenograph results
pca = PCA(n_components=self.n_components,
random_state=self.random_state)
reduced_counts = pca.fit_transform(aug_counts)
print("Clustering augmented data set with Phenograph...\n")
fullcommunities, _, _ = phenograph.cluster(
reduced_counts, **self.phenograph_parameters)
min_ID = min(fullcommunities)
self.communities_ = fullcommunities[:self._num_cells]
self.synth_communities_ = fullcommunities[self._num_cells:]
community_sizes = [
np.count_nonzero(fullcommunities == i)
for i in np.unique(fullcommunities)
]
print("Found communities [{0}, ... {2}], with sizes: {1}\n".format(
min(fullcommunities), community_sizes, max(fullcommunities)))
# Count number of fake doublets in each community and assign score
# Number of synth/orig cells in each cluster.
synth_cells_per_comm = collections.Counter(self.synth_communities_)
orig_cells_per_comm = collections.Counter(self.communities_)
community_IDs = orig_cells_per_comm.keys()
community_scores = {
i: float(synth_cells_per_comm[i]) /
(synth_cells_per_comm[i] + orig_cells_per_comm[i])
for i in community_IDs
}
scores = np.array([community_scores[i] for i in self.communities_])
community_log_p_values = {
i:
hypergeom.logsf(synth_cells_per_comm[i], aug_counts.shape[0],
self._synthetics.shape[0],
synth_cells_per_comm[i] + orig_cells_per_comm[i])
for i in community_IDs
}
log_p_values = np.array(
[community_log_p_values[i] for i in self.communities_])
if min_ID < 0:
scores[self.communities_ == -1] = np.nan
log_p_values[self.communities_ == -1] = np.nan
return scores, log_p_values
