def HDT_Sig_batch(xpdf_matrix, nboot, progressbar=True):
"""
Saves time if calculating dip test on many sample-s of same size.
Only generates the background distribution for significance once.
:param xpdf_matrix: 2-dimensional numpy ndarray
:param nboot: number of times to generate a random test statistic for p-value calculation
:return: dips: dip statistic for each row in xpdf_matrix
ps: p-value for each row in xpdf_matrix
xlows: xlow for each row in xpdf_matrix
xups: xup for each row in xpdf_matrix
"""
dips = np.zeros(xpdf_matrix.shape[0]);
xlows = np.zeros(xpdf_matrix.shape[0]);
xups = np.zeros(xpdf_matrix.shape[0]);
if(progressbar): pbar = ProgressBar(xpdf_matrix.shape[0] + nboot);
for i in xrange(xpdf_matrix.shape[0]):
(dip, xlow, xup, ifault, gcm, lcm, mn, mj) = DipTest(xpdf_matrix[i,:]);
dips[i] = dip;
xlows[i] = xlow;
xups[i] = xup;
if(progressbar): pbar.update();
bootDip=np.zeros(nboot);
for i in np.arange(nboot):
unifpdf=np.sort(np.random.rand(xpdf_matrix.shape[1]))
bootDip[i] = DipTest(unifpdf)[0];
if(progressbar): pbar.update();
dips = np.expand_dims(dips, axis=1); #Make dips Nx1
bootDip = np.expand_dims(bootDip, axis=0); #Make bootDip 1xnboot
ps = np.sum(dips < bootDip, axis=1) / float(nboot);
if(progressbar): pbar.complete();
return(dips, ps, xlows, xups)
def em_exp_norm_mixture(zmat, cutoff, progressbar=True):
with np.errstate(divide="ignore", invalid="ignore"): # nans and infs are handled explicitly, don't need warning
max_iter = 150
if progressbar:
pbar = ProgressBar(max_iter)
# promote to 2d if single gene given
if zmat.ndim == 1:
zmat = zmat.reshape((1, zmat.shape[0]))
cutoff = np.array([cutoff])
cutoff = cutoff.reshape((cutoff.shape[0], 1))
cutoffs = np.tile(cutoff, (1, zmat.shape[1]))
gamma = zmat > cutoffs
Pi = np.mean(gamma, axis=1).reshape((zmat.shape[0], 1))
Pi[Pi == 0] = 1 / zmat.shape[1]
mu_l = weighted_mean(zmat, 1 - gamma)
mu_h = weighted_mean(zmat, gamma)
st_h = weighted_std(zmat, gamma, mu_h)
for niter in xrange(max_iter):
# E
prev_gamma = gamma
p_low = np.exp(-1 * zmat / mu_l) / mu_l
p_high = np.exp(-1 * (zmat - mu_h) ** 2 / (2 * st_h ** 2)) / st_h / np.sqrt(2 * np.pi)
p_low[~np.isfinite(p_low)] = 1e-5
p_low[p_low < 1e-5] = 1e-5
p_high[~np.isfinite(p_high)] = 1e-5
p_high[p_high < 1e-5] = 1e-5
gamma = (Pi * p_high) / (((1 - Pi) * p_low) + (Pi * p_high))
# M
Pi = np.mean(gamma, axis=1).reshape((zmat.shape[0], 1))
mu_l = weighted_mean(zmat, 1 - gamma)
mu_h = weighted_mean(zmat, gamma)
st_h = weighted_std(zmat, gamma, mu_h)
if progressbar:
pbar.update()
if niter % 10 == 0:
biggest_change = np.max(np.abs(gamma - prev_gamma))
if biggest_change < 0.01:
break
# if niter == 1: print mu_l, mu_h, st_l, st_h
# print 'Iteration: ', niter, ' L: ', sum(L);
# if d>0.95:
# break;
if progressbar:
pbar.complete()
L = np.sum(gamma * np.log(p_high) + (1 - gamma) * np.log(p_low), axis=1)
st_l = weighted_std(zmat, 1 - gamma, mu_l)
return (gamma, mu_l, mu_h, st_l, st_h, Pi, L)
def FullOutput(options, args):
start_time = time.time();
if(options.housekeeping):
housekeeping_filename = options.housekeeping;
else:
housekeeping_filename = ''; #If not in interactive mode and no -h specified, just use the default list
def get_housekeeping_file():
fn = housekeeping_filename;
return fn;
#%% Read expression data from file
if(len(args) > 0):
filename = args[0];
else:
raise ValueError("Argument Error: data_file not specified.\nExiting...");
if(not os.path.isfile(filename)):
raise ValueError("Argument Error: data file not found.\nExiting...");
(edata, genes, cells) = FileIO.read_matrix(filename);
print("Imported ", edata.shape[0], " genes across ", edata.shape[1], " samples");
#%% Load Signature file
sigs = [];
if(options.signatures):
sig_file = options.signatures;
if(not os.path.isfile(sig_file)):
raise ValueError("Option Error: signature file " + sig_file + " not found.\nExiting...");
sigs = Signatures.read_signatures(sig_file);
if(options.precomputed):
precomputed_sig_file = options.precomputed;
if(not os.path.isfile(precomputed_sig_file)):
raise ValueError("Option Error: precomputed signature file " + precomputed_sig_file + " not found.\nExiting...");
if(not options.signatures and not options.precomputed): #Need one or the other here
raise ValueError("Option Error: Must specify either a signature file or a pre-computed signature file.\nExiting...");
#Wrap data in ExpressionData object
edata = ExpressionData(edata, genes, cells);
#Hold on to originals so we don't lose data after filtering in case it's needed later
original_data = edata.copy();
#Create directory for all outputs
if(options.output):
dir_name = options.output;
else:
default_dir_name = 'FastProject_Output';
if(os.path.isdir(default_dir_name)):
i = 1;
while(True):
dir_name = default_dir_name + str(i);
if(not os.path.isdir(dir_name)):
break;
else:
i = i+1;
else:
dir_name = default_dir_name;
if(not os.path.isdir(dir_name)):
os.makedirs(dir_name);
#Filtering
filter_dict = {};
if(options.nofilter):
edata = Filters.filter_genes_novar(edata);
filter_dict.update({'None': set()});
else:
edata = Filters.filter_genes_threshold(edata, 0.2);
#HDT Filtering
print("Removing genes with unimodal distribution across samples using Hartigans DT...");
hdt_mask = Filters.filter_genes_hdt(edata, 0.05);
#Fano Filtering
print("Applying Fano-Filtering...");
fano_mask = Filters.filter_genes_fano(edata, 2);
filter_dict.update({
'None': set(edata.row_labels), #None means 'use all genes'. This set only used when outputting filter
'HDT': set([edata.row_labels[i] for i,x in enumerate(hdt_mask) if x]),
'Fano': set([edata.row_labels[i] for i,x in enumerate(fano_mask) if x])
});
edata.filters = filter_dict;
#%% Probability transform
housekeeping_filename = get_housekeeping_file();
print()
print('Fitting expression data to exp/norm mixture model');
(pdata, mu_h) = Transforms.probability_of_expression(edata);
print();
print('Correcting for false-negatives using housekeeping gene levels');
(fit_func, params) = Transforms.create_false_neg_map(original_data, housekeeping_filename);
(pdata, fn_prob) = Transforms.correct_for_fn(pdata, mu_h, fit_func, params);
fn_prob[edata > 0] = 0;
pdata = ProbabilityData(pdata, edata);
edata.weights = 1-fn_prob;
pdata.weights = 1-fn_prob;
sample_passes, sample_scores = Transforms.quality_check(params);
if(options.qc):
pdata = pdata.subset_samples(sample_passes);
edata = edata.subset_samples(sample_passes);
sample_scores = sample_scores[sample_passes];
if(options.subsample_size > edata.shape[1]):
options.subsample_size = None;
Transforms.z_normalize(edata);
model_names = ['Expression', 'Probability'];
model_data = [edata, pdata];
fout_js = open(dir_name + os.sep + "FP_data.jsdata", 'w');
js_models = [];
for name, data in zip(model_names, model_data):
model_dir = os.path.join(dir_name, name);
try:
os.makedirs(model_dir);
except OSError:
pass;
print();
print('Model: ', name)
#Evaluate Signatures
print();
print("Evaluating signature scores on samples...");
sig_scores = dict();
pbar = ProgressBar(len(sigs));
for sig in sigs:
try:
sig_scores[sig.name] = data.eval_signature(sig);
except ValueError: #Only thrown when the signature has no genes in the data
pass #Just discard the signature then
pbar.update();
pbar.complete();
if(options.precomputed):
precomputed_sig_scores = Signatures.load_precomputed(options.precomputed, data.col_labels);
sig_scores.update(precomputed_sig_scores);
#Prompt to save data
out_file = 'SignatureScores.txt';
FileIO.write_signature_scores(os.path.join(model_dir, out_file), sig_scores, data.col_labels);
#Save data to js model as well
js_model_dict = {'model': name};
js_model_dict.update({'signatureScores': sig_scores})
js_model_dict.update({'sampleLabels': data.col_labels});
js_model_dict.update({'projectionData': []})
js_models.append(js_model_dict);
for filter_name in filter_dict.keys():
if(filter_name == "None"):
filter_dir = os.path.join(model_dir, "No_Filter");
else:
filter_dir = os.path.join(model_dir, filter_name + "_Filter");
try:
os.makedirs(filter_dir);
except OSError:
pass;
print();
print("Filter-Level:", filter_name);
#%% Dimensional Reduction procedures
print();
print("Projecting data into 2 dimensions");
projections, pcdata = Projections.generate_projections(data, filter_name, options.subsample_size);
#Evaluate Clusters
print("Evaluating Clusters...");
clusters = Projections.define_clusters(projections);
#%% Evaluating signatures against projections
sp_row_labels, sp_col_labels, sig_proj_matrix, sig_proj_matrix_p = Signatures.sigs_vs_projections_v2(projections, sig_scores,subsample_size=options.subsample_size);
#Save Projections
FileIO.write_projection_file(os.path.join(filter_dir, 'Projections.txt'), data.col_labels, projections);
#Save Clusters
FileIO.write_cluster_file(os.path.join(filter_dir, 'Clusters.txt'), data.col_labels, clusters)
#Output matrix of p-values for conformity scores
FileIO.write_matrix(os.path.join(filter_dir, "DissimilarityMatrix.txt"),sig_proj_matrix, sp_row_labels, sp_col_labels);
FileIO.write_matrix(os.path.join(filter_dir, "PMatrix.txt"),sig_proj_matrix_p, sp_row_labels, sp_col_labels);
#Output genes used in filter
FileIO.write_filter_file(os.path.join(filter_dir, 'ProjectedGenes.txt'), data.filters[filter_name]);
#Output JS
js_filt_dict = dict();
js_model_dict['projectionData'].append(js_filt_dict);
js_filt_dict.update({'filter': filter_name});
js_filt_dict.update({'genes': data.filters[filter_name]});
js_filt_dict.update({'pca': False});
js_filt_dict.update({'projections': projections});
js_filt_dict.update({'sigProjMatrix': sig_proj_matrix});
js_filt_dict.update({'sigProjMatrix_p': sig_proj_matrix_p});
js_filt_dict.update({'projectionKeys': sp_col_labels});
js_filt_dict.update({'signatureKeys': sp_row_labels});
js_filt_dict.update({'clusters': clusters});
#Now do it all again using the principal component data
if(options.pca_filter):
pcdata = Projections.filter_PCA(pcdata, scores=sample_scores, variance_proportion=0.25);
else:
pcdata = Projections.filter_PCA(pcdata, variance_proportion=0.25, min_components = 30);
#%% Dimensional Reduction procedures
print();
print("Projecting data into 2 dimensions");
projections, pcdata2 = Projections.generate_projections(pcdata, filter_name, options.subsample_size);
#Evaluate Clusters
print("Evaluating Clusters...");
clusters = Projections.define_clusters(projections);
#%% Evaluating signatures against projections
sp_row_labels, sp_col_labels, sig_proj_matrix, sig_proj_matrix_p = Signatures.sigs_vs_projections_v2(projections, sig_scores, subsample_size = options.subsample_size);
#Save Projections
FileIO.write_projection_file(os.path.join(filter_dir, 'Projections-PC.txt'), pcdata.col_labels, projections);
#Save Clusters
FileIO.write_cluster_file(os.path.join(filter_dir, 'Clusters-PC.txt'), pcdata.col_labels, clusters)
#Output matrix of p-values for conformity scores
FileIO.write_matrix(os.path.join(filter_dir, "DissimilarityMatrix-PC.txt"),sig_proj_matrix, sp_row_labels, sp_col_labels);
FileIO.write_matrix(os.path.join(filter_dir, "PMatrix-PC.txt"),sig_proj_matrix_p, sp_row_labels, sp_col_labels);
#Output JS
js_filt_dict = dict();
js_model_dict['projectionData'].append(js_filt_dict);
js_filt_dict.update({'filter': filter_name});
js_filt_dict.update({'genes': data.filters[filter_name]});
js_filt_dict.update({'pca': True});
js_filt_dict.update({'projections': projections});
js_filt_dict.update({'sigProjMatrix': sig_proj_matrix});
js_filt_dict.update({'sigProjMatrix_p': sig_proj_matrix_p});
js_filt_dict.update({'projectionKeys': sp_col_labels});
js_filt_dict.update({'signatureKeys': sp_row_labels});
js_filt_dict.update({'clusters': clusters});
fout_js.write(HtmlViewer.toJS_variable("FP_Models", js_models));
#Write signatures to file
#Assemble signatures into an object, then convert to JSON variable and write
sig_dict = {};
for sig in sigs:
sig_genes = sig.sig_dict.keys();
sig_values = sig.sig_dict.values();
sort_i = np.array(sig_values).argsort()[::-1];#Put positive signatures first
sig_genes = [sig_genes[i] for i in sort_i];
sig_values = [sig_values[i] for i in sort_i];
sig_dict.update({sig.name: {'Genes':sig_genes, 'Signs':sig_values}});
fout_js.write(HtmlViewer.toJS_variable("FP_Signatures", sig_dict));
#Write the original data matrix to the javascript file.
#First, cluster genes
from scipy.cluster.hierarchy import leaves_list, linkage;
linkage_matrix = linkage(edata);
leaves_i = leaves_list(linkage_matrix);
edata_clustered = edata[leaves_i, :];
edata_clustered.row_labels = [edata.row_labels[i] for i in leaves_i];
data_json = dict({
'data': edata_clustered,
'gene_labels': edata_clustered.row_labels,
'sample_labels': edata_clustered.col_labels,
});
fout_js.write(HtmlViewer.toJS_variable("FP_ExpressionMatrix", data_json));
fout_js.close();
HtmlViewer.copy_html_files(dir_name);
print();
print("FastProject Analysis Complete")
elapsed_time = time.time() - start_time;
print("Elapsed Time {:.2f} seconds".format(elapsed_time));
