def run_scVI(DataPath, LabelsPath, CV_RDataPath, OutputDir, GeneOrderPath = "", NumGenes = 0):
'''
run scVI
Wrapper script to run scVI on a benchmark dataset with 5-fold cross validation,
outputs lists of true and predicted cell labels as csv files, as well as computation time.
Parameters
----------
DataPath : Data file path (.csv), cells-genes matrix with cell unique barcodes
as row names and gene names as column names.
LabelsPath : Cell population annotations file path (.csv).
CV_RDataPath : Cross validation RData file path (.RData), obtained from Cross_Validation.R function.
OutputDir : Output directory defining the path of the exported file.
GeneOrderPath : Gene order file path (.csv) obtained from feature selection,
defining the genes order for each cross validation fold, default is NULL.
NumGenes : Number of genes used in case of feature selection (integer), default is 0.
'''
# read the Rdata file
robjects.r['load'](CV_RDataPath)
nfolds = np.array(robjects.r['n_folds'], dtype = 'int')
tokeep = np.array(robjects.r['Cells_to_Keep'], dtype = 'bool')
col = np.array(robjects.r['col_Index'], dtype = 'int')
col = col - 1
test_ind = np.array(robjects.r['Test_Idx'])
train_ind = np.array(robjects.r['Train_Idx'])
# read the data
data = pd.read_csv(DataPath,index_col=0,sep=',')
labels = pd.read_csv(LabelsPath, header=0,index_col=None, sep=',', usecols = col)
labels = labels.iloc[tokeep]
data = data.iloc[tokeep]
# read the feature file
if (NumGenes > 0):
features = pd.read_csv(GeneOrderPath,header=0,index_col=None, sep=',')
if (NumGenes == 0):
#save labels as csv file with header and index column
labels.to_csv('Labels_scvi.csv')
data.to_csv('Data_scvi.csv')
train = CsvDataset('Data_scvi.csv', save_path = "", sep = ",", labels_file = "Labels_scvi.csv", gene_by_cell = False)
## this semisupervised trainer automatically uses a part of the input data for training and a part for testing
scanvi = SCANVI(train.nb_genes, train.n_batches, train.n_labels)
trainer_scanvi = SemiSupervisedTrainer(scanvi, train, frequency=5)
n_epochs = 200
truelab = []
pred = []
tr_time = []
ts_time = []
for i in range(np.squeeze(nfolds)):
test_ind_i = np.array(test_ind[i], dtype = 'int') - 1
train_ind_i = np.array(train_ind[i], dtype = 'int') - 1
if (NumGenes > 0):
feat_to_use = features.iloc[0:NumGenes,i]
data2 = data.iloc[:,feat_to_use]
labels.to_csv('Labels_scvi.csv')
data2.to_csv('Data_scvi.csv')
train = CsvDataset('Data_scvi.csv', save_path = "", sep = ",", labels_file = "Labels_scvi.csv", gene_by_cell = False, new_n_genes = False)
## this semisupervised trainer automatically uses a part of the input data for training and a part for testing
scanvi = SCANVI(train.nb_genes, train.n_batches, train.n_labels)
trainer_scanvi = SemiSupervisedTrainer(scanvi, train, frequency=5)
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(indices=(train_ind_i).ravel(), shuffle = False)
trainer_scanvi.labelled_set.to_monitor = ['ll','accuracy']
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(indices=(test_ind_i).ravel(), shuffle = False)
trainer_scanvi.unlabelled_set.to_monitor = ['ll','accuracy']
start = tm.time()
trainer_scanvi.train(n_epochs)
tr_time.append(tm.time()-start)
## labels of test set are in y_pred
## labels are returned in numbers, should be mapped back to the real labels
## indices are permutated
start = tm.time()
y_true, y_pred = trainer_scanvi.unlabelled_set.compute_predictions()
ts_time.append(tm.time()-start)
truelab.extend(y_true)
pred.extend(y_pred)
#write results
os.chdir(OutputDir)
truelab = pd.DataFrame(truelab)
pred = pd.DataFrame(pred)
tr_time = pd.DataFrame(tr_time)
ts_time = pd.DataFrame(ts_time)
if (NumGenes == 0):
truelab.to_csv("scVI_True_Labels.csv", index = False)
pred.to_csv("scVI_Pred_Labels.csv", index = False)
tr_time.to_csv("scVI_Training_Time.csv", index = False)
ts_time.to_csv("scVI_Testing_Time.csv", index = False)
else:
truelab.to_csv("scVI_" + str(NumGenes) + "_True_Labels.csv", index = False)
pred.to_csv("scVI_" + str(NumGenes) + "_Pred_Labels.csv", index = False)
tr_time.to_csv("scVI_" + str(NumGenes) + "_Training_Time.csv", index = False)
ts_time.to_csv("scVI_" + str(NumGenes) + "_Testing_Time.csv", index = False)
def custom_objective_hyperopt(space,
is_best_training=False,
dataset=None,
n_epochs=None):
"""Custom objective function for advanced autotune tutorial."""
space = defaultdict(dict, space)
model_tunable_kwargs = space["model_tunable_kwargs"]
trainer_tunable_kwargs = space["trainer_tunable_kwargs"]
train_func_tunable_kwargs = space["train_func_tunable_kwargs"]
trainer_specific_kwargs = {}
model_specific_kwargs = {}
train_func_specific_kwargs = {}
trainer_specific_kwargs["use_cuda"] = bool(torch.cuda.device_count())
train_func_specific_kwargs["n_epochs"] = n_epochs
# add hardcoded parameters
# disable scVI progbar
trainer_specific_kwargs["show_progbar"] = False
trainer_specific_kwargs["frequency"] = 1
# merge params with fixed param precedence
model_tunable_kwargs.update(model_specific_kwargs)
trainer_tunable_kwargs.update(trainer_specific_kwargs)
train_func_tunable_kwargs.update(train_func_specific_kwargs)
scanvi = SCANVI(dataset.nb_genes, dataset.n_batches, dataset.n_labels,
**model_tunable_kwargs)
trainer_scanvi = SemiSupervisedTrainer(scanvi, dataset,
**trainer_tunable_kwargs)
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=np.squeeze(dataset.batch_indices == 1))
trainer_scanvi.unlabelled_set.to_monitor = [
"reconstruction_error", "accuracy"
]
indices_labelled = np.squeeze(dataset.batch_indices == 0)
if not is_best_training:
# compute k-fold accuracy on a 20% validation set
k = 5
accuracies = np.zeros(k)
indices_labelled = np.squeeze(dataset.batch_indices == 0)
for i in range(k):
indices_labelled_train, indices_labelled_val = train_test_split(
indices_labelled.nonzero()[0], test_size=0.2)
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(
indices=indices_labelled_train)
trainer_scanvi.labelled_set.to_monitor = [
"reconstruction_error",
"accuracy",
]
trainer_scanvi.validation_set = trainer_scanvi.create_posterior(
indices=indices_labelled_val)
trainer_scanvi.validation_set.to_monitor = ["accuracy"]
trainer_scanvi.train(**train_func_tunable_kwargs)
accuracies[i] = trainer_scanvi.history["accuracy_unlabelled_set"][
-1]
return {
"loss": -accuracies.mean(),
"space": space,
"status": STATUS_OK
}
else:
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(
indices=indices_labelled)
trainer_scanvi.labelled_set.to_monitor = [
"reconstruction_error", "accuracy"
]
trainer_scanvi.train(**train_func_tunable_kwargs)
return trainer_scanvi
def run_scVI(input_dir, output_dir, datafile, labfile, Rfile):
'''
Run scVI
Parameters
----------
input_dir : directory of the input files
output_dir : directory of the output files
datafile : name of the data file
labfile : name of the label file
Rfile : file to read the cross validation indices from
'''
os.chdir(input_dir)
# read the Rdata file
robjects.r['load'](Rfile)
nfolds = np.array(robjects.r['n_folds'], dtype='int')
tokeep = np.array(robjects.r['Cells_to_Keep'], dtype='bool')
col = np.array(robjects.r['col_Index'], dtype='int')
col = col - 1
test_ind = np.array(robjects.r['Test_Idx'])
train_ind = np.array(robjects.r['Train_Idx'])
# read the data
os.chdir(input_dir)
data = pd.read_csv(datafile, index_col=0, sep=',')
labels = pd.read_csv(labfile,
header=0,
index_col=None,
sep=',',
usecols=col)
labels = labels.iloc[tokeep]
data = data.iloc[tokeep]
#save labels as csv file with header and index column
labels.to_csv('Labels_scvi.csv')
data.to_csv('Data_scvi.csv')
train = CsvDataset('Data_scvi.csv',
save_path=input_dir,
sep=",",
labels_file="Labels_scvi.csv",
gene_by_cell=False)
## this semisupervised trainer automatically uses a part of the input data for training and a part for testing
scanvi = SCANVI(train.nb_genes, train.n_batches, train.n_labels)
trainer_scanvi = SemiSupervisedTrainer(scanvi, train, frequency=5)
n_epochs = 200
truelab = []
pred = []
tr_time = []
ts_time = []
for i in range(np.squeeze(nfolds)):
test_ind_i = np.array(test_ind[i], dtype='int') - 1
train_ind_i = np.array(train_ind[i], dtype='int') - 1
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(
indices=(train_ind_i).ravel(), shuffle=False)
trainer_scanvi.labelled_set.to_monitor = ['ll', 'accuracy']
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=(test_ind_i).ravel(), shuffle=False)
trainer_scanvi.unlabelled_set.to_monitor = ['ll', 'accuracy']
start = tm.time()
trainer_scanvi.train(n_epochs)
tr_time.append(tm.time() - start)
## labels of test set are in y_pred
## labels are returned in numbers, should be mapped back to the real labels
## indices are permutated
start = tm.time()
y_true, y_pred = trainer_scanvi.unlabelled_set.compute_predictions()
ts_time.append(tm.time() - start)
truelab.extend(y_true)
pred.extend(y_pred)
#write results
os.chdir(output_dir)
truelab = pd.DataFrame(truelab)
pred = pd.DataFrame(pred)
tr_time = pd.DataFrame(tr_time)
ts_time = pd.DataFrame(ts_time)
truelab.to_csv("scVI_" + str(col) + "_true.csv", index=False)
pred.to_csv("scVI_" + str(col) + "_pred.csv", index=False)
tr_time.to_csv("scVI_" + str(col) + "_training_time.csv", index=False)
ts_time.to_csv("scVI_" + str(col) + "_test_time.csv", index=False)
def run_scVI(trainname, testname, n):
#trainDataPath = "/Users/yue/Dropbox (Sydney Uni)/scclassify/scRNAseq_Benchmark_datasets/Pancreatic_data/Segerstolpe/Filtered_Segerstolpe_HumanPancreas_data.csv"
#train = pd.read_csv(trainDataPath,index_col=0,sep=',')
#trainLabelsPath = "/Users/yue/Dropbox (Sydney Uni)/scclassify/scRNAseq_Benchmark_datasets/Pancreatic_data/Segerstolpe/Labels.csv"
#trainlabels = pd.read_csv(trainLabelsPath, header=0,index_col=None, sep=',')
#testDataPath = "/Users/yue/Dropbox (Sydney Uni)/scclassify/scRNAseq_Benchmark_datasets/Pancreatic_data/Xin/Filtered_Xin_HumanPancreas_data.csv"
#test = pd.read_csv(testDataPath,index_col=0,sep=',')
#testLabelsPath = "/Users/yue/Dropbox (Sydney Uni)/scclassify/scRNAseq_Benchmark_datasets/Pancreatic_data/Xin/Labels.csv"
#testlabels = pd.read_csv(testLabelsPath, header=0,index_col=None, sep=',')
train = pd.read_csv(
'/albona/nobackup/biostat/datasets/singlecell/tabulaMuris_benchmark/' +
trainname + '.csv',
index_col=0,
sep=',')
test = pd.read_csv(
'/albona/nobackup/biostat/datasets/singlecell/tabulaMuris_benchmark/' +
testname + '.csv',
index_col=0,
sep=',')
trainlabel = pd.read_csv(
'/albona/nobackup/biostat/datasets/singlecell/tabulaMuris_benchmark/' +
trainname + '_label.csv',
header=0,
index_col=0,
sep=',')
testlabel = pd.read_csv(
'/albona/nobackup/biostat/datasets/singlecell/tabulaMuris_benchmark/' +
testname + '_label.csv',
header=0,
index_col=0,
sep=',')
newdata = pd.concat([train, test], axis=1)
newlabel = pd.concat([trainlabel, testlabel], axis=0)
#train = '/Users/yue/Dropbox (Sydney Uni)/scclassify/countmatrix/logcount/xin.csv'
#save labels as csv file with header and index column
#trainlabels.to_csv('trainLabels_scvi.csv')
#train.to_csv('trainData_scvi.csv')
#testlabels.to_csv('testLabels_scvi.csv')
#test.to_csv('testData_scvi.csv')
os.chdir("/dora/nobackup/yuec/scclassify/benchmark/scVI/vary_test")
newdata.to_csv('data_scvi.csv')
newlabel.to_csv('labels_scvi.csv')
data = CsvDataset('data_scvi.csv',
save_path="",
sep=",",
labels_file="labels_scvi.csv",
gene_by_cell=True)
n_epochs = 100
truelab = []
pred = []
## this semisupervised trainer automatically uses a part of the input data for training and a part for testing
now = time.time()
tracemalloc.start()
scanvi = SCANVI(data.nb_genes, data.n_batches, data.n_labels)
trainer_scanvi = SemiSupervisedTrainer(scanvi, data, frequency=5)
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(
indices=(list(range(0, trainlabel.shape[0]))), shuffle=False)
trainer_scanvi.labelled_set.to_monitor = ['ll', 'accuracy']
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=(list(
range(trainlabel.shape[0],
trainlabel.shape[0] + testlabel.shape[0]))),
shuffle=False)
trainer_scanvi.unlabelled_set.to_monitor = ['ll', 'accuracy']
trainer_scanvi.train(n_epochs)
snapshot = tracemalloc.take_snapshot()
mem_train = display_top(snapshot)
later = time.time()
time_train = int(later - now)
## labels of test set are in y_pred
## labels are returned in numbers, should be mapped back to the real labels
## indices are permutated
now = time.time()
tracemalloc.start()
y_true, y_pred = trainer_scanvi.unlabelled_set.compute_predictions()
snapshot = tracemalloc.take_snapshot()
mem_test = display_top(snapshot)
later = time.time()
time_test = int(later - now)
truelab.extend(y_true)
pred.extend(y_pred)
truelab = pd.DataFrame(truelab)
pred = pd.DataFrame(pred)
os.chdir("/dora/nobackup/yuec/scclassify/benchmark/scVI/vary_test")
truelab.to_csv(n + "_scVI_True.csv", index=False)
pred.to_csv(n + "_scVI_Pred.csv", index=False)
return mem_train, time_train, mem_test, time_test
def runScanvi(adata, batch, labels):
# Use non-normalized (count) data for scanvi!
# Check for counts data layer
if 'counts' not in adata.layers:
raise TypeError(
'Adata does not contain a `counts` layer in `adata.layers[`counts`]`'
)
from scvi.models import VAE, SCANVI
from scvi.inference import UnsupervisedTrainer, SemiSupervisedTrainer
from sklearn.preprocessing import LabelEncoder
from scvi.dataset import AnnDatasetFromAnnData
import numpy as np
# STEP 1: prepare the data
net_adata = adata.copy()
net_adata.X = adata.layers['counts']
del net_adata.layers['counts']
# Ensure that the raw counts are not accidentally used
del net_adata.raw # Note that this only works from anndata 0.7
# Define batch indices
le = LabelEncoder()
net_adata.obs['batch_indices'] = le.fit_transform(
net_adata.obs[batch].values)
net_adata.obs['labels'] = le.fit_transform(net_adata.obs[labels].values)
net_adata = AnnDatasetFromAnnData(net_adata)
print("scANVI dataset object with {} batches and {} cell types".format(
net_adata.n_batches, net_adata.n_labels))
#if hvg is True:
# # this also corrects for different batches by default
# net_adata.subsample_genes(2000, mode="seurat_v3")
# # Defaults from SCVI github tutorials scanpy_pbmc3k and harmonization
n_epochs_scVI = np.min([round((20000 / adata.n_obs) * 400), 400]) #400
n_epochs_scANVI = int(np.min([10, np.max([2, round(n_epochs_scVI / 3.)])]))
n_latent = 30
n_hidden = 128
n_layers = 2
# STEP 2: RUN scVI to initialize scANVI
vae = VAE(
net_adata.nb_genes,
reconstruction_loss='nb',
n_batch=net_adata.n_batches,
n_latent=n_latent,
n_hidden=n_hidden,
n_layers=n_layers,
)
trainer = UnsupervisedTrainer(
vae,
net_adata,
train_size=1.0,
use_cuda=False,
)
trainer.train(n_epochs=n_epochs_scVI, lr=1e-3)
# STEP 3: RUN scANVI
scanvi = SCANVI(net_adata.nb_genes,
net_adata.n_batches,
net_adata.n_labels,
n_hidden=n_hidden,
n_latent=n_latent,
n_layers=n_layers,
dispersion='gene',
reconstruction_loss='nb')
scanvi.load_state_dict(trainer.model.state_dict(), strict=False)
# use default parameter from semi-supervised trainer class
trainer_scanvi = SemiSupervisedTrainer(scanvi, net_adata)
# use all cells as labelled set
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(
trainer_scanvi.model, net_adata, indices=np.arange(len(net_adata)))
# put one cell in the unlabelled set
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=[0])
trainer_scanvi.train(n_epochs=n_epochs_scANVI)
# extract info from posterior
scanvi_full = trainer_scanvi.create_posterior(trainer_scanvi.model,
net_adata,
indices=np.arange(
len(net_adata)))
latent, _, _ = scanvi_full.sequential().get_latent()
adata.obsm['X_emb'] = latent
return adata
trainer_scanvi = SemiSupervisedTrainer(scanvi,
gene_dataset,
classification_ratio=50,
n_epochs_classifier=1,
lr_classification=5 * 1e-3)
# trainer_scanvi = AlternateSemiSupervisedTrainer(scanvi, gene_dataset,
# n_epochs_classifier=5, lr_classification=5 * 1e-3, kl=1)
labelled = np.where(gene_dataset.batch_indices == 0)[0]
np.random.shuffle(labelled)
unlabelled = np.where(gene_dataset.batch_indices == 1)[0]
np.random.shuffle(unlabelled)
trainer_scanvi.labelled_set = trainer_scanvi.create_posterior(indices=labelled)
trainer_scanvi.unlabelled_set = trainer_scanvi.create_posterior(
indices=unlabelled)
trainer_scanvi.train(n_epochs=5)
scanvi_labels = trainer_scanvi.full_dataset.sequential().compute_predictions(
)[1]
# predicted_labels = pd.DataFrame([scVI_labels,scanvi_labels],index=['scVI','scANVI'])
predicted_labels = pd.DataFrame(
[gene_dataset.labels.ravel(), scVI_labels, scanvi_labels],
index=['labels', 'scVI', 'scANVI'])
predicted_labels.T.to_csv(save_path + 'SIM.pred_labels.%i.mis%.2f.csv' %
(rep, misprop))
# get latent space
full_scanvi = trainer.create_posterior(trainer_scanvi.model,
gene_dataset,
indices=np.arange(len(gene_dataset)))