SCOT

SCOT is an implementation of a probabilistic tensor decomposition framework for single-cell multi-omics data integration. SCOT accepts the input of datasets from multiple omics, with missing values allowed.

Getting started

Prerequisite

• numpy
• pytorch 1.9.0

Install

pip install SCOT-bio

Examples

This is an example of multiple datasets when features have corresponding information.

from scot import sc_multi_omics

data = np.array([expression_data, methylation_data])
sc_model = sc_multi_omics()
predict_data = sc_model.fit(data) # the imputed data
np.savetxt("global_cell_embeddings.csv", sc_model.C, delimiter = ',') # global cell embeddings
np.savetxt("global_gene_embeddings.csv", sc_model.G, delimiter = ',') # global gene embeddings
np.savetxt("local_cell_embeddings.csv", sc_model.C, delimiter = ',') # omics-specific cell embeddings
np.savetxt("local_gene_embeddings.csv", sc_model.G, delimiter = ',') # omics-specific gene embeddings

When the features of different omics do not have corresponding information, please use the fit_list function, which accepts the input as a list of matrices.

from scot import sc_multi_omics

data = [expression_data, protein_data]
sc_model = sc_multi_omics()
predict_data = sc_model.fit_list(data)

If the input does not contain missing values ("NA"), we provide fit_complete and fit_list_complete functions to accelerate the optimization since they take advantage of matrix operations.

from scot import sc_multi_omics

data = np.array([expression_data, methylation_data])
sc_model = sc_multi_omics()
predict_data = sc_model.fit_complete(data) # the imputed data

from scot import sc_multi_omics

data = [expression_data, protein_data]
sc_model = sc_multi_omics()
predict_data = sc_model.fit_list_complete(data)

We put the complete scripts for the analysis described in the manuscript under examples/ directory for detailed usage examples and reproduction. The example data can be downloaded from Google Drive.

Parameters

sc_multi_omics

• K1: The local element-wise product parameter, see the manuscript for details (default=20).
• K2: The local element-wise product parameter (default=20).
• K3: The local element-wise product parameter (default=20).
• random_seed: The random seed used in optimization (default=111).

fit

• opt: The optimization algorithm for gradient descent, including SGD, Adam, Adadelta, Adagrad, AdamW, SparseAdam, Adamax, ASGD, LBFGS (default="Adam").
• dist:The distribution used for modeling, including gaussian, poisson, negative_bionomial (default="gaussian").
• lr: The learning rate for gradient descent (default=1e-2).
• n_epochs: The number of optimization epochs (default=1000).
• lambda_C_regularizer: The coefficient for the penalty term of global cell embeddings (default=0.01).
• lambda_G_regularizer: The coefficient for the penalty term of global gene embeddings (default=0.01).
• lambda_O_regularizer: The coefficient list for the penalty term of global omics embeddings; the length of the list should be the same with the number of omics (default=[0.01, 0.01]).
• lambda_OC_regularizer: The coefficient list for the penalty term of omics-specific cell embeddings; the length of the list should be the same with the number of omics, not avaiable for complete functions (default=[1, 1]).
• lambda_OG_regularizer: The coefficient list for the penalty term of omics-specific gene embeddings, the length of the list should be the same with the number of omics, not avaiable for list functions (default=[1, 1]).
• batch_size: The batch size used for gradient descent, not avaiable for complete functions (default=1000).
• device: CPU or GPU (default='cuda' if torch.cuda.is_available() else 'cpu').
• verbose: Whether to print loss for each epoch (default=True).

Maintainer

WANG Ruohan ruohawang2-c@my.cityu.edu.hk