Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data

These are source codes, example data, and example usage of SCOIT (https://github.com/deepomicslab/SCOIT). 

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

The example data contains the eight single-cell multiomic datasets (sc-GEM, SNARE-seq_adult_mouse, SNARE-seq_neonatal_mouse, sci-CAR, PEA-STA, CITE-seq, SCoPE2, scNMT-seq), which are used in the manuscript.

The example usage contains codes to analyse the eight datasets with SCOIT.