  The recent advances in single-cell technologies have enabled us to profile
genomic features at unprecedented resolution and datasets from multiple domains
are available, including datasets that profile different types of genomic
features and datasets that profile the same type of genomic features across
different species. These datasets typically have different powers in
identifying the unknown cell types through clustering, and data integration can
potentially lead to a better performance of clustering algorithms. In this
work, we formulate the problem in an unsupervised transfer learning framework,
which utilizes knowledge learned from auxiliary dataset to improve the
clustering performance of target dataset. The degree of shared information
among the target and auxiliary datasets can vary, and their distributions can
also be different. To address these challenges, we propose an elastic coupled
co-clustering based transfer learning algorithm, by elastically propagating
clustering knowledge obtained from the auxiliary dataset to the target dataset.
Implementation on single-cell genomic datasets shows that our algorithm greatly
improves clustering performance over the traditional learning algorithms. The
source code and data sets are available at
https://github.com/cuhklinlab/elasticC3.
