Elastic Coupled Co-clustering for Single-Cell Genomic Data

The recent advances in single-cell technologies have enabled us to profile genomic features at unprecedented resolution and data sets from multiple domains are available, including data sets that profile different types of genomic features and data sets that profile the same type of genomic features across different species. These data sets 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 data set to improve the clustering performance of target data set. The degree of shared information among the target and auxiliary data sets 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 data set to the target data set. Implementation on single-cell genomic data sets 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.