Dual Graph regularized PCA based on Different Norm Constraints for Bi-clustering Analysis on Single-cell RNA-seq Data

In recent years, single-cell RNA sequencing (scRNA-seq) technology has made significant progress in many fields and become an important means to study cell dynamics. How to effectively mine valuable biological information from these sequencing data is a topic worthy of researching. In this paper, two new methods based on traditional principal component analysis (PCA) are proposed and used to scRNA-seq data. The first method named dual graph regularized PCA (DGPPCA) is based on Frobenius-norm and L
_2,p
-norm constraints, and the method named the dual graph-regularization PCA (DG2PPCA) is based on the nonconvex proximal Lp-norm ( 0 <; p <; 1) and the L
_2,p
-norm constraints. We apply these two new methods to five scRNA-seq datasets, and perform bi-clustering on genes and samples at the same time. Extensive experiments are conducted to explore the influence of the combination of different norm constraints in the two optimization models.