Joint Low Rank Representation with Symmetric Orthogonal Decomposition for Clustering of scRNA-seq Data.

Single-cell RNA transcriptome data offer a fantastic chance to investigate biological mechanisms such as cellular heterogeneity. Accurate identification of subtypes is of great importance for revealing the molecular mechanisms underlying complex diseases. Designing computational methods for cell type identification has been a hot topic recently, and various computational algorithms have been designed to estimate cell type composition. However, owing to the high sparseness, noise, and dimensionality of the obtainable scRNA-seq data, boosting prediction performance remains a challenge. In this work, a new cell type identification method is developed by integrating low rank representation (LRR) and symmetric orthogonal decomposition, named LRRS. Different from the spectral embedding algorithm in which the number of clusters is predefined, LRRS introduces a new orthogonal symmetric decomposition strategy and adaptively characterizes the local properties by measuring the weighted distance under the orthogonal space. To optimize the graph model, an efficient iterative approach is proposed to optimize each variable alternatively utilizing the alternating direction method of multipliers (ADMM). Based on the resulting similarity matrix, the spectral algorithm is adopted to group the individual cells. To evaluate the performance of LRRS, we implemented it on the eleven benchmark datasets and compared it with fourteen other cutting-edge methods in terms of prediction accuracy and normalized mutual information. The comparison results show that LRRS is effective in predicting cell type composition.