Ensemble Adaptive Total Variation Graph Regularized NMF for Single-cell RNA-Seq Data Analysis

Abstract

\n\nSingle-cell RNA sequencing techniques have emerged as effective approaches for finding the heterogeneity between cells and discovering the differentiation stage. Adaptive total variation graph regularized nonnegative matrix factorization (ATV-NMF) has been proposed to capture the inner geometric structure and determine whether to retain feature details or denoise, which is suitable for analyzing single-cell data. However, the rank of matrix factorization significantly affects clustering performance greatly, and it is still challenging to determine the optimal rank.\n\n\n\nTo solve the problem, in this paper, we propose an ensemble clustering method ANMF-CE to integrate several base clustering results corresponding to different parameter rank values.\n\n\n\nFirst, we use the ATV-NMF algorithm to obtain clustering results with different dimension reduction ranks. Second, the consensus function based on connected-triple-based similarity is applied to obtain the similarity matrix. Finally, the spectral clustering method is used to find the final optimal partition.\n\n\n\n\nClustering results on six single-cell sequencing datasets show that our method is more advanced than the individual ATV-NMF method and other comparison methods, which can illustrate that our method is effective in finding the heterogeneity in single-cell datasets. Moreover, the identification of gene markers also achieves accurate results.\n\n\n\nIn summary, our method is effective for analyzing single-cell RNA sequencing datasets.\n