An Advanced NMF-Based Approach for Single Cell Data Clustering

Abstract

Single-cell RNA sequencing (scRNA-seq) provides transcriptomic profiling for individual cells, allowing researchers to study the heterogeneity of tissues, recognize rare cell identities and discover new cellular subtypes. Clustering analysis is usually used to predict cell class assignments and infer cell identities. However, The performance of existing single-cell clustering methods is extremely sensitive to the presence of noise data and outliers. Nevertheless, there is still no consensus on the best performing method. To address this issue, we utilize an advanced NMF for scRNA-seq data clustering based on soft self-paced learning (S3NMF). We will gradually add cells from simple to complex to our model until the model converges. In this way, the influence of noisy data and outliers can be significantly reduced. The proposed method achieves the best performance on both simulation data and real scRNA-seq data.