A data-driven clustering recommendation method for single-cell RNA-sequencing data

Abstract

Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it possible to solve biological problems at the single-cell resolution. One of the critical steps in cellular heterogeneity analysis is the cell type identification. Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters. Among all the methods, hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning, dropout imputation, and dimensionality reduction. In this study, we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions. The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension, while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension. Motivated by this finding, a new strategy, called QRS, is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not. Finally, a data-driven clustering recommendation method, called DDCR, is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data. We perform DDCR on two typical single cell clustering methods, SC3 and RAFSIL, and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.

PICF-LDA: a topic enhanced LDA with probability incremental correction factor for Web API service clustering

“…A data-driven clustering recommendation method, called DDCR, is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data. They perform DDCR on two typical single cell clustering methods, SC3 and RAFSIL, and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results [33]. Hu presented a two-level weighting strategy to measure the importance of views and features.…”

CoSPARP: Collaborative and Social-Personality Aware Recommendation of Programmes

“…In addition to recommender systems in domains such as personalized travel [40], wireless networks [41], data-driven clustering [42], and anchors on live streaming platforms [43], personality-aware recommender systems have shown great success in identifying similar users based on their personality types. In relation to personality-aware recommendations in the domains of education and academia, many researchers have worked in this area.…”

A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition

“…Deep learning has advanced the structure of recommender systems and provides several ways to improve the performance of recommender systems. The development of deep learning-based recommender systems has received great attention because these systems can overcome the limitations of existing CF-models (e.g., data sparsity problem, cold-start problem, scalability problem, and long tail problem) and achieve high recommendation quality [7,8,[27][28][29][30]. Li, et al [31] extracted latent features of user preferences or ratings using restricted Boltzmann machines and an undirected two-layer graphic model as a kind of graphic probabilistic model.…”