Abstract：
:Single-cell RNA sequencing (scRNA-seq) has recently brought new insight into cell differentiation processes and functional variation in cell subtypes from homogeneous cell populations. A lack of prior knowledge makes unsupervised machine learning methods, such as clustering, suitable for analyzing scRNA-seq . However, there are several limitations to overcome, including high dimensionality, clustering result instability, and parameter adjustment complexity. In this study, we propose a method by combining structure entropy and k nearest neighbor to identify cell subpopulations in scRNA-seq data. In contrast to existing clustering methods for identifying cell subtypes, minimized structure entropy results in natural communities without specifying the number of clusters. To investigate the performance of our model, we applied it to eight scRNA-seq datasets and compared our method with three existing methods (nonnegative matrix factorization, single-cell interpretation via multikernel learning, and structural entropy minimization principle). The experimental results showed that our approach achieves, on average, better performance in these datasets compared to the benchmark methods.
journal_name
Genes (Basel)journal_title
Genesauthors
Zhu X,Li HD,Xu Y,Guo L,Wu FX,Duan G,Wang Jdoi
10.3390/genes10020098subject
Has Abstractpub_date
2019-01-29 00:00:00issue
2issn
2073-4425pii
genes10020098journal_volume
10pub_type
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