ECBN: Ensemble Clustering based on Bayesian Network inference for Single-cell RNA-seq Data

With the development of single-cell sequencing technology, it is a hot research topic to identify the cell types using single-cell sequencing data, and many single-cell clustering algorithms have been developed to study this issue. These methods capture partial information of single-cell sequencing data, and obtain the different performance on the same data set. Combining these different results into one can improve the accuracy and validity. Here, we proposed ECBN, Ensemble Clustering based on Bayesian Network. ECBN can ensemble several different results of state-of-the-art single cell clustering methods, such as Seurat, CIDR, SC3 and t-SNE+k-means, and generate a more optimal clustering result through Bayesian network. Experiments are carried on the 5 single cell data sets and compared with 4 individual single cell clustering methods and 3 integrative methods. The size of experiment data sets ranges from 822 to 3605 and the results show that our method can achieve good performance. Moreover, ECBN can also use the graphical regularization to lighten the limitation which is generated by the different basis results.