Accurate Single-Cell Clustering through Ensemble Similarity Learning.

ABSTRACT: Single-cell sequencing provides novel means to interpret the transcriptomic profiles of individual cells. To obtain in-depth analysis of single-cell sequencing, it requires effective computational methods to accurately predict single-cell clusters because single-cell sequencing techniques only provide the transcriptomic profiles of each cell. Although an accurate estimation of the cell-to-cell similarity is an essential first step to derive reliable single-cell clustering results, it is challenging to obtain the accurate similarity measurement because it highly depends on a selection of genes for similarity evaluations and the optimal set of genes for the accurate similarity estimation is typically unknown. Moreover, due to technical limitations, single-cell sequencing includes a larger nu

SUBMITTER: Jeong H 

PROVIDER: S-EPMC8623803 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature