SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples.

ABSTRACT: Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation-maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC6678337 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature