SECANT – a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics

The recent advance of single cell sequencing (scRNA-seq) technology such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) allows researchers to quantify cell surface protein abundance and RNA expression simultaneously at single cell resolution. Although CITE-seq and other similar technologies have gained enormous popularity, novel methods for analyzing this type of single cell multi-omics data are in urgent need. A limited number of available tools utilize data-driven approach, which may undermine the biological importance of surface protein data.

In this study, researchers from the University of Pittsburgh have developed SECANT, a biology-guided SEmi-supervised method for Clustering, classification, and ANnoTation of single-cell multi-omics. SECANT is used to analyze CITE-seq data, or jointly analyze CITE-seq and scRNA-seq data. The novelties of SECANT include (1) using confident cell type label identified from surface protein data as guidance for cell clustering, (2) providing general annotation of confident cell types for each cell cluster, (3) utilizing cells with uncertain or missing cell type label to increase performance, and (4) accurate prediction of confident cell types for scRNA-seq data. Besides, as a model-based approach, SECANT can quantify the uncertainty of the results through easily interpretable posterior probability, and this framework can be potentially extended to handle other types of multi-omics data. The researchers successfully demonstrated the validity and advantages of SECANT via simulation studies and analysis of public and in-house datasets from multiple tissues. They believe this new method will be complementary to existing tools for characterizing novel cell types and make new biological discoveries using single-cell multi-omics data.

General workflow of SECANT

In this study, we used manual gating or Gaussian mixture model (GMM) to classify confident cell types with ADT data, and used scVI for dimensional reduction and batch effect correction with RNA data for data preprocessing.