SCINA: Semi-Supervised Analysis of Single Cells in Silico

• Activity Number: 351 - Statistical Methods for Single-Cell Genomics
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Ze Zhang* and Tao Wang and Payal Kapur and Xinlei Wang and Gary Hon and James Brugarolas
• Companies: University of Texas Southwestern Medical Center at Dallas and University of Texas Southwestern Medical Center and University of Texas Southwestern Medical Center and Southern Methodist University and University of Texas Southwestern Medical Center and University of Texas Southwestern Medical Center
• Keywords: scRNA-seq; CyTOF; SCINA; FH-deficient RCC

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 the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification relies on subjective and inaccurate human inspection. We reversed this paradigm and developed SCINA, a semi-supervised model for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation–maximization (EM) algorithm. We showed SCINA's accuracy, stableness and efficiency exceeded unsupervised approaches. Notably, SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out mouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides methodological advance from perspectives different from traditional analytical methods.