SCINA: Semi-Supervised Analysis of Single Cells in silico

Ze Zhang, M. S. Danni Luo, Xue Zhong, Jin Huk Choi, Yuanqing Ma, Elena Mahrt, Wei Guo, Eric W. Stawiski, Stacy Wang, Zora Modrusan, Somasekar Seshagiri, Payal Kapur, Xinlei Wang, Gary C. Hon, James Brugarolas, Tao Wang

Research output: Contribution to journalArticlepeer-review

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 heavily relies on subjective and inaccurate human inspection afterwards. 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 applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular 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 both methodological advances and biological insights from perspectives different from traditional analytical methods.

Original languageEnglish (US)
JournalUnknown Journal
DOIs
StatePublished - Feb 25 2019

Keywords

  • CyTOF
  • FH-deficient
  • RCC
  • SCINA
  • scRNA-seq

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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