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

Genes

卷:10

Wei Guo¹  Danni Luo²  Xue Zhong³  Elena Mahrt³  JinHuk Choi³  Payal Kapur⁴  Yuanqing Ma⁴  James Brugarolas⁴  GaryC. Hon⁵  Zora Modrusan⁶  Somasekar Seshagiri⁶  EricW Stawiski⁶  Ze Zhang⁷  Tao Wang⁷  Stacy Wang⁷ 

[1] BioHPC, University of Texas Southwestern Medical Center, Dallas, Texas, TX 75390, USA;

[2] Bioinformatics Core Facility, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;

[3] Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;

[4] Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;

[5] Laboratory of Regulatory Genomics, Cecil H. and Ida Green Center for Reproductive Biology Sciences, Division of Basic Reproductive Biology Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;

[6] Molecular Biology Department, Genentech, Inc., South San Francisco, CA 94080, USA;

[7] Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;

关键词: single-cell RNA-seq;    CyTOF;    SCINA;    HLRCC;    RCC;    renal cell carcinoma;    fumarase;    fumarate hydratase;   

DOI  :  10.3390/genes10070531

来源: DOAJ

【 摘 要 】

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 range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

【 授权许可】

Unknown