Journal of University of Science and Technology of China ›› 2020, Vol. 50 ›› Issue (7): 1013-1018.DOI: 10.3969/j.issn.0253-2778.2020.07.019

• Original Paper • Previous Articles     Next Articles

Identification of PBMC-related cells of single cell RNA sequence data

GONG Lejun , ZHOU Shehai, CHENG Yifei, GAO Zhihong, LI Huakang   

  1. 1. School of computer science,Nanjing university of posts and telecommunications, Nanjing 210023, China; 2. Jiangsu Key Lab of Big Data Security& Intelligent Processing, Nanjing 210023, China; 3. Zhejiang Engineering Research Center of Intelligent Medicine, Wenzhou 325035, China; 4. Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518034, China; 5. Suzhou Privacy Information Technology Company, Suzhou 215011, China
  • Received:2020-06-03 Revised:2020-06-21 Accepted:2020-06-21 Online:2020-07-31 Published:2020-06-21

Abstract: Cell type identification is one of the main tasks of single cell RNA sequencing. This paper proposes an automatic identification of cell types based on random forest (AICTRF) method to identify cell types in single-cell sequencing data. This method uses the random forest classification model for training, and then predicts unknown cell types according to the trained model. A random forest classification model was trained on human peripheral blood mononuclear cells (PBMC) sequencing data set to predict the cell types of related subtypes of human PBMC B cells. The results show that the proposed method can help researchers automatically identify cell types in single-cell sequencing data.

Key words: scRNA-seq data mining, cell type, B cell subtype, clustering, classification

CLC Number: