Protocol for Classification Single-Cell PBMC Types from Pathological Samples Using Supervised Machine Learning.

Autor:	Lyu M; School of Computer Science, University of Nottingham, Ningbo, Zhejiang, China., Xin L; School of Computer Science, University of Nottingham, Ningbo, Zhejiang, China., Jin H; School of Computer Science, University of Nottingham, Ningbo, Zhejiang, China., Chitkushev LT; Department of Computer Science, Metropolitan College, Boston University, Boston, MA, USA., Zhang G; Department of Computer Science, Metropolitan College, Boston University, Boston, MA, USA., Keskin DB; Translational Immuno-Genomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA., Brusic V; School of Computer Science, University of Nottingham, Ningbo, Zhejiang, China. vladimir.brusic@nottingham.edu.cn.
Jazyk:	angličtina
Zdroj:	Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2023; Vol. 2673, pp. 53-67.
DOI:	10.1007/978-1-0716-3239-0_4
Abstrakt:	Peripheral blood mononuclear cells (PBMC) are mixed subpopulations of blood cells composed of five cell types. PBMC are widely used in the study of the immune system, infectious diseases, cancer, and vaccine development. Single-cell transcriptomics (SCT) allows the labeling of cell types by gene expression patterns from biological samples. Classifying cells into cell types and states is essential for single-cell analyses, especially in the classification of diseases and the assessment of therapeutic interventions, and for many secondary analyses. Most of the classification of cell types from SCT data use unsupervised clustering or a combination of unsupervised and supervised methods including manual correction. In this chapter, we describe a protocol that uses supervised machine learning (ML) methods with SCT data for the classification of PBMC cell types in samples representing pathological states. This protocol has three parts: (1) data preprocessing, (2) labeling of reference PBMC SCT datasets and training supervised ML models, and (3) labeling new PBMC datasets from disease samples. This protocol enables building classification models that are of high accuracy and efficiency. Our example focuses on 10× Genomics technology but applies to datasets from other SCT platforms. (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze:	MEDLINE
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