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

Minjie Lyu, Lin Xin, Huan Jin, Lou T. Chitkushev, Guanglan Zhang, Derin B. Keskin, Vladimir Brusic

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review


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.

Original languageEnglish
Title of host publicationComputational Vaccine Design
EditorsReche Pedro A.
Place of PublicationNew York
PublisherHumana Press Inc.
Number of pages15
ISBN (Electronic)9781071632390
ISBN (Print)9781071632413, 9781071632383
Publication statusPublished - 2023

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Cell type classification
  • Disease
  • Peripheral blood mononuclear cells
  • Protocol
  • Single-cell transcriptomics
  • Supervised machine learning

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics


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