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Protocol for directly selecting cell type marker genes for single-cell clustering analyses by Festem.

Zihao Chen1, Changhu Wang1, Ruibin Xi1

  • 1School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing 100871, China.

STAR Protocols
|December 19, 2024
PubMed
Summary

Feature selection by expectation maximization test (Festem) directly identifies cell type marker genes for single-cell RNA sequencing (scRNA-seq) data. This protocol enhances biological interpretation by providing both clustering and marker gene identification.

Keywords:
RNA-seqbioinformaticssingle cellsystems biology

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) generates high-dimensional data requiring robust feature selection.
  • Identifying cell type-specific marker genes is crucial for interpreting scRNA-seq data and understanding cellular heterogeneity.
  • Existing methods may not directly facilitate marker gene selection for downstream clustering.

Purpose of the Study:

  • To present a detailed protocol for utilizing the Feature Selection by Expectation Maximization test (Festem) for scRNA-seq data analysis.
  • To demonstrate how Festem enables direct selection of cell type marker genes.
  • To facilitate subsequent clustering and marker gene assignment for enhanced biological interpretation.

Main Methods:

  • Implementation of the Festem algorithm for feature selection.
  • Step-by-step guidance on environment setup for Festem.
  • Detailed procedures for marker gene selection, data clustering, and marker gene assignment.

Main Results:

  • Successful identification of cell type marker genes using Festem.
  • Generation of robust clustering results from scRNA-seq data.
  • Integrated output providing both clustering and marker genes for comprehensive analysis.

Conclusions:

  • The Festem protocol offers a direct and effective approach for marker gene selection in scRNA-seq data.
  • This method enhances the interpretability of biological information derived from scRNA-seq experiments.
  • The protocol provides a valuable tool for researchers analyzing single-cell gene expression data.