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Related Experiment Video

Updated: Nov 22, 2025

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
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Progress and challenge for computational quantification of tissue immune cells.

Ziyi Chen1, Aiping Wu1

  • 1Suzhou Institute of Systems Medicine, Center for Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Jiangsu, Suzhou, China.

Briefings in Bioinformatics
|January 5, 2021
PubMed
Summary

Computational methods predict tissue immune cell composition from bulk transcriptomes. This review summarizes tools, limitations, and proposes solutions like tissue-specific datasets to improve immune cell quantification accuracy.

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • Tissue immune cells are crucial for maintaining bodily homeostasis.
  • Quantifying immune cell abundance enhances understanding of health and disease states.
  • Computational methods for predicting immune cell composition from bulk transcriptomes are advancing.

Purpose of the Study:

  • To review and summarize existing computational tools for immune cell quantification from transcriptomes.
  • To analyze the advantages, disadvantages, challenges, and limitations of these methods.
  • To propose solutions for improving the accuracy and applicability of computational immune cell profiling.

Main Methods:

  • Categorization of computational tools into reference-free, reference-based scoring, and reference-based deconvolution methods.
Keywords:
computational deconvolutionmachine learningtissue immune celltranscriptome

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  • Review of existing literature on computational immune cell quantification techniques.
  • Analysis of common challenges including limited cell types, gene collinearity, tissue microenvironment effects, and lack of validation datasets.

    • Main Results:

    • Existing computational tools vary in their approaches and effectiveness.
    • Key limitations identified include incomplete immune cell type representation, gene collinearity, environmental influences on gene expression, and inadequate validation standards.
    • The study highlights the need for more robust and standardized computational approaches.

    Conclusions:

    • Improved computational models are essential for accurate immune cell quantification.
    • Proposed solutions include developing tissue-specific training datasets, implementing hierarchical computational frameworks, and creating standardized benchmark datasets.
    • These advancements will facilitate a deeper understanding of immune cell roles in various physiological and pathological conditions.