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Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data.

John W Hickey1,2, Yuqi Tan1,2, Garry P Nolan1,2

  • 1Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, United States.

Frontiers in Immunology
|September 6, 2021
PubMed
Summary
This summary is machine-generated.

Multiplexed imaging analysis requires careful processing. Z-score normalization and avoiding overly granular cell types improve accuracy in cell-type identification for CODEX data.

Keywords:
CODEXMultiplexed tissue imagingcell-type identificationcolonnormalizationsingle-cell analysisspatial analysisunsupervised clustering

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Area of Science:

  • Single-cell biology
  • Computational pathology
  • Bioinformatics

Background:

  • Multiplexed imaging is a powerful single-cell research tool.
  • Technical noise in multiplexed imaging data presents challenges for cell-type identification.
  • Standardized processing and analysis protocols are needed.

Purpose of the Study:

  • To evaluate different normalization and clustering techniques for single-cell multiplexed imaging.
  • To compare unsupervised clustering with hand-gating for cell-type annotation.
  • To provide recommendations for accurate cell-type labeling in CODEX data.

Main Methods:

  • Generated single-cell multiplexed imaging datasets using CODEX on human colon tissue.
  • Applied five normalization techniques and four clustering algorithms.
  • Compared 20 unique cell-type annotations against hand-gated and spatially verified labels.

Main Results:

  • Increasing cell-type granularity decreased labeling accuracy.
  • Normalization choice impacted cell-type identification accuracy more than clustering algorithm.
  • Unsupervised clustering handled segmentation noise better than hand-gating.
  • Z-score normalization effectively reduced noise in CODEX data.

Conclusions:

  • Careful selection of normalization and cell-type granularity is crucial for accurate multiplexed imaging analysis.
  • Unsupervised clustering with spatial verification offers advantages over hand-gating.
  • Recommendations are provided for robust cell-type assignment in CODEX studies.