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Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.

Lyla Atta1,2, Kalen Clifton1,2, Manjari Anant2,3

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Biorxiv : the Preprint Server for Biology
|September 11, 2023
PubMed
Summary
This summary is machine-generated.

Normalization methods in imaging-based spatially resolved transcriptomics (im-SRT) can skew results. Using gene panels representative of tissue biology is crucial for reliable im-SRT data analysis.

Keywords:
Normalizationdifferential expressionscaling factorspatial transcriptomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Imaging-based spatially resolved transcriptomics (im-SRT) allows high-throughput gene profiling in fixed tissues.
  • Normalization is essential in im-SRT to correct for technical variations and ensure accurate biological signal interpretation.

Approach:

  • Investigated the impact of various gene count normalization methods on im-SRT data.
  • Utilized simulated gene panels overrepresenting specific tissue regions or cell types.
  • Compared normalization effects across different gene panels and their representativeness.

Key Points:

  • Gene count-based normalization methods can introduce region- or cell type-specific biases in normalized gene expression magnitudes.
  • These biases may lead to unreliable downstream analyses, including differential gene expression and spatially variable gene analysis, causing false positives/negatives.
  • Normalization methods not relying on detected gene counts (e.g., cell volume/area) avoid these observed effects.

Conclusions:

  • Recommends using non-gene count-based normalization approaches for im-SRT data when possible.
  • Emphasizes evaluating gene panel representativeness before applying gene count-based normalization.
  • Highlights that normalization method and gene panel choice significantly influence the biological interpretation of im-SRT data.