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Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial

Levi van Hijfte1,2, Marjolein Geurts1, Wies R Vallentgoed1

  • 1Department of Neurology, Brain Tumor Center at Erasmus MC Cancer Center, 3015 GD Rotterdam, the Netherlands.

Iscience
|January 2, 2023
PubMed
Summary
This summary is machine-generated.

Quality assessment of NanoString GeoMx Digital Spatial Profiling (DSP) data revealed significant signal variations. Quantile normalization and alternative analysis pipelines are crucial for accurate spatial transcriptomics in glioma research.

Keywords:
Biocomputational methodData processing in systems biologyTranscriptomics

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics offers RNA expression data with tissue context.
  • Quality assessment of end-user generated spatial transcriptomics data is often lacking.
  • The NanoString GeoMx Digital Spatial Profiling (DSP) platform is a key technology in this field.

Purpose of the Study:

  • To evaluate the quality of NanoString GeoMx DSP data and standard processing pipelines.
  • To identify and address technical biases in spatial transcriptomics data.
  • To propose improved normalization and analysis strategies for NanoString GeoMx DSP data.

Main Methods:

  • Analysis of 72 regions of interest (ROIs) from 12 glioma samples.
  • Replicate experiments for validation and evaluation of five external datasets.
  • Comparison with bulk RNA sequencing and application of weighted gene co-expression network analysis.

Main Results:

  • Consistent signal intensity variations across samples and experimental conditions were observed, leading to biased analyses.
  • Quantile normalization effectively addressed technical differences in data distributions.
  • NanoString DSP data exhibited a limited dynamic range compared to bulk RNA sequencing, potentially underestimating condition-specific differences.

Conclusions:

  • NanoString GeoMx DSP data necessitate specialized normalization methods beyond standard pipelines.
  • Alternative analysis strategies, including weighted gene co-expression network analysis, can extract meaningful biological insights.
  • Robust quality assessment and tailored data processing are essential for reliable spatial transcriptomics research in oncology.