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

Levi van Hijfte*, Marjolein Geurts, Wies R. Vallentgoed, Paul H.C. Eilers, Peter A.E. Sillevis Smitt, Reno Debets, Pim J. French

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Spatial transcriptomics is a novel technique that provides RNA-expression data with tissue-contextual annotations. Quality assessments of such techniques using end-user generated data are often lacking. Here, we evaluated data from the NanoString GeoMx Digital Spatial Profiling (DSP) platform and standard processing pipelines. We queried 72 ROIs from 12 glioma samples, performed replicate experiments of eight samples for validation, and evaluated five external datasets. The data consistently showed vastly different signal intensities between samples and experimental conditions that resulted in biased analysis. We evaluated the performance of alternative normalization strategies and show that quantile normalization can adequately address the technical issues related to the differences in data distributions. Compared to bulk RNA sequencing, NanoString DSP data show a limited dynamic range which underestimates differences between conditions. Weighted gene co-expression network analysis allowed extraction of gene signatures associated with tissue phenotypes from ROI annotations. Nanostring GeoMx DSP data therefore require alternative normalization methods and analysis pipelines.

Original languageEnglish
Article number105760
JournaliScience
Volume26
Issue number1
Early online date9 Dec 2022
DOIs
Publication statusPublished - 20 Jan 2023

Bibliographical note

Funding Information:
The work presented here was funded by Het Hersentumorfonds (grant nr.: DBTF-RG201901 ).

Publisher Copyright:
© 2022 The Author(s)

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