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

doi:10.1016/j.isci.2022.105760

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

iScience. 2022 Dec 9;26(1):105760. doi: 10.1016/j.isci.2022.105760. eCollection 2023 Jan 20.

Authors

Levi van Hijfte^{1

2}, Marjolein Geurts¹, Wies R Vallentgoed¹, Paul H C Eilers³, Peter A E Sillevis Smitt¹, Reno Debets², Pim J French¹

Affiliations

¹ Department of Neurology, Brain Tumor Center at Erasmus MC Cancer Center, 3015 GD Rotterdam, the Netherlands.
² Laboratory of Tumor Immunology, Department of Medical Oncology, Erasmus MC University Medical Center, 3015 GD Rotterdam, the Netherlands.
³ Department of Biostatistics, Erasmus MC University Medical Center, 3015 GD Rotterdam, the Netherlands.

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.

Keywords: Biocomputational method; Data processing in systems biology; Transcriptomics.