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

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 st...

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Autores principales: van Hijfte, Levi, Geurts, Marjolein, Vallentgoed, Wies R., Eilers, Paul H.C., Sillevis Smitt, Peter A.E., Debets, Reno, French, Pim J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800292/
https://www.ncbi.nlm.nih.gov/pubmed/36590163
http://dx.doi.org/10.1016/j.isci.2022.105760
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author van Hijfte, Levi
Geurts, Marjolein
Vallentgoed, Wies R.
Eilers, Paul H.C.
Sillevis Smitt, Peter A.E.
Debets, Reno
French, Pim J.
author_facet van Hijfte, Levi
Geurts, Marjolein
Vallentgoed, Wies R.
Eilers, Paul H.C.
Sillevis Smitt, Peter A.E.
Debets, Reno
French, Pim J.
author_sort van Hijfte, Levi
collection PubMed
description 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.
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spelling pubmed-98002922022-12-31 Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data van Hijfte, Levi Geurts, Marjolein Vallentgoed, Wies R. Eilers, Paul H.C. Sillevis Smitt, Peter A.E. Debets, Reno French, Pim J. iScience Article 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. Elsevier 2022-12-09 /pmc/articles/PMC9800292/ /pubmed/36590163 http://dx.doi.org/10.1016/j.isci.2022.105760 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
van Hijfte, Levi
Geurts, Marjolein
Vallentgoed, Wies R.
Eilers, Paul H.C.
Sillevis Smitt, Peter A.E.
Debets, Reno
French, Pim J.
Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data
title Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data
title_full Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data
title_fullStr Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data
title_full_unstemmed Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data
title_short Alternative normalization and analysis pipeline to address systematic bias in NanoString GeoMx Digital Spatial Profiling data
title_sort alternative normalization and analysis pipeline to address systematic bias in nanostring geomx digital spatial profiling data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800292/
https://www.ncbi.nlm.nih.gov/pubmed/36590163
http://dx.doi.org/10.1016/j.isci.2022.105760
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