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standR: spatial transcriptomic analysis for GeoMx DSP data.

Ning Liu1,2,3, Dharmesh D Bhuva1,2,3, Ahmed Mohamed1,2

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Summary

This study introduces standR, an R package for analyzing Nanostring GeoMx Digital Spatial Profiler (DSP) data. standR improves the accuracy and statistical power of spatial transcriptomics analysis by addressing technical variability in gene expression data.

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Area of Science:

  • Spatial transcriptomics
  • Bioinformatics
  • Genomics

Background:

  • Understanding gene expression in tissues requires spatial context.
  • Nanostring GeoMx Digital Spatial Profiler (DSP) enables spatially resolved transcriptome measurement.
  • Current bioinformatics pipelines for GeoMx DSP data struggle with technical variability and complex designs.

Purpose of the Study:

  • To present standR, an R/Bioconductor package for end-to-end analysis of GeoMx DSP data.
  • To address limitations in current GeoMx data analysis pipelines.
  • To enhance the accuracy and reliability of spatial transcriptomics studies.

Main Methods:

  • Development of the standR R/Bioconductor package.
  • Implementation of quality control workflows for GeoMx DSP data.
  • Analysis of four previously published GeoMx DSP experiments.

Main Results:

  • The standR workflow effectively accounts for technical variability in GeoMx DSP data.
  • standR enhances the statistical power of spatial transcriptomics data analysis.
  • Case studies demonstrate standR's ability to yield in-depth biological insights.

Conclusions:

  • standR provides a robust solution for analyzing GeoMx DSP data.
  • The package improves the reliability and interpretability of spatial gene expression studies.
  • standR empowers scientists to gain deeper biological insights from spatial profiling experiments.