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Updated: Jun 3, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

standR: spatial transcriptomic analysis for GeoMx DSP data.

Ning Liu^1,2,3, Dharmesh D Bhuva^1,2,3, Ahmed Mohamed^1,2

¹Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia.

Nucleic Acids Research

|November 12, 2023

Summary

This summary is machine-generated.

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.