Standr

standR

Workflows

Standard Workflow

Perform quality control, TMM normalization, and RUV4 batch correction on NanoString GeoMx DSP data.

library(standR)
library(SpatialExperiment)
library(limma)
library(ExperimentHub)

# Load data
eh <- ExperimentHub()
countFile <- eh[["EH7364"]]
sampleAnnoFile <- eh[["EH7365"]]
featureAnnoFile <- eh[["EH7366"]]

spe <- readGeoMx(countFile, sampleAnnoFile, featureAnnoFile = featureAnnoFile, rmNegProbe = TRUE)

# Preprocess and QC
colData(spe)$regions <- paste0(colData(spe)$region,"_",colData(spe)$SegmentLabel) |> 
  (\(.) gsub("_Geometric Segment","",.))() |>
  paste0("_",colData(spe)$pathology) |>
  (\(.) gsub("_NA","_ns",.))()

spe <- addPerROIQC(spe, rm_genes = TRUE)
spe <- spe[,rownames(colData(spe))[colData(spe)$lib_size > 50000]]

# Normalization
colData(spe)$biology <- paste0(colData(spe)$disease_status, "_", colData(spe)$regions)
spe_tmm <- geomxNorm(spe, method = "TMM")

# Batch correction
spe <- findNCGs(spe, batch_name = "SlideName", top_n = 500)
spe_ruv <- geomxBatchCorrection(spe, factors = "biology", NCGs = metadata(spe)$NCGs, k = 5)

Input: Raw NanoString GeoMx DSP count and annotation files from ExperimentHub; Output: A normalized, batch-corrected SpatialExperiment object.

When to Use

• Analyzing NanoString GeoMx DSP data using SpatialExperiment structures.
• Performing gene-level and ROI-level quality control using addPerROIQC and filtering based on library size (lib_size).
• Normalizing spatial transcriptomics data with TMM or other methods via geomxNorm.
• Removing slide-associated batch effects using findNCGs and geomxBatchCorrection.

When NOT to Use

• For single-cell RNA-seq data without spatial coordinates, use standard packages like scran or Seurat because standR is tailored for GeoMx DSP spatial data.
• For spatial datasets requiring continuous cell state modeling across microenvironments, use Statial because standR focuses on ROI-level profiling and batch correction.

Data Requirements

• Input counts, sample annotations, and feature annotations loaded into a SpatialExperiment object using readGeoMx.
• Requires metadata columns such as lib_size for ROI filtering, and slide/batch information (e.g., SlideName) for batch correction.

Key Parameters

• rmNegProbe (TRUE): Parameter in readGeoMx to remove negative probes.
• rm_genes (TRUE): Parameter in addPerROIQC to remove non-expressed genes.
• y_threshold (50000): Library size threshold used in plotROIQC to identify low-quality ROIs.
• method ("TMM"): Normalization method in geomxNorm.
• batch_name ("SlideName"): Column name in colData representing the batch variable in findNCGs.
• top_n (500): Number of top least variable genes to select as negative control genes in findNCGs.
• factors ("biology"): Biological factors to preserve during batch correction in geomxBatchCorrection.
• k (5): Number of factors of unwanted variation to remove in geomxBatchCorrection.

Best Practices

• Merge region-related annotations (e.g., region, SegmentLabel, pathology) to avoid collinearity before batch correction.
• Run gene-level QC using addPerROIQC and visualize removed genes with plotGeneQC.
• Perform ROI-level QC using plotROIQC to identify low library size or low cell count regions.
• Inspect technical variations using Relative Log Expression (plotRLExpr) and Principal Component Analysis (drawPCA) before and after normalization.

Common Pitfalls

• Collinearity in batch correction: Avoid by merging overlapping annotations in colData before running geomxBatchCorrection.
• Incorrect normalization method: Using "RPKM" or "TPM" in geomxNorm without adding a genelength column to rowData will fail.
• Low library size ROIs confounding downstream analysis: Filter out low-quality ROIs (e.g., lib_size > 50000) before normalization.

Alternatives

• edgeR: For general differential expression and TMM normalization without spatial-specific workflows.
• limma: For linear modeling of expression data without built-in GeoMx-specific QC and batch correction.
• DESeq2: For alternative normalization and differential testing on count data.

Citations

• Ning Liu, Dharmesh Bhuva, Ahmed Mohamed, Chin Wee Tan, Melissa Davis (2026). standR: An R package for NanoString GeoMx DSP data analysis.

References

• Homepage: bioconductor.org/packages/standr
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/standr/inst/doc/standR_introduction.html