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