Nanotube

NanoTube

Workflows

Standard Workflow

library(NanoTube)

# Process and normalize NanoString data
dat <- processNanostringData(
  nsFiles = example_data,
  sampleTab = sample_info,
  idCol = "RCC_Name",
  groupCol = "Sample_Diagnosis",
  normalization = "nSolver"
)

# Run differential expression analysis using Limma
limmaResults <- runLimmaAnalysis(dat, base.group = "None")

# Generate volcano plot of differential expression results
deVolcano(limmaResults, plotContrast = "Autoimmune.retinopathy")

# Run gene set enrichment analysis
fgseaResults <- limmaToFGSEA(limmaResults, gene.sets = ExamplePathways)

Input: RCC files or tabular expression data, sample metadata. Output: Normalized ExpressionSet, differential expression statistics, volcano plots, and GSEA results.

When to Use

• Processing, quality control, and normalization of NanoString nCounter data (RCC files or tabular counts) using processNanostringData.
• Performing differential expression analysis on NanoString data using runLimmaAnalysis.
• Performing Gene Set Enrichment Analysis (GSEA) directly from differential expression results using limmaToFGSEA or nsdiffToFGSEA.
• Visualizing NanoString data quality using positiveQC, negativeQC, nanostringPCA, or RLE plots.

When NOT to Use

• For high-throughput RNA-seq data, use standard pipelines like DESeq2 or edgeR.
• For microarray data, use limma directly.

Data Requirements

• Raw NanoString RCC files or a CSV/TXT expression matrix.
• A sample metadata CSV file containing sample characteristics and matching file names.

Key Parameters

• normalization ("nSolver"): Normalization method in processNanostringData ("nSolver", "RUVIII", "RUVg", or "none").
• bgType ("threshold"): Method for background assessment ("threshold" or "t.test").
• bgPVal (0.01): P-value threshold for the t-test background method.
• skip.housekeeping (FALSE): Whether to skip housekeeping normalization.
• n.unwanted (1): Number of dimensions of unwanted variation to remove in RUVg/RUVIII.
• base.group (NULL): Control group for contrast in runLimmaAnalysis.

Best Practices

• Perform quality control checks on positive controls using positiveQC and negative controls using negativeQC before downstream analysis.
• Verify that positive scaling factors are between 0.3 and 3, and R-squared values are greater than 0.95.
• Check housekeeping normalization scale factors to ensure they are within the recommended 0.1-10 range.

Common Pitfalls

• Mismatched sample names between RCC files and the sample metadata table; specify idCol in processNanostringData to ensure correct merging.
• Including empty factor levels in the design matrix; drop them using droplevels() before analysis.

Alternatives

• NanoStringDiff for differential analysis using a generalized linear model.
• limma for general linear modeling.
• RUVSeq for removing unwanted variation.

Citations

• Lundy et al. 2018 (referenced in vignette)
• Ritchie et al. 2015 (limma reference)

References

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