# Nanotube

> NanoTube

- Skill: `biomate-ai/nanotube` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/nanotube`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/nanotube/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/nanotube

---


# NanoTube

## Workflows

### Standard Workflow

```r
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