Start a fresh conversation. Replace the highlighted placeholders.
Use bioconductor-nanotube to perform quality control and nSolver normalization for [NanoString RCC files or count matrix] with matching [sample metadata including file names and group labels], reporting control and normalization checks before downstream analysis.
Required inputs
[NanoString RCC files or count matrix]
[sample metadata including file names and group labels]
User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗
SKILL.md
name: bioconductor-nanotubedescription: NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages.when_to_use: Use when: NanoTube Vignette; Differential expression analysis (NanoTube); Quality control and preprocessing (NanoTube). Not for: Requires R ≥ 4.1 and Bioconductor ≥ 3.16user-invocable: false
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.