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.
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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.
processNanostringData.runLimmaAnalysis.limmaToFGSEA or nsdiffToFGSEA.positiveQC, negativeQC, nanostringPCA, or RLE plots.DESeq2 or edgeR.limma directly.processNanostringData ("nSolver", "RUVIII", "RUVg", or "none").runLimmaAnalysis.positiveQC and negative controls using negativeQC before downstream analysis.idCol in processNanostringData to ensure correct merging.droplevels() before analysis.NanoStringDiff for differential analysis using a generalized linear model.limma for general linear modeling.RUVSeq for removing unwanted variation.No reviews yet. Tell others what you thought.
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