Function reference • scp

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`scpModelComponentMethods` `scpComponentAnalysis()` `scpComponentAggregate()` `scpComponentPlot()` `scpComponentBiplot()`	Component analysis for single cell proteomics
`scpKeepEffect()` `scpRemoveBatchEffect()`	Correct single-cell proteomics data
`scpDifferentialAnalysis()` `scpDifferentialAggregate()` `scpVolcanoPlot()`	Differential abundance analysis for single-cell proteomics
`scpVarianceAnalysis()` `scpVarianceAggregate()` `scpVariancePlot()`	Analysis of variance for single-cell proteomics
`scpModelWorkflow()` `scpModelFilterPlot()`	Modelling single-cell proteomics data
`scpModelFormula()` `scpModelInput()` `scpModelFilterThreshold()` `scpModelFilterNPRatio()` `scpModelResiduals()` `scpModelEffects()` `scpModelNames()` `scpModelFilterThreshold<-`()	Class to store the results of single-cell proteomics modelling
`ScpModelFit` `ScpModelFit-class` `class:ScpModelFit`	Class to store the components of an estimated model for a feature
`addReducedDims()`	Add scplainer Component Analysis Results
`aggregateFeaturesOverAssays()`	Aggregate features over multiple assays
`computeSCR()`	Compute the sample over carrier ratio (SCR)
`cumulativeSensitivityCurve()` `predictSensitivity()`	Cumulative sensitivity curve
`divideByReference()`	Divide assay columns by a reference column
`jaccardIndex()`	Compute the pairwise Jaccard index
`leduc_minimal`	Minimally processed single-cell proteomics data set
`medianCVperCell()`	Compute the median coefficient of variation (CV) per cell
`mqScpData`	Example MaxQuant/SCoPE2 output
`normalizeSCP()`	Normalize single-cell proteomics (SCP) data
`pep2qvalue()`	Compute q-values
`readSCP()` `readSCPfromDIANN()` `readSingleCellExperiment()`	Read single-cell proteomics tabular data
`reportMissingValues()`	Four metrics to report missing values
`sampleAnnotation`	Single cell sample annotation
`scp1`	Single Cell QFeatures data
`scpAnnotateResults()`	Annotate single-cell proteomics analysis output

Reference

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