Package: scp 1.23.0

Christophe Vanderaa

scp: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis

Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics data. The package is an extension to the 'QFeatures' package and relies on 'SingleCellExpirement' to enable single-cell proteomics analyses. The package offers the user the functionality to process quantitative table (as generated by MaxQuant, Proteome Discoverer, and more) into data tables ready for downstream analysis and data visualization.

Authors:Christophe Vanderaa [aut, cre], Laurent Gatto [aut], Léopold Guyot [ctb]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
scp/json (API)

# Install 'scp' in R:
install.packages('scp', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/uclouvain-cbio/scp/issues

Pkgdown/docs site:https://uclouvain-cbio.github.io

Datasets:

On BioConductor:scp-1.23.0(bioc 3.24)scp-1.22.0(bioc 3.23)

geneexpressionproteomicssinglecellmassspectrometrypreprocessingcellbasedassaysbioconductormass-spectrometrysingle-cellsoftware

9.34 score 32 stars 272 scripts 424 downloads 2 mentions 38 exports 100 dependencies

Last updated from:af0dcfbda2. Checks:5 WARNING, 2 OK, 3 ERROR. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING270
linux-devel-x86_64WARNING620
source / vignettesOK464
linux-release-x86_64WARNING643
macos-release-arm64ERROR335
macos-oldrel-arm64ERROR435
windows-develWARNING538
windows-releaseWARNING579
windows-oldrelERROR385
wasm-releaseOK180

Exports:addReducedDimsaggregateFeaturesOverAssayscomputeSCRcumulativeSensitivityCurvedivideByReferencejaccardIndexmedianCVperCellnormalizeSCPpep2qvaluepredictSensitivityreadSCPreadSCPfromDIANNreadSingleCellExperimentreportMissingValuesscpAnnotateResultsscpComponentAggregatescpComponentAnalysisscpComponentBiplotscpComponentPlotscpDifferentialAggregatescpDifferentialAnalysisscpKeepEffectscpModelComponentMethodsscpModelEffectsscpModelFilterNPRatioscpModelFilterPlotscpModelFilterThresholdscpModelFilterThreshold<-scpModelFormulascpModelInputscpModelNamesscpModelResidualsscpModelWorkflowscpRemoveBatchEffectscpVarianceAggregatescpVarianceAnalysisscpVariancePlotscpVolcanoPlot

Dependencies:abindAnnotationFilteraskpassbase64encBiobaseBiocBaseUtilsBiocGenericsbslibcachemcliclueclustercpp11crosstalkcurldata.tableDelayedArraydigestdplyrevaluatefarverfastmapfdrtoolfontawesomefsgenericsGenomicRangesggplot2ggrepelgluegtablehighrhtmltoolshtmlwidgetshttrigraphIHWIRangesisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelpsymphonymagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemetapodmimeMsCoreUtilsMultiAssayExperimentnipalsopensslotelpillarpkgconfigplotlyplyrpromisesProtGenericspurrrQFeaturesR6rappdirsRColorBrewerRcppreshape2rlangrmarkdownS4ArraysS4VectorsS7sassscalesSeqinfoSingleCellExperimentslamSparseArraystringistringrSummarizedExperimentsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunXVectoryaml

Single Cell Proteomics data modelling using scplainer
Introduction | Example data set | Data modelling | Peptide filtering | Model exploration: analysis of variance | Model exploration: differential abundance analysis | Model exploration: component analysis | Batch correction | Session information | License | Reference
Rendered fromscp_data_modelling.Rmdusingknitr::rmarkdown

Last update: 2025-07-02
Started: 2024-04-02

Load Single-Cell Proteomics data using readSCP
The scp data framework | Run identifier column (runCol) | Feature annotations | colData table | readSCP() | Sample names | Special case: empty samples | Running readSCP | Under the hood | License | Reference
Rendered fromread_scp.Rmdusingknitr::rmarkdown

Last update: 2025-07-02
Started: 2021-06-21

Single Cell Proteomics data processing and analysis
The scp package | Before you start | Read in SCP data | Clean missing data | Filter PSMs | Filter features based on feature annotations | Filter assays based on detected features | Filter features based on SCP metrics | Filter features to control for FDR | Process the PSM data | Relative reporter ion intensity | Aggregate PSM data to peptide data | Join the SCoPE2 sets in one assay | Filter single-cells | Filter samples of interest | Filter based on the median relative intensity | Filter based on the median CV | Process the peptide data | Normalization | Remove peptides with high missing rate | Log-transformation | Aggregate peptide data to protein data | Process the protein data | Imputation | Batch correction | Dimension reduction | PCA | UMAP | Monitoring data processing | Session information | License | Reference
Rendered fromscp.Rmdusingknitr::rmarkdown

Last update: 2025-07-02
Started: 2020-08-18

QFeatures in a nutshell
The QFeatures class | Accessing the data | Quantitative data | Feature metadata | Sample metadata | Subsetting the data | Subset assays | Subset samples | Subset features | Common processing steps | Missing data assignment | Feature aggregation | Normalization | Log transformation | Imputation | Data visualization | Session information | License | Reference
Rendered fromQFeatures_nutshell.Rmdusingknitr::rmarkdown

Last update: 2025-05-28
Started: 2021-07-16

Advanced usage of scp
About this vignette | Modify the quantitative data | Create a new assay | Overwrite an existing assay | Check for validity | Modify the sample annotations | Modify the feature annotations | Create a new function for scp | What's next? | Session information | License
Rendered fromadvanced.Rmdusingknitr::rmarkdown

Last update: 2024-04-08
Started: 2021-07-27

Reporting missing values for Single Cell Proteomics
Introduction | Minimal data processing | Report missing values | Advanced criteria | Jaccard index distribution | Assessing the total sensitivity | License | Reference
Rendered fromreporting_missing_values.Rmdusingknitr::rmarkdown

Last update: 2024-04-08
Started: 2023-05-22

Readme and manuals

Help Manual

Help pageTopics
Add scplainer Component Analysis ResultsaddReducedDims
Aggregate features over multiple assaysaggregateFeaturesOverAssays aggregateFeaturesOverAssays-deprecated
Compute the sample over carrier ratio (SCR)computeSCR
Cumulative sensitivity curvecumulativeSensitivityCurve predictSensitivity
Divide assay columns by a reference columndivideByReference
Compute the pairwise Jaccard indexjaccardIndex
Minimally processed single-cell proteomics data setleduc_minimal
Compute the median coefficient of variation (CV) per cellmedianCVperCell
Example MaxQuant/SCoPE2 outputmqScpData
Normalize single-cell proteomics (SCP) datanormalizeSCP
Compute q-valuespep2qvalue
Read single-cell proteomics tabular datareadSCP readSCPfromDIANN readSingleCellExperiment
Four metrics to report missing valuesreportMissingValues
Single cell sample annotationsampleAnnotation
Single Cell QFeatures datascp1
Annotate single-cell proteomics analysis outputscpAnnotateResults
scplainer: linear models to understand mass spectrometry-based single-cell proteomics datascplainer
Class to store the results of single-cell proteomics modellingclass:ScpModel ScpModel ScpModel-class scpModelEffects scpModelFilterNPRatio scpModelFilterThreshold scpModelFilterThreshold<- scpModelFormula scpModelInput scpModelNames scpModelResiduals
Correct single-cell proteomics datascpKeepEffect ScpModel-DataCorrection scpRemoveBatchEffect
Differential abundance analysis for single-cell proteomicsscpDifferentialAggregate scpDifferentialAnalysis ScpModel-DifferentialAnalysis scpVolcanoPlot
Analysis of variance for single-cell proteomicsScpModel-VarianceAnalysis scpVarianceAggregate scpVarianceAnalysis scpVariancePlot
Modelling single-cell proteomics dataScpModel-Workflow scpModelFilterPlot scpModelWorkflow
Component analysis for single cell proteomicsscpComponentAggregate scpComponentAnalysis scpComponentBiplot scpComponentPlot ScpModel-ComponentAnalysis scpModelComponentMethods
Class to store the components of an estimated model for a featureclass:ScpModelFit ScpModelFit ScpModelFit-class