Package: limpa 0.99.10

Gordon Smyth

limpa: Quantification and Differential Analysis of Proteomics Data

Quantification and differential analysis of mass-spectrometry proteomics data, with probabilistic recovery of information from missing values. Estimates the detection probability curve (DPC), which relates the probability of successful detection to the underlying expression level of each peptide, and uses it to incorporate peptide missing values into protein quantification and into subsequent differential expression analyses. The package produces objects suitable for downstream analysis in limma. The package accepts peptide-level data with missing values and produces complete protein quantifications without missing values. The uncertainty introduced by missing value imputation is propagated through to the limma analyses using variance modeling and precision weights. The package name "limpa" is an acronym for "Linear Models for Proteomics Data".

Authors:Mengbo Li [aut], Gordon Smyth [cre, aut]

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limpa.pdf |limpa.html
limpa/json (API)
NEWS

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

On BioConductor:limpa-0.99.8(bioc 3.21)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bayesianbiologicalquestiondataimportdifferentialexpressiongeneexpressionmassspectrometrypreprocessingproteomicsregressionsoftware

3.85 score 6 downloads 52 exports 3 dependencies

Last updated 3 days agofrom:daba68a28d. Checks:6 OK, 3 NOTE. Indexed: yes.

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Exports:completeMomentsONdpcdpcCNdpcDEdpcImputedpcImpute.defaultdpcImpute.EListdpcImputeHyperparamdpcQuantdpcQuant.defaultdpcQuant.EListdpcQuantHyperparamdztbinomestimateDPCInterceptexpTiltByColumnsexpTiltByRowsfilterCompoundProteinsfilterCompoundProteins.defaultfilterCompoundProteins.EListfilterCompoundProteins.EListRawfilterNonProteotypicPeptidesfilterNonProteotypicPeptides.defaultfilterNonProteotypicPeptides.EListfilterNonProteotypicPeptides.EListRawfilterSingletonPeptidesfilterSingletonPeptides.defaultfilterSingletonPeptides.EListfilterSingletonPeptides.EListRawfitZTLogitimputeByExpTiltimputeByExpTilt.defaultimputeByExpTilt.EListimputeByExpTilt.EListRawobservedMomentsCNpeptides2ProteinBFGSpeptides2ProteinNewtonpeptides2Proteinspeptides2ProteinWithoutNAsplotDPCplotMDSUsingSEsplotProteinproteinResVarFromCompletePeptideDatapztbinomreadDIANNreadSpectronautremoveNARowsremoveNARows.defaultremoveNARows.EListsimCompleteDataCNsimCompleteDataONsimProteinDataSetvoomaLmFitWithImputation

Dependencies:data.tablelimmastatmod

Analyzing mass spectrometry data with limpa

Rendered fromlimpa.Rmdusingknitr::rmarkdownon Mar 27 2025.

Last update: 2025-03-25
Started: 2025-01-02

Readme and manuals

Help Manual

Help pageTopics
Linear Models for Proteomics Data (Accounting for Missing Values)limpa-package limpa
Complete Distribution Moments from Observed Normal ModelcompleteMomentsON
Detection Probability Curve Assuming Observed Normal Modeldpc
Detection Probability Curve Assuming Complete Normal ModeldpcCN
Fit Linear Model With Precision WeightsdpcDE
Quantify ProteinsdpcImpute dpcImpute.default dpcImpute.EList dpcQuant dpcQuant.default dpcQuant.EList
Estimate Hyperparameters for DPC-QuantdpcImputeHyperparam dpcQuantHyperparam
Estimate DPC InterceptestimateDPCIntercept
Filtering Based On Protein AnnotationfilterCompoundProteins filterCompoundProteins.default filterCompoundProteins.EList filterCompoundProteins.EListRaw filterNonProteotypicPeptides filterNonProteotypicPeptides.default filterNonProteotypicPeptides.EList filterNonProteotypicPeptides.EListRaw filterSingletonPeptides filterSingletonPeptides.default filterSingletonPeptides.EList filterSingletonPeptides.EListRaw
Fit Capped Logistic Regression To Zero-Truncated Binomial DatafitZTLogit
Impute Missing Values by Exponential TiltingexpTiltByColumns expTiltByRows imputeByExpTilt imputeByExpTilt.default imputeByExpTilt.EList imputeByExpTilt.EListRaw
Observed Distribution Moments from Complete Normal ModelobservedMomentsCN
DPC-Quant for One Proteinpeptides2ProteinBFGS peptides2ProteinNewton peptides2ProteinWithoutNAs
DPC-Quant for Many Proteinspeptides2Proteins
Plot the Detection Probability CurveplotDPC
Multidimensional Scaling Plot of Gene Expression Profiles, Using Standard ErrorsplotMDSUsingSEs
Plot protein summary with error bars by DPC-QuantplotProtein
Protein Residual Variances From Complete Peptide DataproteinResVarFromCompletePeptideData
Read Peptide-Precursor Intensities From DIA-NN OutputreadDIANN
Read Peptide-Precursor Intensities From Spectronaut OutputreadSpectronaut
Remove Entirely NA Rows from Matrix or EListremoveNARows removeNARows.default removeNARows.EList
Simulate Complete Data From Complete or Observed Normal ModelssimCompleteDataCN simCompleteDataON
Simulate Peptide Data with NAs By Complete Normal ModelsimProteinDataSet
Apply vooma-lmFit Pipeline With Automatic Estimation of Sample Weights and Block CorrelationvoomaLmFitWithImputation
Zero-Truncated Binomial Distributiondztbinom pztbinom ZeroTruncatedBinomial