Quantification and Differential Analysis of Proteomics Data

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Documentation for package ‘limpa’ version 0.99.10

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

limpa-package	Linear Models for Proteomics Data (Accounting for Missing Values)
completeMomentsON	Complete Distribution Moments from Observed Normal Model
dpc	Detection Probability Curve Assuming Observed Normal Model
dpcCN	Detection Probability Curve Assuming Complete Normal Model
dpcDE	Fit Linear Model With Precision Weights
dpcImpute	Quantify Proteins
dpcImpute.default	Quantify Proteins
dpcImpute.EList	Quantify Proteins
dpcImputeHyperparam	Estimate Hyperparameters for DPC-Quant
dpcQuant	Quantify Proteins
dpcQuant.default	Quantify Proteins
dpcQuant.EList	Quantify Proteins
dpcQuantHyperparam	Estimate Hyperparameters for DPC-Quant
dztbinom	Zero-Truncated Binomial Distribution
estimateDPCIntercept	Estimate DPC Intercept
expTiltByColumns	Impute Missing Values by Exponential Tilting
expTiltByRows	Impute Missing Values by Exponential Tilting
filterCompoundProteins	Filtering Based On Protein Annotation
filterCompoundProteins.default	Filtering Based On Protein Annotation
filterCompoundProteins.EList	Filtering Based On Protein Annotation
filterCompoundProteins.EListRaw	Filtering Based On Protein Annotation
filterNonProteotypicPeptides	Filtering Based On Protein Annotation
filterNonProteotypicPeptides.default	Filtering Based On Protein Annotation
filterNonProteotypicPeptides.EList	Filtering Based On Protein Annotation
filterNonProteotypicPeptides.EListRaw	Filtering Based On Protein Annotation
filterSingletonPeptides	Filtering Based On Protein Annotation
filterSingletonPeptides.default	Filtering Based On Protein Annotation
filterSingletonPeptides.EList	Filtering Based On Protein Annotation
filterSingletonPeptides.EListRaw	Filtering Based On Protein Annotation
fitZTLogit	Fit Capped Logistic Regression To Zero-Truncated Binomial Data
imputeByExpTilt	Impute Missing Values by Exponential Tilting
imputeByExpTilt.default	Impute Missing Values by Exponential Tilting
imputeByExpTilt.EList	Impute Missing Values by Exponential Tilting
imputeByExpTilt.EListRaw	Impute Missing Values by Exponential Tilting
limpa	Linear Models for Proteomics Data (Accounting for Missing Values)
observedMomentsCN	Observed Distribution Moments from Complete Normal Model
peptides2ProteinBFGS	DPC-Quant for One Protein
peptides2ProteinNewton	DPC-Quant for One Protein
peptides2Proteins	DPC-Quant for Many Proteins
peptides2ProteinWithoutNAs	DPC-Quant for One Protein
plotDPC	Plot the Detection Probability Curve
plotMDSUsingSEs	Multidimensional Scaling Plot of Gene Expression Profiles, Using Standard Errors
plotProtein	Plot protein summary with error bars by DPC-Quant
proteinResVarFromCompletePeptideData	Protein Residual Variances From Complete Peptide Data
pztbinom	Zero-Truncated Binomial Distribution
readDIANN	Read Peptide-Precursor Intensities From DIA-NN Output
readSpectronaut	Read Peptide-Precursor Intensities From Spectronaut Output
removeNARows	Remove Entirely NA Rows from Matrix or EList
removeNARows.default	Remove Entirely NA Rows from Matrix or EList
removeNARows.EList	Remove Entirely NA Rows from Matrix or EList
simCompleteDataCN	Simulate Complete Data From Complete or Observed Normal Models
simCompleteDataON	Simulate Complete Data From Complete or Observed Normal Models
simProteinDataSet	Simulate Peptide Data with NAs By Complete Normal Model
voomaLmFitWithImputation	Apply vooma-lmFit Pipeline With Automatic Estimation of Sample Weights and Block Correlation
ZeroTruncatedBinomial	Zero-Truncated Binomial Distribution