Package: proDA 1.19.0

Constantin Ahlmann-Eltze

proDA: Differential Abundance Analysis of Label-Free Mass Spectrometry Data

Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins.

Authors:Constantin Ahlmann-Eltze [aut, cre], Simon Anders [ths]

proDA_1.19.0.tar.gz
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proDA.pdf |proDA.html✨
proDA/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/const-ae/proda/issues

On BioConductor:proDA-1.19.0(bioc 3.20)proDA-1.18.0(bioc 3.19)

bioconductor-package

19 exports 1.24 score 30 dependencies 1 dependents 1 mentions

Last updated 2 months agofrom:82055d023a

Exports:abundancescoefficient_variance_matricescoefficientsconvergencedesigndist_approxfeature_parametersgenerate_synthetic_datahyper_parametersinvprobitmedian_normalizationpd_lmpd_row_f_testpd_row_t_testpredictproDAreference_levelresult_namestest_diff

Dependencies:abindaskpassBiobaseBiocGenericscrayoncurlDelayedArrayextraDistrGenomeInfoDbGenomeInfoDbDataGenomicRangeshttrIRangesjsonlitelatticeMatrixMatrixGenericsmatrixStatsmimeopensslR6RcppS4ArraysS4VectorsSparseArraySummarizedExperimentsysUCSC.utilsXVectorzlibbioc

Proteomics Data Import

Constantin Ahlmann-Eltze

Rendered fromdata-import.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2019-08-05
Started: 2019-05-31

Introduction

Constantin Ahlmann-Eltze

Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2019-08-05
Started: 2019-05-31

Readme and manuals

Help Manual

Help pageTopics
Fluent use of accessor methods$,proDAFit-method $<-,proDAFit-method .DollarNames.proDAFit dollar_methods
Get the abundance matrixabundances
Get different features and elements of the 'proDAFit' objectabundances,proDAFit-method accessor_methods coefficients,proDAFit-method coefficient_variance_matrices,proDAFit-method convergence,proDAFit-method design,proDAFit-method feature_parameters,proDAFit-method hyper_parameters,proDAFit-method reference_level,proDAFit-method
Get the coefficientscoefficient_variance_matrices
Get the coefficientscoefficients
Get the convergence informationconvergence
Calculate an approximate distance for 'object'dist_approx
Distance method for 'proDAFit' objectdist_approx,ANY-method dist_approx,proDAFit-method dist_approx,SummarizedExperiment-method dist_approx_impl
Get the feature parametersfeature_parameters
Generate a dataset according to the probabilistic dropout modelgenerate_synthetic_data
Get the hyper parametershyper_parameters
Inverse probit functioninvprobit
Column wise median normalization of the data matrixmedian_normalization
Fit a single linear probabilistic dropout modelpd_lm
Row-wise tests of difference using the probabilistic dropout modelpd_row_f_test pd_row_t_test
Predict the parameters or values of additional proteinspredict,proDAFit-method
Main function to fit the probabilistic dropout modelproDA
proDA: Identify differentially abundant proteins in label-free mass spectrometryproDA_package
proDA Class Definition.proDAFit proDAFit-class
Get the reference levelreference_level
Get the result_namesresult_names
Identify differentially abundant proteinsresult_names,proDAFit-method test_diff