Package: prolfqua 0.99.2

Christian Panse

prolfqua: Proteomics Label Free Quantification

The prolfqua package integrates essential steps of the mass spectrometry based label free quantification data analysis workflow: quality control, data normalization, protein aggregation, modeling, hypothesis testing and sample size estimation. We use prolfqua to : develop highly customizable, visually appealing, and interactive data analysis reports in pdf or HTML format; to visualize and model simple experimental designs with a single explanatory variable and complex experiments with multiple factors; to benchmark data acquisition, data preprocessing or data modeling methods. We developed and improved the package by applying the "Eating your own dog food" principle, when analysing dozens of proteomics datasets.

Authors:Witold Wolski [aut], Functional Genomics Center Zurich [fnd], Christian Panse [cre]

prolfqua_0.99.2.tar.gz
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prolfqua_0.99.2.tgz(r-4.6-any)prolfqua_0.99.2.tgz(r-4.5-any)
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prolfqua_0.99.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
prolfqua/json (API)

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

Bug tracker:https://github.com/fgcz/prolfqua/issues

Pkgdown/docs site:https://fgcz.github.io

On CRAN:

Conda:

massspectrometryproteomicsmetabolomicsnormalizationpreprocessingqualitycontrol

5.77 score 1 stars 81 scripts 184 exports 133 dependencies

Last updated from:e4e36d5bc5. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE234
linux-develOK448
source / vignettesOK308
linux-releaseOK453
macos-releaseOK350
macos-oldrelOK347
windows-develOK382
windows-releaseOK387
windows-oldrelOK288
wasm-releaseOK181

Exports:adjust_p_valuesaggregate_intensity_top_nAggregateLimpaAggregateMedpolishAggregateRlmAggregateTopNAnalysisConfigurationannotation_add_contrastsAnovaExtractorapply_to_response_matrixbuild_contrast_analysisbuild_modelbuild_model_glm_peptidebuild_model_glm_proteinbuild_model_imputebuild_model_limmabuild_model_limma_imputebuild_model_limma_voombuild_model_limma_voom_imputebuild_model_limpabuild_model_logistfcenter_to_reference_cfgcomplete_casescompute_contrastcompute_lmer_contrastcompute_pooledContrastConfigurationContrastscontrasts_linfctContrastsBinomialNestedFacadeContrastsDEqMSFacadeContrastsDEqMSVoomFacadeContrastsFacadeBaseContrastsFirthContrastsFirthFacadeContrastsFirthNestedFacadeContrastsInterfaceContrastsLimmaContrastsLimmaFacadeContrastsLimmaImputeFacadeContrastsLimmaVoomFacadeContrastsLimmaVoomImputeFacadeContrastsLimpaFacadeContrastsLimpaNestedFacadeContrastsLmerNestedFacadeContrastsLMFacadeContrastsLMImputeFacadeContrastsModeratedContrastsModeratedDEqMSContrastsPlotterContrastsRfitFacadeContrastsRfitImputeFacadeContrastsRLMFacadeContrastsROPECAContrastsROPECANestedFacadeContrastsTableencode_bin_respestimate_intensityFACADE_REGISTRYfind_package_filegenerate_contrastsgenerate_contrasts_for_factorget_anova_dfget_complete_model_fitget_contrastget_p_values_pbetaget_robscalesget_uniprot_id_from_fasta_headergroup_labelhierarchy_countsHierarchyCountsSampleimpute_from_modelinteraction_contrastsis_contaminantis_decoylevel_specific_contrastslfq_power_t_test_proteinslfq_power_t_test_quantiles_V2LFQDataLFQDataPlotterLFQDataStatsLFQDataSummariserLFQDataToSummarizedExperimentLFQDataTransformerlinfct_from_modellinfct_matrix_contrastslist_facadeslist_to_AnalysisConfigurationlist_to_ContrastConfigurationlookup_facadeLR_testmain_effect_contrastsmake_interaction_columnmake_reduced_hierarchy_configmatrix_to_tibblemerge_contrasts_resultsmissigness_histogramMissingHelpersmissingness_per_conditionmissingness_per_condition_cumsumModelmodel_analysemodel_summaryModelFirthModelInterfaceModelLimmamoderated_p_deqmsmoderated_p_deqms_longmoderated_p_limmamultigroup_volcanonr_children_experimentnr_features_experimentnr_obs_samplepairs_smoothpanel_corpivot_model_contrasts_to_wideplot_estimateplot_heatmapplot_heatmap_corplot_hierarchies_boxplotplot_hierarchies_boxplot_dfplot_hierarchies_lineplot_intensity_distribution_densityplot_intensity_distribution_violinplot_na_heatmapplot_pcaplot_rasterplot_sample_correlationplot_stat_densityplot_stat_density_medianplot_stat_violinplot_stat_violin_medianplot_stdv_vs_meanpoolvarprolfqua_dataR6_extract_valuesrank_peptide_by_intensityregister_facaderesponse_matrix_as_tibblerobust_scalesample_subsetscale_with_subsetscript_copy_helper_vecseparate_hierarchysetup_analysissim_build_models_lmsim_build_models_lmersim_build_models_logistfsim_lfq_datasim_lfq_data_2factor_configsim_lfq_data_peptide_configsim_lfq_data_protein_configsim_make_model_lmsim_make_model_lmerstrategy_binomialstrategy_limmastrategy_limpastrategy_lmstrategy_lmerstrategy_logistfstrategy_rfitstrategy_rlmStrategyBinomialStrategyLimmaStrategyLimpaStrategyLMStrategyLmerStrategyLogistfStrategyRfitStrategyRLMsummarize_hierarchysummarize_statssummarize_stats_factorssummarize_stats_quantilessummary_ROPECA_median_p_scaledtable_facadetable_factorstable_factors_sizetidy_to_wide_configtransform_work_intensityupset_interaction_missing_statsupset_missing_statsvolcano_plotlywhich_missing

Dependencies:askpassbackportsbase64encbeeswarmBiocGenericsbitbit64bootbroombslibcachemcirclizeclicliprclueclustercodetoolscolorspaceComplexHeatmapcpp11crayoncrosstalkcurldata.tabledigestdoParalleldplyrevaluatefarverfastmapfontawesomeforcatsforeachformula.toolsfsgenericsGetoptLongggbeeswarmggplot2ggrepelglmnetGlobalOptionsgluegridExtragtablehavenhighrhmshtmltoolshtmlwidgetshttrIRangesisobanditeratorsjomojquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelimmalme4lmerTestlogistfmagrittrMASSMatrixmatrixStatsmemoisemgcvmicemimeminqamitmlnlmenloptrnnetnumDerivopenssloperator.toolsordinalotelpanpillarpkgconfigplotlyplyrpngprettyunitsprogresspromisespurrrR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreadrreformulasrjsonrlangrmarkdownrpartS4VectorsS7sassscalesshapestatmodstringistringrsurvivalsystibbletidyrtidyselecttinytextzdbucminfUpSetRutf8vctrsviporviridisLitevroomwithrxfunyaml

Comparing Two Groups with prolfqua
Purpose | Simulated protein abundances | Visualization of not normalized data | Visualization of missing data | Computing standard deviations, mean and CV. | Normalize protein intensities and show diagnostic plots | Fitting a linear model | Compute contrasts | Contrasts with missing value imputation | Gene Set Enrichment Analysis (GSEA) | Session Info | References
Rendered fromComparing2Groups.Rmdusingknitr::rmarkdown

Last update: 2026-10-09
Started: 2021-03-03

Introduction to prolfqua
Introduction | Installation | Quick start | Further documentation | Session info | References
Rendered fromprolfqua.Rmdusingknitr::rmarkdown

Last update: 2026-10-09
Started: 2026-10-09

Readme and manuals

Help Manual

Help pageTopics
adjust columnsadjust_p_values
AggregateLimpaAggregateLimpa
AggregateMedpolishAggregateMedpolish
AggregateRlmAggregateRlm
AggregateTopNAggregateTopN
Analysis ConfigurationAnalysisConfiguration
DRY function: process and export annotated contrastsannotation_add_contrasts
R6 class for extracting ANOVA results as a data frameAnovaExtractor
Build a contrast analysis using one of several statistical methodsbuild_contrast_analysis
Build protein models from databuild_model
Build Firth logistic model for nested LFQDatabuild_model_glm_peptide
Build Firth logistic model for aggregated LFQDatabuild_model_glm_protein
Build protein models with LOD imputation for failed fitsbuild_model_impute
Build limma model from LFQDatabuild_model_limma
Build limma model with LOD imputation for failed proteinsbuild_model_limma_impute
Build limma model with vooma precision weights (proteomics)build_model_limma_voom
Build limma-voom model with LOD imputation for failed proteinsbuild_model_limma_voom_impute
Build limpa vooma model from aggregated LFQDatabuild_model_limpa
center to referencecenter_to_reference_cfg
Contrast ConfigurationContrastConfiguration
Estimate contrasts using Wald TestContrasts
Quasibinomial detection-count facade for nested inputContrastsBinomialNestedFacade
DEqMS contrast analysis facadeContrastsDEqMSFacade
DEqMS contrast analysis with vooma weights facadeContrastsDEqMSVoomFacade
Estimate contrasts using Wald TestContrastsFirth
Firth logistic missingness contrast analysis facadeContrastsFirthFacade
Firth logistic missingness contrast analysis facade for nested inputContrastsFirthNestedFacade
Base class for all Contrasts classesContrastsInterface
Limma-based contrasts (direct limma pipeline)ContrastsLimma
Limma contrast analysis facadeContrastsLimmaFacade
Limma contrast analysis with LOD imputation facadeContrastsLimmaImputeFacade
Limma-voom contrast analysis facadeContrastsLimmaVoomFacade
Limma-voom contrast analysis with LOD imputation facadeContrastsLimmaVoomImputeFacade
Limpa contrast analysis facadeContrastsLimpaFacade
Limpa contrast analysis facade for nested inputContrastsLimpaNestedFacade
Lmer (mixed-model) contrast analysis facade for nested inputContrastsLmerNestedFacade
LM contrast analysis facadeContrastsLMFacade
LM contrast analysis with LOD imputation facadeContrastsLMImputeFacade
Limma moderated contrastsContrastsModerated
DEqMS count-dependent moderated contrastsContrastsModeratedDEqMS
plot contrastsContrastsPlotter
Rfit rank-based regression contrast analysis facadeContrastsRfitFacade
Rfit rank-regression contrast analysis with LOD imputation facadeContrastsRfitImputeFacade
RLM contrast analysis facadeContrastsRLMFacade
ROPECA reproducibility-optimization methodContrastsROPECA
ROPECA contrast analysis facade for nested inputContrastsROPECANestedFacade
holds results when contrasts are added.ContrastsTable
Residual degrees of freedom for an augmented rfit fitdf.residual.rfit_prolfqua
Registry of available contrast facade classesFACADE_REGISTRY
find file stored in packagefind_package_file
Combined generate_contrastsgenerate_contrasts
Single-factor contrasts (pairwise comparisons)generate_contrasts_for_factor
Extracts uniprot IDget_uniprot_id_from_fasta_header
group label functiongroup_label
Count distinct elements for each level of hierarchy per sampleHierarchyCountsSample
Fill missing responses with predictions from per-subject linear modelsimpute_from_model
Interaction contrasts (difference of differences)interaction_contrasts
Internal Functions by categoryINTERNAL_FUNCTIONS_BY_FAMILY
Detect contaminant identifiersis_contaminant
Detect decoy / reverse-database identifiersis_decoy
Level-specific contrasts (per secondary level)level_specific_contrasts
LFQData R6 classLFQData
LFQDataPlotter -- Create various visualization of the LFQdataLFQDataPlotter
Decorates LFQData with methods to compute statistics of interactionsLFQDataStats
Summarize LFQDataLFQDataSummariser
converts LFQData object to SummarizedExperimentLFQDataToSummarizedExperiment
Decorate LFQData with Methods for transforming IntensitiesLFQDataTransformer
List currently registered contrast facadeslist_facades
read minimal yaml to reconstruct configurationlist_to_AnalysisConfiguration
Rebuild a ContrastConfiguration from a listlist_to_ContrastConfiguration
Look up a contrast facade by short namelookup_facade
Likelihood ratio testLR_test
main effects contrastsmain_effect_contrasts
matrix or data.frame to tibble (taken from tidyquant)matrix_to_tibble
Merge contrast results coming from two different model.merge_contrasts_results
Group statistics and limit of detectionMissingHelpers
R6 class representing modelling resultModel
R6 class representing modelling resultModelFirth
R6 interface class representing modelling resultModelInterface
R6 class representing a limma modelling resultModelLimma
Max nr_children across samples per hierarchy unitnr_children_experiment
Count distinct child features per hierarchy unitnr_features_experiment
Aggregates e.g. protein abundances from peptide abundancesnr_obs_sample
Package DataPACKAGE_DATA
pivot model contrasts matrix to wide format produced by `contrasts_linfct` and ...pivot_model_contrasts_to_wide
load data from prolfquaprolfqua_data
Extract all value slots in an R6 objectR6_extract_values
Register a contrast facade classregister_facade
Setup a tidy table compatible with a 'AnalysisConfiguration'setup_analysis
Scale estimate for an augmented rfit fitsigma.rfit_prolfqua
simulate protein level data with two groupssim_lfq_data
Simulate data, protein, with config with 2 factors Treatment and Backgroundsim_lfq_data_2factor_config
Simulate data, protein and peptide, with configsim_lfq_data_peptide_config
Simulate data, protein, with configsim_lfq_data_protein_config
Create a quasibinomial detection-count strategystrategy_binomial
Create limma modelling strategystrategy_limma
Create limpa modelling strategystrategy_limpa
Create Firth's logistic regression strategystrategy_lm strategy_lmer strategy_logistf strategy_rfit strategy_rlm
Quasibinomial detection-count strategyStrategyBinomial
R6 class for limma modelling strategyStrategyLimma
R6 class for limpa modelling strategyStrategyLimpa
Linear model strategy (R6 class)StrategyLM
Linear mixed-effects model strategy (R6 class)StrategyLmer
Firth's logistic regression strategy (R6 class)StrategyLogistf
Rank-based regression strategy (R6 class)StrategyRfit
Robust linear model strategy (R6 class)StrategyRLM
compute var sd etc for all factor levelssummarize_stats_factors
table facade to easily switch implementationstable_facade
UpSetR plot from interaction_missing_statsupset_interaction_missing_stats
Named variance-covariance matrix for an augmented rfit fitvcov.rfit_prolfqua
volcano plotlyvolcano_plotly
add missing values to x vector based on the values of xwhich_missing