Package: PRONE 1.1.0

Lis Arend

PRONE: The PROteomics Normalization Evaluator

High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.

Authors:Lis Arend [aut, cre]

PRONE_1.1.0.tar.gz
PRONE_1.1.0.zip(r-4.5)PRONE_1.1.0.zip(r-4.4)
PRONE_1.1.0.tgz(r-4.4-any)
PRONE_1.1.0.tar.gz(r-4.5-noble)PRONE_1.1.0.tar.gz(r-4.4-noble)
PRONE_1.1.0.tgz(r-4.4-emscripten)
PRONE.pdf |PRONE.html
PRONE/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/lisiarend/prone/issues

Datasets:
  • spike_in_de_res - Example data.table of DE results of a spike-in proteomics data set
  • spike_in_se - Example SummarizedExperiment of a spike-in proteomics data set
  • tuberculosis_TMT_de_res - Example data.table of DE results of a real-world proteomics data set
  • tuberculosis_TMT_se - Example SummarizedExperiment of a real-world proteomics data set

On BioConductor:PRONE-1.1.0(bioc 3.21)PRONE-1.0.0(bioc 3.20)

proteomicspreprocessingnormalizationdifferentialexpressionvisualizationdata-analysisevaluation

3.70 score 1 stars 7 scripts 90 exports 270 dependencies

Last updated 2 days agofrom:fd54f01364. Checks:ERROR: 2 WARNING: 3. Indexed: yes.

TargetResultDate
Doc / VignettesFAILOct 31 2024
R-4.5-winWARNINGOct 31 2024
R-4.5-linuxERROROct 31 2024
R-4.4-winWARNINGOct 31 2024
R-4.4-macWARNINGOct 31 2024

Exports:apply_thresholdscheck_input_assayscheck_plot_DE_parameterscheck_stats_spiked_DE_parametersdetect_outliers_POMAeigenMSNormexport_dataexpress_to_DTextract_consensus_DE_candidatesfilter_out_complete_NA_proteinsfilter_out_NA_proteins_by_thresholdfilter_out_proteins_by_IDfilter_out_proteins_by_valueget_color_valueget_condition_valueget_facet_valueget_label_valueget_NA_overviewget_normalization_methodsget_overview_DEget_proteins_by_valueget_shape_valueget_spiked_stats_DEglobalIntNormglobalMeanNormglobalMedianNormimpute_seirsNormlimmaNormload_dataload_spike_dataloessCycNormloessFNormmeanNormmedianAbsDevNormmedianNormnormalize_senormalize_se_combinationnormalize_se_singlenormicsNormplot_boxplotsplot_condition_overviewplot_densitiesplot_fold_changes_spikedplot_heatmapplot_heatmap_DEplot_histogram_spikedplot_identified_spiked_proteinsplot_intersection_enrichmentplot_intragroup_correlationplot_intragroup_PCVplot_intragroup_PEVplot_intragroup_PMADplot_jaccard_heatmapplot_logFC_thresholds_spikedplot_markers_boxplotsplot_NA_densityplot_NA_frequencyplot_NA_heatmapplot_nr_prot_samplesplot_overview_DE_barplot_overview_DE_tileplot_PCAplot_profiles_spikedplot_pvalues_spikedplot_ROC_AUC_spikedplot_stats_spiked_heatmapplot_tot_int_samplesplot_TP_FP_spiked_barplot_TP_FP_spiked_boxplot_TP_FP_spiked_scatterplot_upsetplot_upset_DEplot_volcano_DEquantileNormreadPRONE_exampleremove_assays_from_SEremove_POMA_outliersremove_reference_samplesremove_samples_manuallyrlrMACycNormrlrMANormrlrNormrobnormNormrun_DEspecify_comparisonssubset_SE_by_normtib_to_DFtmmNormvsnNorm

Dependencies:abindaffyaffyioannotateAnnotationDbiAnnotationFilterapeaskpassbabelgenebackportsbase64encBHBiobaseBiocBaseUtilsBiocGenericsBiocManagerBiocParallelBiostringsbitbit64bitopsblobbootbroombslibcachemcarcarDatacaretcirclizeclasscliclockclueclustercodetoolscolorspacecommonmarkComplexHeatmapComplexUpsetcorpcorcowplotcpp11crayoncrosstalkcurldata.tableDBIdbscanDelayedArraydendsortDEqMSDerivDESeq2diagramdigestdoBydoParalleldplyrdqrngdunn.teste1071edgeRellipseevaluatefansifarverfastmapfastmatchfgseaFNNfontawesomeforeachformatRFormulafsFSAfutile.loggerfutile.optionsfuturefuture.applygenefiltergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongggcorrplotggforceggplot2ggrepelggtextglmnetGlobalOptionsglobalsgluegmpgowergprofiler2gridExtragridSVGgridtextgsignalgtablegtoolshardhathighrhmshtmltoolshtmlwidgetshttpuvhttrigraphimputeipredIRangesirlbaisobanditeratorsjanitorjpegjquerylibjsonliteKEGGRESTKernSmoothknitrlabelinglambda.rlaterlatticelavalazyevallifecyclelimmalistenvlme4lmtestlocfitlubridatemagrittrMALDIquantmarkdownMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamixOmicsModelMetricsmodelrMsCoreUtilsmsigdbrMSnbasemultcompMultiAssayExperimentmunsellmvtnormmzIDmzRncdf4nlmenloptrnnetNormalyzerDEnumDerivopensslparallellypatchworkpbkrtestpcaMethodspermutepillarpkgconfigplogrplotlyplotrixplotROCplyrpngpolyclipPOMApracmapreprocessCorepROCprodlimprogressrpromisesProtGenericsproxyPSMatchpurrrQFeaturesquantregR6randomForestRankProdrappdirsrARPACKRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppProgressRCurlrecipesreshape2rglRhdf5librjsonrlangrmarkdownRmpfrROTSrpartRSpectraRSQLiteS4ArraysS4VectorssandwichsassscalesshapeshinysitmosnakecasesnowsourcetoolsSparseArraySparseMSQUAREMstatmodstringistringrSummarizedExperimentsurvivalsvasyssystemfontsTH.datatibbletidyrtidyselecttimechangetimeDatetinytextweenrtzdbUCSC.utilsUpSetRutf8uwotvctrsveganviridisLitevsnwithrxfunXMLxml2xtableXVectoryamlzlibbioczoo

Readme and manuals

Help Manual

Help pageTopics
Apply other thresholds to DE resultsapply_thresholds
Outlier detection via POMA R Packagedetect_outliers_POMA
EigenMS NormalizationeigenMSNorm
Export the SummarizedExperiment object, the meta data, and the normalized data.export_data
Extract consensus DE candidatesextract_consensus_DE_candidates
Extract the DE results from eBayes fit of perform_limma function.extract_limma_DE
Remove proteins with NAs in all samplesfilter_out_complete_NA_proteins
Filter proteins based on their NA pattern using a specific thresholdfilter_out_NA_proteins_by_threshold
Remove proteins by their IDfilter_out_proteins_by_ID
Remove proteins by value in specific columnfilter_out_proteins_by_value
Function to get a long data table of all intensities of all kind of normalizationget_complete_dt
Function to get a long data table of all PCA1 and PCA2 values of all kind of normalizationget_complete_pca_dt
Function returning some values on the numbers of NA in the dataget_NA_overview
Function to return available normalization methods' identifier namesget_normalization_methods
Get overview table of DE resultsget_overview_DE
Get proteins by value in specific columnget_proteins_by_value
Get performance metrics of DE results of spike-in data set.get_spiked_stats_DE
Total Intensity NormalizationglobalIntNorm
Total Intensity Normalization Using the Mean for the Calculation of Scaling FactorsglobalMeanNorm
Total Intensity Normalization Using the Median for the Calculation of Scaling FactorsglobalMedianNorm
Method to impute SummarizedExperiment. This method performs a mixed imputation on the proteins. It uses a k-nearest neighbor imputation for proteins with missing values at random (MAR) and imputes missing values by random draws from a left-shifted Gaussian distribution for proteins with missing values not at random (MNAR).impute_se
Internal Reference Scaling NormalizationirsNorm
limma::removeBatchEffects (limBE)limmaNorm
Load real-world proteomics data into a SummarizedExperimentload_data
Load spike-in proteomics data into a SummarizedExperimentload_spike_data
Cyclic Loess Normalization of limmaloessCycNorm
Fast Loess Normalization of limmaloessFNorm
Mean NormalizationmeanNorm
Median Absolute Deviation NormalizationmedianAbsDevNorm
Median NormalizationmedianNorm
Normalize SummarizedExperiment object using single normalization methods or specified combinations of normalization methodsnormalize_se
Normalize SummarizedExperiment object using combinations of normalization methodsnormalize_se_combination
Normalize SummarizedExperiment object using different normalization methodsnormalize_se_single
Normics Normalization (Normics using VSN or using Median)normicsNorm
Perform DEqMSperform_DEqMS
Fitting a linear model using limmaperform_limma
Performing ROTSperform_ROTS
Plot the distributions of the normalized data as boxplotsplot_boxplots
Barplot showing the number of samples per conditionplot_condition_overview
Plot the densities of the normalized dataplot_densities
Boxplot of log fold changes of spike-in and background proteins for specific normalization methods and comparisons. The ground truth (calculated based on the concentrations of the spike-ins) is shown as a horizontal line.plot_fold_changes_spiked
Plot a heatmap of the sample intensities with optional column annotations for a selection of normalization methodsplot_heatmap
Heatmap of DE resultsplot_heatmap_DE
Plot histogram of the spike-in and background protein intensities per condition.plot_histogram_spiked
Plot number of identified spike-in proteins per sample.plot_identified_spiked_proteins
Intersect top N enrichment terms per normalization methodplot_intersection_enrichment
Plot intragroup correlation of the normalized dataplot_intragroup_correlation
Plot intragroup pooled coefficient of variation (PCV) of the normalized dataplot_intragroup_PCV
Plot intragroup pooled estimate of variance (PEV) of the normalized dataplot_intragroup_PEV
Plot intragroup pooled median absolute deviation (PMAD) of the normalized dataplot_intragroup_PMAD
Jaccard similarity heatmap of DE proteins of the different normalization methodsplot_jaccard_heatmap
Line plot of number of true and false positives when applying different logFC thresholdsplot_logFC_thresholds_spiked
Boxplots of intensities of specific markersplot_markers_boxplots
Plot the intensity distribution of proteins with and without NAsplot_NA_density
Plot protein identification overlap (x = identified in number of Samples, y=number of proteins)plot_NA_frequency
Plot heatmap of the NA patternplot_NA_heatmap
Plot number of non-zero proteins per sampleplot_nr_prot_samples
Overview plots of DE resultsplot_overview_DE_bar
Overview heatmap plot of DE resultsplot_overview_DE_tile
PCA plot of the normalized dataplot_PCA
Plot profiles of the spike-in and background proteins using the log2 average protein intensities as a function of the different concentrations.plot_profiles_spiked
Boxplot of p-values of spike-in and background proteins for specific normalization methods and comparisons. The ground truth (calculated based on the concentrations of the spike-ins) is shown as a horizontal line.plot_pvalues_spiked
Plot ROC curve and barplot of AUC values for each method for a specific comparion or for all comparisonsplot_ROC_AUC_spiked
Heatmap of performance metrics for spike-in data setsplot_stats_spiked_heatmap
Plot total protein intensity per sampleplot_tot_int_samples
Barplot of true and false positives for specific comparisons and normalization methodsplot_TP_FP_spiked_bar
Boxplot of true and false positives for specific comparisons and normalization methodsplot_TP_FP_spiked_box
Scatterplot of true positives and false positives (median with errorbars as Q1, and Q3) for all comparisonsplot_TP_FP_spiked_scatter
Create an UpSet Plot from SummarizedExperiment Dataplot_upset
Upset plots of DE results of the different normalization methodsplot_upset_DE
Volcano plots of DE resultsplot_volcano_DE
Quantile Normalization of preprocessCore package.quantileNorm
Helper function to read example datareadPRONE_example
Remove normalization assays from a SummarizedExperiment objectremove_assays_from_SE
Remove outliers samples detected by the detect_outliers_POMA functionremove_POMA_outliers
Remove reference samples of SummarizedExperiment object (reference samples specified during loading)remove_reference_samples
Remove samples with specific value in column manuallyremove_samples_manually
Cyclic Linear Regression Normalization on MA Transformed DatarlrMACycNorm
Linear Regression Normalization on MA Transformed DatarlrMANorm
Robust Linear Regression Normalization of NormalyzerDE.rlrNorm
RobNorm NormalizationrobnormNorm
Run DE analysis of a selection of normalized data setsrun_DE
Run DE analysis on a single normalized data setrun_DE_single
Create vector of comparisons for DE analysis (either by single condition (sep = NULL) or by combined condition)specify_comparisons
Additional function of the DEqMS packagespectraCounteBayes_DEqMS
Example data.table of DE results of a spike-in proteomics data setspike_in_de_res
Example SummarizedExperiment of a spike-in proteomics data setspike_in_se
Subset SummarizedExperiment object by normalization assayssubset_SE_by_norm
Weighted Trimmed Mean of M Values (TMM) Normalization of edgeR package.tmmNorm
Example data.table of DE results of a real-world proteomics data settuberculosis_TMT_de_res
Example SummarizedExperiment of a real-world proteomics data settuberculosis_TMT_se
Variance Stabilization Normalization of limma package.vsnNorm