{ "_id": "6a1bd6571d7bb097a0a15e2c", "Package": "PRONE", "Type": "Package", "Title": "The PROteomics Normalization Evaluator", "Version": "1.7.0", "Authors@R": "c(person(\"Lis\", \"Arend\", email = \"lis.arend@tum.de\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-7990-8385\")))", "Description": "High-throughput omics data are often affected by\nsystematic biases introduced throughout all the steps of a\nclinical study, from sample collection to quantification.\nNormalization methods aim to adjust for these biases to make\nthe actual biological signal more prominent. However, selecting\nan appropriate normalization method is challenging due to the\nwide range of available approaches. Therefore, a comparative\nevaluation of unnormalized and normalized data is essential in\nidentifying an appropriate normalization strategy for a\nspecific data set. This R package provides different functions\nfor preprocessing, normalizing, and evaluating different\nnormalization approaches. Furthermore, normalization methods\ncan be evaluated on downstream steps, such as differential\nexpression analysis and statistical enrichment analysis.\nSpike-in data sets with known ground truth and real-world data\nsets of biological experiments acquired by either tandem mass\ntag (TMT) or label-free quantification (LFQ) can be analyzed.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "BugReports": "https://github.com/daisybio/PRONE/issues", "URL": "https://github.com/daisybio/PRONE", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "biocViews": "Proteomics, Preprocessing, Normalization,\nDifferentialExpression, Visualization", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibglpk-dev libglu1-mesa-dev libgmp3-dev make texlive\nlibicu-dev libjpeg-dev libpng-dev libuv1-dev libxml2-dev\nlibmpfr-dev libnetcdf-dev libgl1-mesa-dev libssl-dev perl\nzlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 13:03:53 UTC", "RemoteUrl": "https://github.com/bioc/PRONE", "RemoteRef": "HEAD", "RemoteSha": "cee8ec35764284e4a873caa8174703866d189785", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-30 09:40:28 UTC", "User": "root" }, "Author": "Lis Arend [aut, cre] (ORCID: )", "Maintainer": "Lis Arend ", "MD5sum": "4c2abc5cad187073b182640868c1e71f", "_user": "bioc", "_type": "src", "_file": "PRONE_1.7.0.tar.gz", "_fileid": "5859a713de64e9f5fd39ce3aae40ce669fbe520658d47c7d3aacb25c069994ce", "_filesize": 8436668, "_sha256": "5859a713de64e9f5fd39ce3aae40ce669fbe520658d47c7d3aacb25c069994ce", "_created": "2026-05-30T09:40:28.000Z", "_published": "2026-05-31T06:33:59.616Z", "_jobs": [ { "job": 78703928902, "time": 321, "config": "bioc-checks", "r": "4.6.0", "check": "NOTE", "artifact": "7307401166" }, { "job": 78703928936, "time": 575, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "WARNING", "artifact": "7307423991" }, { "job": 78703928973, "time": 577, "config": "linux-release-x86_64", "r": "4.6.0", "check": "WARNING", "artifact": "7307425704" }, { "job": 78703928932, "time": 302, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "WARNING", "artifact": "7308708522" }, { "job": 78703928786, "time": 322, "config": "macos-release-arm64", "r": "4.6.0", "check": "WARNING", "artifact": "7314456389" }, { "job": 78703928764, "time": 403, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7307252623" }, { "job": 78703928970, "time": 267, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7307396299" }, { "job": 78703928957, "time": 389, "config": "windows-devel", "r": "4.7.0", "check": "WARNING", "artifact": "7307406588" }, { "job": 78703928877, "time": 362, "config": "windows-oldrel", "r": "4.5.3", "check": "WARNING", "artifact": "7307404013" }, { "job": 78703928912, "time": 363, "config": "windows-release", "r": "4.6.0", "check": "WARNING", "artifact": "7307404111" } ], "_bioccheck": { "error": 0, "warning": 0, "note": 16 }, "_buildurl": "https://github.com/r-universe/bioc/actions/runs/26676118929", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/bioc/PRONE", "_commit": { "id": "cee8ec35764284e4a873caa8174703866d189785", "author": "A Wokaty ", "committer": "A Wokaty ", "message": "bump x.y.z version to odd y following creation of RELEASE_3_23 branch\n", "time": 1777381433 }, "_maintainer": { "name": "Lis Arend", "email": "lis.arend@tum.de", "login": "lisiarend", "description": "B.Sc. & M.Sc. Bioinformatics TUM /LMU PhD Candidate Data Science in Systems Biology, TUM School of Life Sciences\n", "uuid": 55484713, "orcid": "0000-0001-7990-8385" }, "_distro": "noble", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.4.0", "role": "Depends" }, { "package": "SummarizedExperiment", "role": "Depends" }, { "package": "dplyr", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "data.table", "role": "Imports" }, { "package": "RColorBrewer", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "S4Vectors", "role": "Imports" }, { "package": "ComplexHeatmap", "role": "Imports" }, { "package": "stringr", "role": "Imports" }, { "package": "NormalyzerDE", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "limma", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "edgeR", "role": "Imports" }, { "package": "matrixStats", "role": "Imports" }, { "package": 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"title": "Example SummarizedExperiment of a spike-in proteomics data set", "object": "spike_in_se", "file": "spike_in_se.rda", "class": [ "SummarizedExperiment" ], "fields": [], "table": false, "tojson": false }, { "name": "tuberculosis_TMT_de_res", "title": "Example data.table of DE results of a real-world proteomics data set", "object": "tuberculosis_TMT_de_res", "file": "tuberculosis_TMT_de_res.rda", "class": [ "data.table", "data.frame" ], "fields": [ "Protein.IDs", "logFC", "P.Value", "adj.P.Val", "Change", "Comparison", "Assay", "Gene.Names", "IDs" ], "rows": 9030, "table": true, "tojson": true }, { "name": "tuberculosis_TMT_se", "title": "Example SummarizedExperiment of a real-world proteomics data set", "object": "tuberculosis_TMT_se", "file": "tuberculosis_TMT_se.rda", "class": [ "SummarizedExperiment" ], "fields": [], "table": false, "tojson": false } ], "_help": [ { "page": "apply_thresholds", "title": "Apply other thresholds to DE results", "topics": [ "apply_thresholds" ] }, { "page": "detect_outliers_POMA", "title": "Outlier detection via POMA R Package", "topics": [ "detect_outliers_POMA" ] }, { "page": "eigenMSNorm", "title": "EigenMS Normalization", "topics": [ "eigenMSNorm" ] }, { "page": "export_data", "title": "Export the SummarizedExperiment object, the meta data, and the normalized data.", "topics": [ "export_data" ] }, { "page": "extract_consensus_DE_candidates", "title": "Extract consensus DE candidates", "topics": [ "extract_consensus_DE_candidates" ] }, { "page": "extract_limma_DE", "title": "Extract the DE results from eBayes fit of perform_limma function.", "topics": [ "extract_limma_DE" ] }, { "page": "filter_out_complete_NA_proteins", "title": "Remove proteins with NAs in all samples", "topics": [ "filter_out_complete_NA_proteins" ] }, { "page": "filter_out_NA_proteins_by_threshold", "title": "Filter proteins based on their NA pattern using a specific threshold", "topics": [ "filter_out_NA_proteins_by_threshold" ] }, { "page": "filter_out_proteins_by_ID", "title": "Remove proteins by their ID", "topics": [ "filter_out_proteins_by_ID" ] }, { "page": "filter_out_proteins_by_value", "title": "Remove proteins by value in specific column", "topics": [ "filter_out_proteins_by_value" ] }, { "page": "get_complete_dt", "title": "Function to get a long data table of all intensities of all kind of normalization", "topics": [ "get_complete_dt" ] }, { "page": "get_complete_pca_dt", "title": "Function to get a long data table of all PCA1 and PCA2 values of all kind of normalization", "topics": [ "get_complete_pca_dt" ] }, { "page": "get_NA_overview", "title": "Function returning some values on the numbers of NA in the data", "topics": [ "get_NA_overview" ] }, { "page": "get_normalization_methods", "title": "Function to return available normalization methods' identifier names", "topics": [ "get_normalization_methods" ] }, { "page": "get_overview_DE", "title": "Get overview table of DE results", "topics": [ "get_overview_DE" ] }, { "page": "get_proteins_by_value", "title": "Get proteins by value in specific column", "topics": [ "get_proteins_by_value" ] }, { "page": "get_spiked_stats_DE", "title": "Get performance metrics of DE results of spike-in data set.", "topics": [ "get_spiked_stats_DE" ] }, { "page": "globalIntNorm", "title": "Total Intensity Normalization", "topics": [ "globalIntNorm" ] }, { "page": "globalMeanNorm", "title": "Total Intensity Normalization Using the Mean for the Calculation of Scaling Factors", "topics": [ "globalMeanNorm" ] }, { "page": "globalMedianNorm", "title": "Total Intensity Normalization Using the Median for the Calculation of Scaling Factors", "topics": [ "globalMedianNorm" ] }, { "page": "impute_se", "title": "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).", "topics": [ "impute_se" ] }, { "page": "irsNorm", "title": "Internal Reference Scaling Normalization", "topics": [ "irsNorm" ] }, { "page": "limmaNorm", "title": "limma::removeBatchEffects (limBE)", "topics": [ "limmaNorm" ] }, { "page": "load_data", "title": "Load real-world proteomics data into a SummarizedExperiment", "topics": [ "load_data" ] }, { "page": "load_spike_data", "title": "Load spike-in proteomics data into a SummarizedExperiment", "topics": [ "load_spike_data" ] }, { "page": "loessCycNorm", "title": "Cyclic Loess Normalization of limma", "topics": [ "loessCycNorm" ] }, { "page": "loessFNorm", "title": "Fast Loess Normalization of limma", "topics": [ "loessFNorm" ] }, { "page": "meanNorm", "title": "Mean Normalization", "topics": [ "meanNorm" ] }, { "page": "medianAbsDevNorm", "title": "Median Absolute Deviation Normalization", "topics": [ "medianAbsDevNorm" ] }, { "page": "medianNorm", "title": "Median Normalization", "topics": [ "medianNorm" ] }, { "page": "normalize_se", "title": "Normalize SummarizedExperiment object using single normalization methods or specified combinations of normalization methods", "topics": [ "normalize_se" ] }, { "page": "normalize_se_combination", "title": "Normalize SummarizedExperiment object using combinations of normalization methods", "topics": [ "normalize_se_combination" ] }, { "page": "normalize_se_single", "title": "Normalize SummarizedExperiment object using different normalization methods", "topics": [ "normalize_se_single" ] }, { "page": "normicsNorm", "title": "Normics Normalization (Normics using VSN or using Median)", "topics": [ "normicsNorm" ] }, { "page": "perform_DEqMS", "title": "Perform DEqMS", "topics": [ "perform_DEqMS" ] }, { "page": "perform_limma", "title": "Fitting a linear model using limma", "topics": [ "perform_limma" ] }, { "page": "perform_ROTS", "title": "Performing ROTS", "topics": [ "perform_ROTS" ] }, { "page": "plot_boxplots", "title": "Plot the distributions of the normalized data as boxplots", "topics": [ "plot_boxplots" ] }, { "page": "plot_condition_overview", "title": "Barplot showing the number of samples per condition", "topics": [ "plot_condition_overview" ] }, { "page": "plot_densities", "title": "Plot the densities of the normalized data", "topics": [ "plot_densities" ] }, { "page": "plot_fold_changes_spiked", "title": "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.", "topics": [ "plot_fold_changes_spiked" ] }, { "page": "plot_heatmap", "title": "Plot a heatmap of the sample intensities with optional column annotations for a selection of normalization methods", "topics": [ "plot_heatmap" ] }, { "page": "plot_heatmap_DE", "title": "Heatmap of DE results", "topics": [ "plot_heatmap_DE" ] }, { "page": "plot_histogram_spiked", "title": "Plot histogram of the spike-in and background protein intensities per condition.", "topics": [ "plot_histogram_spiked" ] }, { "page": "plot_identified_spiked_proteins", "title": "Plot number of identified spike-in proteins per sample.", "topics": [ "plot_identified_spiked_proteins" ] }, { "page": "plot_intersection_enrichment", "title": "Functional enrichment analysis for analyzing the DE results of different normalization methods and biologically interpreting the results", "topics": [ "plot_intersection_enrichment" ] }, { "page": "plot_intragroup_correlation", "title": "Plot intragroup correlation of the normalized data", "topics": [ "plot_intragroup_correlation" ] }, { "page": "plot_intragroup_PCV", "title": "Plot intragroup pooled coefficient of variation (PCV) of the normalized data", "topics": [ "plot_intragroup_PCV" ] }, { "page": "plot_intragroup_PEV", "title": "Plot intragroup pooled estimate of variance (PEV) of the normalized data", "topics": [ "plot_intragroup_PEV" ] }, { "page": "plot_intragroup_PMAD", "title": "Plot intragroup pooled median absolute deviation (PMAD) of the normalized data", "topics": [ "plot_intragroup_PMAD" ] }, { "page": "plot_jaccard_heatmap", "title": "Jaccard similarity heatmap of DE proteins of the different normalization methods", "topics": [ "plot_jaccard_heatmap" ] }, { "page": "plot_logFC_thresholds_spiked", "title": "Line plot of number of true and false positives when applying different logFC thresholds", "topics": [ "plot_logFC_thresholds_spiked" ] }, { "page": "plot_markers_boxplots", "title": "Boxplots of intensities of specific markers", "topics": [ "plot_markers_boxplots" ] }, { "page": "plot_NA_density", "title": "Plot the intensity distribution of proteins with and without NAs", "topics": [ "plot_NA_density" ] }, { "page": "plot_NA_frequency", "title": "Plot protein identification overlap (x = identified in number of Samples, y=number of proteins)", "topics": [ "plot_NA_frequency" ] }, { "page": "plot_NA_heatmap", "title": "Plot heatmap of the NA pattern", "topics": [ "plot_NA_heatmap" ] }, { "page": "plot_nr_prot_samples", "title": "Plot number of non-zero proteins per sample", "topics": [ "plot_nr_prot_samples" ] }, { "page": "plot_overview_DE_bar", "title": "Overview plots of DE results", "topics": [ "plot_overview_DE_bar" ] }, { "page": "plot_overview_DE_tile", "title": "Overview heatmap plot of DE results", "topics": [ "plot_overview_DE_tile" ] }, { "page": "plot_PCA", "title": "PCA plot of the normalized data", "topics": [ "plot_PCA" ] }, { "page": "plot_profiles_spiked", "title": "Plot profiles of the spike-in and background proteins using the log2 average protein intensities as a function of the different concentrations.", "topics": [ "plot_profiles_spiked" ] }, { "page": "plot_pvalues_spiked", "title": "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.", "topics": [ "plot_pvalues_spiked" ] }, { "page": "plot_ROC_AUC_spiked", "title": "Plot ROC curve and barplot of AUC values for each method for a specific comparion or for all comparisons", "topics": [ "plot_ROC_AUC_spiked" ] }, { "page": "plot_stats_spiked_heatmap", "title": "Heatmap of performance metrics for spike-in data sets", "topics": [ "plot_stats_spiked_heatmap" ] }, { "page": "plot_tot_int_samples", "title": "Plot total protein intensity per sample", "topics": [ "plot_tot_int_samples" ] }, { "page": "plot_TP_FP_spiked_bar", "title": "Barplot of true and false positives for specific comparisons and normalization methods", "topics": [ "plot_TP_FP_spiked_bar" ] }, { "page": "plot_TP_FP_spiked_box", "title": "Boxplot of true and false positives for specific comparisons and normalization methods", "topics": [ "plot_TP_FP_spiked_box" ] }, { "page": "plot_TP_FP_spiked_scatter", "title": "Scatterplot of true positives and false positives (median with errorbars as Q1, and Q3) for all comparisons", "topics": [ "plot_TP_FP_spiked_scatter" ] }, { "page": "plot_upset", "title": "Create an UpSet Plot from SummarizedExperiment Data", "topics": [ "plot_upset" ] }, { "page": "plot_upset_DE", "title": "Upset plots of DE results of the different normalization methods", "topics": [ "plot_upset_DE" ] }, { "page": "plot_volcano_DE", "title": "Volcano plots of DE results", "topics": [ "plot_volcano_DE" ] }, { "page": "quantileNorm", "title": "Quantile Normalization of preprocessCore package.", "topics": [ "quantileNorm" ] }, { "page": "readPRONE_example", "title": "Helper function to read example data", "topics": [ "readPRONE_example" ] }, { "page": "remove_assays_from_SE", "title": "Remove normalization assays from a SummarizedExperiment object", "topics": [ "remove_assays_from_SE" ] }, { "page": "remove_POMA_outliers", "title": "Remove outliers samples detected by the 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