The PROteomics Normalization Evaluator

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Documentation for package ‘PRONE’ version 1.1.4

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

Help Pages

apply_thresholds	Apply other thresholds to DE results
detect_outliers_POMA	Outlier detection via POMA R Package
eigenMSNorm	EigenMS Normalization
export_data	Export the SummarizedExperiment object, the meta data, and the normalized data.
extract_consensus_DE_candidates	Extract consensus DE candidates
extract_limma_DE	Extract the DE results from eBayes fit of perform_limma function.
filter_out_complete_NA_proteins	Remove proteins with NAs in all samples
filter_out_NA_proteins_by_threshold	Filter proteins based on their NA pattern using a specific threshold
filter_out_proteins_by_ID	Remove proteins by their ID
filter_out_proteins_by_value	Remove proteins by value in specific column
get_complete_dt	Function to get a long data table of all intensities of all kind of normalization
get_complete_pca_dt	Function to get a long data table of all PCA1 and PCA2 values of all kind of normalization
get_NA_overview	Function returning some values on the numbers of NA in the data
get_normalization_methods	Function to return available normalization methods' identifier names
get_overview_DE	Get overview table of DE results
get_proteins_by_value	Get proteins by value in specific column
get_spiked_stats_DE	Get performance metrics of DE results of spike-in data set.
globalIntNorm	Total Intensity Normalization
globalMeanNorm	Total Intensity Normalization Using the Mean for the Calculation of Scaling Factors
globalMedianNorm	Total Intensity Normalization Using the Median for the Calculation of Scaling Factors
impute_se	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).
irsNorm	Internal Reference Scaling Normalization
limmaNorm	limma::removeBatchEffects (limBE)
load_data	Load real-world proteomics data into a SummarizedExperiment
load_spike_data	Load spike-in proteomics data into a SummarizedExperiment
loessCycNorm	Cyclic Loess Normalization of limma
loessFNorm	Fast Loess Normalization of limma
meanNorm	Mean Normalization
medianAbsDevNorm	Median Absolute Deviation Normalization
medianNorm	Median Normalization
normalize_se	Normalize SummarizedExperiment object using single normalization methods or specified combinations of normalization methods
normalize_se_combination	Normalize SummarizedExperiment object using combinations of normalization methods
normalize_se_single	Normalize SummarizedExperiment object using different normalization methods
normicsNorm	Normics Normalization (Normics using VSN or using Median)
perform_DEqMS	Perform DEqMS
perform_limma	Fitting a linear model using limma
perform_ROTS	Performing ROTS
plot_boxplots	Plot the distributions of the normalized data as boxplots
plot_condition_overview	Barplot showing the number of samples per condition
plot_densities	Plot the densities of the normalized data
plot_fold_changes_spiked	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_heatmap	Plot a heatmap of the sample intensities with optional column annotations for a selection of normalization methods
plot_heatmap_DE	Heatmap of DE results
plot_histogram_spiked	Plot histogram of the spike-in and background protein intensities per condition.
plot_identified_spiked_proteins	Plot number of identified spike-in proteins per sample.
plot_intersection_enrichment	Intersect top N enrichment terms per normalization method
plot_intragroup_correlation	Plot intragroup correlation of the normalized data
plot_intragroup_PCV	Plot intragroup pooled coefficient of variation (PCV) of the normalized data
plot_intragroup_PEV	Plot intragroup pooled estimate of variance (PEV) of the normalized data
plot_intragroup_PMAD	Plot intragroup pooled median absolute deviation (PMAD) of the normalized data
plot_jaccard_heatmap	Jaccard similarity heatmap of DE proteins of the different normalization methods
plot_logFC_thresholds_spiked	Line plot of number of true and false positives when applying different logFC thresholds
plot_markers_boxplots	Boxplots of intensities of specific markers
plot_NA_density	Plot the intensity distribution of proteins with and without NAs
plot_NA_frequency	Plot protein identification overlap (x = identified in number of Samples, y=number of proteins)
plot_NA_heatmap	Plot heatmap of the NA pattern
plot_nr_prot_samples	Plot number of non-zero proteins per sample
plot_overview_DE_bar	Overview plots of DE results
plot_overview_DE_tile	Overview heatmap plot of DE results
plot_PCA	PCA plot of the normalized data
plot_profiles_spiked	Plot profiles of the spike-in and background proteins using the log2 average protein intensities as a function of the different concentrations.
plot_pvalues_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_ROC_AUC_spiked	Plot ROC curve and barplot of AUC values for each method for a specific comparion or for all comparisons
plot_stats_spiked_heatmap	Heatmap of performance metrics for spike-in data sets
plot_tot_int_samples	Plot total protein intensity per sample
plot_TP_FP_spiked_bar	Barplot of true and false positives for specific comparisons and normalization methods
plot_TP_FP_spiked_box	Boxplot of true and false positives for specific comparisons and normalization methods
plot_TP_FP_spiked_scatter	Scatterplot of true positives and false positives (median with errorbars as Q1, and Q3) for all comparisons
plot_upset	Create an UpSet Plot from SummarizedExperiment Data
plot_upset_DE	Upset plots of DE results of the different normalization methods
plot_volcano_DE	Volcano plots of DE results
quantileNorm	Quantile Normalization of preprocessCore package.
readPRONE_example	Helper function to read example data
remove_assays_from_SE	Remove normalization assays from a SummarizedExperiment object
remove_POMA_outliers	Remove outliers samples detected by the detect_outliers_POMA function
remove_reference_samples	Remove reference samples of SummarizedExperiment object (reference samples specified during loading)
remove_samples_manually	Remove samples with specific value in column manually
rlrMACycNorm	Cyclic Linear Regression Normalization on MA Transformed Data
rlrMANorm	Linear Regression Normalization on MA Transformed Data
rlrNorm	Robust Linear Regression Normalization of NormalyzerDE.
robnormNorm	RobNorm Normalization
run_DE	Run DE analysis of a selection of normalized data sets
run_DE_single	Run DE analysis on a single normalized data set
specify_comparisons	Create vector of comparisons for DE analysis (either by single condition (sep = NULL) or by combined condition)
spectraCounteBayes_DEqMS	Additional function of the DEqMS package
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
subset_SE_by_norm	Subset SummarizedExperiment object by normalization assays
tmmNorm	Weighted Trimmed Mean of M Values (TMM) Normalization of edgeR package.
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
vsnNorm	Variance Stabilization Normalization of limma package.