# The PROteomics Normalization Evaluator

## Documentation for package ‘PRONE’ version 1.4.0

- 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 | Functional enrichment analysis for analyzing the DE results of different normalization methods and biologically interpreting the results
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.