The PROteomics Normalization Evaluator


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Documentation for package ‘PRONE’ version 1.1.4

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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.