Package index • PRONE

All functions

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_NA_proteins_by_threshold(): Filter proteins based on their NA pattern using a specific threshold

filter_out_complete_NA_proteins(): Remove proteins with NAs in all samples

filter_out_proteins_by_ID(): Remove proteins by their ID

filter_out_proteins_by_value(): Remove proteins by value in specific column

get_NA_overview(): Function returning some values on the numbers of NA in the data

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_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_ROTS(): Performing ROTS

perform_limma(): Fitting a linear model using limma

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_PCA(): PCA plot of the normalized data

plot_ROC_AUC_spiked(): Plot ROC curve and barplot of AUC values for each method for a specific comparion or for all comparisons

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_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_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_intragroup_correlation(): Plot intragroup correlation 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_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_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_stats_spiked_heatmap(): Heatmap of performance metrics for spike-in data sets

plot_tot_int_samples(): Plot total protein intensity per sample

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_POMA_outliers(): Remove outliers samples detected by the detect_outliers_POMA function

remove_assays_from_SE(): Remove normalization assays from a SummarizedExperiment object

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.