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. |