Package: pepdiff 1.0.0

pepdiff: Differential Abundance Analysis for Phosphoproteomics Data

Provides tools for analyzing differential abundance in proteomics experiments. Implements S3 classes for data management and supports Generalized Linear Models (GLM; Nelder and Wedderburn (1972) <doi:10.2307/2344614>), Aligned Rank Transform (ART; Wobbrock et al. (2011) <doi:10.1145/1978942.1978963>), and pairwise test methods for statistical analysis. Includes visualization functions for Principal Component Analysis (PCA), volcano plots, and heatmaps.

Authors:Dan MacLean [aut, cre]

pepdiff_1.0.0.tar.gz
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pepdiff_1.0.0.tgz(r-4.6-any)pepdiff_1.0.0.tgz(r-4.5-any)
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
pepdiff/json (API)

# Install 'pepdiff' in R:
install.packages('pepdiff', repos = c('https://tsl-zz.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.48 score 226 downloads 45 exports 84 dependencies

Last updated from:f6eca2d6d2. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK231
source / vignettesOK290
linux-releaseOK225
macos-releaseOK197
macos-oldrelOK214
windows-develOK137
windows-releaseOK143
windows-oldrelOK147
wasm-releaseOK150

Exports:%>%assess_missingclassify_bf_evidencecombine_tech_repscomparecompare_callscompare_manyestimate_result_clustersfc_qqplotget_bootstrap_percentileget_comparisonget_kruskal_percentileget_peptideget_rp_percentileget_wilcoxon_percentileimport_datakmeans_by_selected_colslong_resultsmissing_peptides_plotnorm_qqplotp_value_histplot_bf_distributionplot_distributions_simpleplot_fcplot_fc_distribution_newplot_fit_diagnosticsplot_heatmapplot_kmeansplot_missingness_simpleplot_pcaplot_pca_simpleplot_pvalue_histogramplot_quant_distributionsplot_resultplot_volcano_bfplot_volcano_newread_pepdiffsignificanttest_bayes_ttest_bootstrap_ttest_rankprodtest_wilcoxontimes_measuredtimes_measured_plotvolcano_plot

Dependencies:abindARToolbackportsbitbit64bootbroomcarcarDataclicliprcolorspacecowplotcpp11crayonDerivdoBydplyremmeansestimabilityfarverforcatsforecastFormulafracdiffgenericsggplot2gluegtablehmsisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvminqamodelrmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigplyrprettyunitsprogresspurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasrlangS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDatetzdburcautf8vctrsviridisLitevroomwithrzoo

Checking GLM Model Fit
Why Check Model Fit? | Quick Check with plot_fit_diagnostics() | Reading the Diagnostic Plots | Panel A: Deviance Distribution | Panel B: Deviance vs Effect Size | Panel C: Sample Residual Plots | Panel D: Pooled QQ Plot | Investigating Flagged Peptides | Example: Data with Poor Fit | Decision: GLM or ART? | Use GLM when: | Consider ART when: | Try ART on the problematic data: | Summary
Rendered fromchecking_fit.Rmdusingknitr::rmarkdown

Last update: 2026-04-02
Started: 2026-04-02

Factorial Designs with GLM
When you need GLM | Why Gamma GLM? | Example: 2×2 factorial design | The simulated peptide groups | What these patterns look like in raw data | Basic GLM analysis: main effects | Did GLM find the treatment effects? | Stratified comparisons with within | Stratified results by group | Handling interactions | When to use main effects vs stratified | Checking convergence | Model checking | Exporting results | Practical recommendations | Quick interaction check | When GLM struggles
Rendered fromglm_analysis.Rmdusingknitr::rmarkdown

Last update: 2026-04-02
Started: 2026-04-02

Getting Started with pepdiff
What is pepdiff? | Installation | Setup | Creating example data | Importing data | Quick look at your data | Running a comparison | Understanding results | Getting significant hits | Visualising results | Comparing with our simulation | Next steps
Rendered frombasic_workflow.Rmdusingknitr::rmarkdown

Last update: 2026-04-02
Started: 2026-04-02

Non-parametric Factorial Analysis with ART
What is ART? | When to consider ART | Example: 2×2 factorial design | The simulated peptide groups | What these patterns look like in raw data | Running ART analysis | Did ART find the treatment effects? | Stratified comparisons with within | Stratified results by group | Handling interactions | ART vs GLM comparison | Diagnostics | Exporting results | Limitations of ART | Practical recommendations | When both methods fail
Rendered fromart_analysis.Rmdusingknitr::rmarkdown

Last update: 2026-04-02
Started: 2026-04-02

Two-Group Comparisons with Pairwise Tests
When to use pairwise tests | Example data | The four pairwise tests | Wilcoxon rank-sum test | Bootstrap t-test | Bayes factor t-test | Rank products | How Rank Products differs from other tests | Comparing results across tests | FDR correction (p-value tests only) | Visualising results | Bayes factor plots | When pairwise isn't enough
Rendered frompairwise_tests.Rmdusingknitr::rmarkdown

Last update: 2026-04-02
Started: 2026-04-02

Visual Quality Control and Interpretation
Why visualise? | Creating example datasets | Data-level diagnostics | PCA plot | Distribution plots | Missingness plot | Results-level diagnostics | Volcano plot | P-value histogram | Fold change distribution | Individual plot functions | Exporting for publication | Summary: what to check
Rendered fromdiagnostic_plots.Rmdusingknitr::rmarkdown

Last update: 2026-04-02
Started: 2026-04-02

Readme and manuals

Help Manual

Help pageTopics
calculate the proportion of peptides with missing values per group in a data set.assess_missing
Classify Bayes factor into evidence categoriesclassify_bf_evidence
Combine technical replicatescombine_tech_reps
Compare peptide abundances between conditionscompare compare.pepdiff_data
compare sets of significant peptides called by the used datacompare_calls
compare many combinations of treatment and controlcompare_many
Default compare method for legacy data framescompare.data.frame
plots a Figure of Merit curve to help estimate the number of clusters in the resultsestimate_result_clusters
plot qqplot of fold changes from a comparisonfc_qqplot
returns a matrix of fold change valuesfold_change_matrix
get p values for contrast using boostrap t testget_bootstrap_percentile
Get results for a specific comparisonget_comparison
get p values for contrast using Kruskal-Wallis testget_kruskal_percentile
Get results for a specific peptideget_peptide
get p values for contrast using Rank Products testget_rp_percentile
works out if a peptide has at least one significant value across the experiment Composes a matrix of the `metric` significance values with peptides in rows, experiments in columns and works out if each peptide row has a value below the stated cut offget_sig_rows
get p values for contrast using Wilcoxon testget_wilcoxon_percentile
read data from a fileimport_data
Perform kmeans of a dataset using just data in selected columns, then return matrices of all columnskmeans_by_selected_cols
converts a results object to a matrix as if for direct use in external heatmap functionslist2mat
Convert wide format results table to long formatlong_results
reports metrics available for significance valuesmetrics
plot the representation of peptides in each group.missing_peptides_plot
draw qqplots for datanorm_qqplot
plot histograms of p-values for each test usedp_value_hist
Bayes factor distribution plotplot_bf_distribution
Simple distribution plot for pepdiff_dataplot_distributions_simple
plot histogram of fold change distribution for a comparisonplot_fc
Fold change distribution for pepdiff_resultsplot_fc_distribution_new
Plot GLM fit diagnosticsplot_fit_diagnostics
makes heatmap from all experiments, filter on a single metric and sig valueplot_heatmap
K-means cluster the data on the samplesplot_kmeans
Simple missingness plot for pepdiff_dataplot_missingness_simple
plots a pca on the treatment, seconds, bio-repplot_pca
Simple PCA plot for pepdiff_dataplot_pca_simple
P-value histogram for pepdiff_resultsplot_pvalue_histogram
draw density plots for dataplot_quant_distributions
plot the p-values against fold change for the tests used in `compare()`plot_result
Volcano plot for Bayes factor resultsplot_volcano_bf
Volcano plot for pepdiff_resultsplot_volcano_new
Plot method for pepdiff_dataplot.pepdiff_data
Plot method for pepdiff_resultsplot.pepdiff_results
Print method for pepdiff_dataprint.pepdiff_data
Print method for pepdiff_resultsprint.pepdiff_results
Read proteomics data into a pepdiff_data objectread_pepdiff
Extract significant resultssignificant
Subset a pepdiff_data objectsubset.pepdiff_data
Summary method for pepdiff_datasummary.pepdiff_data
Summary method for pepdiff_resultssummary.pepdiff_results
Bayes factor t-test for two groupstest_bayes_t
Bootstrap t-test for two groupstest_bootstrap_t
Rank products test for two groupstest_rankprod
Wilcoxon rank-sum test for two groupstest_wilcoxon
calculate number of measurements of each peptide in each treatment and timetimes_measured
plot the count of the number of times peptides were measured.times_measured_plot
volcano plot the datavolcano_plot