Package index • pepdiff

Import & Data

Import data and create pepdiff_data objects

read_pepdiff(): Read proteomics data into a pepdiff_data object
combine_tech_reps(): Combine technical replicates
import_data(): read data from a file
new_pepdiff_data(): Create a new pepdiff_data object
validate_pepdiff_data(): Validate a pepdiff_data object
print(<pepdiff_data>): Print method for pepdiff_data
summary(<pepdiff_data>): Summary method for pepdiff_data
subset(<pepdiff_data>): Subset a pepdiff_data object
plot(<pepdiff_data>): Plot method for pepdiff_data

Analysis

Run differential abundance analysis

compare(): Compare peptide abundances between conditions
compare_glm(): Compare using GLM
compare_art(): Compare using ART
compare_pairwise(): Compare using pairwise tests
compare_pairwise_rankprod(): Compare using Rank Products (full matrix approach)
compare_within_strata(): Compare within strata
significant(): Extract significant results
get_peptide(): Get results for a specific peptide
get_comparison(): Get results for a specific comparison

Results

Results class and methods

new_pepdiff_results(): Create a new pepdiff_results object
validate_pepdiff_results(): Validate a pepdiff_results object
print(<pepdiff_results>): Print method for pepdiff_results
summary(<pepdiff_results>): Summary method for pepdiff_results
plot(<pepdiff_results>): Plot method for pepdiff_results
long_results(): Convert wide format results table to long format
build_results_tibble(): Build results tibble from model output
build_pairwise_results(): Build results tibble from pairwise tests

Diagnostics

Check model fit and convergence

plot_fit_diagnostics(): Plot GLM fit diagnostics
extract_diagnostics(): Extract model diagnostics
extract_stored_residuals(): Extract stored residuals and fitted values for selected peptides
get_all_residuals(): Get standardized residuals from all peptide models
print_diagnostic_summary(): Print diagnostic summary to console

Visualization

Plot functions for data and results

plot_pca(): plots a pca on the treatment, seconds, bio-rep
plot_pca_simple(): Simple PCA plot for pepdiff_data
plot_heatmap(): makes heatmap from all experiments, filter on a single metric and sig value
plot_volcano_new(): Volcano plot for pepdiff_results
plot_volcano_bf(): Volcano plot for Bayes factor results
plot_pvalue_histogram(): P-value histogram for pepdiff_results
plot_fc_distribution_new(): Fold change distribution for pepdiff_results
plot_bf_distribution(): Bayes factor distribution plot
plot_distributions_simple(): Simple distribution plot for pepdiff_data
plot_missingness_simple(): Simple missingness plot for pepdiff_data
plot_quant_distributions(): draw density plots for data
plot_fc(): plot histogram of fold change distribution for a comparison
plot_kmeans(): K-means cluster the data on the samples
plot_result(): plot the p-values against fold change for the tests used in `compare()`
volcano_plot(): volcano plot the data
p_value_hist(): plot histograms of p-values for each test used
fc_qqplot(): plot qqplot of fold changes from a comparison
norm_qqplot(): draw qqplots for data
missing_peptides_plot(): plot the representation of peptides in each group.
times_measured_plot(): plot the count of the number of times peptides were measured.

Statistical Tests

Individual test implementations

test_wilcoxon(): Wilcoxon rank-sum test for two groups
test_bootstrap_t(): Bootstrap t-test for two groups
test_bayes_t(): Bayes factor t-test for two groups
test_rankprod(): Rank products test for two groups
jzs_bf_approx(): JZS Bayes factor approximation
classify_bf_evidence(): Classify Bayes factor into evidence categories
calc_t_statistic(): Calculate t-statistic for two groups
calc_rank_product(): Calculate rank products for two groups
build_replicate_matrix(): Build replicate matrix from pepdiff_data

Model Fitting

GLM and ART model fitting internals

fit_glm(): Fit a Gamma GLM for a single peptide
fit_art(): Fit an Aligned Rank Transform model for a single peptide
fit_and_extract_glm(): Fit GLM and extract contrasts for one peptide
fit_and_extract_art(): Fit ART and extract contrasts for one peptide
extract_contrasts_glm(): Extract contrasts from a fitted GLM using emmeans
extract_contrasts_art(): Extract contrasts from a fitted ART model
run_models(): Run statistical model for all peptides
has_sufficient_variation(): Check if a model has enough variation to fit
build_formula(): Build formula from factor names

Data Utilities

Helper functions for data manipulation

assess_missing(): calculate the proportion of peptides with missing values per group in a data set.
compute_missingness(): Compute missingness statistics per peptide
compute_design_summary(): Compute design summary from data
times_measured(): calculate number of measurements of each peptide in each treatment and time
fold_change_matrix(): returns a matrix of fold change values
matrix_data(): Convert data frame to matrix format (legacy)
list2mat(): converts a results object to a matrix as if for direct use in external heatmap functions
mean_fold_change(): Calculate mean fold change for peptide
log2_fc(): Calculate log2 fold change
apply_fdr_by_comparison(): Apply FDR correction within groups

Legacy Functions

Deprecated functions from earlier versions

compare(<data.frame>): Default compare method for legacy data frames
compare_legacy(): Legacy compare function (deprecated)
compare_many(): compare many combinations of treatment and control
compare_calls(): compare sets of significant peptides called by the used data
get_sig_rows(): 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 off
estimate_result_clusters(): plots a Figure of Merit curve to help estimate the number of clusters in the results
kmeans_by_selected_cols(): Perform kmeans of a dataset using just data in selected columns, then return matrices of all columns
get_bootstrap_percentile(): get p values for contrast using boostrap t test
get_wilcoxon_percentile(): get p values for contrast using Wilcoxon test
get_kruskal_percentile(): get p values for contrast using Kruskal-Wallis test
get_rp_percentile(): get p values for contrast using Rank Products test
get_percentile_lowest_observed_value_iterative(): get p values for contrast using normal percentile
select_columns_for_contrast(): Select columns for contrast (legacy)
min_peptide_values(): Find minimal values for a peptide across replicates (legacy)
replace_vals(): Replace missing values with replacements (legacy)
metrics(): reports metrics available for significance values

Internal Utilities

Internal helper functions

validate_factors(): Validate factor columns exist in data
validate_positive(): Validate positive values for Gamma GLM
check_zeros(): Check for zeros in values (used before GLM fitting)
is_useable(): Check if values are useable (non-NA and non-NaN)