Differential Abundance Analysis for Phosphoproteomics Data

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Documentation for package ‘pepdiff’ version 1.0.0

• DESCRIPTION file
• User guides, package vignettes and other documentation
• Package NEWS
• Package README

Help Pages

assess_missing	calculate the proportion of peptides with missing values per group in a data set.
classify_bf_evidence	Classify Bayes factor into evidence categories
combine_tech_reps	Combine technical replicates
compare	Compare peptide abundances between conditions
compare.data.frame	Default compare method for legacy data frames
compare.pepdiff_data	Compare peptide abundances between conditions
compare_calls	compare sets of significant peptides called by the used data
compare_many	compare many combinations of treatment and control
estimate_result_clusters	plots a Figure of Merit curve to help estimate the number of clusters in the results
fc_qqplot	plot qqplot of fold changes from a comparison
fold_change_matrix	returns a matrix of fold change values
get_bootstrap_percentile	get p values for contrast using boostrap t test
get_comparison	Get results for a specific comparison
get_kruskal_percentile	get p values for contrast using Kruskal-Wallis test
get_peptide	Get results for a specific peptide
get_rp_percentile	get p values for contrast using Rank Products test
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
get_wilcoxon_percentile	get p values for contrast using Wilcoxon test
import_data	read data from a file
kmeans_by_selected_cols	Perform kmeans of a dataset using just data in selected columns, then return matrices of all columns
list2mat	converts a results object to a matrix as if for direct use in external heatmap functions
long_results	Convert wide format results table to long format
metrics	reports metrics available for significance values
missing_peptides_plot	plot the representation of peptides in each group.
norm_qqplot	draw qqplots for data
plot.pepdiff_data	Plot method for pepdiff_data
plot.pepdiff_results	Plot method for pepdiff_results
plot_bf_distribution	Bayes factor distribution plot
plot_distributions_simple	Simple distribution plot for pepdiff_data
plot_fc	plot histogram of fold change distribution for a comparison
plot_fc_distribution_new	Fold change distribution for pepdiff_results
plot_fit_diagnostics	Plot GLM fit diagnostics
plot_heatmap	makes heatmap from all experiments, filter on a single metric and sig value
plot_kmeans	K-means cluster the data on the samples
plot_missingness_simple	Simple missingness plot for pepdiff_data
plot_pca	plots a pca on the treatment, seconds, bio-rep
plot_pca_simple	Simple PCA plot for pepdiff_data
plot_pvalue_histogram	P-value histogram for pepdiff_results
plot_quant_distributions	draw density plots for data
plot_result	plot the p-values against fold change for the tests used in 'compare()'
plot_volcano_bf	Volcano plot for Bayes factor results
plot_volcano_new	Volcano plot for pepdiff_results
print.pepdiff_data	Print method for pepdiff_data
print.pepdiff_results	Print method for pepdiff_results
p_value_hist	plot histograms of p-values for each test used
read_pepdiff	Read proteomics data into a pepdiff_data object
significant	Extract significant results
subset.pepdiff_data	Subset a pepdiff_data object
summary.pepdiff_data	Summary method for pepdiff_data
summary.pepdiff_results	Summary method for pepdiff_results
test_bayes_t	Bayes factor t-test for two groups
test_bootstrap_t	Bootstrap t-test for two groups
test_rankprod	Rank products test for two groups
test_wilcoxon	Wilcoxon rank-sum test for two groups
times_measured	calculate number of measurements of each peptide in each treatment and time
times_measured_plot	plot the count of the number of times peptides were measured.
volcano_plot	volcano plot the data