Package index • EasyProtein

Data import and preprocessing

fix_duplicate_protein_ids(): Fix duplicated protein IDs

rawdata2se(): Construct a SummarizedExperiment object from raw expression table

impute_low1pct_or_median_raw(): Impute missing values in raw intensity using low-percentile sampling or geometric mean

calculate_cv(): Calculate coefficient of variation

calc_gene_CV_by_condition(): Calculate coefficient of variation (CV) for each feature within each condition

calc_gene_mean_by_condition(): Calculate mean expression per gene per condition

summarize_se_by_coldata(): Summarize assay values by groups in colData(se)

se2raw(): Convert SummarizedExperiment to raw intensity data.frame

se2intensity(): Convert SummarizedExperiment to intensity data.frame

se2conc(): Convert SummarizedExperiment to concentration (CPM) data.frame

se2DEGs(): Extract DEG results from SummarizedExperiment

se2gene_group(): Assign genes into dynamic groups based on expression, correlation, and CV

se2scale(): Convert SummarizedExperiment to z-scaled CPM data.frame

scale_mtx(): Scale matrix by row

df2mtx(): Convert data.frame to matrix using first column as row names

Analysis

limma_protein_DE(): Limma-based differential expression analysis for proteomics (unpaired)

limma_protein_DE_pair(): Paired differential expression analysis using limma

enrichment_analysis(): Functional enrichment analysis (GO / KEGG)

run_gsea(): Run GSEA (Gene Set Enrichment Analysis)

run_mfuzz_clustering(): Perform fuzzy clustering on gene-level time-course matrix using Mfuzz

get_batch_DEGs(): Run pairwise DEG comparisons for all conditions except a reference

correlate_genes_to_target(): Calculate gene-wise correlation to target gene profile

classify_timecourse_by_round(): Classify genes by multi-round monotonic time-course patterns

get_turning_point(): Count turning points in vector

get_pc_contributors(): Extract positive and negative contributors for a given PCA component

Visualization

ivolcano(): Interactive or static volcano plot

saveplot(): Save plots in multiple formats with standardized resolution

plot_pca(): Plot PCA embedding

plotSE_density(): Plot intensity distribution for each sample

plotCV_density(): Plot CV distribution across conditions

plotSE_missing_value(): Plot missing values per sample

plotSE_protein_number(): Plot number of detected proteins per sample

plot_gene_expression(): Plot gene expression across samples or conditions

.parse_ratio_to_numeric(): Dot plot for GO enrichment (style 1)

plot_GO_dot2(): Dot plot for GO enrichment (style 2)

plot_GO_dot3(): Dot plot for GO/Pathway output (style 3, generic)

plot_FC_trend(): Plot multi-round logFC trends for monotonic gene groups

plot_deg_trends(): Visualize DEG dynamic trends by Mfuzz cluster

plot_heatmap_withline(): Plot heatmap with row-wise summary line using ComplexHeatmap

catable(): Category table summarizer

geom_mean(): Geometric mean

auto_cluster_matrix_pca_one(): PCA-based clustering using DynamicTreeCut

App and package utilities

EasyProtein-package EasyProtein: EasyProtein: A comprehensive proteomics analysis toolkit

launch_EasyProtein(): Launch EasyProtein Shiny App

detect_species_from_symbol(): Detect species (human or mouse) from gene symbols