A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Analysis of Proteomics and Metabolomics Data


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Documentation for package ‘LimROTS’ version 1.1.0

Help Pages

bootstrapS Generate Bootstrap Samples
bootstrapSamples_limRots Generate Stratified Bootstrap Samples for limRots
Boot_parallel Parallel processing handling function
calculateFalseDiscoveryRate Calculate False Discovery Rate (FDR) Using Permuted Values (Adjusted)
calOverlaps Calculate Overlaps Between Observed and Permuted Data
calOverlaps_slr Calculate Overlaps for Single-Label Replicates (SLR)
Check_meta_info Check if meta info is correct
Check_SummarizedExperiment Check if SummarizedExperiment or data is correct
countLargerThan Count Larger Permuted Values (Modified)
Limma_bootstrap Perform Linear Modeling with Covariates using Limma
Limma_fit Perform Linear Modeling with Covariates using Limma
Limma_permutating Perform Permutation-Based Linear Modeling with Covariates using Limma
LimROTS 'LimROTS': A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Analysis of Proteomics and Metabolomics Data
Optimizing Optimize Parameters Based on Overlap Calculations
SanityChecK Sanity Check for Input Data and Parameters
UPS1.Case4 Spectronaut and ScaffoldDIA UPS1 Spiked Dataset case 4