biostudies-literatureKillinger BJNational Institute of Environmental Health SciencesNIEHS NIH HHS554-562https://www.ebi.ac.uk/biostudies/studies/S-EPMC9514008biostudies-literatureUnknown16(6)The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of thMolecular omicsDetecting differential protein abundance by combining peptide level P-values.PMC9514008P42 ES016465ES016465ES029319R21 ES029319Petyuk VAKillinger BJWright ATfalseDetecting differential protein abundance by combining peptide level P-values.The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of th2020-01-01T00:00:00Z2020 Dec2026-05-08T03:12:24.843Z2026-05-08T03:06:23.867ZS-EPMC95140083292405310.1039/d0mo00045k