Hello everyone. I have a nLC-MS/MS proteomics dataset with protein abundances for every sample (n = 250). The problem I have is that the dataset is highly variable, with a mean CV over 90% and even after normalization it stays at 40%. I have filtered the data so that I only keep proteins with less than 50% missingness, I have tried several normalization and imputation methods and I have even tried removing proteins with CV lower than 40% after normalization. I don´t get any significant differential exression after computing adjusted p-values.

All the upstream analysis was performed buy a proteomics company and don´t have access to the raw data.

Has anyone ever encountered this problem before? Is it bad sample quality or am I missing something? :(

Thank you everyone!