Missingness in label-free mass spectrometry (MS) is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in MS data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns.