As encouraged by the Metabolomics Society , a proper and detailed reporting of the data analysis methodology used in a metabolomic study is essential and “ethically” obligatory, for clarity of the etiology of the inferences of the study and ascertaining the reproducibility of the latter. Furthermore, the growing call for submission of metabolomic studies (and raw data) into the repositories, such as MetaboLights , is to be encouraged, as further mining of datasets (with different data analysis scopes) can yield more information and more biological insights . Untargeted metabolomic studies, in general, generate large amounts of data that are exceedingly rich in information and, consequently, realistically challenging to mine, interpret, and pursue mechanistically in a comprehensive biological context. Furthermore, even though the current study used only one method for peak detection (and varying its parameters), it should be noted that there are a wide variety of workflows (vendor-specific, commercial software, and freeware) available for peak picking/detection. Since various algorithms are used by these different peak detection methods, the choice thereof can have a significant influence on the processed end results of a study . Hence, careful and thoughtful use of pre-processing and processing tools is mandatory to be able to make a critical judgment on the outcomes following those two essential steps in the metabolomics workflows. Furthermore, exploration of data analysis methods (as demonstrated in this study) and data sharing (via data repositories) are encouraged so as to maximize the value of these metabolomic datasets.