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This module supports functional analysis of untargeted metabolomics data generated from high-resolution mass spectrometry (HRMS). The basic assumption is that putative annotation at individual compound level can collectively predict changes at functional levels as defined by metabolite sets or pathways. This is because changes at group level rely on "collective behavior" which is more tolerant to random errors in compound annotation as demonstrated by Li et al. To use this approach,

  • The input peak list or peak table must contain the complete data, not just significant data - we need the complete data to estimate the null model (background);
  • [Required] Feature or peak names must be their numeric mass (m/z) values for putative annotation;
  • [Optional] You can also provide retention time (RT) to further improve peak annotation


Three columns (m.z, p.value, t.score)
Peak list data (12 pediatric IBD patients and 12 controls) obtained using a Q-Exactive Plus Orbitrap (negative ion mode) from the Integrative Human Microbiome Project (iHMP).
Four columns (m.z, p.value, t.score, rt)
Same as above
Dendritic Cells
One column (m.z ranked by p.value)
Peak list data obtained from human monocyte-derived dendritic cells stimulated by yellow fever vaccine strain (YF17D), collected using an Orbitrap LC-MS (positive ion mode).
Four columns (m.z, p.value, t.score, mode)
Peak list from mice alveolar macrophages, with or without mTOR knock-out (details), collected using an Orbitrap LC-MS (C18 negative ion mode and HILIC positive ion mode)