MetaCyto

MetaCyto is a software tool designed to address the challenge of applying meta-analysis to cytometry data, which often involves diverse sets of markers and inconsistent detected values of the same marker between studies. Meta-analysis has demonstrated increased statistical power and more robust estimations in studies. However, applying this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies.

MetaCyto is developed for automated meta-analysis of flow and mass cytometry (CyTOF) data to overcome these challenges. The software tool uses clustering methods and a silhouette scanning method to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. MetaCyto enables researchers to generate more meaningful biological insights from large-scale cytometry data.

MetaCyto has been used to identify multiple cell populations exhibiting differences in abundance between demographic groups in a set of ten heterogeneous cytometry studies totaling 2,926 samples. In one study, MetaCyto identified a subset of T cells enriched in patients with systemic lupus erythematosus. In another study, MetaCyto identified a subset of B cells depleted in patients with multiple sclerosis. Such findings demonstrate the potential of MetaCyto to enable researchers to identify meaningful biological differences across multiple studies.