TY - JOUR T1 - Meta-analysis of Cytometry Data Reveals Racial Differences in Immune Cells JF - bioRxiv DO - 10.1101/130948 SP - 130948 AU - Zicheng Hu AU - Chethan Jujjavarapu AU - Jake J. Hughey AU - Sandra Andorf AU - Pier Federico Gherardini AU - Matthew H. Spitzer AU - Patrick Dunn AU - Cristel G Thomas AU - John Campbell AU - Jeff Wiser AU - Garry P. Nolan AU - Sanchita Bhattacharya AU - Atul J. Butte Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/06/09/130948.abstract N2 - While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of 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. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify common cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto on a set of 10 heterogeneous cytometry studies with a total of 5966 samples allowed us to identify multiple cell populations exhibiting differences in phenotype and abundance across races. Software is released to the public through GitHub (github.com/hzc363/MetaCyto). ER -