RT Journal Article
SR Electronic
T1 Meta-analysis of Cytometry Data Reveals Racial Differences in Immune Cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 130948
DO 10.1101/130948
A1 Zicheng Hu
A1 Chethan Jujjavarapu
A1 Jake J. Hughey
A1 Sandra Andorf
A1 Pier Federico Gherardini
A1 Matthew H. Spitzer
A1 Patrick Dunn
A1 Cristel G Thomas
A1 John Campbell
A1 Jeff Wiser
A1 Garry P. Nolan
A1 Sanchita Bhattacharya
A1 Atul J. Butte
YR 2017
UL http://biorxiv.org/content/early/2017/06/09/130948.abstract
AB 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).