CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors

Yu-Chen Lo, Timothy J. Keyes, Astraea Jager, Jolanda Sarno, Pablo Domizi, Ravindra Majeti, Kathleen M. Sakamoto, Norman Lacayo, Charles G. Mullighan, Jeffrey Waters, Bita Sahaf, Sean C. Bendall and Kara L. Davis ()
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Yu-Chen Lo: Stanford University School of Medicine
Timothy J. Keyes: Stanford University School of Medicine
Astraea Jager: Stanford University School of Medicine
Jolanda Sarno: Stanford University School of Medicine
Pablo Domizi: Stanford University School of Medicine
Ravindra Majeti: Stanford University School of Medicine
Kathleen M. Sakamoto: Stanford University School of Medicine
Norman Lacayo: Stanford University School of Medicine
Charles G. Mullighan: St. Jude Children’s Research Hospital
Jeffrey Waters: Stanford University School of Medicine
Bita Sahaf: Stanford University School of Medicine
Sean C. Bendall: Stanford University School of Medicine
Kara L. Davis: Stanford University School of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract The increasing use of mass cytometry for analyzing clinical samples offers the possibility to perform comparative analyses across public datasets. However, challenges in batch normalization and data integration limit the comparison of datasets not intended to be analyzed together. Here, we present a data integration strategy, CytofIn, using generalized anchors to integrate mass cytometry datasets from the public domain. We show that low-variance controls, such as healthy samples and stable channels, are inherently homogeneous, robust against stimulation, and can serve as generalized anchors for batch correction. Single-cell quantification comparing mass cytometry data from 989 leukemia files pre- and post normalization with CytofIn demonstrates effective batch correction while recapitulating the gold-standard bead normalization. CytofIn integration of public cancer datasets enabled the comparison of immune features across histologies and treatments. We demonstrate the ability to integrate public datasets without necessitating identical control samples or bead standards for fast and robust analysis using CytofIn.

Date: 2022
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DOI: 10.1038/s41467-022-28484-5

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