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Lo YC, Keyes TJ, Jager A, Sarno J, Domizi P, Majeti R, Sakamoto KM, Lacayo N, Mullighan CG, Waters J, Sahaf B, Bendall SC, Davis KL. CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors. Nat Commun 2022; 13:934. [PMID: 35177627 PMCID: PMC8854441 DOI: 10.1038/s41467-022-28484-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 01/27/2022] [Indexed: 11/09/2022] Open
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. Challenges in batch normalization and data integration limit the comparison of existing mass cytometry datasets. Here, the authors report CytofIn that can integrate mass cytometry datasets from the public domain and reveal cellular features associated with immune oncology by analyzing five public cancer datasets.
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Affiliation(s)
- Yu-Chen Lo
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Timothy J Keyes
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.,Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Astraea Jager
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jolanda Sarno
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Pablo Domizi
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ravindra Majeti
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Kathleen M Sakamoto
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Norman Lacayo
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Charles G Mullighan
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jeffrey Waters
- Center for Cancer Cellular Therapy, Cancer Correlative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA
| | - Bita Sahaf
- Center for Cancer Cellular Therapy, Cancer Correlative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA
| | - Sean C Bendall
- Center for Cancer Cellular Therapy, Cancer Correlative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA.,Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kara L Davis
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA. .,Center for Cancer Cellular Therapy, Cancer Correlative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA.
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