Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE
Open accessAbstract
The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.
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Factors defining cell identity may be discrete categories, continuous spectra, or combinations of discrete and continuous categories.
“These factors may not only be the discrete categories that are often assumed when classifying cells into major types4,30,94 but may also represent a continuous spectrum1, or a combination of discrete and continuous categories36,95–97,227.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [96]
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References 23 · Cited by 956
- Authors
- Qiu, P., Simonds, E. F., Bendall, S. C., Gibbs, K. D., Bruggner, R. V., Linderman, M. D., Sachs, K., Nolan, G. P., & Plevritis, S. K.
- Affiliations
- Stanford Medicine (US)
- Stanford University (US)
- Corresponding author
- Qiu, Peng
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2011-10
- Volume
- 29
- Issue
- 10
- Pages
- 886-891
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3196363
- OpenAlex
- W2073256527
- Cited by
- 956
- References
- 23
- Citations by year
- 2026: 15 · 2025: 24 · 2024: 41 · 2023: 41 · 2022: 67 · 2021: 105 · 2020: 94 · 2019: 95 · 2018: 89 · 2017: 97 · 2016: 101 · 2015: 79 · 2014: 50 · 2013: 30 · 2012: 25
- FWCI
- 13.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5u54ca143907-05)
- National Heart, Lung, and Blood Institute (N01-HV 00242)
- National Eye Institute (PN2EY018228)
- National Cancer Institute (5U54 CA143907, U54CA149145, U54-CA-143907, U56CA112973, R01CA130826, P01CA034233)
- National Institute of Allergy and Infectious Diseases (HHSN272200700038C, U19AI057229)
- U.S. National Library of Medicine (T15LM007033)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Spades, cellular hierarchy, high-dimensional cytometry, mass cytometry, flow cytometry, single-cell data analysis, cellular heterogeneity, hematopoiesis, bone marrow, cell type annotation, measurement noise robustness
- MeSH terms
- Adult, Algorithms, Animals, Bone Marrow Cells, Computer Simulation, Flow Cytometry, Hematopoiesis, Humans, Staining and Labeling, Tumor Necrosis Factor-alpha, Biomarkers, Databases as Topic, Mice
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