destiny : diffusion maps for large-scale single-cell data in R
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Abstract
Summary: Diffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single-cell expression data. Here we present destiny, an efficient R implementation of the diffusion map algorithm. Our package includes a single-cell specific noise model allowing for missing and censored values. In contrast to previous implementations, we further present an efficient nearest-neighbour approximation that allows for the processing of hundreds of thousands of cells and a functionality for projecting new data on existing diffusion maps. We exemplarily apply destiny to a recent time-resolved mass cytometry dataset of cellular reprogramming. Availability and implementation: destiny is an open-source R/Bioconductor package “bioconductor.org/packages/destiny” also available at www.helmholtz-muenchen.de/icb/destiny. A detailed vignette describing functions and workflows is provided with the package. Contact: carsten.marr@helmholtz-muenchen.de or f.buettner@helmholtz-muenchen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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Pseudotime order was calculated using DPT per cell type using harmony-corrected PCs and topics from snRNA-seq and snATAC-seq.
“Pseudotime order was calculated using the DPT() function of destiny (v.3.2.0)80 per cell type using as input the harmony corrected PCs and topics from the snRNA-seq and snATAC–seq analyses, respectively.”
From Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation (2024) · cites this paper as [80]
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References 12 · Cited by 640
Research works printed but not linked: 2
- Coifman, R. R., S. Lafon, et al. (2005) Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. Proceedings of the National Academy of Sciences 102.21, 7426-7431.
- Homrighausen, Darren and Daniel J. McDonald (2011) Spectral approximations in machine learning. [stat.ML] arXiv:1107.4340. · arXiv
- Authors
- Angerer, P., Haghverdi, L., Büttner, M., Theis, F. J., Marr, C., & Buettner, F.
- Affiliations
- Helmholtz Munich (DE)
- Corresponding author
- Marr, Carsten
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-12-14
- Volume
- 32
- Issue
- 8
- Pages
- 1241-1243
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2951764674
- Cited by
- 640
- References
- 12
- Citations by year
- 2026: 60 · 2025: 76 · 2024: 64 · 2023: 72 · 2022: 73 · 2021: 79 · 2020: 71 · 2019: 57 · 2018: 39 · 2017: 35 · 2016: 14
- FWCI
- 12.73
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- Site cant be reached (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft
- Medical Research Council (MR/M01536X/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- diffusion maps, single-cell gene expression, nonlinear dimensionality reduction, noise models, mass cytometry, cellular reprogramming, data visualization, R/Bioconductor package
- MeSH terms
- Algorithms, Diffusion, Software, Cluster Analysis, Single-Cell Analysis
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