The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
flowCore: a Bioconductor package for high throughput flow cytometry
Open accessAbstract
BACKGROUND: Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates. RESULTS: We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry. CONCLUSION: The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.
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Cites
Phenotype and function of human T lymphocyte subsets: Consensus and issues
Appay, V., van Lier, R. A. W., Sallusto, F., & Roederer, M. (2008) · Cytometry Part A · doi:10.1002/cyto.a.20643 · Cited by 729
Cited by
Henrik Heiland, D., Ravi, V. M., Behringer, S. P., Frenking, J. H., Wurm, J., Joseph, K., Garrelfs, N. W. C., Strähle, J., Heynckes, S., Grauvogel, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10493-6 · Cited by 437
NBB-Psy, Böttcher, C., Schlickeiser, S., Sneeboer, M. A. M., Kunkel, D., Knop, A., Paza, E., Fidzinski, P., Kraus, L., Snijders, G. J. L., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0290-2 · Cited by 436
Single-cell analysis defines a pancreatic fibroblast lineage that supports anti-tumor immunity
Hutton, C., Heider, F., Blanco-Gomez, A., Banyard, A., Kononov, A., Zhang, X., Karim, S., Paulus-Hock, V., Watt, D., Steele, N., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.06.017 · Cited by 337
Testing for differential abundance in mass cytometry data
Lun, A. T. L., Richard, A. C., & Marioni, J. C. (2017) · Nature Methods · doi:10.1038/nmeth.4295 · Cited by 181
Autoreactive CD8+ T cell exhaustion distinguishes subjects with slow type 1 diabetes progression
Wiedeman, A. E., Muir, V. S., Rosasco, M. G., DeBerg, H. A., Presnell, S., Haas, B., Dufort, M. J., Speake, C., Greenbaum, C. J., Serti, E., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci126595 · Cited by 168
Metadata
- Authors
- Hahne, F., LeMeur, N., Brinkman, R. R., Ellis, B., Haaland, P., Sarkar, D., Spidlen, J., Strain, E., & Gentleman, R.
- Venue
- BMC Bioinformatics
- Published
- 2009-04-09
- Volume
- 10
- Issue
- 1
- Pages
- 106-106
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W2150756255
- Cited by
- 581
- References
- 1
- FWCI
- 28.14
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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