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cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies

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Harvard

Pedersen, CB, Dam, SH, Barnkob, MB, Leipold, MD, Purroy, N, Rassenti, LZ, Kipps, TJ, Nguyen, J, Lederer, JA, Gohil, SH, Wu, CJ & Olsen, LR 2022, 'cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies', Nature Communications, bind 13, nr. 1, 1698. https://doi.org/10.1038/s41467-022-29383-5

APA

Pedersen, C. B., Dam, S. H., Barnkob, M. B., Leipold, M. D., Purroy, N., Rassenti, L. Z., Kipps, T. J., Nguyen, J., Lederer, J. A., Gohil, S. H., Wu, C. J., & Olsen, L. R. (2022). cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies. Nature Communications, 13(1), [1698]. https://doi.org/10.1038/s41467-022-29383-5

CBE

Pedersen CB, Dam SH, Barnkob MB, Leipold MD, Purroy N, Rassenti LZ, Kipps TJ, Nguyen J, Lederer JA, Gohil SH, Wu CJ, Olsen LR. 2022. cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies. Nature Communications. 13(1):Article 1698. https://doi.org/10.1038/s41467-022-29383-5

MLA

Vancouver

Author

Pedersen, Christina Bligaard ; Dam, Søren Helweg ; Barnkob, Mike Bogetofte ; Leipold, Michael D. ; Purroy, Noelia ; Rassenti, Laura Z. ; Kipps, Thomas J. ; Nguyen, Jennifer ; Lederer, James Arthur ; Gohil, Satyen Harish ; Wu, Catherine J. ; Olsen, Lars Rønn. / cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies. I: Nature Communications. 2022 ; Bind 13, Nr. 1.

Bibtex

@article{d11dbe6a2e8c418a99d20249f4632894,
title = "cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies",
abstract = "Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.",
author = "Pedersen, {Christina Bligaard} and Dam, {S{\o}ren Helweg} and Barnkob, {Mike Bogetofte} and Leipold, {Michael D.} and Noelia Purroy and Rassenti, {Laura Z.} and Kipps, {Thomas J.} and Jennifer Nguyen and Lederer, {James Arthur} and Gohil, {Satyen Harish} and Wu, {Catherine J.} and Olsen, {Lars R{\o}nn}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
month = dec,
doi = "10.1038/s41467-022-29383-5",
language = "English",
volume = "13",
journal = "Nature Communications",
issn = "2041-1722",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies

AU - Pedersen, Christina Bligaard

AU - Dam, Søren Helweg

AU - Barnkob, Mike Bogetofte

AU - Leipold, Michael D.

AU - Purroy, Noelia

AU - Rassenti, Laura Z.

AU - Kipps, Thomas J.

AU - Nguyen, Jennifer

AU - Lederer, James Arthur

AU - Gohil, Satyen Harish

AU - Wu, Catherine J.

AU - Olsen, Lars Rønn

N1 - Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.

AB - Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.

UR - http://www.scopus.com/inward/record.url?scp=85127453024&partnerID=8YFLogxK

U2 - 10.1038/s41467-022-29383-5

DO - 10.1038/s41467-022-29383-5

M3 - Journal article

C2 - 35361793

AN - SCOPUS:85127453024

VL - 13

JO - Nature Communications

JF - Nature Communications

SN - 2041-1722

IS - 1

M1 - 1698

ER -

ID: 79408779