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Minimizing Batch Effects in Mass Cytometry Data

Cytometry by Time-Of-Flight (CyTOF) uses antibodies conjugated to isotopically pure metals to identify and quantify a large number of cellular features with single-cell resolution. A barcoding approach allows for 20 unique samples to be pooled and processed together in one tube, reducing the intra-b...

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Autores principales: Schuyler, Ronald P., Jackson, Conner, Garcia-Perez, Josselyn E., Baxter, Ryan M., Ogolla, Sidney, Rochford, Rosemary, Ghosh, Debashis, Rudra, Pratyaydipta, Hsieh, Elena W. Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803429/
https://www.ncbi.nlm.nih.gov/pubmed/31681275
http://dx.doi.org/10.3389/fimmu.2019.02367
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author Schuyler, Ronald P.
Jackson, Conner
Garcia-Perez, Josselyn E.
Baxter, Ryan M.
Ogolla, Sidney
Rochford, Rosemary
Ghosh, Debashis
Rudra, Pratyaydipta
Hsieh, Elena W. Y.
author_facet Schuyler, Ronald P.
Jackson, Conner
Garcia-Perez, Josselyn E.
Baxter, Ryan M.
Ogolla, Sidney
Rochford, Rosemary
Ghosh, Debashis
Rudra, Pratyaydipta
Hsieh, Elena W. Y.
author_sort Schuyler, Ronald P.
collection PubMed
description Cytometry by Time-Of-Flight (CyTOF) uses antibodies conjugated to isotopically pure metals to identify and quantify a large number of cellular features with single-cell resolution. A barcoding approach allows for 20 unique samples to be pooled and processed together in one tube, reducing the intra-barcode technical variability. However, with only 20 samples per barcode, multiple barcode sets (batches) are required to address questions in robustly powered study designs. A batch adjustment procedure is required to reduce variability across batches and to facilitate direct comparison of runs performed across multiple barcodes run over weeks, months, or years. We describe a method using technical replicates that are included in each run to determine and apply an appropriate adjustment per batch without manual intervention. The use of technical replicate samples (i.e., anchors or reference samples) avoids assumptions of sample homogeneity among batches, and allows direct estimation of batch effects and appropriate adjustment parameters applicable to all samples within a batch. Quantification of cell subpopulations and mean signal intensity pre- and post-adjustment using both manual gating and unsupervised clustering demonstrate substantial mitigation of batch effects in the anchor samples used for this adjustment calculation, and in a second validation set of technical replicates.
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spelling pubmed-68034292019-11-03 Minimizing Batch Effects in Mass Cytometry Data Schuyler, Ronald P. Jackson, Conner Garcia-Perez, Josselyn E. Baxter, Ryan M. Ogolla, Sidney Rochford, Rosemary Ghosh, Debashis Rudra, Pratyaydipta Hsieh, Elena W. Y. Front Immunol Immunology Cytometry by Time-Of-Flight (CyTOF) uses antibodies conjugated to isotopically pure metals to identify and quantify a large number of cellular features with single-cell resolution. A barcoding approach allows for 20 unique samples to be pooled and processed together in one tube, reducing the intra-barcode technical variability. However, with only 20 samples per barcode, multiple barcode sets (batches) are required to address questions in robustly powered study designs. A batch adjustment procedure is required to reduce variability across batches and to facilitate direct comparison of runs performed across multiple barcodes run over weeks, months, or years. We describe a method using technical replicates that are included in each run to determine and apply an appropriate adjustment per batch without manual intervention. The use of technical replicate samples (i.e., anchors or reference samples) avoids assumptions of sample homogeneity among batches, and allows direct estimation of batch effects and appropriate adjustment parameters applicable to all samples within a batch. Quantification of cell subpopulations and mean signal intensity pre- and post-adjustment using both manual gating and unsupervised clustering demonstrate substantial mitigation of batch effects in the anchor samples used for this adjustment calculation, and in a second validation set of technical replicates. Frontiers Media S.A. 2019-10-15 /pmc/articles/PMC6803429/ /pubmed/31681275 http://dx.doi.org/10.3389/fimmu.2019.02367 Text en Copyright © 2019 Schuyler, Jackson, Garcia-Perez, Baxter, Ogolla, Rochford, Ghosh, Rudra and Hsieh. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Schuyler, Ronald P.
Jackson, Conner
Garcia-Perez, Josselyn E.
Baxter, Ryan M.
Ogolla, Sidney
Rochford, Rosemary
Ghosh, Debashis
Rudra, Pratyaydipta
Hsieh, Elena W. Y.
Minimizing Batch Effects in Mass Cytometry Data
title Minimizing Batch Effects in Mass Cytometry Data
title_full Minimizing Batch Effects in Mass Cytometry Data
title_fullStr Minimizing Batch Effects in Mass Cytometry Data
title_full_unstemmed Minimizing Batch Effects in Mass Cytometry Data
title_short Minimizing Batch Effects in Mass Cytometry Data
title_sort minimizing batch effects in mass cytometry data
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803429/
https://www.ncbi.nlm.nih.gov/pubmed/31681275
http://dx.doi.org/10.3389/fimmu.2019.02367
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