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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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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. |
format | Online Article Text |
id | pubmed-6803429 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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