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A targeted solution for estimating the cell-type composition of bulk samples

BACKGROUND: To avoid false-positive findings and detect cell-type specific associations in methylation and transcription investigations with bulk samples, it is critical to know the proportions of the major cell-types. RESULTS: We present a novel approach that allows for precise estimation of cell-t...

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Autores principales: van den Oord, Edwin J. C. G., Xie, Lin Y., Tran, Charles J., Zhao, Min, Aberg, Karolina A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8474864/
https://www.ncbi.nlm.nih.gov/pubmed/34565346
http://dx.doi.org/10.1186/s12859-021-04385-0
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author van den Oord, Edwin J. C. G.
Xie, Lin Y.
Tran, Charles J.
Zhao, Min
Aberg, Karolina A.
author_facet van den Oord, Edwin J. C. G.
Xie, Lin Y.
Tran, Charles J.
Zhao, Min
Aberg, Karolina A.
author_sort van den Oord, Edwin J. C. G.
collection PubMed
description BACKGROUND: To avoid false-positive findings and detect cell-type specific associations in methylation and transcription investigations with bulk samples, it is critical to know the proportions of the major cell-types. RESULTS: We present a novel approach that allows for precise estimation of cell-type proportions using only a few highly informative methylation markers. The most reliable estimates were obtained with 17 amplicons (34 CpGs) using the MuSiC estimator, for which the average correlations between the estimated and the true cell-type proportions were 0.889. Furthermore, the estimates were not significantly different from the true values (P = 0.95) indicating that the estimator is unbiased and the standard deviation of the estimates further indicate high precision. Moreover, the overall variability of the estimates as measured by the Root Mean Squared Error (RMSE), which is a function of both bias and precision, was low (mean RMSE = 0.038). Taken together, these results indicate that the approach produced reliable estimates that are both unbiased and highly precise. CONCLUSION: This cost-effective approach for estimating cell-type proportions in bulk samples allows for enhanced targeted analysis, which in turn will minimize the risk of reporting false-positive findings and allowing for detection of cell-type specific associations. The approach is applicable across platforms and can be extended to assess cell-type proportions for various tissues. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04385-0.
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spelling pubmed-84748642021-09-28 A targeted solution for estimating the cell-type composition of bulk samples van den Oord, Edwin J. C. G. Xie, Lin Y. Tran, Charles J. Zhao, Min Aberg, Karolina A. BMC Bioinformatics Methodology Article BACKGROUND: To avoid false-positive findings and detect cell-type specific associations in methylation and transcription investigations with bulk samples, it is critical to know the proportions of the major cell-types. RESULTS: We present a novel approach that allows for precise estimation of cell-type proportions using only a few highly informative methylation markers. The most reliable estimates were obtained with 17 amplicons (34 CpGs) using the MuSiC estimator, for which the average correlations between the estimated and the true cell-type proportions were 0.889. Furthermore, the estimates were not significantly different from the true values (P = 0.95) indicating that the estimator is unbiased and the standard deviation of the estimates further indicate high precision. Moreover, the overall variability of the estimates as measured by the Root Mean Squared Error (RMSE), which is a function of both bias and precision, was low (mean RMSE = 0.038). Taken together, these results indicate that the approach produced reliable estimates that are both unbiased and highly precise. CONCLUSION: This cost-effective approach for estimating cell-type proportions in bulk samples allows for enhanced targeted analysis, which in turn will minimize the risk of reporting false-positive findings and allowing for detection of cell-type specific associations. The approach is applicable across platforms and can be extended to assess cell-type proportions for various tissues. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04385-0. BioMed Central 2021-09-26 /pmc/articles/PMC8474864/ /pubmed/34565346 http://dx.doi.org/10.1186/s12859-021-04385-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology Article
van den Oord, Edwin J. C. G.
Xie, Lin Y.
Tran, Charles J.
Zhao, Min
Aberg, Karolina A.
A targeted solution for estimating the cell-type composition of bulk samples
title A targeted solution for estimating the cell-type composition of bulk samples
title_full A targeted solution for estimating the cell-type composition of bulk samples
title_fullStr A targeted solution for estimating the cell-type composition of bulk samples
title_full_unstemmed A targeted solution for estimating the cell-type composition of bulk samples
title_short A targeted solution for estimating the cell-type composition of bulk samples
title_sort targeted solution for estimating the cell-type composition of bulk samples
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8474864/
https://www.ncbi.nlm.nih.gov/pubmed/34565346
http://dx.doi.org/10.1186/s12859-021-04385-0
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