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Bringing Statistics Up to Speed with Data in Analysis of Lymphocyte Motility
Two-photon (2P) microscopy provides immunologists with 3D video of the movement of lymphocytes in vivo. Motility parameters extracted from these videos allow detailed analysis of lymphocyte motility in lymph nodes and peripheral tissues. However, standard parametric statistical analyses such as the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4431811/ https://www.ncbi.nlm.nih.gov/pubmed/25973755 http://dx.doi.org/10.1371/journal.pone.0126333 |
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author | Letendre, Kenneth Donnadieu, Emmanuel Moses, Melanie E. Cannon, Judy L. |
author_facet | Letendre, Kenneth Donnadieu, Emmanuel Moses, Melanie E. Cannon, Judy L. |
author_sort | Letendre, Kenneth |
collection | PubMed |
description | Two-photon (2P) microscopy provides immunologists with 3D video of the movement of lymphocytes in vivo. Motility parameters extracted from these videos allow detailed analysis of lymphocyte motility in lymph nodes and peripheral tissues. However, standard parametric statistical analyses such as the Student’s t-test are often used incorrectly, and fail to take into account confounds introduced by the experimental methods, potentially leading to erroneous conclusions about T cell motility. Here, we compare the motility of WT T cell versus PKCθ-/-, CARMA1-/-, CCR7-/-, and PTX-treated T cells. We show that the fluorescent dyes used to label T cells have significant effects on T cell motility, and we demonstrate the use of factorial ANOVA as a statistical tool that can control for these effects. In addition, researchers often choose between the use of “cell-based” parameters by averaging multiple steps of a single cell over time (e.g. cell mean speed), or “step-based” parameters, in which all steps of a cell population (e.g. instantaneous speed) are grouped without regard for the cell track. Using mixed model ANOVA, we show that we can maintain cell-based analyses without losing the statistical power of step-based data. We find that as we use additional levels of statistical control, we can more accurately estimate the speed of T cells as they move in lymph nodes as well as measure the impact of individual signaling molecules on T cell motility. As there is increasing interest in using computational modeling to understand T cell behavior in in vivo, these quantitative measures not only give us a better determination of actual T cell movement, they may prove crucial for models to generate accurate predictions about T cell behavior. |
format | Online Article Text |
id | pubmed-4431811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44318112015-05-27 Bringing Statistics Up to Speed with Data in Analysis of Lymphocyte Motility Letendre, Kenneth Donnadieu, Emmanuel Moses, Melanie E. Cannon, Judy L. PLoS One Research Article Two-photon (2P) microscopy provides immunologists with 3D video of the movement of lymphocytes in vivo. Motility parameters extracted from these videos allow detailed analysis of lymphocyte motility in lymph nodes and peripheral tissues. However, standard parametric statistical analyses such as the Student’s t-test are often used incorrectly, and fail to take into account confounds introduced by the experimental methods, potentially leading to erroneous conclusions about T cell motility. Here, we compare the motility of WT T cell versus PKCθ-/-, CARMA1-/-, CCR7-/-, and PTX-treated T cells. We show that the fluorescent dyes used to label T cells have significant effects on T cell motility, and we demonstrate the use of factorial ANOVA as a statistical tool that can control for these effects. In addition, researchers often choose between the use of “cell-based” parameters by averaging multiple steps of a single cell over time (e.g. cell mean speed), or “step-based” parameters, in which all steps of a cell population (e.g. instantaneous speed) are grouped without regard for the cell track. Using mixed model ANOVA, we show that we can maintain cell-based analyses without losing the statistical power of step-based data. We find that as we use additional levels of statistical control, we can more accurately estimate the speed of T cells as they move in lymph nodes as well as measure the impact of individual signaling molecules on T cell motility. As there is increasing interest in using computational modeling to understand T cell behavior in in vivo, these quantitative measures not only give us a better determination of actual T cell movement, they may prove crucial for models to generate accurate predictions about T cell behavior. Public Library of Science 2015-05-14 /pmc/articles/PMC4431811/ /pubmed/25973755 http://dx.doi.org/10.1371/journal.pone.0126333 Text en © 2015 Letendre et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Letendre, Kenneth Donnadieu, Emmanuel Moses, Melanie E. Cannon, Judy L. Bringing Statistics Up to Speed with Data in Analysis of Lymphocyte Motility |
title | Bringing Statistics Up to Speed with Data in Analysis of Lymphocyte Motility |
title_full | Bringing Statistics Up to Speed with Data in Analysis of Lymphocyte Motility |
title_fullStr | Bringing Statistics Up to Speed with Data in Analysis of Lymphocyte Motility |
title_full_unstemmed | Bringing Statistics Up to Speed with Data in Analysis of Lymphocyte Motility |
title_short | Bringing Statistics Up to Speed with Data in Analysis of Lymphocyte Motility |
title_sort | bringing statistics up to speed with data in analysis of lymphocyte motility |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4431811/ https://www.ncbi.nlm.nih.gov/pubmed/25973755 http://dx.doi.org/10.1371/journal.pone.0126333 |
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