Interpretation of cellular proliferation data: Avoid the panglossian
There are several statistics that may be calculated to characterize a cellular proliferation experiment. By far, the most commonly-reported statistic is the percent of cells in the final culture that have divided; however, this statistic has significant limitations. Other statistics provided by soft...
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| Published in | Cytometry. Part A Vol. 79A; no. 2; pp. 95 - 101 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.02.2011
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| Abstract | There are several statistics that may be calculated to characterize a cellular proliferation experiment. By far, the most commonly-reported statistic is the percent of cells in the final culture that have divided; however, this statistic has significant limitations. Other statistics provided by software modeling provide a much richer characterization of the biological response; however, their use also comes with caveats. Here, I discuss the practical application of these statistics, including their limitations and interdependencies, using hypothetical data. The goal of this perspective is to prevent the blind reliance or overly optimistic (“panglossian”) interpretation of the statistics generated by software, so that researchers and reviewers have a more-informed basis for drawing conclusions from the data. Published 2011 Wiley-Liss, Inc. |
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| AbstractList | There are several statistics that may be calculated to characterize a cellular proliferation experiment. By far, the most commonly-reported statistic is the percent of cells in the final culture that have divided; however, this statistic has significant limitations. Other statistics provided by software modeling provide a much richer characterization of the biological response; however, their use also comes with caveats. Here, I discuss the practical application of these statistics, including their limitations and interdependencies, using hypothetical data. The goal of this perspective is to prevent the blind reliance or overly optimistic ('panglossian') interpretation of the statistics generated by software, so that researchers and reviewers have a more-informed basis for drawing conclusions from the data. Published 2011 Wiley-Liss, Inc. There are several statistics that may be calculated to characterize a cellular proliferation experiment. By far, the most commonly-reported statistic is the percent of cells in the final culture that have divided; however, this statistic has significant limitations. Other statistics provided by software modeling provide a much richer characterization of the biological response; however, their use also comes with caveats. Here, I discuss the practical application of these statistics, including their limitations and interdependencies, using hypothetical data. The goal of this perspective is to prevent the blind reliance or overly optimistic ("panglossian") interpretation of the statistics generated by software, so that researchers and reviewers have a more-informed basis for drawing conclusions from the data. There are several statistics that may be calculated to characterize a cellular proliferation experiment. By far, the most commonly-reported statistic is the percent of cells in the final culture that have divided; however, this statistic has significant limitations. Other statistics provided by software modeling provide a much richer characterization of the biological response; however, their use also comes with caveats. Here, I discuss the practical application of these statistics, including their limitations and interdependencies, using hypothetical data. The goal of this perspective is to prevent the blind reliance or overly optimistic ("panglossian") interpretation of the statistics generated by software, so that researchers and reviewers have a more-informed basis for drawing conclusions from the data.There are several statistics that may be calculated to characterize a cellular proliferation experiment. By far, the most commonly-reported statistic is the percent of cells in the final culture that have divided; however, this statistic has significant limitations. Other statistics provided by software modeling provide a much richer characterization of the biological response; however, their use also comes with caveats. Here, I discuss the practical application of these statistics, including their limitations and interdependencies, using hypothetical data. The goal of this perspective is to prevent the blind reliance or overly optimistic ("panglossian") interpretation of the statistics generated by software, so that researchers and reviewers have a more-informed basis for drawing conclusions from the data. |
| Author | Roederer, Mario |
| Author_xml | – sequence: 1 fullname: Roederer, Mario |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21265003$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/0022-1759(94)90236-4 10.1002/0471142735.im0409s84 10.1038/nprot.2007.297 10.1007/s11538-006-9094-8 10.1080/08820130701712461 10.1002/cyto.a.20935 10.1046/j.1440-1711.1999.00869.x 10.1002/0471142956.cy0911s27 10.1038/nprot.2007.296 10.1186/1471-2105-8-196 |
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| Notes | http://dx.doi.org/10.1002/cyto.a.21010 This article is a US government work and, as such, is in the public domain in the United States of America. The author has a financial interest in FlowJo, one of the software packages discussed in this report. ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Correspondence-1 |
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| SubjectTerms | Cell culture cell division Cell Division - physiology Cells, Cultured CFSE Computer programs Cytometry Data Interpretation, Statistical Data processing Flow Cytometry Fluoresceins - chemistry Fluoresceins - pharmacokinetics Fluorescent Dyes - chemistry Fluorescent Dyes - pharmacokinetics mathematical modeling Models, Biological Models, Statistical precursor frequency software Statistics Succinimides - chemistry Succinimides - pharmacokinetics |
| Title | Interpretation of cellular proliferation data: Avoid the panglossian |
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