Open AccessComment on analyzing flow cytometric data for comparison of mean values of the coefficient of variation of the G1 peak
Author(s) -
Misra R.K.,
Easton Michael D.L.
Publication year - 1999
Publication title -
cytometry
Language(s) - English
Resource type - Journals
eISSN - 1097-0320
pISSN - 0196-4763
DOI - 10.1002/(sici)1097-0320(19990601)36:2<112::aid-cyto4>3.0.co;2-7
Subject(s) - statistics , coefficient of variation , mathematics , data set , least squares function approximation , population , set (abstract data type) , standard deviation , variation (astronomy) , variance (accounting) , analysis of variance , non linear least squares , generalized least squares , explained sum of squares , computer science , medicine , physics , environmental health , accounting , estimator , astrophysics , business , programming language
Background: The coefficient of variation (CV) is often used to characterize and summarize the flow cytometry analysis of nuclear DNA of the Go/G1 peak in a cell population within an individual organism. CV values are frequently used in subsequent statistical analysis to compare experimental groups of individuals. Methods: We explain why the conventional analysis of variance, linear comparisons and regressions that employ the F and t ‐tests are not appropriate for analyzing CV data sets. The weighted least squares procedure which utilizes the chi‐square test is presented as an adequate method. We further explain why this type of data needs to be analyzed by this procedure. Results: To illustrate the application of the weighted least squares procedure, we analyzed a real data set that had been previously analyzed by conventional methods. We found that a non‐significant result (p = 1) using the latter was significant when re‐analyzed with the weighted least squares procedure (p = 0.032). Conclusions: Significant differences between treatments established by the weighted least squares often go unidentified by the conventional analysis. Use of the weighted least squares procedure is recommended for analyzing CV data sets. Cytometry 36:112–116, 1999. © 1999 Wiley‐Liss, Inc.