## Flow Cytometry Data Analysis: Basic Concepts and StatisticsThis book covers very basic number handling techniques, regression analysis, probability functions, statistical tests and methods of analyzing dynamic processes from flow cytometry data. These are developed for the analysis of not only individual DNA histograms to obtain the proportion of cells in the cell cycle phases, but also time courses of DNA histograms to yield cell cycle kinetic information; overlapping immunofluorescence distributions with confidence limits for the estimated proportions; enzyme kinetic and membrane transport parameters and a brief introduction to multivariate analysis is given. A distinction is made between data handling, for example gating and counting the numbers of cells within that gate, a process commonly regarded as data analysis but which, in reality, is data handling, and data analysis itself which is the means by which information is extracted. |

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### Contents

1 Introduction | 1 |

2 Fundamental concepts | 4 |

3 Probability functions | 20 |

4 Significance testing and fit criteria | 31 |

5 Regression analysis | 58 |

6 Flow cytometric sources of variation | 82 |

7 Immunofluorescence data | 101 |

8 DNA histogram analysis | 126 |

Numerical integrating routine | 249 |

Normal distribution probabilities | 250 |

Variance ratio tables | 252 |

MannWhitney U tables | 256 |

Appendix 5 | 262 |

Regression analysis for y on x | 265 |

Appendix 7 | 266 |

Appendix 8 | 269 |

### Other editions - View all

Flow Cytometry Data Analysis: Basic Concepts and Statistics James V. Watson No preview available - 1992 |

### Common terms and phrases

abscissa acridine orange ampliﬁers array associated BrdUrd calculated channel colcemid compared conﬁdence limits correlation cumulative frequency cumulative frequency distributions curve cycle data sets daunorubicin deﬁned degrees of freedom DNA content DNA histogram duration enzyme equation estimate experimental data exponential ﬁlter ﬁnd ﬁnding ﬁrst ﬁt ﬁve ﬂow cytometry ﬂuorescein ﬂuorescence fraction frequency distributions function G1 mean G1 peak G2+M Gaussian give given hence immunoﬂuorescence increase initial interval kinetics labelled linear linear transform mathematical measurements methotrexate mitosis normal distribution number of cells obtained panel parameters photomultiplier plotted Poisson population predicted probability problem propidium iodide pulse quenching rate constant ratio regression analysis regression line represent respectively S-phase fraction Section shown in Figure shows signal signiﬁcant skewed-normal slope speciﬁc squared deviations staining standard deviation standard error Student’s substrate concentration test sample tion total number transform tumour unity unlabelled uptake variable variance versus Watson y-values zero