## Flow Cytometry Data Analysis: Basic Concepts and StatisticsFlow cytometry is now well established in research laboratories and is gaining increasing use in clinical medicine and pathology. The technique enables multiple simultaneous light scatter and fluorescence measurements to be made at the individual cell level at very rapid rates and results in very large quantities of data being collected. Data, however, is just a series of numbers which have to be converted to information which, in turn, must be shown to have meaning. This is the most important single aspect of flow cytometry but it has received relatively little attention. One of the frequently voiced advantages of the technology is that it produces 'good statistics' because large numbers of cells have been analysed. However, it is not very often that confidence limits are placed on results, hence the reader has little or no feel for the inherent variability in the information produced. This book covers very basic number handling techniques, regression analysis, probability functions, statistical tests and methods of analysing dynamic processes. All those who use flow cytometry in their research will find this book an invaluable guide to interpreting the data produced by flow cytometers. |

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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