| Chapter title |
Single Cell and Population Level Analysis of HCA Data
|
|---|---|
| Chapter number | 15 |
| Book title |
High Content Screening
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7357-6_15 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7355-2, 978-1-4939-7357-6
|
| Authors |
David Novo, Kaya Ghosh, Sean Burke |
| Abstract |
High Content Analysis instrumentation has undergone tremendous hardware advances in recent years. It is now possible to obtain images of hundreds of thousands to millions of individual objects, across multiple wells, channels, and plates, in a reasonable amount of time. In addition, it is possible to extract dozens, or hundreds, of features per object using commonly available software tools. Analyzing this data provides new challenges to the scientists. The magnitude of these numbers is reminiscent of flow cytometer, where practitioners have long been taking what effectively amounted to very low resolution, multi-parametric measurements from individual cells for many decades. Flow cytometrists have developed a wide range of tools to effectively analyze and interpret these types of data. This chapter will review the techniques used in flow cytometry and show how they can easily and effectively be applied to High Content Analysis. |
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