"Cell biology is becoming an increasingly quantitative field, as technical advances mean researchers now routinely capture vast amounts of data. This handbook is an essential guide to the computational approaches, image processing and analysis techniques, and basic programming skills that are now part of the skill set of anyone working in the field"--
Describing the new quantitative practices that cell biology, like other specialties of biology, are adopting, Royle covers the digital cell philosophy, dealing with data, imaging data, image processing and analysis, statistics, coding, and putting it together. Among specific topics are using spreadsheets for experimental data, software selection, how to analyze an image, designing an experiment, how to write a basic R script for analysis, how to validate and check data, and unacceptable manipulation in figures. Annotation ©2020 Ringgold, Inc., Portland, OR (protoview.com)
Cell and molecular biology are becoming increasingly data driven. Technological advances and increased computing power mean that researchers now increasingly quantify experimental results, rather than simply report qualitative, representative observations. The Digital Cell provides a comprehensive guide for scientists seeking to make this transition. It describes how data should be generated and processed, discussing research workflows, pipelines, and storage solutions. A key focus of the book is imaging--image types and formats are explained, as is software for image processing and analysis, along with techniques such as segmentation analysis and automated particle tracking.
The book examines the wide variety of statistical approaches that can be used for data analysis, emphasizing concepts such as significance and reproducibility. It also includes an introduction to coding, including examples of how to write and use R scripts to analyze results. In addition, there is useful advice on how to plot and present data to convey results most effectively. The Digital Cell is thus an essential resource for all cell and molecular biologists--from students embarking on research for the first time to experienced scientists who need to acquire, process, and present their data accurately and efficiently.
