The Digital Cell Cell Biology as a Data Science

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.

(preliminary)

Preface

1. The Digital Cell Philosophy
Workflows and pipelines
Using spreadsheets for experimental data
Software for Digital Cell Biology
Focusing on imaging
Golden rules

2. Dealing With Data
Why is organization so important?
Getting organized
Experiment-based organization
Databases for resources
Electronic Lab Notebooks (ELNs)
Databases for imaging data
Sharing your data externally
Backing up your data
Golden rules

3. Imaging Data
Software selection
FIJI
RStudio
What is an image?
Image formats
Image types
Multi-dimensional image files
Metadata
Image transformation
Imaging information
Trade-offs in imaging
Focus and dealing with drift
Phototoxicity and photobleaching
Choice of fluorophores
Dynamic range
Golden rules

4. Image Processing and Analysis
How to analyze an image
Tutorial: quantifying cell protein levels from immunofluorescence images
Segmentation
Further segmentation approaches
Image filters
Gel densitometry
Tutorial: quantifying bands on a gel
How to analyze a movie
Tutorial: counting vesicles in cells
Particle tracking
Tutorial: manual particle tracking
Tutorial: automated particle tracking
Kymographs
Tutorial: Generating a kymograph
Colocalization
Tutorial: using R to measure colocalization over time
Getting the right data out of the image
Validation
Back to square one
Golden rules

5. Statistics
Designing an experiment
What is n?
Why does n matter?
Power analysis for cell biologists
Basic statistics that you'll need
A refresher of summary statistics
Always plot out your data
Descriptive statistics
Statistical tests
Compare one group to a value
Compare two groups
Comparing three or more groups
More complicated experimental designs
Problems with data
p-value
What it really means
Statistically significant vs biologically significant
Effect size
Golden rules

6. Coding
Where to start
Basic principles: workflow, reproducibility, benefits
Mastering the command line
Getting started with coding
Variables and strings
Arrays and vectors
Loops
How to write a basic ImageJ macro
Working on all files in a directory
Tutorial: blinding files for manual image analysis
How to write a basic R script for analysis
What can go wrong?
How to validate and check your data
Debugging
Getting help
Getting good
Ugly code
Write modular code
Version control and git
Sharing your code
Golden rules

7. Putting it Together
Plotting data
Best practice
Making figures
Best practice
Making figures that look great
Color blindness
Contrast adjustments
Crops and expansion
Scale bars
Movie files
Unacceptable manipulation in figures
Golden rules

Bibliography