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Updated: Mar 2, 2026
10:35
A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
Published on: April 3, 2014
21.5K
The rainfall plot: its motivation, characteristics and pitfalls.
Diana Domanska1, Daniel Vodák2, Christin Lund-Andersen2
1Department of Informatics, University of Oslo, Oslo, Norway. dianadom@ifi.uio.no.
BMC Bioinformatics
|May 20, 2017
Summary
Rainfall plots visualize somatic cancer mutation distribution along genomes, identifying hotspots. However, plot congestion and logarithmic y-axes can obscure data interpretation, necessitating guidelines for proper usage.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Rainfall plots are increasingly popular for visualizing somatic cancer mutation distribution.
- They illustrate mutation locations against inter-mutation distances to identify hotspots.
- The underlying principles and optimal application of rainfall plots remain underexplored.
Purpose of the Study:
- To critically evaluate the motivation and appropriateness of rainfall plot usage in genome data analysis.
- To provide a comprehensive survey of rainfall plot characteristics and usage guidelines.
Main Methods:
- Analysis of rainfall plot properties using simulated and real genomic mutation data.
- Demonstration of potential pitfalls such as plot congestion and logarithmic y-axis effects.
Main Results:
- Rainfall plots effectively detect high-frequency, short-distance events and multi-scale clustering.
- Overlapping events and plot congestion can lead to obscured data interpretation.
- A logarithmic y-axis can also hinder accurate visual assessment.
Conclusions:
- Rainfall plots offer rich, parameter-free data visualization for mutation analysis.
- Understanding limitations like plot congestion and axis choices is crucial for accurate interpretation.
- Practical guidelines are provided to enhance the productive utilization of rainfall plots in research.
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