A Practical Guide to Visualization and Statistical Analysis of R. solanacearum Infection Data Using R

Area of Science:

• Plant pathology
• Microbial genomics
• Statistical analysis in biology

Background:

• Ralstonia solanacearum species complex causes lethal bacterial wilt disease in plants.
• Bacterial wilt has been studied for over a century, with diverse methods for disease quantification and data analysis.
• Effective visualization and statistical analysis are crucial for interpreting complex plant disease data.

Purpose of the Study:

• To describe and summarize broadly applicable approaches for visualization and statistical analysis of Ralstonia solanacearum infection data.
• To provide a general background on commonly used disease measures: area under the disease progression curve, longitudinal disease severity, and host survival.
• To guide researchers in visualization, statistical analysis, and interpretation of plant disease datasets.

Main Methods:

• Discussion of three distinct disease measures: area under the disease progression curve (AUDPC), longitudinal disease severity recordings, and host survival analysis.
• Explanation of visualization techniques applicable to these disease measures.
• Overview of statistical analysis concepts and their application to plant disease data.

Main Results:

• The paper provides a framework for analyzing Ralstonia solanacearum infection data.
• It highlights the utility of AUDPC, longitudinal severity, and survival analysis for disease assessment.
• Included R code and datasets facilitate practical application of the discussed methods.

Conclusions:

• The presented methods offer a robust approach to analyzing bacterial wilt disease data.
• Researchers can utilize these techniques for better understanding of Ralstonia solanacearum pathogenesis and host responses.
• The study emphasizes the importance of appropriate statistical analysis and visualization in plant pathology research.