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Published on: October 26, 2017
Statistical Issues and Group Classification in Plasma MicroRNA Studies With Data Application
Shesh N Rai1,2,3,4, Chen Qian1,2, Jianmin Pan1
1Biostatistics and Bioinformatics Facility, James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA.
Abstract:
The analysis of plasma microRNAs (miRNAs) has been widely used as a method for finding potential biomarkers for human diseases, especially those with a link to cancer. Methods of analyzing plasma miRNA have been thoroughly discussed from sample extraction to data modeling. However, some issues exist within the process that have rarely been talked about. Rice et al. discussed some issues in plasma miRNA studies, such as the lack of standard methodology including the use of different cycle threshold, time to plasma extraction, among others. These issues can lead to inconsistent data, and thus impact the result and assay reproducibility. Other external issues, such as batch effect and operator effect, may also indirectly impact the statistical analysis. Here, we discuss issues in plasma miRNA studies from a statistical point of view. The interaction effect of different ways of calculating fold-change, the choice of housekeeping genes, and methods of normalization are among the issues we discuss, with data demonstrations. P values are calculated and compared to determine the effect of those issues on statistical conclusions. Statistical methods such as analysis of variance and analysis of covariance are crucial in the analysis of miRNA but investigators are often confused about them; therefore, a brief explanation of these statistical methods is also included. In addition, 3-group classification is discussed, as it is often challenging, compared with 2-group classification.
Insights
Statistical challenges in plasma microRNA (miRNA) analysis can affect disease biomarker discovery. This study addresses issues in miRNA data normalization, housekeeping gene selection, and fold-change calculation to improve assay reproducibility and statistical conclusions for cancer research.
Area of Science:
- Biochemistry
- Genetics
- Biostatistics
Background:
- Plasma microRNAs (miRNAs) are investigated as potential biomarkers for human diseases, particularly cancer.
- Existing research highlights a lack of standardized methodologies in plasma miRNA analysis, leading to inconsistent data and reproducibility issues.
- External factors like batch and operator effects can also compromise statistical analysis in miRNA studies.
Purpose of the Study:
- To address statistical challenges in plasma miRNA analysis from a biostatistical perspective.
- To evaluate the impact of different statistical approaches on miRNA data interpretation and reproducibility.
- To provide clarity on crucial statistical methods for miRNA analysis.
Main Methods:
- Analysis of statistical issues including fold-change calculation, housekeeping gene selection, and normalization methods.
- Data demonstrations comparing P values to assess the impact of these issues on statistical conclusions.
- Explanation of statistical methods such as analysis of variance (ANOVA) and analysis of covariance (ANCOVA).
Main Results:
- Variations in statistical methods significantly affect miRNA analysis outcomes and P values.
- The choice of housekeeping genes and normalization techniques influences the reliability of fold-change calculations.
- Statistical method selection critically impacts the interpretation of miRNA data, especially in complex classifications.
Conclusions:
- Standardizing statistical approaches is crucial for reliable plasma miRNA biomarker discovery.
- Understanding the influence of statistical choices enhances the validity of miRNA assay results.
- Proper application of statistical methods like ANOVA and ANCOVA is essential for robust miRNA data analysis.
