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OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data
Hyungjun Cho1, Yang-Jin Kim, Hee Jung Jung
1Department of Statistics, Department of Biostatistics, Institute of Statistics and Department of Chemistry, Korea University, Seoul, Korea.
Bioinformatics (Oxford, England)
|January 12, 2008
Summary
Preprocessing high-throughput mass spectrometry data requires effective outlier detection. We developed R software using quantile regression to address heterogeneous variability, improving proteomics analysis accuracy.
Area of Science:
- Proteomics
- Bioinformatics
- Data Analysis
Background:
- High-throughput mass spectrometry generates complex data.
- Effective preprocessing, including outlier detection, is crucial for proteomics.
- Existing methods struggle with heterogeneous variability in such data.
Purpose of the Study:
- To develop a robust outlier detection method for high-throughput proteomics data.
- To account for heterogeneous variability common in mass spectrometry datasets.
- To provide a user-friendly software tool within the R environment.
Main Methods:
- Developed an outlier detection software using R.
- Employed linear, non-linear, and non-parametric quantile regression techniques.
- Addressed heterogeneous variability in data.
Main Results:
- The developed software effectively detects outliers in high-throughput data.
- The method accounts for complex data variability.
- The R package is available for interactive use.
Conclusions:
- The novel outlier detection approach improves proteomics analysis.
- Quantile regression is suitable for handling heterogeneous variability.
- The R package offers a convenient tool for researchers.
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