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ClusterSignificance: a bioconductor package facilitating statistical analysis of class cluster separations in
Jason T Serviss1, Jesper R Gådin2, Per Eriksson2
1Department of Oncology and Pathology, Karolinska University Hospital Solna, Cancer Center Karolinska, Stockholm, Sweden.
Bioinformatics (Oxford, England)
|September 29, 2017
Summary
This study introduces ClusterSignificance, an R package for statistically assessing class separations after dimensionality reduction. It helps determine if specific gene subsets, like long non-coding RNAs, can identify hematological malignancies.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- High-throughput experiments generate multi-dimensional data.
- Dimensionality reduction is used to visualize data and identify class separations.
- Current evaluation of class separations is often subjective and relies on visualization.
Purpose of the Study:
- To present the ClusterSignificance package for statistically assessing class separations.
- To demonstrate the utility of ClusterSignificance in evaluating variable subsets.
- To determine the role of long non-coding RNA expression in hematological malignancies.
Main Methods:
- Development of the ClusterSignificance R package.
- Application of ClusterSignificance to assess statistical significance of class separations.
- Utilizing the package to analyze hematological malignancy data.
Main Results:
- ClusterSignificance provides statistical assessment for class separations.
- The package aids in determining if specific variable subsets can distinguish classes.
- Long non-coding RNA expression was found to be important in hematological malignancy identity.
Conclusions:
- ClusterSignificance offers a statistically rigorous method for evaluating class separations.
- The package is valuable for identifying key biological variables driving sample classification.
- This approach enhances the interpretation of high-dimensional data in cancer research.
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