Unsupervised feature selection under perturbations: meeting the challenges of biological data.
BACKGROUND
Feature selection methods aim to reduce the complexity of data and to uncover the most relevant biological variables. In reality, information in biological datasets is often incomplete as a result of untrustworthy samples and missing values. The reliability of selection methods may therefore be questioned.


METHODS
Information loss is incorporated into a perturbation scheme, testing which features are stable under it. This method is applied to data analysis by unsupervised feature filtering (UFF). The latter has been shown to be a very successful method in analysis of gene-expression data.


RESULTS
We find that the UFF quality degrades smoothly with information loss. It remains successful even under substantial damage. Our method allows for selection of a best imputation method on a dataset treated by UFF. More importantly, scoring features according to their stability under information loss is shown to be correlated with biological importance in cancer studies. This scoring may lead to novel biological insights.
Varshavsky
Roy
R
School of Computer Science and Engineering, The Hebrew University of Jerusalem 91904, Israel. royke@cs.huji.ac.il
Gottlieb
Assaf
A
Horn
David
D
Linial
Michal
M
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity