Novel unsupervised feature filtering of biological data

Roy Varshavsky*, Assaf Gottlieb, Michal Linial, David Horn

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

129 Scopus citations


Motivation: Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component. We propose a novel unsupervised criterion, based on SVD-entropy, selecting a feature according to its contribution to the entropy (CE) calculated on a leave-one-out basis. This can be implemented in four ways: simple ranking according to CE values (SR); forward selection by accumulating features according to which set produces highest entropy (FS1); forward selection by accumulating features through the choice of the best CE out of the remaining ones (FS2); backward elimination (BE) of features with the lowest CE. Results: We apply our methods to different benchmarks. In each case we evaluate the success of clustering the data in the selected feature spaces, by measuring Jaccard scores with respect to known classifications. We demonstrate that feature filtering according to CE outperforms the variance method and gene-shaving. There are cases where the analysis, based on a small set of selected features, outperforms the best score reported when all information was used. Our method calls for an optimal size of the relevant feature set. This turns out to be just a few percents of the number of genes in the two Leukemia datasets that we have analyzed. Moreover, the most favored selected genes turn out to have significant GO enrichment in relevant cellular processes.

Original languageAmerican English
Pages (from-to)e507-e513
Issue number14
StatePublished - 15 Jul 2006

Bibliographical note

Funding Information:
We thank Alon Kaufman and Nati Linial for stimulating discussions and suggestions, and Orly Alter for technical and theoretical assistance. R.V. is supported by SCCB, the Sudarsky Center for Computational Biology in the Hebrew University of Jerusalem.

Funding Information:
DIAMONDS consortium, and also partially supported by the Israel Science Foundation (grant No. 39/02).

Funding Information:
This study was supported by the EU Framework VI NoE


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