Enabling network inference methods to handle missing data and outliers

BMC Bioinformatics
Abel Folch-FortunyJulio R Banga

Abstract

The inference of complex networks from data is a challenging problem in biological sciences, as well as in a wide range of disciplines such as chemistry, technology, economics, or sociology. The quantity and quality of the data greatly affect the results. While many methodologies have been developed for this task, they seldom take into account issues such as missing data or outlier detection and correction, which need to be properly addressed before network inference. Here we present an approach to (i) handle missing data and (ii) detect and correct outliers based on multivariate projection to latent structures. The method, called trimmed scores regression (TSR), enables network inference methods to analyse incomplete datasets by imputing the missing values coherently with the latent data structure. Furthermore, it substitutes the faulty values in a dataset by proper estimations. We provide an implementation of this approach, and show how it can be integrated with any network inference method as a preliminary data curation step. This functionality is demonstrated with a state of the art network inference method based on mutual information distance and entropy reduction, MIDER. The methodology presented here enables network infe...Continue Reading

References

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Methods Mentioned

BETA
PCA

Software Mentioned

Octave
Matlab
ARACNE
TSR
CLR
MIDER
MI3
Cytoscape
MRNET
Deduce

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