Enabling network inference methods to handle missing data and outliers.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
The methodology presented here enables network inference methods to analyse a large number of incomplete and faulty datasets that could not be reliably analysed so far. Our comparative studies show the superiority of TSR over other missing data approaches used by practitioners. Furthermore, the method allows for outlier detection and correction.
Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain. abfolfor@upv.es.
Abel
A
Folch-Fortuny
BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.|Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, Braga, 4710-057, Portugal.|Department of Systems and Control Engineering, Universidade de Vigo, Rua Maxwell, Vigo, 36310, Spain.
Alejandro F
AF
Villaverde
Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain.
Alberto
A
Ferrer
BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.
Julio R
JR
Banga
D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D007257:Information Theory; D013223:Statistics as Topic / Q000379:methods*