LPDA: A new classification method based on linear programming.

The search of separation hyperplanes is an efficient way to find rules with classification purposes. This paper presents an alternative mathematical programming formulation to existing methods to find a discriminant hyperplane. The hyperplane H is found by minimizing the sum of all the distances to the area assigned to the group each individual belongs to. It results in a convex optimization problem for which we find an equivalent linear programming problem. We demonstrate that H exists when the centroids of the two groups are not equal. The method is effective dealing with low and high dimensional data where reduction of the dimension is proposed to avoid overfitting problems. We show the performance of this approach with different data sets and comparisons with other classifications methods. The method is called LPDA and it is implemented in a R package available in https://github.com/mjnueda/lpda.

Chitosan modulates Pochonia chlamydosporia gene expression during nematode egg parasitism.

Climate change makes plant-parasitic nematodes (PPN) an increasing threat to commercial crops. PPN can be managed sustainably by the biocontrol fungus Pochonia chlamydosporia (Pc). Chitosan generated from chitin deacetylation enhances PPN parasitism by Pc. In this work, we investigate the molecular mechanisms of Pc for chitosan resistance and root-knot nematode (RKN) parasitism, using transcriptomics. Chitosan and RKN modify the expression of Pc genes, mainly those involved in oxidation-reduction processes. Both agents significantly modify the expression of genes associated to 113 GO terms and 180 Pc genes. Genes encoding putative glycoproteins (Pc adhesives) to nematode eggshell, as well as genes involved in redox, carbohydrate and lipid metabolism trigger the response to chitosan. We identify genes expressed in both the parasitic and endophytic phases of the Pc lifecycle; these include proteases, chitosanases and transcription factors. Using the Pathogen-Host Interaction database (PHI-base), our previous RNA-seq data and RT-PCR of Pc colonizing banana we have investigated genes expressed both in the parasitic and endophytic phases of Pc lifecycle.

ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments.

Transcriptomic profiling experiments that aim to the identification of responsive genes in specific biological conditions are commonly set up under defined experimental designs that try to assess the effects of factors and their interactions on gene expression. Data from these controlled experiments, however, may also contain sources of unwanted noise that can distort the signal under study, affect the residuals of applied statistical models, and hamper data analysis. Commonly, normalization methods are applied to transcriptomics data to remove technical artifacts, but these are normally based on general assumptions of transcript distribution and greatly ignore both the characteristics of the experiment under consideration and the coordinative nature of gene expression. In this paper, we propose a novel methodology, ARSyN, for the preprocessing of microarray data that takes into account these 2 last aspects. By combining analysis of variance (ANOVA) modeling of gene expression values and multivariate analysis of estimated effects, the method identifies the nonstructured part of the signal associated to the experimental factors (the noise within the signal) and the structured variation of the ANOVA errors (the signal of the noise). By removing these noise fractions from the original data, we create a filtered data set that is rich in the information of interest and includes only the random noise required for inferential analysis. In this work, we focus on multifactorial time course microarray (MTCM) experiments with 2 factors: one quantitative such as time or dosage and the other qualitative, as tissue, strain, or treatment. However, the method can be used in other situations such as experiments with only one factor or more complex designs with more than 2 factors. The filtered data obtained after applying ARSyN can be further analyzed with the appropriate statistical technique to obtain the biological information required. To evaluate the performance of the filtering strategy, we have applied different statistical approaches for MTCM analysis to several real and simulated data sets, studying also the efficiency of these techniques. By comparing the results obtained with the original and ARSyN filtered data and also with other filtering techniques, we can conclude that the proposed method increases the statistical power to detect biological signals, especially in cases where there are high levels of structural noise. Software for ARSyN is freely available at http://www.ua.es/personal/mj.nueda.