Alexandre Perera

A portable electronic nose based on embedded PC technology and GNU/Linux: hardware, software and applications

This paper describes a portable electronic nose based on embedded PC technology. The instrument combines a small footprint with the versatility offered by embedded technology in terms of software development and digital communications services. A summary of the proposed hardware and software solutions is provided with an emphasis on data processing. Data evaluation procedures available in the instrument include automatic feature selection by means of SFFS, feature extraction with linear discriminant analysis (LDA) and principal component analysis (PCA), multi-component analysis with partial least squares (PLS) and classification through k-NN and Gaussian mixture models. In terms of instrumentation, the instrument makes use of temperature modulation to improve the selectivity of commercial metal oxide gas sensors. Field applications of the instrument, including experimental results, are also presented.

Aberrant DNA methylation in non-small cell lung cancer-associated fibroblasts

Summary DNA methylation profiling of TAFs reveals global demethylation and a selective impact on the TGF-β pathway. Moreover, it suggests the fibrocyte origin of a fraction of TAFs, and identifies a novel prognostic biomarker in non-small cell lung cancer.

An R package to analyse LC/MS metabolomic data: MAIT (Metabolite Automatic Identification Toolkit)

Summary: Current tools for liquid chromatography and mass spectrometry for metabolomic data cover a limited number of processing steps, whereas online tools are hard to use in a programmable fashion. This article introduces the Metabolite Automatic Identification Toolkit (MAIT) package, which makes it possible for users to perform metabolomic end-to-end liquid chromatography and mass spectrometry data analysis. MAIT is focused on improving the peak annotation stage and provides essential tools to validate statistical analysis results. MAIT generates output files with the statistical results, peak annotation and metabolite identification. Availability and implementation: http://b2slab.upc.edu/software-and-downloads/metabolite-automatic-identification-toolkit/. Contact: francesc.fernandez.albert@upc.edu Supplementary information: Supplementary data are available at Bioinformatics online

Intensity drift removal in LC/MS metabolomics by common variance compensation

UNLABELLED Liquid chromatography coupled to mass spectrometry (LC/MS) has become widely used in Metabolomics. Several artefacts have been identified during the acquisition step in large LC/MS metabolomics experiments, including ion suppression, carryover or changes in the sensitivity and intensity. Several sources have been pointed out as responsible for these effects. In this context, the drift effects of the peak intensity is one of the most frequent and may even constitute the main source of variance in the data, resulting in misleading statistical results when the samples are analysed. In this article, we propose the introduction of a methodology based on a common variance analysis before the data normalization to address this issue. This methodology was tested and compared with four other methods by calculating the Dunn and Silhouette indices of the quality control classes. The results showed that our proposed methodology performed better than any of the other four methods. As far as we know, this is the first time that this kind of approach has been applied in the metabolomics context. AVAILABILITY AND IMPLEMENTATION The source code of the methods is available as the R package intCor at http://b2slab.upc.edu/software-and-downloads/intensity-drift-correction/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis.

MOTIVATION Finding association between genetic variants and phenotypes related to disease has become an important vehicle for the study of complex disorders. In this context, multi-loci genetic association might unravel additional information when compared with single loci search. The main goal of this work is to propose a non-linear methodology based on information theory for finding combinatorial association between multi-SNPs and a given phenotype. RESULTS The proposed methodology, called MISS (mutual information statistical significance), has been integrated jointly with a feature selection algorithm and has been tested on a synthetic dataset with a controlled phenotype and in the particular case of the F7 gene. The MISS methodology has been contrasted with a multiple linear regression (MLR) method used for genetic association in both, a population-based study and a sib-pairs analysis and with the maximum entropy conditional probability modelling (MECPM) method, which searches for predictive multi-locus interactions. Several sets of SNPs within the F7 gene region have been found to show a significant correlation with the FVII levels in blood. The proposed multi-site approach unveils combinations of SNPs that explain more significant information of the phenotype than their individual polymorphisms. MISS is able to find more correlations between SNPs and the phenotype than MLR and MECPM. Most of the marked SNPs appear in the literature as functional variants with real effect on the protein FVII levels in blood. AVAILABILITY The code is available at http://sisbio.recerca.upc.edu/R/MISS_0.2.tar.gz

On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Leakage detection is a common chemical-sensing application. Leakage detection by thresholds on a single sensor signal suffers from important drawbacks when sensors show drift effects or when they are affected by other long-term cross sensitivities. In this paper, we present an adaptive method based on a recursive dynamic principal component analysis (RDPCA) algorithm that models the relationships between the sensors in the array and their past history. In normal conditions, a certain variance distribution characterizes sensor signals, however, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drift, the model is adaptive, and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic and real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method

Principal discriminants analysis for small-sample-size problems: application to chemical sensing

Two dimensionality reduction techniques are widely used to analyze data from chemical sensor arrays: Fishers linear discriminants analysis (LDA) and principal components analysis (PCA). LDA finds the directions of maximum discrimination in classification problems, but has a tendency to overfit when the ratio of training samples to dimensionality is low, as is commonly the case in chemical sensor array problems. PCA is more robust to overfitting but, being a variance model, fails to capture discriminatory information in low-variance sensors. In this article we propose a hybrid model, termed principal discriminants analysis (PDA), which incorporates both LDA and PCA criteria by means of a regularization parameter. The model is characterized on a synthetic dataset and validated with experimental data from an array of 15 metal-oxide sensors exposed to five varieties of roasted coffee beans. Our results show that PDA provides higher predictive accuracy than LDA or PCA alone. In addition, the model is able to find a trade-off between discriminant- and variance-based projections according to where information is located in the distribution of the data.

Fault detection, identification, and reconstruction of faulty chemical gas sensors under drift conditions, using Principal Component Analysis and Multiscale-PCA

Statistical methods like Principal Components Analysis (PCA) or Partial Least Squares (PLS) and multiscale approaches, have been reported to be very useful in the task of fault diagnosis of malfunctioning sensors for several types of faults. In this work, we compare the performance of PCA and Multiscale-PCA on a fault based on a change of sensor sensitivity. This type of fault affects chemical gas sensors and it is one of the effects of the sensor poisoning. These two methods will be applied on a dataset composed by the signals of 17 conductive polymer gas sensors, measuring three analytes at several concentration levels during 10 months. Therefore, additionally to performances comparison, both methods stability along the time will be tested. The comparison between both techniques will be made regarding three aspects; detection, identification of the faulty sensors and correction of faulty sensors response.

Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation

Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for that purpose. More recently, independent component analysis (ICA) has been reported as an alternative to MCR. Those algorithms attempt to infer a model describing the observed data and, therefore, the least squares regression used in MCR assumes that the data is a linear combination of that model. However, due to the high complexity of real data, the construction of a model to describe optimally the observed data is a critical step and these algorithms should prevent the influence from outlier data. This study proves independent component regression (ICR) as an alternative for GC-MS compound identification. Both ICR and MCR though require least squares regression to correctly resolve the mixtures. In this paper, a novel orthogonal signal deconvolution (OSD) approach is introduced, which uses principal component analysis to determine the compound spectra. The study includes a compound identification comparison between the results by ICA-OSD, MCR-OSD, ICR and MCR-ALS using pure standards and human serum samples. Results shows that ICR may be used as an alternative to multivariate curve methods, as ICR efficiency is comparable to MCR-ALS. Also, the study demonstrates that the proposed OSD approach achieves greater spectral resolution accuracy than the traditional least squares approach when compounds elute under undue interference of biological matrices.

A subspace method for the detection of transcription factor binding sites

MOTIVATION The identification of the sites at which transcription factors (TFs) bind to Deoxyribonucleic acid (DNA) is an important problem in molecular biology. Many computational methods have been developed for motif finding, most of them based on position-specific scoring matrices (PSSMs) which assume the independence of positions within a binding site. However, some experimental and computational studies demonstrate that interdependences within the positions exist. RESULTS In this article, we introduce a novel motif finding method which constructs a subspace based on the covariance of numerical DNA sequences. When a candidate sequence is projected into the modeled subspace, a threshold in the Q-residuals confidence allows us to predict whether this sequence is a binding site. Using the TRANSFAC and JASPAR databases, we compared our Q-residuals detector with existing PSSM methods. In most of the studied TF binding sites, the Q-residuals detector performs significantly better and faster than MATCH and MAST. As compared with Motifscan, a method which takes into account interdependences, the performance of the Q-residuals detector is better when the number of available sequences is small.