Aurora Torrente

Aurora Torrente
University Carlos III de Madrid | UC3M · Department of Mathematics

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43
Publications
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328
Citations

Publications

Publications (43)
Article
Full-text available
The k -Means algorithm is one of the most popular choices for clustering data but is well-known to be sensitive to the initialization process. There is a substantial number of methods that aim at finding optimal initial seeds for k -Means, though none of them is universally valid. This paper presents an extension to longitudinal data of one of such...
Article
Full-text available
The concept of depth induces an ordering from centre outwards in multivariate data. Most depth definitions are unfeasible for dimensions larger than three or four, but the Modified Band Depth (MBD) is a notable exception that has proven to be a valuable tool in the analysis of high-dimensional gene expression data. This depth definition relates the...
Preprint
Full-text available
The $k$-Means algorithm is one of the most popular choices for clustering data but is well-known to be sensitive to the initialization process. There is a substantial number of methods that aim at finding optimal initial seeds for $k$-Means, though none of them are universally valid. This paper presents an extension to longitudinal data of one of s...
Article
Full-text available
We study a granular gas of viscoelastic particles (kinetic energy loss upon collision is a function of the particles' relative velocities at impact) subject to a stochastic thermostat. We show that the system displays anomalous cooling and heating rates during thermal relaxation processes, this causing the emergence of thermal memory. In particular...
Article
The k-means algorithm is widely used in various research fields because of its fast convergence to the cost function minima; however, it frequently gets stuck in local optima as it is sensitive to initial conditions. This paper explores a simple, computationally feasible method, which provides k-means with a set of initial seeds to cluster datasets...
Preprint
Full-text available
We study a granular gas of viscoelastic particles, i.e, the kinetic energy loss upon collision, characteristic of granular materials, is a function of the particles relative velocities at impact. In order to characterize thermal memory in this system, we study the temperature relaxation curves when the granular gas is subject to sudden thermostat c...
Article
A system of smooth “frozen” Janus-type disks is studied. Such disks cannot rotate and are divided by their diameter into two sides of different inelasticities. Taking as a reference a system of colored elastic disks, we find differences in the behavior of the collisions once the anisotropy is included. A homogeneous state, akin to the homogeneous c...
Preprint
Full-text available
A system of smooth "frozen" Janus-type disks is studied. Such disks cannot rotate and are divided by their diameter into two sides of different inelasticities. Taking as a reference a system of colored elastic disks, we find differences in the behavior of the collisions once the anisotropy is included. A homogeneous state, akin to the homogeneous c...
Article
Full-text available
We report the emergence of a giant Mpemba effect in the uniformly heated gas of inelastic rough hard spheres: The initially hotter sample may cool sooner than the colder one, even when the initial temperatures differ by more than one order of magnitude. In order to understand this behavior, it suffices to consider the simplest Maxwellian approximat...
Preprint
Full-text available
We report the emergence of a giant Mpemba effect in the uniformly heated gas of inelastic rough hard spheres. For this purpose, it suffices to consider the simplest Maxwellian approximation for the velocity distribution. Within this framework, the rotational and translational granular temperatures obey two coupled evolution equations, which predict...
Article
Full-text available
clustComp is an open source Bioconductor package that implements different techniques for the comparison of two gene expression clustering results. These include flat versus flat and hierarchical versus flat comparisons. The visualisation of the similarities is provided by means of a bipartite graph, whose layout is heuristically optimised. Its fle...
Article
Full-text available
Rapid accumulation and availability of gene expression datasets in public repositories have enabled large-scale meta-analyses of combined data. The richness of cross-experiment data has provided new biological insights, including identification of new cancer genes. In this study, we compiled a human gene expression dataset from ∼40,000 publicly ava...
Data
Heatmap; solid groups vs 1,000 most variable probesets. Heatmap for the expression level of the 1,000 most variable probesets averaged over the samples included in each biological group with at least 20 observations. The range for this similarity measure is (2.6984, 14.4581). The colour labels display the same clusters as those in S11 Fig. The prob...
Data
Significant probesets. The list of 1,835 significant probes, for which there is a significant effect of the disease status, along with the corrected p-values, and the genes or set of genes they are mapping to. They are ordered according to increasing p-values. There are 1,285 unique genes and 97 multiple matchings. (TXT)
Data
Heatmap; all biological groups and 10,000 most variable probesets. Heatmap for the average pairwise correlations between samples from any two biological groups with at least 20 observations. Only the 10,000 most variable probesets are accounted for in the computation of the correlations. The range for the similarity measure is (0.1352, 0.9938). The...
Data
Heatmap; all biological groups and 1,000 most variable probesets. Heatmap for the average pairwise correlations between samples from any two biological groups with at least 20 observations. Only the 1,000 most variable probesets are accounted for in the computation of the correlations. The range for the similarity measure is (−0.3591, 0.9960). The...
Data
BGV for all probesets across paired tissues. The BGV ranges from 0.051 to 2,047.575, but only 10.85% of the probesets show a BGV really high (greater than 128.1, the ‘maximum’ whisker). (PDF)
Data
a) Permutation test QQ-plot. Quantiles of the adjusted permutation and observed p-values in log10 scale. Except for very extreme results observed due to resolution of attainable p-values in the permutation test, the observed p-values are larger than those obtained with the permutation test. b) QQ-plot of correct vs shuffled disease labels. After ra...
Data
Genes found in the Atlas. The 135 unique genes found in the list L1 from the Atlas of Genetics and Cytogenetics in Oncology and Haematology are alphabetically ordered and displayed in bold-face. The types of cancer they have been related to are also shown. (XLS)
Data
Genes overexpressed in cancer. The 210 unique genes found in the list L3 identified in [34] are alphabetically ordered and displayed in bold-face. The types of cancer they have been related to are also shown. (XLS)
Data
Samples and Biological groups. Collection of 27,887 annotated samples retrieved from ArrayExpress along with the biological group; the original experiments and assay names are given in the format ‘Experiment_CELfile’. (XLS)
Data
Heatmap; all biological groups and 5,000 most variable probesets. Heatmap for the average pairwise correlations between samples from any two biological groups with at least 20 observations. Only the 5,000 most variable probesets are accounted for in the computation of the correlations. The range for the similarity measure is (−0.0288, 0.9940). The...
Data
Heatmap; all biological groups and 500 most variable probesets. Heatmap for the average pairwise correlations between samples from any two biological groups with at least 20 observations. Only the 500 most variable probesets are accounted for in the computation of the correlations. The range for the similarity measure is (−0.4359, 0.9965). The colo...
Data
Heatmap; solid groups and 20,000 most variable probesets. Heatmap for the average pairwise correlations between samples from any two solid groups with at least 20 observations, accounting for the 20,000 most variable probesets in the computation of the correlations. The range for the similarity measure is (0.3869, 0.9907). The colour labels display...
Data
Heatmap; solid groups and 10,000 most variable probesets. Heatmap for the average pairwise correlations between samples from any two solid groups with at least 20 observations, accounting for the 10,000 most variable probesets in the computation of the correlations. The range for the similarity measure is (0.1704, 0.9896). The colour labels display...
Data
Heatmap; solid groups and 5,000 most variable probesets. Heatmap for the average pairwise correlations between samples from any two solid groups with at least 20 observations, accounting for the 5,000 most variable probesets in the computation of the correlations. The range for the similarity measure is (0.0247, 0.9893). The colour labels display s...
Data
Heatmap; solid groups and 500 most variable probesets. Heatmap for the average pairwise correlations between samples from any two solid groups with at least 20 observations, accounting for the 500 most variable probesets in the computation of the correlations. The range for the similarity measure is (−0.2424, 0.9955). The colour labels display smal...
Data
Disease effect volcano plot. Plot of the disease effect, irrespective of the tissue type, versus the negative log 10-transformed p-values. (PDF)
Data
Data pre-processing and quality control. Description of the pre-processing and quality control steps and parameters. (PDF)
Data
Heatmap; all biological groups and all probesets. Heatmaps for the average pairwise correlations between samples from any two biological groups with at least 20 observations. All probesets are accounted for in the computation of the correlations. The range for the similarity measure is (0.6317, 0.9953). The colour labels display smaller clusters in...
Data
Heatmap; all biological groups and 20,000 most variable probesets. Heatmap for the average pairwise correlations between samples from any two biological groups with at least 20 observations. Only the 20,000 most variable probesets are accounted for in the computation of the correlations. The range for the similarity measure is (0.3012, 0.9946). The...
Data
Heatmap; solid groups and all probesets. Heatmap for the average pairwise correlations between samples from any two solid groups with at least 20 observations, accounting for all the probesets in the computation of the correlations. The range for the similarity measure is (0.7164, 0.9920). The colour labels display smaller clusters in the hierarchi...
Data
Heatmap; solid groups and 1,000 most variable probesets. Heatmap for the average pairwise correlations between samples from any two solid groups with at least 20 observations, accounting for the 1,000 most variable probesets in the computation of the correlations. The range for the similarity measure is (−0.1312, 0.9938). The colour labels display...
Article
Full-text available
The use of DNA microarrays and oligonucleotide chips of high density in modern biomedical research provides complex, high dimensional data which have been proven to convey crucial information about gene expression levels and to play an important role in disease diagnosis. Therefore, there is a need for developing new, robust statistical techniques...
Article
Full-text available
Rapid accumulation of large and standardized microarray data collections is opening up novel opportunities for holistic characterization of genome function. The limited scalability of current preprocessing techniques has, however, formed a bottleneck for full utilization of these data resources. Although short oligonucleotide arrays constitute a ma...
Article
Full-text available
Microwave tomographic imaging is an inexpensive, noninvasive modality of media dielectric properties reconstruction which can be utilized as a screening method in clinical applications such as breast cancer and brain stroke detection. For breast cancer detection, the iterative algorithm of structural inversion with level sets provides well-defined...
Article
Full-text available
Microarray experiments provide data on the expression levels of thousands of genes and, therefore, statistical methods applicable to the analysis of such high-dimensional data are needed. In this paper, we propose robust nonparametric tools for the description and analysis of microarray data based on the concept of functional depth, which measures...
Article
Full-text available
Clustering is one of the most widely used methods in unsupervised gene expression data analysis. The use of different clustering algorithms or different parameters often produces rather different results on the same data. Biological interpretation of multiple clustering results requires understanding how different clusters relate to each other. It...
Article
Full-text available
Expression Profiler (EP, http://www.ebi.ac.uk/expressionprofiler) is a web-based platform for microarray gene expression and other functional genomics-related data analysis. The new architecture, Expression Profiler: next generation (EP:NG), modularizes the original design and allows individual analysis-task-related components to be developed by di...
Article
Full-text available
Many iterative techniques are sensitive to the initial conditions, thus getting stuck in local optima. This paper explores two simple, computationally fast methods that allow the reflnement of the initial points of k-means to cluster a given data set. They are based on alternating k-means and the search of the deepest (most representative) point of...

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