Penalized Partial Least Square applied to structured data

Nowadays, data analysis applied to high dimension has arisen. The edification of high-dimensional data can be achieved by the gathering of different independent data. However, each independent set can introduce its own bias. We can cope with this bias introducing the observation set structure into our model. The goal of this article is to build theoretical background for the dimension reduction method sparse Partial Least Square (sPLS) in the context of data presenting such an observation set structure. The innovation consists in building different sPLS models and linking them through a common-Lasso penalization. This theory could be applied to any field, where observation present this kind of structure and, therefore, improve the sPLS in domains, where it is competitive. Furthermore, it can be extended to the particular case, where variables can be gathered in given a priori groups, where sPLS is defined as a sparse group Partial Least Square

Are the statistical tests the best way to deal with the biomarker selection problem?

Statistical tests are a powerful set of tools when applied correctly, but unfortunately the extended misuse of them has caused great concern. Among many other applications, they are used in the detection of biomarkers so as to use the resulting p-values as a reference with which the candidate biomarkers are ranked. Although statistical tests can be used to rank, they have not been designed for that use. Moreover, there is no need to compute any p-value to build a ranking of candidate biomarkers. Those two facts raise the question of whether or not alternative methods which are not based on the computation of statistical tests that match or improve their performances can be proposed. In this paper, we propose two alternative methods to statistical tests. In addition, we propose an evaluation framework to assess both statistical tests and alternative methods in terms of both the performance and the reproducibility. The results indicate that there are alternative methods that can match or surpass methods based on statistical tests in terms of the reproducibility when processing real data, while maintaining a similar performance when dealing with synthetic data. The main conclusion is that there is room for the proposal of such alternative methods.This work is partially supported by the Basque Government (IT1244-19, Elkartek BID3A and Elkartek project 3KIA, KK2020/00049) and the Spanish Ministry of Economy and Competitiveness MINECO (PID2019-104966GB-I00) and a University-Society Project 15/19 (Basque Government and University of the Basque Country UPV/EHU). Ari Urkullu has been supported by the Basque Government through a predoctoral grant (PRE_2013_1_1313, PRE_2014_2_87, PRE_2015_2_0280 and PRE_2016_2_0314). Aritz Perez has been supported by the Basque Government through the BERC 2022-2025 and Elkartek programs and by the Ministry of Science, Innovation and Universities: BCAM Severo Ochoa accreditation SEV-2017-0718. Borja Calvo has been partially supported by the IT1244-19 project and the ELKARTEK program from Basque Government, and the project PID2019-104966GB-I00 from the Spanish Ministry of Economy and Competitiveness

Ideafix: a decision tree-based method for the refinement of variants in FFPE DNA sequencing data

[EN]Increasingly, treatment decisions for cancer patients are being made from next-generation sequencing results generated from formalin-fixed and paraffin-embedded (FFPE) biopsies. However, this material is prone to sequence artefacts that cannot be easily identified. In order to address this issue, we designed a machine learning-based algorithm to identify these artefacts using data from >1600000 variants from 27 paired FFPE and fresh-frozen breast cancer samples. Using these data, we assembled a series of variant features and evaluated the classification performance of five machine learning algorithms. Using leave-one-sample-out cross-validation, we found that XGBoost (extreme gradient boosting)and random forest obtained AUC (area under the receiver operating characteristic curve) values >0.86. Performance was further tested using two independent datasets that resulted in AUC values of 0.96, whereas a comparison with previously published tools resulted in a maximum AUC value of 0.92. The most discriminating features were read pair orientation bias, genomic context and variant allele frequency. In summary, our results show a promising future for the use of these samples in molecular testing. We built the algorithm into an R package called Ideafix (DEAmination FIXing) that is freely available at https://github.com/mmaitenat/ideafix.Departamento de Educaci ́on, Universidades e Investi- gaci ́on of the Basque Government [PRE 2019 2 0211 to M.T.A]; Ikerbasque, Basque Foundation for Science [to C.L.]; Starmer–Smith Memorial Fund [to C.L.]; Ministerio de Econom ́ıa, Industria y Competitividad (MINECO) of the Spanish Central Government [to C.L., PID2019- 104933GB-10 to B.C.]; ISCIII and FEDER Funds [PI12/00663, PIE13/00048, DTS14/00109, PI15/00275 and PI18/01710 to C.L.]; Departamento de Desarrollo Econ ́omico y Competitividad and Departamento de Sanidad of the Basque Government [to C.L.]; Aso- ciaci ́on Espa ̃nola Contra el Cancer (AECC) [to C.L.]; Diputaci ́on Foral de Guipuzcoa (DFG) [to C.L.]; Depar- tamento de Industria of the Basque Government [ELKA- RTEK Programme, project code: KK-2018/00038 to C.L., ELKARTEK Programme, project code: KK-2020/00049 to B.C., IT-1244-19 to B.C.

Statistical model for the reproducibility in ranking based feature selection

Recently, concerns about the reproducibility of scientific studies have been growing among the scientific community, mainly due to the existing large quantity of irreproducible results. This has reach such an extent that a perception of a reproducibility crisis has spread through the scientific community (Baker, 2016). Among others, researchers point out “insufficient replication in the lab, poor oversight or low statistical power” as the reasons behind this crisis. Indeed, the A.S.A. warned almost two years ago that the problem derived from an inappropriate use of some statistical tools (Wasserstein & Lazar, 2016). Motivated to work on this reproducibility problem, in this paper we present a framework that allows to model the reproducibility in ranking based feature subset selection problems. In that context, among n features that could be relevant for a given objective, an attempt is made to choose the best subset of a prefixed size i ∈ {1,..., n} through a method capable of ranking the features. In this situation, we will analyze the reproducibility of a given method which is defined as the consistency of the selection in different repetitions of the same experiment

A Preprocessing Procedure for Haplotype Inference by Pure Parsimony

Haplotype data is especially important in the study of complex diseases since it contains more information than genotype data. However, obtaining haplotype data is technically difficult and expensive. Computational methods have proved to be an effective way of inferring haplotype data from genotype data. One of these methods, the haplotype inference by pure parsimony approach (HIPP), casts the problem as an optimization problem and as such has been proved to be NP-hard. We have designed and developed a new preprocessing procedure for this problem. Our proposed algorithm works with groups of haplotypes rather than individual haplotypes. It iterates searching and deleting haplotypes that are not helpful in order to find the optimal solution. This preprocess can be coupled with any of the current solvers for the HIPP that need to preprocess the genotype data. In order to test it, we have used two state-of-the-art solvers, RTIP and GAHAP, and simulated and real HapMap data. Due to the computational time and memory reduction caused by our preprocess, problem instances that were previously unaffordable can be now efficiently solved

Statistical model for the reproducibility in ranking based feature selection

Recently, concerns about the reproducibility of scientific studies have been growing among the scientific community, mainly due to the existing large quantity of irreproducible results. This has reach such an extent that a perception of a reproducibility crisis has spread through the scientific community (Baker, 2016). Among others, researchers point out “insufficient replication in the lab, poor oversight or low statistical power” as the reasons behind this crisis. Indeed, the A.S.A. warned almost two years ago that the problem derived from an inappropriate use of some statistical tools (Wasserstein & Lazar, 2016). Motivated to work on this reproducibility problem, in this paper we present a framework that allows to model the reproducibility in ranking based feature subset selection problems. In that context, among n features that could be relevant for a given objective, an attempt is made to choose the best subset of a prefixed size i ∈ {1,..., n} through a method capable of ranking the features. In this situation, we will analyze the reproducibility of a given method which is defined as the consistency of the selection in different repetitions of the same experiment

Sampling and learning the Mallows and Weighted Mallows models under the Hamming distance

[EN]In this paper we deal with distributions over permutation spaces. The Mallows model is the mode l in use. The associated distance for permutations is the Hamming distance

An R package for permutations, Mallows and Generalized Mallows models

[EN]Probability models on permutations associate a probability value to each of the permutations on n items. This paper considers two popular probability models, the Mallows model and the Generalized Mallows model. We describe methods for making inference, sampling and learning such distributions, some of which are novel in the literature. This paper also describes operations for permutations, with special attention in those related with the Kendall and Cayley distances and the random generation of permutations. These operations are of key importance for the efficient computation of the operations on distributions. These algorithms are implemented in the associated R package. Moreover, the internal code is written in C++

Sampling and learning the Mallows model under the Ulam distance

[EN]In this paper we deal with probability distributions over permutation spaces. The Probability model in use is the Mallows model. The distance for permutations that the model uses in the Ulam distance

Microarray analysis of autoimmune diseases by machine learning procedures

—Microarray-based global gene expression profiling, with the use of sophisticated statistical algorithms is providing new insights into the pathogenesis of autoimmune diseases. We have applied a novel statistical technique for gene selection based on machine learning approaches to analyze microarray expression data gathered from patients with systemic lupus erythematosus (SLE) and primary antiphospholipid syndrome (PAPS), two autoimmune diseases of unknown genetic origin that share many common features. The methodology included a combination of three data discretization policies, a consensus gene selection method, and a multivariate correlation measurement. A set of 150 genes was found to discriminate SLE and PAPS patients from healthy individuals. Statistical validations demonstrate the relevance of this gene set from an univariate and multivariate perspective. Moreover, functional characterization of these genes identified an interferon-regulated gene signature, consistent with previous reports. It also revealed the existence of other regulatory pathways, including those regulated by PTEN, TNF, and BCL-2, which are altered in SLE and PAPS. Remarkably, a significant number of these genes carry E2F binding motifs in their promoters, projecting a role for E2F in the regulation of autoimmunity