Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

An update on statistical boosting in biomedicine

Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine-learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine

Challenges of Big Data Analysis

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article give overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasis on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions

Sparse N-way Partial Least Squares by L1-penalization

[EN] N-PLS, as the natural extension of PLS to N-way structures, tries to maximize the covariance between an X and a Y N-way data arrays. It provides a useful framework for fitting prediction models to N-way data. However, N-PLS by itself does not perform variable selection, which indeed can facilitate interpretation in different situations (e.g. the so-called ¿¿omics¿ data). In this work, we propose a method for variable selection within N-PLS by introducing sparsity in the weights matrices WJ and WK by means of L1-penalization. The sparse version of N-PLS is able to provide lower prediction errors by filtering all the noise variables and to further improve interpretability and usability of the N-PLS results. To test Sparse N-PLS performance two different simulated data sets were used, whereas to show its utility in a biological context a real time course metabolomics data set was used.Hervás-Marín, D.; Prats-Montalbán, JM.; Garcia-Cañaveras, J.; Lahoz Rodríguez, AG.; Ferrer, A. (2019). Sparse N-way Partial Least Squares by L1-penalization. Chemometrics and Intelligent Laboratory Systems. 185:85-91. https://doi.org/10.1016/j.chemolab.2019.01.004S859118

Sufficient principal component regression for pattern discovery in transcriptomic data

Methods for global measurement of transcript abundance such as microarrays and RNA-seq generate datasets in which the number of measured features far exceeds the number of observations. Extracting biologically meaningful and experimentally tractable insights from such data therefore requires high-dimensional prediction. Existing sparse linear approaches to this challenge have been stunningly successful, but some important issues remain. These methods can fail to select the correct features, predict poorly relative to non-sparse alternatives, or ignore any unknown grouping structures for the features. We propose a method called SuffPCR that yields improved predictions in high-dimensional tasks including regression and classification, especially in the typical context of omics with correlated features. SuffPCR first estimates sparse principal components and then estimates a linear model on the recovered subspace. Because the estimated subspace is sparse in the features, the resulting predictions will depend on only a small subset of genes. SuffPCR works well on a variety of simulated and experimental transcriptomic data, performing nearly optimally when the model assumptions are satisfied. We also demonstrate near-optimal theoretical guarantees.Comment: 26 pages, 9 figures, 9 table

Use of multivariate statistical methods for the analysis of metabolomic data

[ES] En las últimas décadas los avances tecnológicos han tenido como consecuencia la generación de una creciente cantidad de datos en el campo de la biología y la biomedicina. A día de hoy, las así llamadas tecnologías "ómicas", como la genómica, epigenómica, transcriptómica o metabolómica entre otras, producen bases de datos con cientos, miles o incluso millones de variables. El análisis de datos ómicos presenta una serie de complejidades tanto metodoló-gicas como computacionales que han llevado a una revolución en el desarrollo de nuevos métodos estadísticos específicamente diseñados para tratar con este tipo de datos. A estas complejidades metodológicas hay que añadir que, en la mayor parte de los casos, las restricciones logísticas y/o económicas de los proyectos de investigación suelen conllevar que los tamaños muestrales en estas bases de datos con tantas variables sean muy bajos, lo cual no hace sino empeorar las dificultades de análisis, ya que se tienen muchísimas más variables que observaciones. Entre las técnicas desarrolladas para tratar con este tipo de datos podemos encontrar algunas basadas en la penalización de los coeficientes, como lasso o elastic net, otras basadas en técnicas de proyección sobre estructuras latentes como PCA o PLS y otras basadas en árboles o combinaciones de árboles como random forest. Todas estas técnicas funcionan muy bien sobre distintos datos ómicos presentados en forma de matriz (IxJ). Sin embargo, en ocasiones los datos ómicos pueden estar expandidos, por ejemplo, al tomar medidas repetidas en el tiempo sobre los mismos individuos, encontrándonos con estructuras de datos que ya no son matrices, sino arrays tridimensionales o three-way (IxJxK). En estos casos, la mayoría de las técnicas citadas pierden parte de su aplicabilidad, quedando muy pocas opciones viables para el análisis de este tipo de estructuras de datos. Una de las técnicas que sí es útil para el análisis de estructuras three-way es N-PLS, que permite ajustar modelos predictivos razonablemente precisos, así como interpretarlos mediante distintos gráficos. Sin embargo, relacionado con el problema de la escasez de tamaño muestral relativa al desorbitado número de variables, aparece la necesidad de realizar una selección de variables relacionadas con la variable respuesta. Esto es especialmente cierto en el ámbito de la biología y la biomedicina, ya que no solo se quiere poder predecir lo que va a suceder, sino entender por qué sucede, qué variables están implicadas y, a poder ser, no tener que volver a recoger los cientos de miles de variables para realizar una nueva predicción, sino utilizar unas cuantas, las más importantes, para poder diseñar kits predictivos coste/efectivos de utilidad real. Por ello, el objetivo principal de esta tesis es mejorar las técnicas existentes para el análisis de datos ómicos, específicamente las encaminadas a analizar datos three-way, incorporando la capacidad de selección de variables, mejorando la capacidad predictiva y mejorando la interpretabilidad de los resultados obtenidos. Todo ello se implementará además en un paquete de R completamente documentado, que incluirá todas las funciones necesarias para llevar a cabo análisis completos de datos three-way. El trabajo incluido en esta tesis por tanto, consta de una primera parte teórico-conceptual de desarrollo de la idea del algoritmo, así como su puesta a punto, validación y comprobación de su eficacia; de una segunda parte empírico-práctica de comparación de los resultados del algoritmo con otras metodologías de selección de variables existentes, y de una parte adicional de programación y desarrollo de software en la que se presenta todo el desarrollo del paquete de R, su funcionalidad y capacidades de análisis. El desarrollo y validación de la técnica, así como la publicación del paquete de R, ha permitido ampliar las opciones actuales para el análisis[CA] En les últimes dècades els avançaments tecnològics han tingut com a conseqüència la generació d'una creixent quantitat de dades en el camp de la biologia i la biomedicina. A dia d'avui, les anomenades tecnologies "òmiques", com la genòmica, epigenòmica, transcriptòmica o metabolòmica entre altres, produeixen bases de dades amb centenars, milers o fins i tot milions de variables. L'anàlisi de dades 'òmiques' presenta una sèrie de complexitats tant metodolò-giques com computacionals que han portat a una revolució en el desenvolupament de nous mètodes estadístics específicament dissenyats per a tractar amb aquest tipus de dades. A aquestes complexitats metodològiques cal afegir que, en la major part dels casos, les restriccions logístiques i / o econòmiques dels projectes de recerca solen comportar que les magnituts de les mostres en aquestes bases de dades amb tantes variables siguen molt baixes, el que no fa sinó empitjorar les dificultats d'anàlisi, ja que es tenen moltíssimes més variables que observacions Entre les tècniques desenvolupades per a tractar amb aquest tipus de dades podem trobar algunes basades en la penalització dels coeficients, com lasso o elastic net, altres basades en tècniques de projecció sobre estructures latents com PCA o PLS i altres basades en arbres o combinacions d'arbres com random forest. Totes aquestes tècniques funcionen molt bé sobre diferents dades 'òmiques' presentats en forma de matriu (IxJ), però, en ocasions les dades òmiques poden estar expandits, per exemple, cuan ni ha mesures repetides en el temps sobre els mateixos individus, trobant-se amb estructures de dades que ja no són matrius, sinó arrays tridimensionals o three-way (IxJxK). En aquestos casos, la majoria de les tècniques mencionades perden tota o bona part de la seua aplicabilitat, quedant molt poques opcions viables per a l'anàlisi d'aquest tipus d'estructures de dades. Una de les tècniques que sí que és útil per a l'anàlisi d'estructures three-way es N-PLS, que permet ajustar models predictius raonablement precisos, així com interpretar-los mitjançant diferents gràfics. No obstant això, relacionat amb el problema de l'escassetat de mostres relativa al desorbitat nombre de variables, apareix la necessitat de realitzar una selecció de variables relacionades amb la variable resposta. Això és especialment cert en l'àmbit de la biologia i la biomedicina, ja que no només es vol poder predir el que va a succeir, sinó entendre per què passa, quines variables estan implicades i, si pot ser, no haver de tornar a recollir els centenars de milers de variables per realitzar una nova predicció, sinó utilitzar unes quantes, les més importants, per poder dissenyar kits predictius cost / efectius d'utilitat real. Per això, l'objectiu principal d'aquesta tesi és millorar les tècniques existents per a l'anàlisi de dades òmiques, específicament les encaminades a analitzar dades three-way, incorporant la capacitat de selecció de variables, millorant la capacitat predictiva i millorant la interpretabilitat dels resultats obtinguts. Tot això s'implementarà a més en un paquet de R completament documentat, que inclourà totes les funcions necessàries per a dur a terme anàlisis completes de dades three-way. El treball inclòs en aquesta tesi per tant, consta d'una primera part teorica-conceptual de desenvolupament de la idea de l'algoritme, així com la seua posada a punt, validació i comprovació de la seua eficàcia, d'una segona part empíric-pràctica de comparació dels resultats de l'algoritme amb altres metodologies de selecció de variables existents i d'una part adicional de programació i desenvolupament de programació en la qual es presenta tot el desenvolupament del paquet de R, la seua funcionalitat i capacitats d'anàlisi. El desenvolupament i validació de la tècnica, així com la publicació del paquet de R, ha permès ampliar les opcions actuals per a l'anàlis[EN] In the last decades, advances in technology have enabled the gathering of an increasingly amount of data in the field of biology and biomedicine. The so called "-omics" technologies such as genomics, epigenomics, transcriptomics or metabolomics, among others, produce hundreds, thousands or even millions of variables per data set. The analysis of 'omic' data presents different complexities that can be methodological and computational. This has driven a revolution in the development of new statistical methods specifically designed for dealing with these type of data. To this methodological complexities one must add the logistic and economic restrictions usually present in scientific research projects that lead to small sample sizes paired to these wide data sets. This makes the analyses even harder, since there is a problem in having many more variables than observations. Among the methods developed to deal with these type of data there are some based on the penalization of the coefficients, such as lasso or elastic net, others based on projection techniques, such as PCA or PLS, and others based in regression or classification trees and ensemble methods such as random forest. All these techniques work fine when dealing with different 'omic' data in matrix format (IxJ), but sometimes, these IxJ data sets can be expanded by taking, for example, repeated measurements at different time points for each individual, thus having IxJxK data sets that raise more methodological complications to the analyses. These data sets are called three-way data. In this cases, the majority of the cited techniques lose all or a good part of their applicability, leaving very few viable options for the analysis of this type of data structures. One useful tool for analyzing three-way data, when some Y data structure is to be predicted, is N-PLS. N-PLS reduces the inclusion of noise in the models and obtains more robust parameters when compared to PLS while, at the same time, producing easy-to-understand plots. Related to the problem of small sample sizes and exorbitant variable numbers, comes the issue of variable selection. Variable selection is essential for facilitating biological interpretation of the results when analyzing 'omic' data sets. Often, the aim of the study is not only predicting the outcome, but also understanding why it is happening and also what variables are involved. It is also of interest being able to perform new predictions without having to collect all the variables again. Because all of this, the main goal of this thesis is to improve the existing methods for 'omic' data analysis, specifically those for dealing with three-way data, incorporating the ability of variable selection, improving predictive capacity and interpretability of results. All this will be implemented in a fully documented R package, that will include all the necessary functions for performing complete analyses of three-way data. The work included in this thesis consists in a first theoretical-conceptual part where the idea and development of the algorithm takes place, as well as its tuning, validation and assessment of its performance. Then, a second empirical-practical part comes where the algorithm is compared to other variable selection methodologies. Finally, an additional programming and software development part is presented where all the R package development takes place, and its functionality and capabilities are exposed. The development and validation of the technique, as well as the publication of the R package, has opened many future research lines.Hervás Marín, D. (2019). Use of multivariate statistical methods for the analysis of metabolomic data [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/130847TESI

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data