Associating Multi-modal Brain Imaging Phenotypes and Genetic Risk Factors via A Dirty Multi-task Learning Method

Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternative method in multi-modal brain imaging genetics

Analysis of High-dimensional and Left-censored Data with Applications in Lipidomics and Genomics

Recently, there has been an occurrence of new kinds of high- throughput measurement techniques enabling biological research to focus on fundamental building blocks of living organisms such as genes, proteins, and lipids. In sync with the new type of data that is referred to as the omics data, modern data analysis techniques have emerged. Much of such research is focusing on finding biomarkers for detection of abnormalities in the health status of a person as well as on learning unobservable network structures representing functional associations of biological regulatory systems. The omics data have certain specific qualities such as left-censored observations due to the limitations of the measurement instruments, missing data, non-normal observations and very large dimensionality, and the interest often lies in the connections between the large number of variables. There are two major aims in this thesis. First is to provide efficient methodology for dealing with various types of missing or censored omics data that can be used for visualisation and biomarker discovery based on, for example, regularised regression techniques. Maximum likelihood based covariance estimation method for data with censored values is developed and the algorithms are described in detail. Second major aim is to develop novel approaches for detecting interactions displaying functional associations from large-scale observations. For more complicated data connections, a technique based on partial least squares regression is investigated. The technique is applied for network construction as well as for differential network analyses both on multiple imputed censored data and next- generation sequencing count data.Uudet mittausteknologiat ovat mahdollistaneet kokonaisvaltaisen ymmärryksen lisäämisen elollisten organismien molekyylitason prosesseista. Niin kutsutut omiikka-teknologiat, kuten genomiikka, proteomiikka ja lipidomiikka, kykenevät tuottamaan valtavia määriä mittausdataa yksittäisten geenien, proteiinien ja lipidien ekspressio- tai konsentraatiotasoista ennennäkemättömällä tarkkuudella. Samanaikaisesti tarve uusien analyysimenetelmien kehittämiselle on kasvanut. Kiinnostuksen kohteena ovat olleet erityisesti tiettyjen sairauksien riskiä tai prognoosia ennustavien merkkiaineiden tunnistaminen sekä biologisten verkkojen rekonstruointi. Omiikka-aineistoilla on useita erityisominaisuuksia, jotka rajoittavat tavanomaisten menetelmien suoraa ja tehokasta soveltamista. Näistä tärkeimpiä ovat vasemmalta sensuroidut ja puuttuvat havainnot, sekä havaittujen muuttujien suuri lukumäärä. Tämän väitöskirjan ensimmäisenä tavoitteena on tarjota räätälöityjä analyysimenetelmiä epätäydellisten omiikka-aineistojen visualisointiin ja mallin valintaan käyttäen esimerkiksi regularisoituja regressiomalleja. Kuvailemme myös sensuroidulle aineistolle sopivan suurimman uskottavuuden estimaattorin kovarianssimatriisille. Toisena tavoitteena on kehittää uusia menetelmiä omiikka-aineistojen assosiaatiorakenteiden tarkasteluun. Monimutkaisempien rakenteiden tarkasteluun, visualisoimiseen ja vertailuun esitetään erilaisia variaatioita osittaisen pienimmän neliösumman menetelmään pohjautuvasta algoritmista, jonka avulla voidaan rekonstruoida assosiaatioverkkoja sekä multi-imputoidulle sensuroidulle että lukumääräaineistoille.Siirretty Doriast

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Introduction: Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up.Methods: Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations.Results: We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. The clustering derived from netMUG achieved an adjusted Rand index of 1 with respect to the synthesized true labels. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these subgroups.Discussion: netMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity.

[en] UNLABELLED: Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures. AUTHOR SUMMARY: In recent years, we see the increasing possibility of collecting data from multiple modalities in various fields, requesting novel methods to exploit the consensus among different data types. As exemplified in systems biology or epistasis analyses, the interactions between features may contain more information than the features themselves, thereby necessitating the use of feature networks. Furthermore, in real-life scenarios, subjects, such as patients or individuals, may originate from diverse populations, which underscores the importance of subtyping or clustering these subjects to account for their heterogeneity. In this study, we present a novel pipeline for selecting the most relevant features from multiple data types, constructing a feature network for each subject, and obtaining a subgrouping of samples informed by a phenotype of interest. We validated our method on synthetic data and demonstrated its superiority over several state-of-the-art multi-view clustering approaches. Additionally, we applied our method to a real-life, large-scale dataset of genomic data and facial images, where it effectively identified a meaningful BMI subtyping that complemented existing BMI categories and offered new biological insights. Our proposed method has wide applicability to complex multi-view or multi-omics datasets for tasks such as disease subtyping or personalized medicine