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

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data

Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available from the Comprehensive R Archive Network at https://CRAN.R-project.org/package= SeqNet and on GitHub at https://github.com/tgrimes/SeqNet

Connect the dots: sketching out microbiome interactions through networking approaches

Microbiome networking analysis has emerged as a powerful tool for studying the complex interactions among microorganisms in various ecological niches, including the human body and several environments. This analysis has been used extensively in both human and environmental studies, revealing key taxa and functional units peculiar to the ecosystem considered. In particular, it has been mainly used to investigate the effects of environmental stressors, such as pollution, climate change or therapies, on host-associated microbial communities and ecosystem function. In this review, we discuss the latest advances in microbiome networking analysis, including methods for constructing and analyzing microbiome networks, and provide a case study on how to use these tools. These analyses typically involve constructing a network that represents interactions among microbial taxa or functional units, such as genes or metabolic pathways. Such networks can be based on a variety of data sources, including 16S rRNA sequencing, metagenomic sequencing, and metabolomics data. Once constructed, these networks can be analyzed to identify key nodes or modules important for the stability and function of the microbiome. By providing insights into essential ecological features of microbial communities, microbiome networking analysis has the potential to transform our understanding of the microbial world and its impact on human health and the environment

Functional analysis of structural variants in single cells using Strand-seq

Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations

Benchmarking of differential abundance methods and development of bioinformatics and statistical tools for metagenomics data analysis

L'analisi di dati nell'ambito del microbioma e della metagenomica è stato il tema principale del mio dottorato. L'obiettivo primario di questa tesi si muove attorno all'osservazione dei limiti dei metodi per lo studio dell'abbondanza differenziale e culmina con la creazione di un framework analitico che permette la loro misurazione e comparazione. Come obiettivo secondario, inoltre, la tesi vuole enfatizzare la necessità di una solida analisi statistica esplorativa ed inferenziale nei dati di metabarcoding, tramite la presentazione di alcuni casi studio. Inizio presentando 2 studi strettamente collegati in cui i metodi per l'analisi di abbondanza differenziale sono i protagonisti. L'analisi di abbondanza differenziale è lo strumento principale per individuare differenze nelle composizioni delle comunità microbiche in gruppi di campioni di diversa provenienza. Rappresenta quindi il primo passo per la comprensione delle comunità microbiche, delle relazioni tra i loro membri e di questi con l'ambiente. Il primo studio riguarda un lavoro di confronto tra metodi. A partire da una collezione di dataset metagenomici, l'obiettivo era di valutare le performance di metodi per l'analisi dell'abbondanza differenziale, anche nati in altri ambiti di ricerca (e.g., RNA-Seq e single-cell RNA-Seq). Invece, con il secondo studio presento un software che ho sviluppato grazie ai risultati ottenuti dalla precedente ricerca. Attualmente, il pacchetto software, in linguaggio R, è disponibile su Bioconductor (i.e., una piattaforma open-source per l'analisi e la visualizzazione di dati biologici). Esso consente agli utenti di replicare sui propri dataset il confronto tra metodi per lo studio dell'abbondanza differenziale e la conseguente analisi delle performance. Infine, mostro alcune delle sfide che ho incontrato nell'analisi di questo tipo di dato attraverso 2 casi studio riguardanti il microbioma umano, la sua composizione e dinamica, sia in stato di salute che malattia. Nel primo studio, dei soggetti sani sono stati trattati con una mistura di probiotici per valutare variazioni del microbiota intestinale ed eventuali associazioni con alcuni aspetti psicologici. Un'attenta analisi esplorativa, l'impiego di tecniche di clustering e l'utilizzo di modelli di regressione lineare ad effetti misti hanno consentito di svelare un forte effetto soggetto-specifico e la presenza di diversi batteriotipi di partenza che mascheravano l'effetto complessivo del trattamento probiotico. Invece, nel secondo studio mostro come, a partire da campioni salivari, sono stati individuati dei biomarcatori associati all'esofagite eosinofila (i.e., una malattia cronica immuno-mediata a carico dell'esofago che causa disfagia, occlusioni e stenosi esofagee). Nonostante la bassa numerosità campionaria è stato possibile costruire un modello per discriminare tra casi e controlli con una buona accuratezza. Anche se ancora prematuro, questo risultato rappresenta un passo promettente verso la diagnosi non invasiva di questa malattia che per il momento viene fatta solo tramite biopsia esofagea.Microbiome and metagenomics data analysis has been the main theme of my PhD programme. As a main goal, the thesis moves from the observed limitations of the differential abundance analysis tools to a benchmark and a framework against which they could be measured and compared. Furthermore, as a secondary goal, the presentation of some case studies wants to emphasise the need for a sound exploratory and inferential statistical analysis in metabarcoding data. Firstly, I present two closely related studies in which differential abundance analysis methods play the main role. The differential abundance analysis is the principal approach to detect differences in microbial community compositions between different sample groups, and hence, for understanding microbial community structures and the relationships between microbial compositions and the environment. I start by introducing a benchmarking study in which differential abundance analysis methods, even from different domains (e.g., RNA-Seq and single-cell RNA-Seq), were used in a collection of microbiome datasets to evaluate their performance. Then, I continue with the presentation of software package that I developed from the results obtained in the previous research. The software package, in R language, is currently available on Bioconductor (i.e., an open-source software platform for analysing and visualising biological data). It allows users to replicate the benchmarking of differential abundance analysis methods and evalute their performances on their own datasets. Secondly, I highlight the microbiome data analysis challenges presenting two case studies about the human microbiome and its composition and dynamics in both disease and healthy states. In the first study, healthy volunteers were treated with a probiotic mixture and the changes in the gut microbiome were studied in conjunction with some psychological aspects. A careful data exploration, clustering, and mixed-effects regression models, unveiled subject-specific effects and the presence of different bacteriotypes which masked the probiotic effect. Instead, in the second study I show how to identify disease-related microbial biomarkers for eosinophilic oesophagitis (i.e., a chronic immune-mediated inflammatory disease of the oesophagus that causes dysphagia, food impaction of the oesophagus, and esophageal strictures) from saliva. Despite the low sample size it was possible to train a model to discriminate between case and control states with a decent accuracy. While still premature, this represents a promising step for the non-invasive diagnosis of eosinophilic oesophagitis which is now possible only through esophageal biopsy

Natural Selection For Disease Resistance In Hybrid Poplars Targets Stomatal Patterning Traits And Regulatory Genes.

The evolution of disease resistance in plants occurs within a framework of interacting phenotypes, balancing natural selection for life-history traits along a continuum of fast-growing and poorly defended, or slow-growing and well-defended lifestyles. Plant populations connected by gene flow are physiologically limited to evolving along a single axis of the spectrum of the growth-defense trade-off, and strong local selection can purge phenotypic variance from a population or species, making it difficult to detect variation linked to the trade-off. Hybridization between two species that have evolved different growth-defense trade-off optima can reveal trade-offs hidden in either species by introducing phenotypic and genetic variance. Here, I investigated the phenotypic and genetic basis for variation of disease resistance in a set of naturally formed hybrid poplars. The focal species of this dissertation were the balsam poplar (Populus balsamifera), black balsam poplar (P. trichocarpa), narrowleaf cottonwood (P. angustifolia), and eastern cottonwood (P. deltoides). Vegetative cuttings of samples were collected from natural populations and clonally replicated in a common garden. Ecophysiology and stomata traits, and the severity of poplar leaf rust disease (Melampsora medusae) were collected. To overcome the methodological bottleneck of manually phenotyping stomata density for thousands of cuticle micrographs, I developed a publicly available tool to automatically identify and count stomata. To identify stomata, a deep con- volutional neural network was trained on over 4,000 cuticle images of over 700 plant species. The neural network had an accuracy of 94.2% when applied to new cuticle images and phenotyped hundreds of micrographs in a matter of minutes. To understand how disease severity, stomata, and ecophysiology traits changed as a result of hybridization, statistical models were fit that included the expected proportion of the genome from either parental species in a hybrid. These models in- dicated that the ratio of stomata on the upper surface of the leaf to the total number of stomata was strongly linked to disease, was highly heritable, and wass sensitive to hybridization. I further investigated the genomic basis of stomata-linked disease variation by performing an association genetic analysis that explicitly incorporated admixture. Positive selection in genes involved in guard cell regulation, immune sys- tem negative regulation, detoxification, lipid biosynthesis, and cell wall homeostasis were identified. Together, my dissertation incorporated advances in image-based phenotyping with evolutionary theory, directed at understanding how disease frequency changes when hybridization alters the genomes of a population

Temporal and Causal Inference with Longitudinal Multi-omics Microbiome Data

Microbiomes are communities of microbes inhabiting an environmental niche. Thanks to next generation sequencing technologies, it is now possible to study microbial communities, their impact on the host environment, and their role in specific diseases and health. Technology has also triggered the increased generation of multi-omics microbiome data, including metatranscriptomics (quantitative survey of the complete metatranscriptome of the microbial community), metabolomics (quantitative profile of the entire set of metabolites present in the microbiome\u27s environmental niche), and host transcriptomics (gene expression profile of the host). Consequently, another major challenge in microbiome data analysis is the integration of multi-omics data sets and the construction of unified models. Finally, since microbiomes are inherently dynamic, to fully understand the complex interactions that take place within these communities, longitudinal studies are critical. Although the analysis of longitudinal microbiome data has been attempted, these approaches do not attempt to probe interactions between taxa, do not offer holistic analyses, and do not investigate causal relationships. In this work we propose approaches to address all of the above challenges. We propose novel analysis pipelines to analyze multi-omic longitudinal microbiome data, and to infer temporal and causal relationships between the different entities involved. As a first step, we showed how to deal with longitudinal metagenomic data sets by building a pipeline, PRIMAL, which takes microbial abundance data as input and outputs a dynamic Bayesian network model that is highly predictive, suggests significant interactions between the different microbes, and proposes important connections from clinical variables. A significant innovation of our work is its ability to deal with differential rates of the internal biological processes in different individuals. Second, we showed how to analyze longitudinal multi-omic microbiome datasets. Our pipeline, PALM, significantly extends the previous state of the art by allowing for the integration of longitudinal metatranscriptomics, host transcriptomics, and metabolomics data in additional to longitudinal metagenomics data. PALM achieves prediction powers comparable to the PRIMAL pipeline while discovering a web of interactions between the entities of far greater complexity. An important innovation of PALM is the use of a multi-omic Skeleton framework that incorporates prior knowledge in the learning of the models. Another major innovation of this work is devising a suite of validation methods, both in silico and in vitro, enhancing the utility and validity of PALM. Finally, we propose a suite of novel methods (unrolling and de-confounding), called METALICA, consisting of tools and techniques that make it possible to uncover significant details about the nature of microbial interactions. We also show methods to validate such interactions using ground truth databases. The proposed methods were tested using an IBD multi-omics dataset

Functional analysis of structural variants in single cells using Strand-seq

Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations

Integration and visualisation of clinical-omics datasets for medical knowledge discovery

In recent decades, the rise of various omics fields has flooded life sciences with unprecedented amounts of high-throughput data, which have transformed the way biomedical research is conducted. This trend will only intensify in the coming decades, as the cost of data acquisition will continue to decrease. Therefore, there is a pressing need to find novel ways to turn this ocean of raw data into waves of information and finally distil those into drops of translational medical knowledge. This is particularly challenging because of the incredible richness of these datasets, the humbling complexity of biological systems and the growing abundance of clinical metadata, which makes the integration of disparate data sources even more difficult. Data integration has proven to be a promising avenue for knowledge discovery in biomedical research. Multi-omics studies allow us to examine a biological problem through different lenses using more than one analytical platform. These studies not only present tremendous opportunities for the deep and systematic understanding of health and disease, but they also pose new statistical and computational challenges. The work presented in this thesis aims to alleviate this problem with a novel pipeline for omics data integration. Modern omics datasets are extremely feature rich and in multi-omics studies this complexity is compounded by a second or even third dataset. However, many of these features might be completely irrelevant to the studied biological problem or redundant in the context of others. Therefore, in this thesis, clinical metadata driven feature selection is proposed as a viable option for narrowing down the focus of analyses in biomedical research. Our visual cortex has been fine-tuned through millions of years to become an outstanding pattern recognition machine. To leverage this incredible resource of the human brain, we need to develop advanced visualisation software that enables researchers to explore these vast biological datasets through illuminating charts and interactivity. Accordingly, a substantial portion of this PhD was dedicated to implementing truly novel visualisation methods for multi-omics studies.Open Acces