HIGH-DIMENSIONAL DATA ANALYSIS PROBLEMS IN INFECTIOUS DISEASE STUDIES

Recent technological developments give researchers the opportunity to obtain large informative datasets when studying infectious disease. Such datasets are often high-dimensional, which presents challenges for classical multivariate analysis methods. It is critical to develop novel methods that can solve problems arising in infectious disease studies when the data is high-dimensional or has complex structure. In the first project, we focus on a Plasmodium vivax malaria infection study. A standard competing risks set-up requires both time-to-event and cause-of-failure to be fully observable for all subjects. However, in practice, the cause of failure may not always be observable, thus impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of Plasmodium vivax malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. Therefore, we developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios in simulation experiments. The method was applied to Plasmodium vivax infection data to classify recurrent infections of malaria. In the second project, we investigate data collected from a Chlamydia trachomatis genital tract infection study. Many biomedical studies collect data of mixed types of variables from multiple groups of subjects. Some of these studies aim to find the group-specific and the common variation among all these variables. Even though similar problems have been studied by some previous works, their methods mainly rely on the Pearson correlation, which cannot handle mixed data. To address this issue, we propose a Latent Mixed Gaussian Copula model that can quantify the correlations among binary, categorical, continuous, and truncated variables in a unified framework. We also provide a tool to decompose the variation into the group-specific and the common variation over multiple groups via solving a regularized M -estimation problem. We conduct extensive simulation studies to show the advantage of our proposed method over the Pearson correlation-based methods. We also demonstrate that by jointly solving the M-estimation problem over multiple groups, our method is better than decomposing the variation group-by-group. We apply our method to a Chlamydia trachomatis genital tract infection study to demonstrate how it can be used to discover informative biomarkers that differentiate patients. When performing variance decomposition for data collected from the Chlamydia trachomatis genital tract infection study, so far we only considered subjects with complete data for all data modalities and removed subjects with missing values. The fact that not all subjects have complete data from all data modalities results in a block-wise missing structure of the mixed type data. Simply removing subjects with block-wise missing values would lead to a great reduction in sample size and thereby losing valuable information. To utilize as much data as possible when the mixed type data has a block-wise missing structure, we propose to impute the missing values by the Latent Mixed Gaussian Copula model in the third project, where we perform imputation for block-wise missing values by the underlying correlations between fully observed and partially observed variables. The method proposed can be applied to multi-modal data with various data types. We performed extensive simulation experiments to examine the effect of true latent correlation, missing mechanism and dimensionality on the performance of our proposed method, and compare it with three other popular approach. Our method shows superior performance for imputing the mixed type data compared with the other methods under different scenarios. We also applied the method to the multi-modal data collected from a Chlamydia trachomatis genital tract infection study for imputation of missing endometrial infection status, endometrial diagnosis results, and truncated cytokine values.Doctor of Philosoph

Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis

Identifying complex biological processes associated to patients\u27 survival time at the cellular and molecular level is critical not only for developing new treatments for patients but also for accurate survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges in survival analysis. We developed a novel family of pathway-based, sparse deep neural networks (PASNet) for cancer survival analysis. PASNet family is a biologically interpretable neural network model where nodes in the network correspond to specific genes and pathways, while capturing nonlinear and hierarchical effects of biological pathways associated with certain clinical outcomes. Furthermore, integration of heterogeneous types of biological data from biospecimen holds promise of improving survival prediction and personalized therapies in cancer. Specifically, the integration of genomic data and histopathological images enhances survival predictions and personalized treatments in cancer study, while providing an in-depth understanding of genetic mechanisms and phenotypic patterns of cancer. Two proposed models will be introduced for integrating multi-omics data and pathological images, respectively. Each model in PASNet family was evaluated by comparing the performance of current cutting-edge models with The Cancer Genome Atlas (TCGA) cancer data. In the extensive experiments, PASNet family outperformed the benchmarking methods, and the outstanding performance was statistically assessed. More importantly, PASNet family showed the capability to interpret a multi-layered biological system. A number of biological literature in GBM supported the biological interpretation of the proposed models. The open-source software of PASNet family in PyTorch is publicly available at https://github.com/DataX-JieHao

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms competing methods and can identify new associations. Furthermore, through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problems.Comment: 18 page

Regularisoitu riippuvuuksien mallintaminen geeniekpressio- ja metabolomiikkadatan välillä metabolian säätelyn tutkimuksessa

Fusing different high-throughput data sources is an effective way to reveal functions of unknown genes, as well as regulatory relationships between biological components such as genes and metabolites. Dependencies between biological components functioning in the different layers of biological regulation can be investigated using canonical correlation analysis (CCA). However, the properties of the high-throughput bioinformatics data induce many challenges to data analysis: the sample size is often insufficient compared to the dimensionality of the data, and the data pose multi-collinearity due to, for example, co-expressed and co-regulated genes. Therefore, a regularized version of classical CCA has been adopted. An alternative way of introducing regularization to statistical models is to perform Bayesian data analysis with suitable priors. In this thesis, the performance of a new variant of Bayesian CCA called gsCCA is compared to a classical ridge regression regularized CCA (rrCCA) in revealing relevant information shared between two high-throughput data sets. The gsCCA produces a partly similar regulatory effect as the classical CCA but, in addition, the gsCCA introduces a new type of regularization to the data covariance matrices. Both CCA methods are applied to gene expression and metabolic concentration measurements obtained from an oxidative-stress tolerant Arabidopsis thaliana ecotype Col-0, and an oxidative stress sensitive mutant rcd1 as time series under ozone exposure and in a control condition. The aim of this work is to reveal new regulatory mechanisms in the oxidative stress signalling in plants. For the both methods, rrCCA and gsCCA, the thesis illustrates their potential to reveal both already known and new regulatory mechanisms in Arabidopsis thaliana oxidative stress signalling.Bioinformatiikassa erityyppisten mittausaineistojen yhdistäminen on tehokas tapa selvittää tuntemattomien geenien toiminnallisuutta sekä säätelyvuorovaikutuksia eri biologisten komponenttien, kuten geenien ja metaboliittien, välillä. Riippuvuuksia eri biologisilla säätelytasoilla toimivien komponenttien välillä voidaan tutkia kanonisella korrelaatioanalyysilla (canonical correlation analysis, CCA). Bioinformatiikan tietoaineistot aiheuttavat kuitenkin monia haasteita data-analyysille: näytteiden määrä on usein riittämätön verrattuna aineiston piirteiden määrään, ja aineisto on multikollineaarista johtuen esim. yhdessä säädellyistä ja ilmentyvistä geeneistä. Tästä syystä usein käytetään regularisoitua versiota kanonisesta korrelaatioanalyysistä aineiston tilastolliseen analysointiin. Vaihtoehto regularisoidulle analyysille on bayesilainen lähestymistapa yhdessä sopivien priorioletuksien kanssa. Tässä diplomityössä tutkitaan ja vertaillaan uuden bayesilaisen CCA:n sekä klassisen harjanneregressio-regularisoidun CCA:n kykyä löytää oleellinen jaettu informaatio kahden bioinformatiikka-tietoaineiston välillä. Uuden bayesilaisen menetelmän nimi on ryhmittäin harva kanoninen korrelaatioanalyysi. Ryhmittäin harva CCA tuottaa samanlaisen regularisointivaikutuksen kuin harjanneregressio-CCA, mutta lisäksi uusi menetelmä regularisoi tietoaineistojen kovarianssimatriiseja uudella tavalla. Molempia CCA-menetelmiä sovelletaan geenien ilmentymisaineistoon ja metaboliittien konsentraatioaineistoon, jotka on mitattu Arabidopsis thaliana:n hapetus-stressiä sietävästä ekotyypistä Col-0 ja hapetus-stressille herkästä rcd1 mutantista aika-sarjana, sekä otsoni-altistuksessa että kontrolliolosuhteissa. Diplomityö havainnollistaa harjanneregressio-CCA:n ja ryhmittäin harvan CCA:n kykyä paljastaa jo tunnettuja ja mahdollisesti uusia säätelymekanismeja geenien ja metabolittien välillä kasvisolujen viestinnässä hapettavan stressin aikana

Meta-analysis of Incomplete Microarray Studies

Meta-analysis of microarray studies to produce an overall gene list is relatively straightforward when complete data are available. When some studies lack information, providing only a ranked list of genes, for example, it is common to reduce all studies to ranked lists prior to combining them. Since this entails a loss of information, we consider a hierarchical Bayes approach to meta-analysis using different types of information from different studies: the full data matrix, summary statistics or ranks. The model uses an informative prior for the parameter of interest to aid the detection of differentially expressed genes. Simulations show that the new approach can give substantial power gains compared to classical meta analysis and list aggregation methods. A meta-analysis of 11 published ovarian cancer studies with different data types identifies genes known to be involved in ovarian cancer, shows significant enrichment, while controlling the number of false positives. Independence of genes is a common assumption in microarray data analysis, and in the previous model, although it is not true in practice. Indeed, genes are activated in groups called modules: sets of co-regulated genes. These modules are usually defined by biologists, based on the position of the genes on the chromosome or known biological pathways (KEGG, GO for example). Our goal in the second part of this work is to be able to define modules common to several studies, in an automatic way. We use an empirical Bayes approach to estimate a sparse correlation matrix common to all studies, and identify modules by clustering. Simulations show that our approach performs as well or better than existing methods in terms of detection of modules across several datasets. We also develop a method based on extreme value theory to detect scattered genes, which do not belong to any module. This automatic module detection is very fast and produces accurate modules in our simulation studies. Application to real data results in a huge dimension reduction, which allows us to fit the hierarchical Bayesian model to modules, without the computational burden. Differentially expressed modules identified by this analysis present significant enrichment, indicating promising results of the method for future applications

Probabilistic Models for Aggregate Analysis of Non-Gaussian Data in Biomedicine

Aggregate association analysis is a popular way in genome-wide association studies (GWAS) that analyzes the association between the trait of interest and regions of functionally related genes, which has the advantage of capturing the missing heritability from the joint effects of correlated genetic variants while providing a better understanding of disease etiology from a systematic perspective. However, traditional methods lose their power for biomedical data with non-Gaussian data types. We proposed innovative statistical models to derive more accurate aggregated signals to enhance the power by taking account of the special data types. Based on general exponential family distribution assumptions, we developed supervised logistic PCA and supervised categorical PCA for pathway based GWAS and rare variant analysis. A general framework, sparse exponential family PCA (SePCA), is further developed for aggregate analyses for various types of biomedical data with good interpretation. We derived an efficient algorithm to find the optimal aggregated signals by solving its equivalent dual problem with closed-form updating rules. SePCA is extended for aggregate association analysis in hierarchical levels for better biological interpretation, from groups to individual variables. Both simulation studies and real world applications have demonstrated that our methods can achieve higher power in association analysis and population stratification by taking good care of the correlations among the non-Gaussian variables in biomedical data. Another analytic issue in aggregate analysis is that biomedical data often have special stratified data structures due to the experiment design to solve confounding issues. We extended SePCA to low-rank and full-rank matched models to take account of the stratified data structures. The simulation study has demonstrated their capability of reconstructing more relevant PCs for the signals of interest compared to standard ePCA. A sparse low-rank matched PCA model outperforms the existing Bayesian methods in detecting differentially expressed genes for a benchmark spike-in gene study with technical replicates. In summary, our proposed statistical models for non-Gaussian biomedical data can derive more accurate and robust aggregated signals that help reveal underlying biological principles of human disease. Other than bioinformatics, these probabilistic models also have rich applications in data mining, computer vision, and social science areas

Probabilistic Models for Aggregate Analysis of Non-Gaussian Data in Biomedicine

Aggregate association analysis is a popular way in genome-wide association studies (GWAS) that analyzes the association between the trait of interest and regions of functionally related genes, which has the advantage of capturing the missing heritability from the joint effects of correlated genetic variants while providing a better understanding of disease etiology from a systematic perspective. However, traditional methods lose their power for biomedical data with non-Gaussian data types. We proposed innovative statistical models to derive more accurate aggregated signals to enhance the power by taking account of the special data types. Based on general exponential family distribution assumptions, we developed supervised logistic PCA and supervised categorical PCA for pathway based GWAS and rare variant analysis. A general framework, sparse exponential family PCA (SePCA), is further developed for aggregate analyses for various types of biomedical data with good interpretation. We derived an efficient algorithm to find the optimal aggregated signals by solving its equivalent dual problem with closed-form updating rules. SePCA is extended for aggregate association analysis in hierarchical levels for better biological interpretation, from groups to individual variables. Both simulation studies and real world applications have demonstrated that our methods can achieve higher power in association analysis and population stratification by taking good care of the correlations among the non-Gaussian variables in biomedical data. Another analytic issue in aggregate analysis is that biomedical data often have special stratified data structures due to the experiment design to solve confounding issues. We extended SePCA to low-rank and full-rank matched models to take account of the stratified data structures. The simulation study has demonstrated their capability of reconstructing more relevant PCs for the signals of interest compared to standard ePCA. A sparse low-rank matched PCA model outperforms the existing Bayesian methods in detecting differentially expressed genes for a benchmark spike-in gene study with technical replicates. In summary, our proposed statistical models for non-Gaussian biomedical data can derive more accurate and robust aggregated signals that help reveal underlying biological principles of human disease. Other than bioinformatics, these probabilistic models also have rich applications in data mining, computer vision, and social science areas

Optimization of logical networks for the modelling of cancer signalling pathways

Cancer is one of the main causes of death throughout the world. The survival of patients diagnosed with various cancer types remains low despite the numerous progresses of the last decades. Some of the reasons for this unmet clinical need are the high heterogeneity between patients, the differentiation of cancer cells within a single tumor, the persistence of cancer stem cells, and the high number of possible clinical phenotypes arising from the combination of the genetic and epigenetic insults that confer to cells the functional characteristics enabling them to proliferate, evade the immune system and programmed cell death, and give rise to neoplasms. To identify new therapeutic options, a better understanding of the mechanisms that generate and maintain these functional characteristics is needed. As many of the alterations that characterize cancerous lesions relate to the signaling pathways that ensure the adequacy of cellular behavior in a specific micro-environment and in response to molecular cues, it is likely that increased knowledge about these signaling pathways will result in the identification of new pharmacological targets towards which new drugs can be designed. As such, the modeling of the cellular regulatory networks can play a prominent role in this understanding, as computational modeling allows the integration of large quantities of data and the simulation of large systems. Logical modeling is well adapted to the large-scale modeling of regulatory networks. Different types of logical network modeling have been used successfully to study cancer signaling pathways and investigate specific hypotheses. In this work we propose a Dynamic Bayesian Network framework to contextualize network models of signaling pathways. We implemented FALCON, a Matlab toolbox to formulate the parametrization of a prior-knowledge interaction network given a set of biological measurements under different experimental conditions. The FALCON toolbox allows a systems-level analysis of the model with the aim of identifying the most sensitive nodes and interactions of the inferred regulatory network and point to possible ways to modify its functional properties. The resulting hypotheses can be tested in the form of virtual knock-out experiments. We also propose a series of regularization schemes, materializing biological assumptions, to incorporate relevant research questions in the optimization procedure. These questions include the detection of the active signaling pathways in a specific context, the identification of the most important differences within a group of cell lines, or the time-frame of network rewiring. We used the toolbox and its extensions on a series of toy models and biological examples. We showed that our pipeline is able to identify cell type-specific parameters that are predictive of drug sensitivity, using a regularization scheme based on local parameter densities in the parameter space. We applied FALCON to the analysis of the resistance mechanism in A375 melanoma cells adapted to low doses of a TNFR agonist, and we accurately predict the re-sensitization and successful induction of apoptosis in the adapted cells via the silencing of XIAP and the down-regulation of NFkB. We further point to specific drug combinations that could be applied in the clinics. Overall, we demonstrate that our approach is able to identify the most relevant changes between sensitive and resistant cancer clones

Transcriptional decomposition reveals active chromatin architectures and cell specific regulatory interactions

Transcriptional regulation is coupled with chromosomal positioning and chromatin architecture. Here the authors develop a transcriptional decomposition approach to separate expression associated with genome structure from independent effects not directly associated with genomic positioning