Medoidshift clustering applied to genomic bulk tumor data.

Despite the enormous medical impact of cancers and intensive study of their biology, detailed characterization of tumor growth and development remains elusive. This difficulty occurs in large part because of enormous heterogeneity in the molecular mechanisms of cancer progression, both tumor-to-tumor and cell-to-cell in single tumors. Advances in genomic technologies, especially at the single-cell level, are improving the situation, but these approaches are held back by limitations of the biotechnologies for gathering genomic data from heterogeneous cell populations and the computational methods for making sense of those data. One popular way to gain the advantages of whole-genome methods without the cost of single-cell genomics has been the use of computational deconvolution (unmixing) methods to reconstruct clonal heterogeneity from bulk genomic data. These methods, too, are limited by the difficulty of inferring genomic profiles of rare or subtly varying clonal subpopulations from bulk data, a problem that can be computationally reduced to that of reconstructing the geometry of point clouds of tumor samples in a genome space. Here, we present a new method to improve that reconstruction by better identifying subspaces corresponding to tumors produced from mixtures of distinct combinations of clonal subpopulations. We develop a nonparametric clustering method based on medoidshift clustering for identifying subgroups of tumors expected to correspond to distinct trajectories of evolutionary progression. We show on synthetic and real tumor copy-number data that this new method substantially improves our ability to resolve discrete tumor subgroups, a key step in the process of accurately deconvolving tumor genomic data and inferring clonal heterogeneity from bulk data

RNA 시퀀싱 데이터의 해독과 활용을 위한 기계학습 기법

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 생물정보학전공,2019. 8. 김선.진핵 세포 시스템에서는 mRNA 분자가 전사된 이후 완전히 처리되어 단백질로 번역될 때까지 여러 단계의 전사 후 조절 과정을 거치게 된다. 전사 후 조절 과정은 RNA 편집, 선택적 접합, 선택적 아데닐화 등을 포함한다. 즉 어느 한 시점에서 전사체를 들여다보면 그 내부는 다양한 중간체들의 혼합물로 구성되어 있는 것이다. 이러한 복잡한 조절 시스템 때문에 전사체를 전체적인 수준에서 이해하기가 쉽지 않다. 본 학위 연구는 RNA 시퀀싱 데이터를 해독하고 활용하기 위한 기계학습 기법들에 대한 연구이며 RNA 편집, 선택적 접합 및 유전자 발현의 관점에서 수행된 세 가지 연구로 구성된다. RNA 편집은 ADAR(A=>I) 과 APOBEC(C=>U) 두 가지 효소에 의해 촉매 되는 전사 후 RNA 서열 조절 기작이다. RNA 편집은 단백질 활성도, 선택적 접합 및 miRNA 표적 조절 등 다양한 세포 기작을 제어하는 것으로 알려진 중요한 새포 내 조절 시스템이다. RNA 시퀀싱을 이용해 RNA 편집 현상을 검출하는 것은 RNA 편집 현상의 생물학적 기능을 이해하는 데에 매우 중요하다. 문제는 이 과정에서 상당한 양의 위양성이 발생한다는 점이다. 샘플당 수만 개 이상 발생하는 RNA 편집 잔기들 모두를 실험적으로 검증할 수 없기 때문에 이를 걸러내기 위한 전산학적 모델이 요구된다. RDDpred는 RNA 시퀀싱 데이터로부터 RNA 편집 현상을 검출하는 과정에서 발생하는 위양성 잔기들을 기계학습 기술에 기반하여 구분하는 모델이다. RDDpred는 두 개의 기 발표된 RNA 편집 연구 데이터를 이용하여 검증되었다. RNA 시퀀싱 기술이 활용될 수 있는 또 하나의 복잡한 문제로 접합체 차원에서의 종양 이질성 (ITH) 측정 문제가 있다. ITH는 암 조직을 구성하는 세포 집단의 다양성의 지표이며, 최근 출판된 연구들의 결과는 유전자 발현량 데이터에 기반하여 측정된 전사체 수준에서의 ITH가 암 환자의 예후예측에 유용함을 시사한다. 접합체는 유전자 발현량과 함께 전사체를 구성하는 주요 요소 중 하나이며 따라서 접합체 수준에서 ITH를 측정하는 것은 보다 전체적인 수준에서 전사체 ITH를 연구하기 위한 자연스러운 흐름이다. RNA 시퀀싱 데이터를 이용하여 암 접합체 수준에서 ITH를 측정하는 과정에는 복잡한 접합 패턴과 광범위한 인트론 연장 변이 및 짧은 시퀀싱 판독 길이 등의 심각한 기술적 난관들이 있다. SpliceHetero는 이러한 문제들을 고려하여 접합체 수준에서의 ITH (즉, sITH)를 측정하기 위한 도구이며 내부적으로 정보이론을 활용한다. SpliceHetero는 시뮬레이션 데이터, 이종이식 종양 데이터 및 TCGA pan-cancer 데이터 등을 활용하여 광범위하게 검증되었으며 ITH를 잘 반영하는 것으로 확인되었다. 이뿐 아니라 sITH는 암의 진행과 암 환자의 예후 및 PAM50와 같은 잘 알려진 분자 아형들과도 높은 상관관계를 가지는 것으로 확인되었다. 마지막 연구 주제는 유전자 발현량 데이터에 기반하여 특정 암 표현형에 특이적인 환자 부분 공간을 정의하는 기계학습 알고리즘을 개발하는 것이다. RNA 시퀀싱 데이터는 암 환자의 유전자 발현량 프로파일을 얻는 데에 유용한 도구이지만, 2만 개 이상의 차원을 가진 매우 고차원의 데이터이기 때문에 실질적인 용도로 사용되기 위해서는 그 차원의 크기를 축소할 필요가 있다. 이때 각 유전자들은 복잡하지만 고유한 방식으로 서로 상호작용한다는 점을 이용할 수 있다. 실험적으로 검증된 단백질 간의 상호작용 정보를 모아 네트워크 형태로 묶은 것을 단백질 상호작용 네트워크 (혹은 PIN)라 부른다. 이 PIN을 활용하여 RNA 시퀀싱 데이터의 차원을 줄이면서도 데이터로부터 생물학적으로 유의미한 특징들을 추출할 수 있다. Tumor2Vec은 이렇게 추출된 PIN 수준의 특징들을 활용하여 특정 암 표현형에 특이적인 환자 부분 공간을 정의한다. Tumor2Vec은 조기 구강 암에서 림프절 전이를 예측하기 위한 파일럿 연구에 적용되었으며 그 결과 RNA 시퀀싱 데이터의 차원을 줄여 림프절 전이 예측 모델을 생성했고 이 과정에서 암 표현형을 잘 설명하는 PIN 수준의 특징들을 보존하는 데에도 성공했다.In eukaryotic cells, there are several post-transcriptional modification steps such as RNA editing and alternative splicing, until mRNA molecules are fully matured and translated into proteins. Thus, the transcriptome is a complex mixture of various intermediates that are processed in multiple steps. This complex regulatory structure makes it difficult to fully understand the landscape of transcriptome. My doctoral study consists of three studies that enable RNA-seq to be decoded and utilized in terms of RNA editing, alternative splicing, and gene expression. RNA editing is a post-transcriptional RNA sequence modification performed by two catalytic enzymes ADAR (A-to-I) and APOBEC (C-to-U). RNA editing is considered an important regulatory system that controls a variety of cellular functions such as protein activation, alternative splicing, and miRNA targeting. Therefore, detecting RNA editing events in RNA-seq data is important for understanding its biological functions. However, it is known that a significant amount of false-positives occur when detecting RNA editing in RNA-seq. Since it is not possible to experimentally validate all RNA editing residues extracted from RNA-seq, a computational model is needed to filter potential false-positive RNA editing calls. RDDpred, an RNA editing predictor based on machine learning techniques, was developed to filter out false-positive RNA editing calls in RNA-seq. It uses prior knowledge bases to collect training instances directly from the input data, and then trains the random forest (RF) predictors that are specific to the input data. RDDpred was tested using two publicly available datasets of RNA editing studies and has shown good performance. Another complex problem in RNA-seq decoding is spliceomic intratumor heterogeneity (ie, sITH). Intratumor heterogeneity (ITH) represents the diversity of cell populations that make up the cancer tissue. Recent studies have identified ITH at the transcriptome level and suggested that ITH at gene expression levels is useful for predicting prognosis. Measuring ITH levels at the spliceome level is a natural extension. There is a serious technical challenge in measuring sITH from bulk tumor RNA-seq, such as complex splicing patterns, widespread intron retentions, and short sequencing read lengths. SpliceHetero, an information-theoretic method for measuring the sITH of a tumor, was developed to address the aforementioned technical problems. SpliceHetero was extensively tested in experiments using synthetic data, xenograft tumor data and TCGA pan-cancer data and measured sITH successfully. Also, sITH was shown to be closely related to cancer progression and clonal heterogeneity, along with clinically significant features such as cancer stage, survival outcome, and PAM50 subtype. The last research topic is to develop a machine learning algorithm that defines patient subspaces specific to particular cancer phenotypes based on gene expression data. Since RNA-seq data is high-dimensional data composed of 20,000 or more genes in general, it is not easy to apply a machine learning algorithm. A network that collects information of experimentally verified interaction of proteins is called a Protein Interaction Network (PIN). Tumor2Vec defines the patient subspace by defining the subnetwork communities that interact with each other by applying the Graph Embedding technique to PIN. Tumor2Vec proposed a clinical model by defining a subspace for patients with different lymph node metastases in early oral cancer and found biologically significant features in the PIN subnetwork unit in the process.Chapter 1 Introduction 1 1.1 Biological background . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Challenges in decoding and utilizing RNA-seq data . . . . . . . . 5 1.2.1 false-positives in RNA editing calls . . . . . . . . . . . . . 6 1.2.2 Absence of a model for measuring spliceomic intratumor heterogeneity considering complex cancer spliceome . . . 6 1.2.3 Lack of biological interpretation of dimension reduction techniques using gene expression . . . . . . . . . . . . . . 8 1.3 Machine learning techniques to solve difficulties in using RNA-seq 9 1.4 Outline of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 2 RDDpred: A condition specific machine learning model for filtering false-positive RNA editing calls in RNAseq data 11 2.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 A preliminary study . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.5.1 Design of experiments for evaluation . . . . . . . . . . . . 18 2.5.2 Evaluation using data from Bahn et al. . . . . . . . . . . 19 2.5.3 Evaluation using data from Peng et al. . . . . . . . . . . . 19 2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Chapter 3 SpliceHetero: An information-theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq data 24 3.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3 A preliminary study . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.1 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.2 Xenograft tumor data . . . . . . . . . . . . . . . . . . . . 36 3.5.3 TCGA pan-cancer data . . . . . . . . . . . . . . . . . . . 38 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Chapter 4 Tumor2Vec: A supervised learning algorithm for extracting subnetwork representations of cancer RNAseq data using protein interaction networks 48 4.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.1 Lymph node metastasis in early oral cancer . . . . . . . . 57 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Chapter 5 Conclusion 62 초록 78Docto

A survey on data integration for multi-omics sample clustering

Due to the current high availability of omics, data-driven biology has greatly expanded, and several papers have reviewed state-of-the-art technologies. Nowadays, two main types of investigation are available for a multi-omics dataset: extraction of relevant features for a meaningful biological interpretation and clustering of the samples. In the latter case, a few reviews refer to some outdated or no longer available methods, whereas others lack the description of relevant clustering metrics to compare the main approaches. This work provides a general overview of the major techniques in this area, divided into four groups: graph, dimensionality reduction, statistical and neural-based. Besides, eight tools have been tested both on a synthetic and a real biological dataset. An extensive performance comparison has been provided using four clustering evaluation scores: Peak Signal-to-Noise Ratio (PSNR), Davies-Bouldin(DB) index, Silhouette value and the harmonic mean of cluster purity and efficiency. The best results were obtained by using the dimensionality reduction, either explicitly or implicitly, as in the neural architecture

Sparse group sufficient dimension reduction and covariance cumulative slicing estimation

This dissertation contains two main parts: In Part One, for regression problems with grouped covariates, we adopt the idea of sparse group lasso (Friedman et al., 2010) to the framework of the sufficient dimension reduction. We propose a method called the sparse group sufficient dimension reduction (sgSDR) to conduct group and within group variable selections simultaneously without assuming a specific model structure on the regression function. Simulation studies show that our method is comparable to the sparse group lasso under the regular linear model setting, and outperforms sparse group lasso with higher true positive rates and substantially lower false positive rates when the regression function is nonlinear or (and) the error distributions are non-Gaussian. One immediate application of our method is to the gene pathway data analysis where genes naturally fall into groups (pathways). An analysis of a glioblastoma microarray data is included for illustration of our method. In Part Two, for many-valued or continuous Y , the standard practice of replacing the response Y by a discrete version of Y usually results in the loss of power due to the ignorance of intra-slice information. Most of the existing slicing methods highly reply on the selection of the number of slices h. Zhu et al. (2010) proposed a method called the cumulative slicing estimation (CUME) which avoids the otherwise subjective selection of h. In this dissertation, we revisit CUME from a different perspective to gain more insights, and then refine its performance by incorporating the intra-slice covariances. The resulting new method, which we call the covariance cumulative slicing estimation (COCUM), is comparable to CUME when the predictors are normally distributed, and outperforms CUME when the predictors are non-Gaussian, especially in the existence of outliers. The asymptotic results of COCUM are also well proved. --Abstract, page iv

Integrated Graph Theoretic, Radiomics, and Deep Learning Framework for Personalized Clinical Diagnosis, Prognosis, and Treatment Response Assessment of Body Tumors

Purpose: A new paradigm is beginning to emerge in radiology with the advent of increased computational capabilities and algorithms. The future of radiological reading rooms is heading towards a unique collaboration between computer scientists and radiologists. The goal of computational radiology is to probe the underlying tissue using advanced algorithms and imaging parameters and produce a personalized diagnosis that can be correlated to pathology. This thesis presents a complete computational radiology framework (I GRAD) for personalized clinical diagnosis, prognosis and treatment planning using an integration of graph theory, radiomics, and deep learning. Methods: There are three major components of the I GRAD framework–image segmentation, feature extraction, and clinical decision support. Image Segmentation: I developed the multiparametric deep learning (MPDL) tissue signature model for segmentation of normal and abnormal tissue from multiparametric (mp) radiological images. The segmentation MPDL network was constructed from stacked sparse autoencoders (SSAE) with five hidden layers. The MPDL network parameters were optimized using k-fold cross-validation. In addition, the MPDL segmentation network was tested on an independent dataset. Feature Extraction: I developed the radiomic feature mapping (RFM) and contribution scattergram (CSg) methods for characterization of spatial and inter-parametric relationships in multiparametric imaging datasets. The radiomic feature maps were created by filtering radiological images with first and second order statistical texture filters followed by the development of standardized features for radiological correlation to biology and clinical decision support. The contribution scattergram was constructed to visualize and understand the inter-parametric relationships of the breast MRI as a complex network. This multiparametric imaging complex network was modeled using manifold learning and evaluated using graph theoretic analysis. Feature Integration: The different clinical and radiological features extracted from multiparametric radiological images and clinical records were integrated using a hybrid multiview manifold learning technique termed the Informatics Radiomics Integration System (IRIS). IRIS uses hierarchical clustering in combination with manifold learning to visualize the high-dimensional patient space on a two-dimensional heatmap. The heatmap highlights the similarity and dissimilarity between different patients and variables. Results: All the algorithms and techniques presented in this dissertation were developed and validated using breast cancer as a model for diagnosis and prognosis using multiparametric breast magnetic resonance imaging (MRI). The deep learning MPDL method demonstrated excellent dice similarity of 0.87±0.05 and 0.84±0.07 for segmentation of lesions on malignant and benign breast patients, respectively. Furthermore, each of the methods, MPDL, RFM, and CSg demonstrated excellent results for breast cancer diagnosis with area under the receiver (AUC) operating characteristic (ROC) curve of 0.85, 0.91, and 0.87, respectively. Furthermore, IRIS classified patients with low risk of breast cancer recurrence from patients with medium and high risk with an AUC of 0.93 compared to OncotypeDX, a 21 gene assay for breast cancer recurrence. Conclusion: By integrating advanced computer science methods into the radiological setting, the I-GRAD framework presented in this thesis can be used to model radiological imaging data in combination with clinical and histopathological data and produce new tools for personalized diagnosis, prognosis or treatment planning by physicians

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.Peer reviewe

Integrative Analysis Methods for Biological Problems Using Data Reduction Approaches

The "big data" revolution of the past decade has allowed researchers to procure or access biological data at an unprecedented scale, on the front of both volume (low-cost high-throughput technologies) and variety (multi-platform genomic profiling). This has fueled the development of new integrative methods, which combine and consolidate across multiple sources of data in order to gain generalizability, robustness, and a more comprehensive systems perspective. The key challenges faced by this new class of methods primarily relate to heterogeneity, whether it is across cohorts from independent studies or across the different levels of genomic regulation. While the different perspectives among data sources is invaluable in providing different snapshots of the global system, such diversity also brings forth many analytic difficulties as each source introduces a distinctive element of noise. In recent years, many styles of data integration have appeared to tackle this problem ranging from Bayesian frameworks to graphical models, a wide assortment as diverse as the biology they intend to explain. My focus in this work is dimensionality reduction-based methods of integration, which offer the advantages of efficiency in high-dimensions (an asset among genomic datasets) and simplicity in allowing for elegant mathematical extensions. In the course of these chapters I will describe the biological motivations, the methodological directions, and the applications of three canonical reductionist approaches for relating information across multiple data groups.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138564/1/yangzi_1.pd