The combined effects of rivers and refugia generate extreme cryptic fragmentation within the common ground skink (Scincella lateralis)

Rivers can act as both islands of mesic refugia for terrestrial organisms during times of aridification and barriers to gene flow, though evidence for long-term isolation by rivers is mixed. Understanding the extent to which riverine barrier effects can be heightened for populations trapped in mesic refugia can help explain maintenance and generation of diversity in the face of Pleistocene climate change. Herein, we implement phylogenetic and population genetic approaches to investigate the phylogeographic structure and history of the ground skink, Scincella lateralis, using mtDNA and eight nuclear loci. We then test several predictions of a river-refugia model of diversification. We recover 14 well-resolved mtDNA lineages distributed east-west along the Gulf Coast with a subset of lineages extending northward. In contrast, ncDNA exhibits limited phylogenetic structure or congruence among loci. However, multilocus population structure is broadly congruent with mtDNA patterns and suggests that deep coalescence rather than differential gene flow is responsible for mtDNA-ncDNA discordance. The observed patterns suggest that most lineages originated from population vicariance due to riverine barriers strengthened during the Plio-Pleistocene by a climate-induced coastal distribution. Diversification due to rivers is likely a special case, contingent upon other environmental or biological factors that reinforce riverine barrier effects. © 2009 The Society for the Study of Evolution

표현형 기반 고속 레이저 분리 및 염기서열 분석 기술을 이용한 암 유전체 분석

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2019. 2. 권성훈.지금까지는 기술적인 한계로 인해 NGS 를 이용하여 생산된 세포의 유전체 정보가 세포의 조직학적 정보와 연결될 수 없었다. 본 박사학위 논문에서는 단일 세포 혹은 소량의 세포를 조직으로부터 고속으로 분리하여 유전체 분석을 수행하는 PHLI- seq 기술을 소개한다. 그리고 PHLI-seq 기술을 이용하여 암 조직을 대상으로 유전체 정보와 조직학적 정보를 연결하는 연구를 소개한다. 유방암 조직에 대하여 PHLI-seq 을 적용하여 유방암 세포의 유전체 분석을 수행하고 분석된 세포의 유전체 정보와 조직학적 이미지를 연동하여 유방암 조직의 유전적 다양성을 시각화 한다. 또한, 난소암에 대하여 PHLI-seq 을 적용하여 암의 발생 및 진행의 과정을 연구한 내용을 소개한다. 이 연구들을 통하여 암 세포들의 서브클론 형성 및 공간적인 배열이 암 발생 초기에 이루어짐을 알 수 있다.Despite advances in next-generation sequencing technologies, it has been challenging to map molecular information of cells to their tissue context due to technical limitations. In this dissertation, I describe PHLI-seq, a novel approach that enables high-throughput isolation and genome-wide sequence analysis of single-cells or small numbers of cells from a tissue to construct genomic maps within biological samples in relation to the images or phenotypes of the cells. By applying PHLI-seq to breast cancer tissues, genetic heterogeneity of cancer is revealed at a high spatial resolution. Also, in a case of ovarian cancer, a history of tumor development is explored using PHLI-seq. Through these studies, it is concluded that genetic subclonality and spatial separation of the subclones are formed in early stage of cancer development.Table of Contents Abstract____________________________________________________ i List of Figures ______________________________________________ iv List of Tables_______________________________________________ xi Chapter 1 Introduction _______________________________________ 1 1.1 Identifying Molecular Information of Cells ____________________ 3 1.2 Spatially Resolved Sequencing of Cells_______________________ 6 1.3 Comparison of Technologies _______________________________ 7 Chapter 2 Techical Overview of PHLI-seq ______________________ 11 2.1 Phenotype-based High-throughput Laser-aided Isolation and Sequencing (PHLI-seq) _____________________________________ 12 2.2 Instrumentation_________________________________________ 14 2.3 Whole Genome Amplification _____________________________ 20 2.4 Performance Validation of PHLI-seq________________________ 28 2.5 Performance Comparison with Commercial Laser Microdissection 36 Chapter 3 Spatially Resolved Sequencing of Breast Cancer ________ 42 3.1 PHLI-seq Analysis of a HER2 Positive Breast Tumor___________ 43 3.1.1 Experimental Procedure ______________________________ 43 3.1.2 Somatic Copy Number Alteration (CNA) Analysis _________ 45 3.1.3 Somatic Single Nucleotide Variant (SNV) Analysis_________ 50 3.1.4 Inferring Tumor Evolution ____________________________ 57 3.2 PHLI-seq Analysis of a Triple-negative Breast Tumor __________ 60 3.3 A Summary____________________________________________ 65 Chapter 4 Inferring Tumor Evolution of Ovarian Cancer__________ 67 4.1 Sample Preparation______________________________________ 70 4.2 Somatic CNA and SNV Analysis___________________________ 73 4.3 Inferring Evolutionary Trajectory of the Tumor Cells ___________ 80 4.4 A Summary____________________________________________ 85 Chapter 5 Conclusion________________________________________ 87 Bibliography _______________________________________________ 90 Abstract in Korean _________________________________________ 100Docto

The empirical replicability of task-based fMRI as a function of sample size

Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these

Statistical validation of megavariate effects in ASCA

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Leveraging variational autoencoders for multiple data imputation

Missing data persists as a major barrier to data analysis across numerous applications. Recently, deep generative models have been used for imputation of missing data, motivated by their ability to capture highly non-linear and complex relationships in the data. In this work, we investigate the ability of deep models, namely variational autoencoders (VAEs), to account for uncertainty in missing data through multiple imputation strategies. We find that VAEs provide poor empirical coverage of missing data, with underestimation and overconfident imputations, particularly for more extreme missing data values. To overcome this, we employ $\beta$-VAEs, which viewed from a generalized Bayes framework, provide robustness to model misspecification. Assigning a good value of $\beta$ is critical for uncertainty calibration and we demonstrate how this can be achieved using cross-validation. In downstream tasks, we show how multiple imputation with $\beta$-VAEs can avoid false discoveries that arise as artefacts of imputation.Comment: 17 pages, 3 main figures, 6 supplementary figure