Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 147 for “"scRNA-seq"”.
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Identifying and Characterizing Transition Cells in Developmental Processes from scRNA-Seq Data
… and the etiology of diseases. Single-cell RNA-sequencing (scRNA-seq), which is widely used to study complex cell states and dynamic gene expression patterns, enables us to investigate molecular mechanisms of cellular state transitions. Currently, however, computational tools available for …
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Autoencoding variational inference for the visualization of velocity-enriched scRNA-seq data
… of the lower-dimensional data. By augmenting scRNA-seq data with velocities for each cell, we can develop better visualization methodologies that use the richer information we may have describing cellular expression dynamics. Current techniques for dimensionality reduction, such as t-SNE and …
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Differential analysis of scRNA-Seq data to characterize epithelial cells in health and disease
… of high-resolution methods like single-cell RNA-Seq (scRNASeq) have enabled the study of these tissues with unprecedented resolution, revealing substantial cell-to-cell heterogeneity and the role of diverse cell states in health and disease. Increased accessibility of these technologies led to a …
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Unveiling Phenotype–Genotype Interplay with Deep Learning Foundation Models for scRNA-seq: A Quantitative Perspective
… as powerful tools for analyzing single-cell RNA sequencing (scRNA-seq) data, leveraging large-scale pretraining to capture complex gene expression patterns. However, a comprehensive quantitative framework for understanding the interplay between phenotypes and genotypes remains underdeveloped. …
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Alternative splicing and single-cell RNA-sequencing: a feasibility assessment
… most spliced genes. In theory, single-cell RNA-sequencing (scRNA-seq) could enable us to investigate isoform choice at cellular resolution. Therefore, scRNA-seq could give insight into the fundamental molecular biology process of how alternative splicing is regulated within cells. However, …
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Machine Learning Methods for Single Cell RNA-Sequencing Data to Improve Clinical Oncology
Single-cell RNA sequencing (scRNA-seq) offers a detailed view of the cellular and phenotypic composition of healthy and diseased tissues. While machine learning (ML) methods are well-suited for the high-dimensional nature of scRNA-seq data, current computational tools face limitations, particularly …
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Single Cell RNA Sequencing Data Analysis and Applications in Caenorhabditis Elegans Embryo Development
Single cell RNA sequencing (hereinafter “scRNA-seq”) has become a routine assay in molecular biological research, allowing in-depth assessments of individual cell transcriptome. Here, we used scRNA-seq datasets to study the global cellular communications in the Caenorhabditis elegans (hereinafter …
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Towards Network-Guided Large-Scale Foundation Models on Single-Cell Transcriptomics
… foundation models tailored for single-cell RNA sequencing (scRNA-seq) data have shown significant potential in interpreting the 'languages' of cells through self-supervised learning on huge amounts of unlabeled scRNA-seq datasets. These models could significantly enhance our understanding of …
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Stitching and sketching large-scale single-cell transcriptomic data
Researchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems [1]-[7] and every cell type in the human body [8] at an unprecedented scale, with scRNA-seq experiments regularly profiling gene expression in hundreds of thousands or even millions of cells …
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Methods for Dissecting High Dimensional Single Cell RNA Sequencing Data
… being described in 2009 [ 1], single cell RNA sequencing (scRNA-seq) has rapidly advanced into a staple for interrogating cellular identity in heterogeneous populations. Researchers routinely capture transcriptome-wide snapshots of thousands or even millions of individual cells. From these …
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Novel Techniques for Single-cell RNA Sequencing Data Imputation and Clustering
… one of the major challenges in analyzing scRNA-seq data is the prevalence of dropouts, which are instances where gene expression is not detected despite being present in the cell. Dropouts occur due to technical limitations and can introduce excessive noise into the data, obscuring the …
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Modeling sources of variation to interpret single cell transcriptomic maps
… for studying biological systems. Single-cell RNA sequencing profiles thousands of individual cells and captures coexisting gene expression programs within a cell. Analyzing data from multiple samples and conditions introduces additional biological variability, including genetic background, age, …
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Understanding Developmental Decision Making in Malaria Parasites using Single-Cell Transcriptomics
Single-cell sequencing has the potential to revolutionise our understanding of malaria parasites. Malaria is caused by single-celled parasitic organisms of the genus Plasmodium which display remarkable cellular plasticity during their complex life cycle with large variations in size (1.2 to 50 μm) …
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Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells
… spliced and unspliced mRNA in single cell RNA-seq (scRNA-seq) data, resulting in noisy and biased approximations. Recent advancements in metabolic labeling enabled the direct, unbiased measurement of nascent RNA, yielding significantly improved RNA velocity estimates. However, there is still a …
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Recovery of T cell receptor variable sequences from 3' barcoded single-cell RNA sequencing libraries
… techniques, including flow cytometry, RNA-seq, and mass spectrometry, to decipher the immune underpinnings of various diseases such as cancer and autoimmune disorders. In recent years, high-throughput single-cell RNA sequencing (scRNA-seq) has gained popularity among immunologists due to …
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Investigating intratumoural heterogeneity and plasticity with multiplexed single-cell assays
… nature of cancer cells through single-cell RNA-sequencing (scRNA-seq) of patient tumours. Drug combinations are a promising strategy to overcome treatment resistance but little is known about how CRC cells invoke cellular plasticity by shifting their cell states following treatment with KRAS …
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Supervised Inference of Gene Regulatory Networks
… GRNs increasingly rely on single-cell level RNA-sequencing (scRNA-seq) data. Most of these methods rely on unsupervised or association based strategies, which cannot leverage known regulatory interactions by design. To facilitate supervised learning, we propose a novel graph convolutional neural …
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Using heterogeneous Graph Neural Networks(hGNN) to predict cell-cell communication
… cellular interactions using single-cell RNA sequencing (scRNA-seq) data. We evaluate the performance of Graph Neural Networks (GNNs) both with and without gene-gene edges, Contrastive Learning, and Variational Autoencoders (VAEs) across multiple datasets. Our study compares these methods and …
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Oncologia di Precisione: Il Ruolo della Radiomica, della Genomica e della Trascrittomica nelle Strategie di Trattamento Personalizzato del Cancro
… intratumorale (ITH) utilizzando il sequenziamento dell'RNA a singola cellula (scRNA-seq) nel mieloma multiplo (MM). Questo studio evidenzia il potenziale dello scRNA-seq di catturare le variazioni di espressione genica tra i sottocloni tumorali, affrontando le sfide poste dai metodi …
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A Transformer for scATAC-scRNA Translation
scATAC-seq gives a comprehensive picture of the chromatin accessibility profile of a cell, covering not only protein-coding regions but also non-coding regulatory regions which are in theory missed by scRNA-seq. However, scATAC-seq data is highdimensional and noisy, aspects which when compounded …
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