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 67 for “"single-cell RNA sequencing (scRNA-seq)"”.
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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 …
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Using heterogeneous Graph Neural Networks(hGNN) to predict cell-cell communication
… diverse computational methodologies for modeling 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 …
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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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The design and statistical analysis of single-cell RNA-sequencing experiments
<p>Next-generation DNA- and RNA-sequencing (RNA-seq) technologies have expanded rapidly in both throughput and accuracy within the last decade. The momentum continues as emerging techniques become increasingly capable of profiling molecular content at the level of individual cells. One goal of this …
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Harnessing Cancer Omics to Inform Precision Oncology
… colon cancer via whole genome and targeted UCE sequencing from two cohorts. We characterized the tumor microenvironment of melanoma brain metastasis (MBM) during anti-PD1 therapy using single cell RNA sequencing (scRNA-seq) of patients’ cerebrospinal fluid (CSF) samples. We profiled functional …
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Multi-omic Analysis of Neurodegeneration in Alzheimer’s Disease and Related Dementias
The advent of single cell sequencing has revolutionized the granularity at which we can understand genetics and underlying cell biology. This enables us to analyze both the transcriptome and epigenome of various tissues, offering new insights into the molecular mechanisms that underlie disease such …
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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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Ageing and regeneration-associated transcriptional state transitions and tumour-stroma interaction in early renal carcinogenesis
… common cancers in the world, among which clear cell renal cell carcinoma (ccRCC) is the most common subtype. VHL inactivation is the most common mutation seen in ccRCC with more than 90% of tumours had clonal disruption of the VHL pathway. Although it is the initial oncogenic insult in ccRCC, it …
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Uncertainty and Generality of Transfer Learning Models in Predicting Signaling History
Proper cell-cell communication is essential for multicellular development, from embryogenesis to stem cell differentiation. To map these networks, we developed IRIS (Intracellular Response to Infer Signaling state), a semi-supervised deep learning method that fits conditional variational …
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Unveiling Phenotype–Genotype Interplay with Deep Learning Foundation Models for scRNA-seq: A Quantitative Perspective
… have emerged 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 …
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Characterizing slc8a4b as an Osteoblast Marker in Danio rerio
… demonstrate the utility of slc8a4b to demarcate cells poorly captured by other markers. The analysis of pre-existing single-cell RNA-sequencing (scRNA-seq) data showed osteoblasts expressing slc8a4b had similar transcriptional profiles as cells marked by other osteoblast markers but reduced …
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Highly Constrained Kinetic Models for Quantitative Single-Cell Gene Expression Analysis
Cells are for the most part genetically identical, and it is the gene expression which determines different cell types in the body. Transcription, the first step in gene expression, is a complex, multi-step process initiated by transcription factors (TFs) binding to DNA promoter regions, followed …
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Computational analysis of cell-cell communication in the tumor microenvironment
Cell-cell communication between malignant, immune, and stromal cells influences many aspects of in vivo tumor biology, including tumorigenesis, tumor progression, and therapeutic resistance. As a result, targeting receptor-ligand interactions, for instance with immune check-point inhibitors, can …
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scPhen: Single-Cell Phenotype Predictor for Alzheimer’s Disease
… our ability to model complex biological systems. Single-cell RNA sequencing (scRNA-seq) provides unprecedented resolution into cellular heterogeneity, offering a powerful substrate for modeling disease circuitry. However, predicting patient-level phenotypes from scRNA-seq remains challenging due …
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Towards Network-Guided Large-Scale Foundation Models on Single-Cell Transcriptomics
… transformer-based 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 …
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Discovery of microenvironment drivers of cell states, plasticity and drug response
Cell state can be influenced by both intrinsic and extrinsic factors with functional consequences. Illustratively, in cancer, intrinsic genome level alterations or extrinsic microenvironmental immune cell activity can drive tumorigenesis. Similarly, in viral infections like COVID-19, the responses …
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LINKING TRANSCRIPTIONAL AND MORPHOLOGICAL HETEROGENEITY TO THERAPEUTIC VULNERABILITIES IN GLIOBLASTOMA
… elicit a gene-independent detrimental effect on cellular survival when targeting genomic copy-number amplified regions (CN bias) and spurious effect due to Cas9-mediated whole chromosome arm truncations (proximity bias). Subsequently, I have focused on the integration of transcriptomics and …
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Identifying and Characterizing Transition Cells in Developmental Processes from scRNA-Seq Data
<p>During the development of multicellular organisms, individual cells make distinct decisions about their cell types and states. Understanding the molecular mechanisms underlying cellular state transitions at different developmental stages provides deep insights into physiology, morphology and the …
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The role of tumor-associated macrophages in pancreatic cancer
… unknown. In this study, comparative analysis of single-cell RNA sequencing (scRNA-Seq) data and validated experiments demonstrated that more exhausted effector CD8+ T cells and increased M2-like TAMs with a reduced capacity of antigen presentation are detected in resistant Panc02-formed tumors …
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Intratumoral B and T cell receptors: reconstruction and analysis
When cells divide, mistakes happen. However, an intricate surveillance system has evolved to detect and eliminate anomalous cells before they become detrimental to the host organism. In cancer, abnormal cells manage to escape the immune system and grow uncontrollably. In this sense, cancer can be …
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