Novel Techniques for Single-cell RNA Sequencing Data Imputation and Clustering
type: CreativeWork
description: Advances in single-cell technologies have shifted genomics research from the analysis of bulk tissues toward a comprehensive characterization of individual cells. These cutting-edge approaches enable the in-depth analysis of individual cells, unveiling the remarkable heterogeneity and complexity of cellular systems. By unraveling the unique signatures and functions of distinct cell types, single-cell technologies have not only deepened our understanding of fundamental biological processes but also unlocked new avenues for disease diagnostics and therapeutic interventions.The applications of single-cell technologies extend beyond basic research, with significant implications for precision medicine, drug discovery, and regenerative medicine. By capturing the cellular heterogeneity within tumors, these methods have shed light on the mechanisms of tumor evolution, metastasis, and therapy resistance. Additionally, they have facilitated the identification of rare cell populations with specialized functions, such as stem cells and tissue-resident immune cells, which hold great promise for cell-based therapies.However, 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 true biological signals. As a result, imputation methods are used to estimate missing values and reduce the impact of dropouts on downstream analyses. Furthermore, the high-dimensionality of scRNA-seq data presents additional challenges in effectively partitioning cell populations. Thus, robust computational approaches are required to overcome these challenges and extract meaningful biological insights from single-cell data.There have been numerous imputation and clustering methods developed specifically to address the unique challenges associated with scRNA-seq data analysis. …
abstract: Advances in single-cell technologies have shifted genomics research from the analysis of bulk tissues toward a comprehensive characterization of individual cells. These cutting-edge approaches enable the in-depth analysis of individual cells, unveiling the remarkable heterogeneity and complexity of cellular systems. By unraveling the unique signatures and functions of distinct cell types, single-cell technologies have not only deepened our understanding of fundamental biological processes but also unlocked new avenues for disease diagnostics and therapeutic interventions.The applications of single-cell technologies extend beyond basic research, with significant implications for precision medicine, drug discovery, and regenerative medicine. By capturing the cellular heterogeneity within tumors, these methods have shed light on the mechanisms of tumor evolution, metastasis, and therapy resistance. Additionally, they have facilitated the identification of rare cell populations with specialized functions, such as stem cells and tissue-resident immune cells, which hold great promise for cell-based therapies.However, 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 true biological signals. As a result, imputation methods are used to estimate missing values and reduce the impact of dropouts on downstream analyses. Furthermore, the high-dimensionality of scRNA-seq data presents additional challenges in effectively partitioning cell populations. Thus, robust computational approaches are required to overcome these challenges and extract meaningful biological insights from single-cell data.There have been numerous imputation and clustering methods developed specifically to address the unique challenges associated with scRNA-seq data analysis. …
author: Tran, Bang Sy
keywords: bioinformatics, computational biology, machine learning, scRNA-seq, scRNA-seq clustering, scRNA-seq imputatation
date published: 2023
date modified: 2026-07-27T21:46:08Z
provider: University of Nevada - Reno
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Nevada - Reno

PropertyValue
property ID: Handle
value: http://hdl.handle.net/11714/10515