Next-Generation Sequencing (NGS) technology has been successfully applied in disease diagnostics, oncological immunotherapy, and drug repurposing, especially for precision medicine where optimized medication is tailored to individual patients. Recently, the development of single-cell techniques makes it possible to examine gene expression and mutation at individual cell resolution, which provides an unprecedented opportunity to study cell development and differentiation and reveal cell-to-cell heterogeneity during disease development, treatment and drug response for individual patients. With the exponential increase of single-cell sequencing data, it is critical to develop appropriate bioinformatics and machine learning tools to mine the rules behind them. However, due to the technical barriers in single-cell sequencing and the noisy nature of raw sequencing data, this task is challenging especially in the context of disease diagnosis and drug development.
To promote the translation and efficient usage of single-cell sequencing data to precision medicine, it is necessary to develop new analysis tools for analyzing and integrating multi-level single-cell data including DNA, RNA, protein and so on, comparing existing methods and results derived from different studies, and enhancing disease diagnostics and drug development. For example, the quality control, normalization, differential gene calling and clustering methods are quite different between single-cell sequencing and traditional bulk cell sequencing. Thus, it is critical to develop a best practice specifically for dealing with single-cell sequencing data. For disease treatment, it is also important to identify disease driver genes common to all cell types as well as those specific to a particular cell type or subgroup as revealed by single-cell techniques, based on existing or novel network and machine learning-based methods. Finally, more translational work should be done to bridge the bioinformatics analyses and clinical applications for single-cell researchers.
In our first volume, Bioinformatics Analysis of Single-Cell Sequencing Data and Applications in Precision Medicine we found the application of single-cell data analysis in disease prognostics, diagnostics and treatment; methods integrating multi-level single-cell data including DNA, RNA, protein, and so on and for single-cell data analysis including quality control, normalization, dropout imputation, clustering, differential and highly variable gene calling, cell-to-cell heterogeneity identification
In the past two years since our Research Topic we are particularly interested in:
• Best practices for dealing with single-cell sequencing data;
• Methods for single-cell data analysis including quality control, normalization, dropout imputation, clustering, differential and highly variable gene calling, cell-to-cell heterogeneity identification, and so on;
• Single-cell trajectory analysis and data visualization;
• Methods comparing and integrating single-cell and bulk RNA sequencing data;
• Methods integrating multi-level single-cell data including DNA, RNA, protein, and so on;
• Methods for aligning single-cell RNA sequencing data and spatial transcriptome sequencing data;
• Cell type-specific gene network inference and mining;
• Integrating single-cell sequencing and spatial sequencing in studying tumor microenvironment;
• Disease gene identification and biomarker discovery using single-cell techniques;
• Application of single-cell data analysis in disease prognostics, diagnostics and treatment;
• Single cell-based drug response and repurposing analyses;
• Single cell-based cancer evolutionary study and cell development model inference;
• Translational applications of single-cell sequencing analysis
We welcome investigators to contribute Original Research as well as Review articles on methods and clinical applications of single-cell sequencing data analysis especially in the context of precision medicine.
Disclosure: Jialiang Yang is currently employed by Geneis Beijing Co., Ltd.; All other Topic Editors declared that they have no conflict of interest to this work.
Next-Generation Sequencing (NGS) technology has been successfully applied in disease diagnostics, oncological immunotherapy, and drug repurposing, especially for precision medicine where optimized medication is tailored to individual patients. Recently, the development of single-cell techniques makes it possible to examine gene expression and mutation at individual cell resolution, which provides an unprecedented opportunity to study cell development and differentiation and reveal cell-to-cell heterogeneity during disease development, treatment and drug response for individual patients. With the exponential increase of single-cell sequencing data, it is critical to develop appropriate bioinformatics and machine learning tools to mine the rules behind them. However, due to the technical barriers in single-cell sequencing and the noisy nature of raw sequencing data, this task is challenging especially in the context of disease diagnosis and drug development.
To promote the translation and efficient usage of single-cell sequencing data to precision medicine, it is necessary to develop new analysis tools for analyzing and integrating multi-level single-cell data including DNA, RNA, protein and so on, comparing existing methods and results derived from different studies, and enhancing disease diagnostics and drug development. For example, the quality control, normalization, differential gene calling and clustering methods are quite different between single-cell sequencing and traditional bulk cell sequencing. Thus, it is critical to develop a best practice specifically for dealing with single-cell sequencing data. For disease treatment, it is also important to identify disease driver genes common to all cell types as well as those specific to a particular cell type or subgroup as revealed by single-cell techniques, based on existing or novel network and machine learning-based methods. Finally, more translational work should be done to bridge the bioinformatics analyses and clinical applications for single-cell researchers.
In our first volume, Bioinformatics Analysis of Single-Cell Sequencing Data and Applications in Precision Medicine we found the application of single-cell data analysis in disease prognostics, diagnostics and treatment; methods integrating multi-level single-cell data including DNA, RNA, protein, and so on and for single-cell data analysis including quality control, normalization, dropout imputation, clustering, differential and highly variable gene calling, cell-to-cell heterogeneity identification
In the past two years since our Research Topic we are particularly interested in:
• Best practices for dealing with single-cell sequencing data;
• Methods for single-cell data analysis including quality control, normalization, dropout imputation, clustering, differential and highly variable gene calling, cell-to-cell heterogeneity identification, and so on;
• Single-cell trajectory analysis and data visualization;
• Methods comparing and integrating single-cell and bulk RNA sequencing data;
• Methods integrating multi-level single-cell data including DNA, RNA, protein, and so on;
• Methods for aligning single-cell RNA sequencing data and spatial transcriptome sequencing data;
• Cell type-specific gene network inference and mining;
• Integrating single-cell sequencing and spatial sequencing in studying tumor microenvironment;
• Disease gene identification and biomarker discovery using single-cell techniques;
• Application of single-cell data analysis in disease prognostics, diagnostics and treatment;
• Single cell-based drug response and repurposing analyses;
• Single cell-based cancer evolutionary study and cell development model inference;
• Translational applications of single-cell sequencing analysis
We welcome investigators to contribute Original Research as well as Review articles on methods and clinical applications of single-cell sequencing data analysis especially in the context of precision medicine.
Disclosure: Jialiang Yang is currently employed by Geneis Beijing Co., Ltd.; All other Topic Editors declared that they have no conflict of interest to this work.
