About this Research Topic
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.
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. Potential topics include but are not limited to the following:
• 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;
• Cell type-specific gene network inference and mining;
• 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.
Keywords: Single cell sequencing, Precison medicine, Drug repurposing, Bioinformatics, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.