About this Research Topic
Single-cell research has revealed important technical progress and biological insights, such as unexpected heterogeneity in tissues and dynamical landscapes of cellular processes, which on the other hand have stimulated numerous computational methods. Through new data-generation techniques, such as single-cell-level genomics, transcriptomics, proteomics and epigenomics, single-cell research has become a wide and fast-growing area for pattern recognition and intelligent computation. For example, research areas such as data processing, model inference, dynamical reconstruction, and functional interpretation, have developed rapidly. The single-cell resolution achieved through this research could gain significant new insights into complex biological systems. Many important biological and engineering applications have been found for these newly developed wet and dry techniques, such as stem cell biology, development, cancer biology, immune systems, neural sciences, tissue engineering, regenerative medicine, aging, and challenging human diseases.
Single-cell studies have previously been characterized by large sample sizes and low sequencing depth per sample. This often causes highly repeated observations with few detectable features and less stable signals. It is therefore expected that machine learning methods may provide powerful analytic frameworks in this field. This is already exemplified by the growing number of implementations of sophisticated methods for various single-cell applications, such as gene selection, data recalibration, data transformation, dimension reduction, cell type identification, trajectory inference, lineage or clonal evolution, and data integration. These applications allow a more advanced and flexible analysis of single-cell signals detected at different molecular levels (DNA, RNA, protein) as well as in challenging biological and medical contexts.
The aim of the current Research Topic is to cover exciting advances in single-cell studies with technical and computational emphasis, but also addressing unsolved biological issues. The approaches taken are expected to be applicable in a wider range of related studies and highly effective in tackling the complexity or dynamics of biological or engineering systems. The article types may include original research papers, methods, case reports, and reviews. Single-cell-related areas to be preferentially covered may include, but are not limited to:
• novel techniques for data generation or tracking
• novel computational methods for data analysis
• theoretical issues of machine learning methods
• practical pipelines with wider applications
• application studies of pathological processes or regenerative medicine
Single-cell studies have previously been characterized by large sample sizes and low sequencing depth per sample. This often causes highly repeated observations with few detectable features and less stable signals. It is therefore expected that machine learning methods may provide powerful analytic frameworks in this field. This is already exemplified by the growing number of implementations of sophisticated methods for various single-cell applications, such as gene selection, data recalibration, data transformation, dimension reduction, cell type identification, trajectory inference, lineage or clonal evolution, and data integration. These applications allow a more advanced and flexible analysis of single-cell signals detected at different molecular levels (DNA, RNA, protein) as well as in challenging biological and medical contexts.
The aim of the current Research Topic is to cover exciting advances in single-cell studies with technical and computational emphasis, but also addressing unsolved biological issues. The approaches taken are expected to be applicable in a wider range of related studies and highly effective in tackling the complexity or dynamics of biological or engineering systems. The article types may include original research papers, methods, case reports, and reviews. Single-cell-related areas to be preferentially covered may include, but are not limited to:
• novel techniques for data generation or tracking
• novel computational methods for data analysis
• theoretical issues of machine learning methods
• practical pipelines with wider applications
• application studies of pathological processes or regenerative medicine
Keywords: Single cell, Genomics, Proteomics, Machine learning, Clustering, Tissue engineering, Regenerative medicine
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