About this Research Topic
Nearly two decades have passed since bioinformatics and systems biology were introduced into the language of modern biology as a consequence of the spread of high-throughput bio-technologies. In recent years, single-cell technologies have become established, allowing the analysis of thousands of genes, mRNAs, and proteins within a single cell, for hundreds or even thousands of cells in parallel rather than, from cells in bulk. These technologies give the possibility to observe how tissues and organs are spatially and temporally organized as a system of multiple cells, able to communicate and interact with each other and to orchestrate self-assembly and response to stimuli as a whole.
Despite its power and high resolution, single cell analysis has some open challenges related to the higher level of technical noise and data complexity with respect to bulk data. Moreover, the number of measured variables (thousands, in possibly thousands of cells and a multitude of samples), their heterogeneity and the complexity of the systems under analysis, pose a number of methodological challenges that require new theoretical and applicative approaches.
In this Research Topic, we welcome submissions in any of the following topics:
1. New statistical models, algorithms, and software packages to analyze single cell data.
2. Visualization tools for single cell data analysis and interpretation.
3. Methods to relate single cell data with disease classification and prognosis.
4. Comprehensive evaluation and comparison of single cell data analytic methods.
5. Methods and tools to discover spatial/temporal organization of tissues at a single cell level.
6. Models for describing and mining cell-cell communication.
7. Techniques to model and simulate tissue/organ development at a single cell level.
8. Scalable mathematical and computer-science approaches for analysis of mega-scale single cell data.
9. Review or mini-review or practical guidance for interpreting and analyzing single cell data.
10. Other topics relevant to single-cell data analysis not explicitly listed here, such as combining mixed platform data, noise filtering, and robust normalization.
Despite its power and high resolution, single cell analysis has some open challenges related to the higher level of technical noise and data complexity with respect to bulk data. Moreover, the number of measured variables (thousands, in possibly thousands of cells and a multitude of samples), their heterogeneity and the complexity of the systems under analysis, pose a number of methodological challenges that require new theoretical and applicative approaches.
In this Research Topic, we welcome submissions in any of the following topics:
1. New statistical models, algorithms, and software packages to analyze single cell data.
2. Visualization tools for single cell data analysis and interpretation.
3. Methods to relate single cell data with disease classification and prognosis.
4. Comprehensive evaluation and comparison of single cell data analytic methods.
5. Methods and tools to discover spatial/temporal organization of tissues at a single cell level.
6. Models for describing and mining cell-cell communication.
7. Techniques to model and simulate tissue/organ development at a single cell level.
8. Scalable mathematical and computer-science approaches for analysis of mega-scale single cell data.
9. Review or mini-review or practical guidance for interpreting and analyzing single cell data.
10. Other topics relevant to single-cell data analysis not explicitly listed here, such as combining mixed platform data, noise filtering, and robust normalization.
Keywords: Single cell data analysis, Cell-cell communication, Spatial-temporal cell system organization, Data visualization for single cell, Single cell data interpretation
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.
