With new statistical and computational methods, single-cell sequencing technologies have been used to profile various cellular features, such as genetic variation, transcriptome abundance, protein abundance, methylation, chromatin accessibility, and location. Single-cell sequencing analysis has revealed cellular heterogeneity and lineage, leading to new findings in biology and medicine.
Despite the current success, there remain fundamental challenges in single-cell sequencing analysis. Some examples are a) quantifying the uncertainties in data measurements and analysis results; b) inferring heterogeneous and complex cellular feature changes after intervention or across samples; c) Integrative analysis of multi-modality single-cell data. These challenges have hindered the robustness, accuracy, and completeness of the findings generated from the single-cell sequencing analysis.
The rapid development of single-cell sequencing technology leads to a massive amount of cellular-level data. What can be obtained from the data heavily relies on the statistical and computational methods used to analyze them. An improper method or an improper implementation of an existing method may distort or bias the information contained in the data and subsequently lead to incorrect conclusions. To help researchers to extract biomedically meaningful information from the massive complex data, this research topic aims to a) benchmark the existing methods to understand their accuracy, robustness, and uncertainties in results, and b) develop new powerful and robust methods to conquer the existing limitations and tackle the new challenges in single-cell studies.
This Research Topic will focus on new statistical and computational methods to tackle the main challenges in single-cell sequencing analysis. The topics include, but are not limited to:
- Models and methods that quantify the uncertainties in single-cell sequencing data and the analysis results
- Robust methods for single-cell analysis
- Methods to characterize cell evolutions in biomedical processes, such as cancer development, aging, or drug interventions
- Methods for integrative analysis on multi-modality data that involve single-cell sequencing
- Methods for integrative analysis of single-cell sequencing data across samples, experiments, and types of measurements
- Artificial intelligence (AI) and explainable AI methods to analyze single-cell sequencing data
- Validating and benchmarking the accuracy and robustness of single-cell analysis methods and tools.
Software development is often coupled with methodology development. However, software development without a novel method does not fall in the scope.
With new statistical and computational methods, single-cell sequencing technologies have been used to profile various cellular features, such as genetic variation, transcriptome abundance, protein abundance, methylation, chromatin accessibility, and location. Single-cell sequencing analysis has revealed cellular heterogeneity and lineage, leading to new findings in biology and medicine.
Despite the current success, there remain fundamental challenges in single-cell sequencing analysis. Some examples are a) quantifying the uncertainties in data measurements and analysis results; b) inferring heterogeneous and complex cellular feature changes after intervention or across samples; c) Integrative analysis of multi-modality single-cell data. These challenges have hindered the robustness, accuracy, and completeness of the findings generated from the single-cell sequencing analysis.
The rapid development of single-cell sequencing technology leads to a massive amount of cellular-level data. What can be obtained from the data heavily relies on the statistical and computational methods used to analyze them. An improper method or an improper implementation of an existing method may distort or bias the information contained in the data and subsequently lead to incorrect conclusions. To help researchers to extract biomedically meaningful information from the massive complex data, this research topic aims to a) benchmark the existing methods to understand their accuracy, robustness, and uncertainties in results, and b) develop new powerful and robust methods to conquer the existing limitations and tackle the new challenges in single-cell studies.
This Research Topic will focus on new statistical and computational methods to tackle the main challenges in single-cell sequencing analysis. The topics include, but are not limited to:
- Models and methods that quantify the uncertainties in single-cell sequencing data and the analysis results
- Robust methods for single-cell analysis
- Methods to characterize cell evolutions in biomedical processes, such as cancer development, aging, or drug interventions
- Methods for integrative analysis on multi-modality data that involve single-cell sequencing
- Methods for integrative analysis of single-cell sequencing data across samples, experiments, and types of measurements
- Artificial intelligence (AI) and explainable AI methods to analyze single-cell sequencing data
- Validating and benchmarking the accuracy and robustness of single-cell analysis methods and tools.
Software development is often coupled with methodology development. However, software development without a novel method does not fall in the scope.
