About this Research Topic
The rapid development of single-cell technologies enables us to collect high-throughput, high-dimensional, and multimodal data with unprecedented cellular resolution, which provides unique opportunity for us to explore cellular states and dynamics for different tissues and disease conditions. However, modern single-cell datasets differ significantly from other biological datasets due to limited input material from a single cell and the nature the acquiring technologies. Not only are there significant large amount of missing value (dropout) in each dataset, but there is also more significant batch effect between datasets. Traditional statistical and machine learning algorithm designed for genomic analyses are not suitable for single-cell datasets which contain complex structures. Recently, various computational methods have been developed for single-cell data such as clustering, visualization, integration, and trajectory analyses. However, there are many challenges new computational algorithms need to overcome to release the full potential of single-cell data. Those challenges are summarized on Eleven Grand Challenges in Single-Cell Data Science by Lahnemann et. al.
In this research topic, we welcome manuscripts that help to address some of the following important questions:
1) How to detect rare cell population?
2) How to accurately impute missing values?
3) How to effectively integrate data from different batches?
4) How to effectively utilize multimodal single-cell data?
5) How to map single cells to a reference atlas?
6) How to utilize spatial information for spatial transcriptomics data?
We welcome contributions in the form of original research, review, mini review, case report, hypothesis and theory, perspective, both experimental and computational studies that cover, but are not limited to, following themes:
a) Cell clustering and cell type annotation
b) Imputation
c) Data integration
d) Statistical methods for differential testing between clusters or states
e) Trajectory analyses
f) Multimodality data analyses
g) Spatial transcriptomics data analyses
In this research topic, we welcome manuscripts that help to address some of the following important questions:
1) How to detect rare cell population?
2) How to accurately impute missing values?
3) How to effectively integrate data from different batches?
4) How to effectively utilize multimodal single-cell data?
5) How to map single cells to a reference atlas?
6) How to utilize spatial information for spatial transcriptomics data?
We welcome contributions in the form of original research, review, mini review, case report, hypothesis and theory, perspective, both experimental and computational studies that cover, but are not limited to, following themes:
a) Cell clustering and cell type annotation
b) Imputation
c) Data integration
d) Statistical methods for differential testing between clusters or states
e) Trajectory analyses
f) Multimodality data analyses
g) Spatial transcriptomics data analyses
Keywords: Systems Biology, Single-Cell, Algorithms
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.
