Computational Analysis of Single-Cell Transcriptome Data
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "RNA".
Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 1856
Special Issue Editor
Interests: single-cell RNA sequencing; cancer drug resistance; cell cycle; cell fate analysis; transcription networks; DNA replication stress; bladder cancer; stemness; bioinformatics
Special Issue Information
Dear Colleagues,
Since it first became widely available to researchers in life sciences around 2016, single-cell RNA-sequencing (scRNA-seq) technology has greatly improved our understanding of individual cell function in healthy and diseased tissues. Since then, there has been an exponential growth in the numbers of cells profiled. Moreover, new technologies, such as spatially resolved single-cell transcriptomics and single-cell (epi)genomics, are rapidly improving. However, proper analysis of large amounts of single-cell data requires efficient and innovative computational and statistical methodology.
Various computational and statistical tools now allow researchers to perform transcription factor network analysis, trajectory inference, and predictions of cell–cell interactions. However, there is still a big need for innovative approaches to address challenges in handling sparsity in scRNA-seq, integration of scRNA-seq datasets across experiments and studies, integration of scRNA-seq with other types of single-cell data, recognition of patterns in spatial single-cell transcriptomic data, and validation and benchmarking of analysis tools.
We welcome the submission of original studies that present and validate innovative tools and methodology for computational analysis of single-cell RNA-seq data, and integration with other data types, such as (single-cell) proteomic, metabolomic, or epigenomic data. In addition, we invite comprehensive literature reviews and perspectives about the rapidly expanding toolbox of single-cell transcriptome analysis tools.
Dr. Bart Westendorp
Guest Editor
Manuscript Submission Information
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Keywords
- single-cell transcriptomics
- rare cell populations
- trajectory inference
- tumor heterogeneity
- biomarker discovery
- multi-omic analysis
- benchmarking computational methods
- stem cell biology
