About Single-Cell RNA-Sequencing
Single-Cell RNA-Sequencing
About this Collection
This collection is now closed to submissions.
Single-cell RNA sequencing (scRNA-seq) has led to great advances in revealing tissue heterogeneity, cell differentiation dynamics and the complex responses to perturbations. For example, using scRNA-seq Wen et al. comprehensively characterised transcriptional changes in the immune system of individuals recovering from COVID19, highlighting sustained inflammatory signatures. The application of scRNA-seq is now common in biomedical research and on the rise in other biological fields due to commercialization of the assays and availability of specialised analysis tools.
The complexity and high dimensionality of the resulting data have posed unique challenges for the analysis of scRNA-seq experiments. Specialised tools are required for nearly all the stages of the scRNA-seq analysis workflow. While there is already a plethora of existing tools, new tools are still required to address challenges outlined in the Open Problems in Single Cell Analysis Project. These challenges include integrating multiple samples and using the resulting atlases as references for analysing other datasets, handling the inherent sparsity of single-cell data and devising appropriate statistical methods to investigate changes caused by perturbations and disease.
This collection aims at promoting the latest tools that offer processing, statistical analysis, visualisation, integration of further modalities and advanced machine learning for scRNA-seq data.
Keywords: single cell RNA-sequencing; single cell transcriptomics; single cell workflow; single cell methods; multi-omic integration; visualisation; machine learning; processing; machine learning; perturbations
This collection is part of the Cell & Molecular Biology Gateway.
Any questions about this collection? Please get in contact directly with research@f1000.com.
Single-cell RNA sequencing (scRNA-seq) has led to great advances in revealing tissue heterogeneity, cell differentiation dynamics and the complex responses to perturbations. For example, using scRNA-seq Wen et al. comprehensively characterised transcriptional changes in the immune system of individuals recovering from COVID19, highlighting sustained inflammatory signatures. The application of scRNA-seq is now common in biomedical research and on the rise in other biological fields due to commercialization of the assays and availability of specialised analysis tools.
The complexity and high dimensionality of the resulting data have posed unique challenges for the analysis of scRNA-seq experiments. Specialised tools are required for nearly all the stages of the scRNA-seq analysis workflow. While there is already a plethora of existing tools, new tools are still required to address challenges outlined in the Open Problems in Single Cell Analysis Project. These challenges include integrating multiple samples and using the resulting atlases as references for analysing other datasets, handling the inherent sparsity of single-cell data and devising appropriate statistical methods to investigate changes caused by perturbations and disease.
This collection aims at promoting the latest tools that offer processing, statistical analysis, visualisation, integration of further modalities and advanced machine learning for scRNA-seq data.
Keywords: single cell RNA-sequencing; single cell transcriptomics; single cell workflow; single cell methods; multi-omic integration; visualisation; machine learning; processing; machine learning; perturbations
This collection is part of the Cell & Molecular Biology Gateway.
Any questions about this collection? Please get in contact directly with research@f1000.com.
Collection Advisors
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Saskia Freytag
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Luke Zappia
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