Computational approaches for interpreting scRNA‐seq data
The recent developments in high‐throughput single‐cell RNA sequencing technology (scRNA‐seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high‐dimensional data mining techniques. Here, we c...
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| Published in | FEBS letters Vol. 591; no. 15; pp. 2213 - 2225 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
John Wiley and Sons Inc
01.08.2017
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| Subjects | |
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| Abstract | The recent developments in high‐throughput single‐cell RNA sequencing technology (scRNA‐seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high‐dimensional data mining techniques. Here, we consider biological questions for which scRNA‐seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene‐level analyses are particularly informative areas of computational analysis. |
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| AbstractList | The recent developments in high‐throughput single‐cell RNA sequencing technology (scRNA‐seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high‐dimensional data mining techniques. Here, we consider biological questions for which scRNA‐seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene‐level analyses are particularly informative areas of computational analysis. The recent developments in high‐throughput single‐cell RNA sequencing technology (sc RNA ‐seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high‐dimensional data mining techniques. Here, we consider biological questions for which sc RNA ‐seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene‐level analyses are particularly informative areas of computational analysis. The recent developments in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high-dimensional data mining techniques. Here, we consider biological questions for which scRNA-seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene-level analyses are particularly informative areas of computational analysis.The recent developments in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high-dimensional data mining techniques. Here, we consider biological questions for which scRNA-seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene-level analyses are particularly informative areas of computational analysis. |
| Author | Teichmann, Sarah A. Kar, Gozde Rostom, Raghd Svensson, Valentine |
| AuthorAffiliation | 2 The European Bioinformatics Institute (EMBL‐EBI) Cambridge UK 1 Wellcome Trust Sanger Institute Cambridge UK |
| AuthorAffiliation_xml | – name: 2 The European Bioinformatics Institute (EMBL‐EBI) Cambridge UK – name: 1 Wellcome Trust Sanger Institute Cambridge UK |
| Author_xml | – sequence: 1 givenname: Raghd surname: Rostom fullname: Rostom, Raghd organization: Wellcome Trust Sanger Institute – sequence: 2 givenname: Valentine surname: Svensson fullname: Svensson, Valentine organization: The European Bioinformatics Institute (EMBL‐EBI) – sequence: 3 givenname: Sarah A. surname: Teichmann fullname: Teichmann, Sarah A. organization: Wellcome Trust Sanger Institute – sequence: 4 givenname: Gozde surname: Kar fullname: Kar, Gozde email: gkar@ebi.ac.uk organization: The European Bioinformatics Institute (EMBL‐EBI) |
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| Keywords | single-cell analysis methods and tools single-cell genomics |
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| Snippet | The recent developments in high‐throughput single‐cell RNA sequencing technology (scRNA‐seq) have enabled the generation of vast amounts of transcriptomic data... The recent developments in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of vast amounts of transcriptomic data... The recent developments in high‐throughput single‐cell RNA sequencing technology (sc RNA ‐seq) have enabled the generation of vast amounts of transcriptomic... |
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| SubjectTerms | Animals Cluster Analysis Computational Biology - methods Gene Expression genes Humans Review RNA Sequence Analysis, RNA - methods Single-Cell Analysis - methods single‐cell analysis methods and tools single‐cell genomics transcriptomics |
| Title | Computational approaches for interpreting scRNA‐seq data |
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