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Computational approaches for interpreting scRNA-seq data
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DOI：10.1002/1873-3468.12684.png)
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摘要
En 中文
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.
Keyword:
single-cell analysis methods and tools
single-cell genomics
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期刊
IF:
3
论文数:
2.3W
被引数:
3.8W
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