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Computational and analytical challenges in single-cell transcriptomics
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DOI：10.1038/nrg3833.png)
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Abstract
En 中文
The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the study of global patterns of stochastic gene expression. Alongside the technological breakthroughs that have facilitated the large-scale generation of single-cell transcriptomic data, it is important to consider the specific computational and analytical challenges that still have to be overcome. Although some tools for analysing RNA-seq data from bulk cell populations can be readily applied to single-cell RNA-seq data, many new computational strategies are required to fully exploit this data type and to enable a comprehensive yet detailed study of gene expression at the single-cell level.
Keywords:
DIFFERENTIAL EXPRESSION ANALYSIS
STOCHASTIC GENE-EXPRESSION
MESSENGER-RNA-SEQ
REVEALS
RECONSTRUCTION
NETWORKS
NOISE
QUANTIFICATION
HETEROGENEITY
NORMALIZATION
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