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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.1
- 수록면
- 189 - 199 (11page)
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Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.
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참고문헌 (125)
참고문헌 신청
Aibar, S. / 2017 / SCENIC : single-cell regulatory network inference and clustering / Nat. Methods 14:1083~1086
Alavi, A. / 2018 / A web server for comparative analysis of single-cell RNA-seq data / Nat. Commun 9:4768
Alquicira-Hernandez, J. / 2018 / scPred:scPred: Cell type prediction at single-cell resolution / bioRxiv :369538
Amir, E. D. / 2013 / viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia / Nat. Biotechnol 31:545~552
Anders, S. / 2010 / Differential expression analysis for sequence count data / Genome Biol 11:R106
Alavi, A. / 2018 / A web server for comparative analysis of single-cell RNA-seq data / Nat. Commun 9:4768
Alquicira-Hernandez, J. / 2018 / scPred:scPred: Cell type prediction at single-cell resolution / bioRxiv :369538
Amir, E. D. / 2013 / viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia / Nat. Biotechnol 31:545~552
Anders, S. / 2010 / Differential expression analysis for sequence count data / Genome Biol 11:R106
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