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Tytuł pozycji:

Tools for the analysis of high-dimensional single-cell RNA sequencing data.

Tytuł:
Tools for the analysis of high-dimensional single-cell RNA sequencing data.
Autorzy:
Wu Y; Department of Bioengineering, University of California at San Diego, La Jolla, CA, USA.
Zhang K; Department of Bioengineering, University of California at San Diego, La Jolla, CA, USA. .
Źródło:
Nature reviews. Nephrology [Nat Rev Nephrol] 2020 Jul; Vol. 16 (7), pp. 408-421. Date of Electronic Publication: 2020 Mar 27.
Typ publikacji:
Journal Article; Research Support, N.I.H., Extramural; Review
Język:
English
Imprint Name(s):
Original Publication: London Nature Pub. Group
MeSH Terms:
Data Analysis*
Data Visualization*
Datasets as Topic*
RNA-Seq*
Single-Cell Analysis*
Electronic Data Processing ; Gene Expression Profiling ; High-Throughput Nucleotide Sequencing ; Humans ; Sequence Analysis, RNA ; Software ; Workflow
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Grant Information:
U01 MH098977 United States MH NIMH NIH HHS; R01 HL123755 United States HL NHLBI NIH HHS; U54 HL145608 United States HL NHLBI NIH HHS; UH3 DK114933 United States DK NIDDK NIH HHS; R01 HG009285 United States HG NHGRI NIH HHS
Entry Date(s):
Date Created: 20200330 Date Completed: 20201009 Latest Revision: 20210313
Update Code:
20240105
DOI:
10.1038/s41581-020-0262-0
PMID:
32221477
Czasopismo naukowe
Breakthroughs in the development of high-throughput technologies for profiling transcriptomes at the single-cell level have helped biologists to understand the heterogeneity of cell populations, disease states and developmental lineages. However, these single-cell RNA sequencing (scRNA-seq) technologies generate an extraordinary amount of data, which creates analysis and interpretation challenges. Additionally, scRNA-seq datasets often contain technical sources of noise owing to incomplete RNA capture, PCR amplification biases and/or batch effects specific to the patient or sample. If not addressed, this technical noise can bias the analysis and interpretation of the data. In response to these challenges, a suite of computational tools has been developed to process, analyse and visualize scRNA-seq datasets. Although the specific steps of any given scRNA-seq analysis might differ depending on the biological questions being asked, a core workflow is used in most analyses. Typically, raw sequencing reads are processed into a gene expression matrix that is then normalized and scaled to remove technical noise. Next, cells are grouped according to similarities in their patterns of gene expression, which can be summarized in two or three dimensions for visualization on a scatterplot. These data can then be further analysed to provide an in-depth view of the cell types or developmental trajectories in the sample of interest.

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