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Tytuł pozycji:

CHARTS: a web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq data sets.

Tytuł:
CHARTS: a web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq data sets.
Autorzy:
Bernstein MN; Morgridge Institute for Research, Madison, WI, 53715, USA.
Ni Z; Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA.
Collins M; Morgridge Institute for Research, Madison, WI, 53715, USA.
Burkard ME; Department of Medicine, Hematology/Oncology, University of Wisconsin - Madison, Madison, WI, 53705, USA.; University of Wisconsin Carbone Cancer Center, Madison, WI, 53705, USA.
Kendziorski C; Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53792, USA. .
Stewart R; Morgridge Institute for Research, Madison, WI, 53715, USA. .
Źródło:
BMC bioinformatics [BMC Bioinformatics] 2021 Feb 23; Vol. 22 (1), pp. 83. Date of Electronic Publication: 2021 Feb 23.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2000-
MeSH Terms:
Neoplasms*/genetics
Sequence Analysis, RNA*
Single-Cell Analysis*
Gene Expression Profiling ; Humans ; RNA-Seq ; Software ; Tumor Microenvironment
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Grant Information:
R01 GM102756 United States GM NIGMS NIH HHS; CA234904 United States CA NCI NIH HHS; NIHGM102756 National Institutes of Health (US)
Entry Date(s):
Date Created: 20210224 Date Completed: 20210412 Latest Revision: 20211105
Update Code:
20240105
PubMed Central ID:
PMC7903756
DOI:
10.1186/s12859-021-04021-x
PMID:
33622236
Czasopismo naukowe
Background: Single-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. This is especially important in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer data sets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data.
Results: We present CHARacterizing Tumor Subpopulations (CHARTS), a web application for exploring publicly available scRNA-seq cancer data sets in the NCBI's Gene Expression Omnibus. More specifically, CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across tumors and data sets. Along with the web application, we also make available the backend computational pipeline that was used to produce the analyses that are available for exploration in the web application.
Conclusion: CHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer data sets. CHARTS is freely available at charts.morgridge.org.
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