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chevron_left Gene set enrichment analysis Gene expression clustering Immune cells abundance estimation chevron_right
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xCell specifications

Information


Unique identifier OMICS_16733
Name xCell
Interface Web user interface
Restrictions to use None
Input data The gene expression data.
Input format TXT, CSV
Output data The cell type enrichment scores.
Programming languages R
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Dvir Aran

Publication for xCell

xCell citations

 (3)
library_books

Deviations of the immune cell landscape between healthy liver and hepatocellular carcinoma

2018
Sci Rep
PMCID: 5906687
PMID: 29670256
DOI: 10.1038/s41598-018-24437-5

[…] into macrophages, and resting and activated NK cells into NK cells. The agreement between EPIC and CIBERSORT results was estimated with Pearson’s correlation.For computation of abundance scores with xCell, all expression data have been concatenated in a single file and duplicate gene symbols have been resolved by selecting the gene with the highest mean across all samples. Abundance scores were t […]

library_books

Integrative analysis of imaging and transcriptomic data of the immune landscape associated with tumor metabolism in lung adenocarcinoma: Clinical and prognostic implications

2018
Theranostics
PMCID: 5858511
PMID: 29556367
DOI: 10.7150/thno.23767

[…] Sixty-four cell types enrichment scores were assigned to each tumor sample via the xCell analysis. To visualize cellular heterogeneity in TME, a dimension reduction method, t-distributed stochastic neighbor embedding (t-SNE), was employed . Cell types enrichment scores were normaliz […]

library_books

xCell: digitally portraying the tissue cellular heterogeneity landscape

2017
Genome Biol
PMCID: 5688663
PMID: 29141660
DOI: 10.1186/s13059-017-1349-1

[…] signature-based methods only provide enrichment scores and thus do not allow comparison across cell types and cannot provide insights into the abundance of cell types in the mixture.Here, we present xCell, a novel method that integrates the advantages of gene set enrichment with deconvolution approaches. We present a compendium of newly generated gene signatures for 64 cell types, spanning multip […]


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xCell institution(s)
Institute for Computational Health Sciences, University of California, San Francisco, CA, USA
xCell funding source(s)
This work was supported by a Gruss Lipper Postdoctoral Fellowship, the National Cancer Institute (U24 CA195858) and the National Institute of Allergy and Infectious Diseases (Bioinformatics Support Contract HHSN272201200028C).

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