xCell: digitally portraying the tissue cellular heterogeneity landscape
Abstract Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we...
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doaj-a1e6b5f2ff8d4f0aa2c933ed6facf09d2020-11-24T21:46:01ZengBMCGenome Biology1474-760X2017-11-0118111410.1186/s13059-017-1349-1xCell: digitally portraying the tissue cellular heterogeneity landscapeDvir Aran0Zicheng Hu1Atul J. Butte2Institute for Computational Health Sciences, University of CaliforniaInstitute for Computational Health Sciences, University of CaliforniaInstitute for Computational Health Sciences, University of CaliforniaAbstract Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/ .http://link.springer.com/article/10.1186/s13059-017-1349-1 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dvir Aran Zicheng Hu Atul J. Butte |
spellingShingle |
Dvir Aran Zicheng Hu Atul J. Butte xCell: digitally portraying the tissue cellular heterogeneity landscape Genome Biology |
author_facet |
Dvir Aran Zicheng Hu Atul J. Butte |
author_sort |
Dvir Aran |
title |
xCell: digitally portraying the tissue cellular heterogeneity landscape |
title_short |
xCell: digitally portraying the tissue cellular heterogeneity landscape |
title_full |
xCell: digitally portraying the tissue cellular heterogeneity landscape |
title_fullStr |
xCell: digitally portraying the tissue cellular heterogeneity landscape |
title_full_unstemmed |
xCell: digitally portraying the tissue cellular heterogeneity landscape |
title_sort |
xcell: digitally portraying the tissue cellular heterogeneity landscape |
publisher |
BMC |
series |
Genome Biology |
issn |
1474-760X |
publishDate |
2017-11-01 |
description |
Abstract Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/ . |
url |
http://link.springer.com/article/10.1186/s13059-017-1349-1 |
work_keys_str_mv |
AT dviraran xcelldigitallyportrayingthetissuecellularheterogeneitylandscape AT zichenghu xcelldigitallyportrayingthetissuecellularheterogeneitylandscape AT atuljbutte xcelldigitallyportrayingthetissuecellularheterogeneitylandscape |
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1725902567776452608 |