Abstract
Transcriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: https://github.com/wwylab/DeMixTallmaterials. Computational Bioinformatics; Cancer; Transcriptomics
| Original language | English (US) |
|---|---|
| Pages (from-to) | 451-460 |
| Number of pages | 10 |
| Journal | Food Science and Human Wellness |
| Volume | 9 |
| DOIs | |
| State | Published - Nov 30 2018 |
| Externally published | Yes |
Fingerprint
Keywords
- Cancer
- Computational Bioinformatics
- Transcriptomics
ASJC Scopus subject areas
- Food Science
- General
Cite this
Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. / Wang, Zeya; Cao, Shaolong; Morris, Jeffrey S.; Ahn, Jaeil; Liu, Rongjie; Tyekucheva, Svitlana; Gao, Fan; Li, Bo; Lu, Wei; Tang, Ximing; Wistuba, Ignacio I.; Bowden, Michaela; Mucci, Lorelei; Loda, Massimo; Parmigiani, Giovanni; Holmes, Chris C.; Wang, Wenyi.
In: Food Science and Human Wellness, Vol. 9, 30.11.2018, p. 451-460.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration
AU - Wang, Zeya
AU - Cao, Shaolong
AU - Morris, Jeffrey S.
AU - Ahn, Jaeil
AU - Liu, Rongjie
AU - Tyekucheva, Svitlana
AU - Gao, Fan
AU - Li, Bo
AU - Lu, Wei
AU - Tang, Ximing
AU - Wistuba, Ignacio I.
AU - Bowden, Michaela
AU - Mucci, Lorelei
AU - Loda, Massimo
AU - Parmigiani, Giovanni
AU - Holmes, Chris C.
AU - Wang, Wenyi
PY - 2018/11/30
Y1 - 2018/11/30
N2 - Transcriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: https://github.com/wwylab/DeMixTallmaterials. Computational Bioinformatics; Cancer; Transcriptomics
AB - Transcriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: https://github.com/wwylab/DeMixTallmaterials. Computational Bioinformatics; Cancer; Transcriptomics
KW - Cancer
KW - Computational Bioinformatics
KW - Transcriptomics
UR - http://www.scopus.com/inward/record.url?scp=85065297851&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065297851&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2018.10.028
DO - 10.1016/j.isci.2018.10.028
M3 - Article
VL - 9
SP - 451
EP - 460
JO - Food Science and Human Wellness
JF - Food Science and Human Wellness
SN - 2213-4530
ER -