DTD: An R Package for Digital Tissue Deconvolution
2020 | journal article; research paper
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DTD: An R Package for Digital Tissue Deconvolution
Schön, M.; Simeth, J.; Heinrich, P.; Görtler, F.; Solbrig, S.; Wettig, T. & Oefner, P. J. et al. (2020)
Journal of Computational Biology, 27(3) pp. 386-389. DOI: https://doi.org/10.1089/cmb.2019.0469
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Details
- Authors
- Schön, Marian; Simeth, Jakob; Heinrich, Paul; Görtler, Franziska; Solbrig, Stefan; Wettig, Tilo; Oefner, Peter J.; Altenbuchinger, Michael ; Spang, Rainer
- Abstract
- Digital tissue deconvolution (DTD) estimates the cellular composition of a tissue from its bulk gene-expression profile. For this, DTD approximates the bulk as a mixture of cell-specific expression profiles. Different tissues have different cellular compositions, with cells in different activation states, and embedded in different environments. Consequently, DTD can profit from tailoring the deconvolution model to a specific tissue context. Loss-function learning adapts DTD to a specific tissue context, such as the deconvolution of blood, or a specific type of tumor tissue. We provide software for loss-function learning, for its validation and visualization, and for applying the DTD models to new data.
- Issue Date
- 2020
- Journal
- Journal of Computational Biology
- ISSN
- 1557-8666
- Language
- English