Schoen, Marian and Simeth, Jakob and Heinrich, Paul and Goertler, Franziska and Solbrig, Stefan and Wettig, Tilo and Oefner, Peter J. and Altenbuchinger, Michael and Spang, Rainer (2020) DTD: An R Package for Digital Tissue Deconvolution. JOURNAL OF COMPUTATIONAL BIOLOGY, 27 (3). pp. 386-389. ISSN 1066-5277, 1557-8666
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ; cell-type deconvolution; loss-function learning; model adaptation; R package |
| Subjects: | 500 Science > 530 Physics 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner) Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) Physics > Institute of Theroretical Physics Physics > Institute of Theroretical Physics > Chair Professor Braun > Group Tilo Wettig |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 01 Apr 2021 08:01 |
| Last Modified: | 01 Apr 2021 08:01 |
| URI: | https://pred.uni-regensburg.de/id/eprint/45305 |
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