期刊
JOURNAL OF COMPUTATIONAL BIOLOGY
卷 27, 期 3, 页码 386-389出版社
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2019.0469
关键词
cell-type deconvolution; loss-function learning; model adaptation; R package
类别
资金
- BMBF [031L0173]
- DFG [FOR-2127, SFB/TRR-55]
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.
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