Cargando…
imply: improving cell-type deconvolution accuracy using personalized reference profiles
Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that t...
Autores principales: | Meng, Guanqun, Pan, Yue, Tang, Wen, Zhang, Lijun, Cui, Ying, Schumacher, Fredrick R., Wang, Ming, Wang, Rui, He, Sijia, Krischer, Jeffrey, Li, Qian, Feng, Hao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557724/ https://www.ncbi.nlm.nih.gov/pubmed/37808714 http://dx.doi.org/10.1101/2023.09.27.559579 |
Ejemplares similares
-
ISLET: individual-specific reference panel recovery improves cell-type-specific inference
por: Feng, Hao, et al.
Publicado: (2023) -
Uncertainty quantification of reference-based cellular deconvolution algorithms
por: Vellame, Dorothea Seiler, et al.
Publicado: (2022) -
A Novel Framework for the Identification of Reference DNA Methylation Libraries for Reference-Based Deconvolution of Cellular Mixtures
por: Bell-Glenn, Shelby, et al.
Publicado: (2022) -
Deconvolution of heterogeneous tumor samples using partial reference signals
por: Qin, Yufang, et al.
Publicado: (2020) -
RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics
por: Singh, Roopali, et al.
Publicado: (2023)