Cargando…
Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets
Deconvolution of cell mixtures in “bulk” transcriptomic samples from homogenate human tissue is important for understanding the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, es...
Autores principales: | Maden, Sean K., Kwon, Sang Ho, Huuki-Myers, Louise A., Collado-Torres, Leonardo, Hicks, Stephanie C., Maynard, Kristen R. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cornell University
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10197733/ https://www.ncbi.nlm.nih.gov/pubmed/37214135 |
Ejemplares similares
-
Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue
por: Huuki-Myers, Louise A., et al.
Publicado: (2023) -
Deconvolving the contributions of cell-type heterogeneity on cortical gene expression
por: Patrick, Ellis, et al.
Publicado: (2020) -
Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors
por: Hippen, Ariel A., et al.
Publicado: (2023) -
escheR: Unified multi-dimensional visualizations with Gestalt principles
por: Guo, Boyi, et al.
Publicado: (2023) -
Digitally deconvolving the tumor microenvironment
por: Aran, Dvir, et al.
Publicado: (2016)