Cargando…
The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data
BACKGROUND: In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study inves...
Autores principales: | de Torrenté, Laurence, Zimmerman, Samuel, Suzuki, Masako, Christopeit, Maximilian, Greally, John M., Mar, Jessica C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768656/ https://www.ncbi.nlm.nih.gov/pubmed/33371881 http://dx.doi.org/10.1186/s12859-020-03892-w |
Ejemplares similares
-
scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data
por: Dharmaratne, Malindrie, et al.
Publicado: (2023) -
How effective is incidental learning of the shape of probability distributions?
por: Tran, Randy, et al.
Publicado: (2017) -
A strategy for residual error modeling incorporating scedasticity of variance and distribution shape
por: Dosne, Anne-Gaëlle, et al.
Publicado: (2015) -
How conductance distributions are shaped by activity-dependent regulation rules
por: O'Leary, Timothy, et al.
Publicado: (2013) -
Strong interaction effects in kaonic atoms (and nuclear matter distribution shape)
por: Koch, H, et al.
Publicado: (1972)