The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data

The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data

Abstract 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 stu...

Full description

Bibliographic Details
Main Authors: Laurence de Torrenté, Samuel Zimmerman, Masako Suzuki, Maximilian Christopeit, John M. Greally, Jessica C. Mar
Format: Article
Language:English
Published: BMC 2020-12-01
Series:BMC Bioinformatics
Subjects:
Gene expression
Multi-modality
Non-normal distribution
Survival analysis
Cancer genomics
Online Access:https://doi.org/10.1186/s12859-020-03892-w

Internet

https://doi.org/10.1186/s12859-020-03892-w

Similar Items