A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes.
Proc Natl Acad Sci U S A
; 117(32): 19339-19346, 2020 08 11.
Article
en En
| MEDLINE
| ID: mdl-32709743
ABSTRACT
Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets-where we demonstrate its ability to correctly infer true (known) expression states-and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-sequencing (RNA-seq) datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely available R package zigzag.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Primates
Tipo de estudio:
Prognostic_studies
/
Qualitative_research
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Año:
2020
Tipo del documento:
Article