PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.
PLoS Comput Biol
; 20(7): e1012241, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38985831
ABSTRACT
Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Biologia Computacional
/
Neuroimagem
Limite:
Humans
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos