Visualizing structure and transitions in high-dimensional biological data.
Nat Biotechnol
; 37(12): 1482-1492, 2019 12.
Article
en En
| MEDLINE
| ID: mdl-31796933
ABSTRACT
The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. We compare PHATE to other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data, including continual progressions, branches and clusters, better than other tools. We define a manifold preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as compared to existing visualization methods. An analysis of a newly generated single-cell RNA sequencing dataset on human germ-layer differentiation demonstrates how PHATE reveals unique biological insight into the main developmental branches, including identification of three previously undescribed subpopulations. We also show that PHATE is applicable to a wide variety of data types, including mass cytometry, single-cell RNA sequencing, Hi-C and gut microbiome data.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
3_ND
Problema de salud:
3_zoonosis
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Genómica
/
Ensayos Analíticos de Alto Rendimiento
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Nat Biotechnol
Asunto de la revista:
BIOTECNOLOGIA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos