scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction.
Bioinformatics
; 38(20): 4745-4753, 2022 10 14.
Article
em En
| MEDLINE
| ID: mdl-36040148
ABSTRACT
MOTIVATION With the recent surge of large-cohort scale single cell research, it is of critical importance that analytical methods can fully utilize the comprehensive characterization of cellular systems that single cell technologies produce to provide insights into samples from individuals. Currently, there is little consensus on the best ways to compress information from the complex data structures of these technologies to summary statistics that represent each sample (e.g. individuals). RESULTS:
Here, we present scFeatures, an approach that creates interpretable cellular and molecular representations of single-cell and spatial data at the sample level. We demonstrate that summarizing a broad collection of features at the sample level is both important for understanding underlying disease mechanisms in different experimental studies and for accurately classifying disease status of individuals. AVAILABILITY AND IMPLEMENTATION scFeatures is publicly available as an R package at https//github.com/SydneyBioX/scFeatures. All data used in this study are publicly available with accession ID reported in the Section 2. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Austrália