Removal of batch effects using distribution-matching residual networks.
Bioinformatics
; 33(16): 2539-2546, 2017 Aug 15.
Article
en En
| MEDLINE
| ID: mdl-28419223
ABSTRACT
MOTIVATION Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq (scRNA-seq), are plagued with systematic errors that may severely affect statistical analysis if the data are not properly calibrated. RESULTS:
We propose a novel deep learning approach for removing systematic batch effects. Our method is based on a residual neural network, trained to minimize the Maximum Mean Discrepancy between the multivariate distributions of two replicates, measured in different batches. We apply our method to mass cytometry and scRNA-seq datasets, and demonstrate that it effectively attenuates batch effects. AVAILABILITY AND IMPLEMENTATION our codes and data are publicly available at https//github.com/ushaham/BatchEffectRemoval.git. CONTACT yuval.kluger@yale.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Estadística como Asunto
/
Biología Computacional
/
Exactitud de los Datos
/
Aprendizaje Automático
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2017
Tipo del documento:
Article
País de afiliación:
Estados Unidos