An efficient scRNA-seq dropout imputation method using graph attention network

An efficient scRNA-seq dropout imputation method using graph attention network

Background: Single-cell sequencing technology can address the amount of single-cell library data at the same time and display the heterogeneity of different cells. However, analyzing single-cell data is a computationally challenging problem. Because there are low counts in the gene expression region...

Full description

Bibliographic Details
Main Authors: Cai, L. (Author), Gao, J. (Author), Xu, C. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
article
attention network
Auto encoders
autoencoder
comparative effectiveness
Convolution
correlation coefficient
Correlation methods
Cosine similarity
Cytology
Dropout imputation
Gene expression
Graph attention convolution
Imputation methods
Learning systems
Mean absolute error
Mean square error
Means square errors
noise
Pearson correlation coefficients
scRNA-seq
ScRNA-seq
single cell RNA seq
Single cells
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items