Genomic data imputation with variational auto-encoders.

ABSTRACT:

Background

As missing values are frequently present in genomic data, practical methods to handle missing data are necessary for downstream analyses that require complete data sets. State-of-the-art imputation techniques, including methods based on singular value decomposition and K-nearest neighbors, can be computationally expensive for large data sets and it is difficult to modify these algorithms to handle certain cases not missing at random.

Results

In this work, we use a deep-learning framework based on the variational auto-encoder (VAE) for genomic missing value imputation and demonstrate its effectiveness in transcriptome and methylome data analysis. We show that in the vast majority of our testing scenarios, VAE achieves similar or better performances than the most wi

SUBMITTER: Qiu YL 

PROVIDER: S-EPMC7407276 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature