A deep learning framework for imputing missing values in genomic data

Qiu, Y. L.; Zheng, H.; Gavaert, O.

doi:10.1101/406066

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bioRxiv · 10.1101/406066

A deep learning framework for imputing missing values in genomic data

Qiu, Y. L.·Zheng, H.·Gavaert, O.

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Abstract

MotivationThe presence of missing values is a frequent problem encountered in genomic data analysis. Lost data can be an obstacle to downstream analyses that require complete data matrices. State-of-the-art imputation techniques including Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) based methods usually achieve good performances, but are computationally expensive especially for large datasets such as those involved in pan-cancer analysis.\n\nResultsThis study describes a new method: a denoising autoencoder with partial loss (DAPL) as a deep learning based alternative for data imputation. Results on pan-cancer gene expression data and DNA methylation data from over 11,000 samples demonstrate significant improvement over standard denoising autoencoder for both data missing-at-random cases with a range of missing percentages, and missing-not-at-random cases based on expression level and GC-content. We discuss the advantages of DAPL over traditional imputation methods and show that it achieves comparable or better performance with less computational burden.\n\nAvailabilityhttps://github.com/gevaertlab/DAPL\n\nContactogevaert@stanford.edu

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Qiu, Y. L., Zheng, H., Gavaert, O.. 2018-08-31. A deep learning framework for imputing missing values in genomic data. https://doi.org/10.1101/406066

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