Benchmarking Variational AutoEncoders on cancer transcriptomics data

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer. However, these models are difficult to train due to the large number of hyperparameters that need to be tuned. To get a better understanding of the importance of the different hyperparameters, we examined six different VAE models when trained on TCGA transcriptomics data and evaluated on the downstream task of cluster agreement with cancer subtypes. We studied the effect of the latent space dimensionality, learning rate, optimizer and initialization on the quality of subsequent clustering of the TCGA samples. We found β -TCVAE and DIP-VAE to have a good performance, on average, despite being more sensitive to hyperparameters selection. Based on these experiments, we derived recommendations for selecting the different hyperparameters settings. In addition, we examined whether the learned latent spaces capture biologically relevant information. Hereto, we correlated the different representations with various data characteristics such as age, days to metastasis, immune infiltration, and mutation signatures. We found that for all models the latent factors, in general, do not uniquely correlate with one of the data characteristics even for models specifically designed for disentanglement.

Authors 6

1. Mostafa Eltager

   Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

   Delft Bioinformatics Lab, Delft University of Technology

2. Tamim Abdelaal Aachen Department of Radiology, Leiden University Medical Center Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

   Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands

   Delft Bioinformatics Lab, Delft University of Technology

   Department of Radiology, Leiden University Medical Center

   Leiden Computational Biology Center, Leiden University Medical Center

3. Mohammed Charrout

   Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

   Delft Bioinformatics Lab, Delft University of Technology

4. Ahmed Essam Mahfouz Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands

   Delft Bioinformatics Lab, Delft University of Technology

   Department of Human Genetics, Leiden University Medical Center

   Leiden Computational Biology Center, Leiden University Medical Center

5. Marcel J. T. Reinders Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands

   Delft Bioinformatics Lab, Delft University of Technology

   Department of Human Genetics, Leiden University Medical Center

   Leiden Computational Biology Center, Leiden University Medical Center

6. Stavros Makrodimitris corresponding

   Erasmus MC · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands

   Department of Medical Oncology, Erasmus Medical Center, Rotterdam, The Netherlands

   Delft Bioinformatics Lab, Delft University of Technology

   Department of Medical Oncology, Erasmus Medical Center