Print Email Facebook Twitter Benchmarking variational AutoEncoders on cancer transcriptomics data Title Benchmarking variational AutoEncoders on cancer transcriptomics data Author Eltager, M.A.M.E. (TU Delft Pattern Recognition and Bioinformatics) Abdelaal, T.R.M. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Charrout, M. (TU Delft Pattern Recognition and Bioinformatics) Mahfouz, A.M.E.T.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Makrodimitris, S. (TU Delft Pattern Recognition and Bioinformatics; Erasmus MC) Date 2023 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 tasks of cluster agreement with cancer subtypes and survival analysis. We studied the effect of the latent space dimensionality, learning rate, optimizer, initialization and activation function on the quality of subsequent downstream tasks on 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. To ensure generalization, we tested all hyperparameter configurations on the GTEx dataset. We found a significant correlation (ρ = 0.7) between the hyperparameter effects on clustering performance in the TCGA and GTEx datasets. This highlights the robustness and generalizability of our recommendations. In addition, we examined whether the learned latent spaces capture biologically relevant information. Hereto, we measured the correlation and mutual information of the different representations with various data characteristics such as gender, 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 nor capture separable information in the latent factors even for models specifically designed for disentanglement. To reference this document use: http://resolver.tudelft.nl/uuid:0f616121-ebda-4f83-b2d9-a68200c5f5ab DOI https://doi.org/10.1371/journal.pone.0292126 ISSN 1932-6203 Source PLoS ONE, 18 (10) Part of collection Institutional Repository Document type journal article Rights © 2023 M.A.M.E. Eltager, T.R.M. Abdelaal, M. Charrout, A.M.E.T.A. Mahfouz, M.J.T. Reinders, S. Makrodimitris Files PDF journal.pone.0292126_1_.pdf 2.9 MB Close viewer /islandora/object/uuid:0f616121-ebda-4f83-b2d9-a68200c5f5ab/datastream/OBJ/view