Additional file 1 of MethylNet: an automated and modular deep learning approach for DNA methylation analysis
Creators: name: Levy, Joshua J.
name: Titus, Alexander J.
name: Petersen, Curtis L.
name: Youdinghuan Chen
name: Salas, Lucas A.
name: Christensen, Brock C.
Publisher: name: figshare
Year: 2020
Subjects: subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Sociology
subject: FOS: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics

description: Additional file 1: Supplementary Table 1. Male to Female Ratio Across Both Datasets. Supplementary Figure 1. Distribution of Age Across Datasets. Supplementary Table 2. Number of Samples for Each Cancer Subtype in Training, Validation, and Test Sets. Supplementary Figure 2. a) Visual flow diagram of method used to find CpG groupings and recapitulation of DNAm profiles. First, the 300 k CpGs are projected into a 6-dimensional embedding using UMAP. Each point in the low dimensional space represents a CpG and proximity between points denotes a shared methylation profile across all of the training samples (n = 503). Then, KMeans clustering was used to find 25 clusters of CpGs with similar profiles. The number of clusters of CpG features were reduced to 8 by filtering out clusters if their variance was above 1 in the 6D space. After that, the CpG features found in each cluster were used to select CpGs to form independent MethylationArrays across the training, validation and test sets. Finally, one autoencoder was trained per each array and the test samples were recapitulated and compared to the original input data; b) Descriptive statistics for final groupings of CpGs and recapitulation scores for each resultant set of CpGs versus the original methylation profiles input into each model. Supplementary Figure 3. Generated/recapitulated beta values versus original beta values for each CpG per individual of the held-out test set (n = 144); b)-f) corresponds to each of eight chosen clusters in order of low to high cluster variance as previously described; a) is an aggregation of the generated/recapitulated versus true beta values of all of the CpG clusters. Supplementary Figure 4. Hierarchically clustered cosine distance matrix between test samples’ VAE-embedded methylation profiles of the held-out test set for the TCGA cohort, colored by: a) Labels assigned to the hierarchical clustering labels for the samples; b) Original TCGA cancer labels; c) RPMM-derived clustering labels on 20 k CpGs. Agreement scores between the RPMM and hierarchal clustering results and the original cancer subtypes were calculated using the v-measure, which takes into account the homogeneity and completeness of the labeling. Note that the clustering colors are not the same because the number of clusters is different from the number of cancer labels. Supplementary Table 3. MethylNet Results on Training (n = 503) and Validation (n = 72) Sets for Sample Age Prediction. Supplementary Table 4. MethylNet Results on Training (n = 503) and Validation (n = 72) Sets for Cell Type Deconvolution. Supplementary Table 5. MethylNet Results on Training (n = 5860) and Validation (n = 840) Sets for Pan-Cancer Classification. Supplementary Table 6. Tukey’s Studentized Range Tests for identifying which of six hierarchical clusters from unsupervised VAE embeddings differ in cellular proportions; VAE embeddings derived from blood test dataset. Supplementary Table 7. Correlation of MethylNet Cellular Proportions to Other Estimation Methods’ Proportions. Supplementary Figure 5. Proportion of IDOL CpGs that are Overlapped by the Top 1 k CpGs for Each Cell-Type. Supplementary Figure 6. Bar Charts of CpGs with the 10 Largest Shapley Scores for Each Cell-Type, linked by red, blue or green lines if shared across subtype for: a-d) Lymphocytes; e-f) Myeloids. Not sharing top 10 CpGs does not indicate that two cell-types do not share similar CpG profiles. Supplementary Figure 7. Bar Charts of CpGs with the 10 Largest Shapley Scores for Age Groups: a) 14–24 and b) 84–94. These CpGs are linked if shared across the age groups, but this does not indicate that they are not shared outside of this top 10 list of CpGs. The top 10 CpGs that are associated with lower age are similar to the older age group; c) Distribution of Shapley Scores for these two age groups. CpG contributions tend to be negative for the younger age groups and positive for the older age groups. Supplementary Table 8. Confusion Matrix Pan-cancer Classification (Colored Superclass). Supplementary Table 9. Breakdown Pancancer Classification Results (Colored by Superclass). Supplementary Table 10. Average Cosine Distance Between Embeddings of Cancer Subtypes Supplementary Figure 8. Micro F1-Scores of the held-out test samples (n = 1676) of the TCGA cohort as they relate to: a) the fraction of training samples included for the training process, b) the number of CpGs. Test performance scales linearly with the number of training samples and logarithmically with the number of CpGs. Confidence intervals were calculated using a 1 k nonparametric bootstrap of the test results for each dataset size point in the line plot, and the resulting bootstrapped f1-scores were used to compute the confidence interval for each point in the line plots; c) performance of MethylNet, pretrained using a VAE, is compared to performance using an MLP with the same architecture; F1-Score confidence intervals were derived using a 1 k nonparametric bootstrap; validation loss for each model is compared at the first training epoch and their ultimate convergence point. Supplementary Figure 9. V-Measure scores of the held-out test samples (n = 1676) of the TCGA cohort as they relate to: a) the fraction of training samples included for the training process, b) the number of CpGs. V-measure scores were derived by applying and comparing hierarchical clustering on the VAE embeddings to known cancer subtype assignments and using a knee point detection algorithm to identify the ideal number of clusters for each tested dataset. In this figure, scores were smoothed using an exponential moving average smoothing technique to illustrate general trends. Supplementary Figure 10. Smoking EWAS study via MethylNet: a) final embeddings derived when finetuning the MethylNet VAE demonstrates cluster separation of the “never” versus “current” smokers; b) confusion matrix for the true and predicted “never” versus “current” smokers; c) plotted average ranks found using SHAP for the CpGs that intersected with CpGs identified by Liu et al. versus the ranks of those corresponding p-values of the EWAS meta-analysis. Supplementary Table 11. Preliminary Results for PAM50 Breast Tumor Classification (n = 1018); 95% confidence intervals of scores estimated via 1000-sample non-parametric bootstrap. Supplementary Figure 11. Internal and External Validation Cohorts: a) Boxenplot demonstrating distribution of ages for internal and external cohorts; notice how age for external validation cohort is greater than that of the internal validation cohort; b) plotted MethylNet predicted age versus actual age. Supplementary Table 12. Select Hyperparameters for Embedding Tasks. Supplementary Table 13. Select Hyperparameters for Prediction Tasks. Supplementary Figure 12. Fine-tuned embeddings (few parts highlighted) for: a) Age Prediction, b) Cell-Type Deconvolution (colored by Neutrophil Cell Type Proportions), and c) Pan-Cancer Classification (Labeled kidney cancers, lung cancers, brain cancers). Supplementary Figure 13. Model Training Curves for a) Age Predictions, b) Cell-Type Predictions, c) Pan-Cancer Predictions. Please note that the learning rates for the prediction curve of a) oscillates quickly every 10 training epochs as compared to a larger timescale.