Additional file 1 of BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin
Creators: name: Kshirsagar, Meghana
name: Yuan, Han
name: Ferres, Juan Lavista
name: Leslie, Christina
Publisher: name: figshare
Year: 2022
Subjects: subject: Space Science
subject: Genetics
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: Cancer
subject: 60506 Virology
subject: Computational Biology

description: Additional file 1 ∙Table S1: Details of the ATAC-seq datasets used in the analyses shown in the supplementary material. ∙Figure S1: The top 100 8-mers from the latent dimension #37, which captures genomic background in the GM12878 model. ∙Figure S2: The latent representation learned for top low complexity regions (LCRs) are similar as shown in the heatmap, where most of them have a high value for the same latent factor (#37). ∙Figure S3: Latent projection of peaks with only repeat regions and peaks with some low-complexity repeating patterns and TFBS for a TF. This shows the disentanglement achieved by BindVAE. ∙Figure S4: CIS-BP motifs for TFs from the same family or for paralogous TFs are shown, to illustrate the difficulty of learning TF-specific patterns for these. We show the TFs from the heatmap of Figure 2b (TFs in the boxes). Each group of TFs gets projected to the same latent factor by our model as discussed in the main paper. ∙Figure S5: GM12878: Top 50 8-mers from some latent dimensions, aligned using Clustal Omega to summarize the patterns found. The CIS-BP motif corresponding to the TF that was assigned to each latent dimension (using Algorithm 1) is also shown. Since CTCF is assigned to multiple latent dimensions, the top 25 8-mers from each are shown. ∙Figure S6: PCA performed on the decoder parameters θ ∈ ℝ M × D $ \theta \in \mathbb{R}^{M \times D}$ that capture the k-mer distributions, from models trained on various cell-types. Each dot on a plot represents a TF binding pattern or k-mer distribution. (Top left) k-mer distributions from two models trained on naive B cells from two human donors, male and female. (Top right) k-mer distributions from two models from two repeat experiments on female naive B cells. In both cases, there is not much variance in the learned patterns, as these are biologically close samples. (Bottom left) k-mer distributions from the two models trained on GM12878 and A549. Since these are distinct cell types, we see two distinct manifolds in the patterns being learned. There is also a cluster of latent dimensions at the bottom of the plot that captures similar k-mer patterns. (Bottom right) k-mer distributions from four models: GM12878, A549, mouse CD8 T cells, and human CD8 T cells. ∙Table S2: Motifs constructed from latent factors learned for GM12878 and A549 for TFs learned by our model. HNF4A, NFIA, SRY have the same ‘accessibility score’ in both GM12878 and A549 trained models (see Figure 3d in the main paper). ELF5 and OLIG3 are both expressed in both cell types. ∙Table S3: MYBL1:MAX motifs computed from the GM12878 model are shown in the top row. The bottom table shows all cooperative binding pairs of TFs found for GM12878 by our model using the CAP-SELEX data are shown. ∙Figure S7: Examples of CAP-SELEX probes scoring higher and lower than individual TF probes. (Top) Example of cooperative binding CAP-SELEX probes of TFAP4:FLI1 being enriched while the individual TF probes are not enriched for TFAP4 or FLI1 (p = 1). (Bottom) Example where individual TF probes from the SELEX experiment for EOMES are enriched, while CAP-SELEX probes of cooperative binding between MYBL1:EOMES are not. ∙Figure S8: Accessibility scores of TFs obtained by summing the latent representations over all ATAC-seq peaks showing the possible extent of accessibility for each TF. The datasets used here are described in Table S1. (Left) TFs found in naive B cells by our model and their ‘accessibility scores’ in the two human donors: male donor (light blue) and female donor (orange).(Right) Averaged relative accessibility scores in female donor over 5 runs. ∙Table S4: Precision, recall, F1 achieved by the various de novo motif discovery approaches in retrieving TFs from ATAC-seq peaks of the two cell types. Expressed TFs (from RNA-seq data) that intersect with our HT-SELEX set of TFs are used as the gold-standard for retrieval. (a) Performance with default p-value and e-value cut-offs for all methods. (b) For the two best approaches, HOMER and BindVAE, performance upon varying the cut-offs is shown. The performance of a naive match-every-motif classifier (label everything positive) is shown. ∙Figure S9: Select latent dimensions (topics) from the GM12878 model that are referred to in the main text. The shown matrix is a sub-matrix of θ ∈ ℝ M × D $\theta \in \mathbb{R}^{M \times D}$ . Dimensions 69 and 83 are redundant, in that they both assign high weights to the same k-mer features. ∙Figure S10: Redundant dimensions: Latent dimensions with similar k-mer distributions are shown. In each plot, the two dimensions were mapped to the same TF; for example: #69 and #83 are both mapped to HEY1. Along the x-axis is the union of the top 1000 8-mers from both dimensions. The values in each row are the decoder parameters learned by the model: θ → i $\vec{\theta}_{i}$ for the ith dimension. ∙Figure S11: GM12878 peaks projected onto the 10 noisy latent dimensions. ∙Figure S12: Interpreting the models learned on random regions of the DNA. (Top) Matrix showing the learned decoder parameter θ (that shows the k-mer distributions for each latent factor) for a model trained on 150,000 random regions of naked DNA from mouse E16.5 sorted germ cells. (Bottom) Matrix showing the learned decoder parameter θ for a model trained on flanking genomic regions from GM12878 cells, where regions that are 10kb away from the ATAC-seq peaks were chosen as training data. ∙Table S5: TFs learned by the model trained on the GM12878 ATAC-seq data. ∙Section 2: Description of latent factors capturing non-TF related patterns. ∙Section 3: Details of the comparison to other baselines. ∙Section 4: Further details on datasets. ∙Figure S13: GM12878 peaks that contain sequences with the most repeats (top 5 are shown), as ranked by the Tandem Repeat Finder score (TRF algorithm [41]). We find that the highest-scoring topic for all of these sequences is the same. The genomic coordinates correspond to the hg19 assembly. ∙Figure S14: A549: Heatmap showing the top 20 k-mers learned by our model for each latent dimension. ∙Figure S15: A549: Heatmap of the latent space obtained by our A549-trained model, upon doing inference on 11,600 SELEX probes from 51 TF experiments. Each row is the latent representation z → $\vec{z}$ of a HT-SELEX probe, with the rows being colored by the TF experiment that the probe comes from. There are 200 enriched probes per TF/HT-SELEX experiment. ∙Figure S16: PCA of the k-mer distributions in two isogenic replicates of T cells. ∙Figure S17: Box-plots showing the distribution of topic scores for ATAC-seq peaks that overlap with ChIP-seq peaks and those that do not, for several TFs. ∙Figure S18: Box-plots showing the distribution of topic scores for ATAC-seq peaks that overlap with ChIP-seq peaks and those that do not, for several TFs.