diff --git a/.gitignore b/.gitignore index fcf296e..55b1bef 100644 --- a/.gitignore +++ b/.gitignore @@ -1,30 +1,30 @@ +# Raw sequencing and contact data (large files; download via run_pipeline.sh) +data/raw/ + # Python __pycache__/ -*.pyc +*.py[cod] +*.pyo +.pytest_cache/ +*.egg-info/ +dist/ +build/ -# Conda / mamba envs -.env/ -*.yml.lock +# Environments +.env +*.env +.venv/ -# Data -*.hic -*.mcool -*.cool -*.bam -*.bw -*.bigwig -*.bed -*.pairs* -*.pt -*.npy -*.csv -*.png +# Editors +.vscode/ +.idea/ +*.swp +*~ -# Jupyter and logs -*.ipynb_checkpoints/ -*.log +# Jupyter +.ipynb_checkpoints/ +*.ipynb + +# OS .DS_Store - -# Results / temp -results/ -data/ +Thumbs.db diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..d4123f2 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,60 @@ +cff-version: 1.2.0 +message: >- + If you use this software in your research, please cite it using the + following metadata. +type: software +title: >- + chromatin-gnn: Variational Graph Autoencoder for learning latent + representations of chromatin topology from Hi-C data +authors: + - family-names: Okada + given-names: Toru + alias: ToruOkadaOi + # orcid: "https://orcid.org/XXXX-XXXX-XXXX-XXXX" # add your ORCID +version: "1.0.0" +date-released: "2024-01-01" # update to actual release date +doi: "10.5281/zenodo.XXXXXXX" # replace with actual Zenodo DOI after deposit +repository-code: "https://github.com/ToruOkadaOi/chromatin-gnn" +url: "https://github.com/ToruOkadaOi/chromatin-gnn" +license: MIT +abstract: >- + A Variational Graph Autoencoder (VGAE) applied to Hi-C chromatin contact + data to learn unsupervised latent representations of chromatin topology. + Genomic bins are modelled as graph nodes with ChIP-seq features (CTCF, + H3K27me3); normalised contact frequencies define weighted edges. + The model is trained on GM12878 lymphoblastoid cells and evaluated on + both link-prediction (AUROC/AP) and the biological interpretability of + the latent space against known A/B compartments. +keywords: + - chromatin + - Hi-C + - graph neural network + - variational autoencoder + - VGAE + - A/B compartments + - topologically associating domains + - TAD + - epigenomics + - 3D genome organisation +references: + - type: article + title: >- + A 3D Map of the Human Genome at Kilobase Resolution Reveals + Principles of Chromatin Looping + authors: + - family-names: Rao + given-names: "Suhas S. P." + - family-names: Huntley + given-names: "Miriam H." + year: 2014 + journal: Cell + doi: 10.1016/j.cell.2014.11.021 + - type: article + title: "Variational Graph Auto-Encoders" + authors: + - family-names: Kipf + given-names: "Thomas N." + - family-names: Welling + given-names: Max + year: 2016 + url: "https://arxiv.org/abs/1611.07308" diff --git a/README.md b/README.md index 096be89..d58d85a 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,302 @@ -# Graph learning for genome architecture +# chromatin-gnn -### workflow +**Variational Graph Autoencoder for learning latent representations of chromatin topology from Hi-C data** + +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.XXXXXXX.svg)](https://doi.org/10.5281/zenodo.XXXXXXX) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) + +--- + +## Overview + +The three-dimensional organisation of chromatin in the nucleus is not random. Chromosomes fold into compartments, topologically associating domains (TADs), and loop structures that correlate strongly with gene regulation. Hi-C sequencing captures these contacts genome-wide, but the resulting data are high-dimensional and require principled dimensionality reduction to extract biologically interpretable structure. + +This repository applies a **Variational Graph Autoencoder (VGAE)** to Hi-C contact data to learn a compact, continuous latent representation of chromatin topology. Genomic bins are treated as graph nodes; normalised contact frequencies define weighted edges; ChIP-seq tracks for CTCF and H3K27me3 supply node features. The model is trained end-to-end on a link-prediction objective and evaluated for its ability to recover known biological structure — A/B compartments — in an entirely unsupervised manner. + +--- + +## Scientific question + +> Can a VGAE learn biologically meaningful latent representations of chromatin topology — capturing A/B compartments and cell-type-specific reorganisation — from Hi-C contact data alone, in an unsupervised manner? + +--- + +## Architecture + +``` +Node features (2D: CTCF, H3K27me3) + │ + BatchNorm + │ + GCNConv(64) ← shared message-passing layer + │ + ReLU + Dropout(0.2) + / \ +GCNConv(32) GCNConv(32) + μ log σ + \ / + Reparameterisation + │ + z ∈ ℝ³² (node embeddings) + │ + Inner-product decoder + (link prediction objective: binary cross-entropy + KL divergence) +``` + +The encoder is a two-layer Graph Convolutional Network (Kipf & Welling 2016, 2017) with a BatchNorm input layer. The decoder is the standard dot-product decoder used in the original VGAE paper. Training uses a link-prediction objective: the model is asked to distinguish real Hi-C contacts from randomly sampled non-contacts. + +--- + +## Dataset + +All data are from the GRCh38/hg38 reference genome, chromosome 21 at 25 kb resolution. + +| File | Cell line | Type | Source | Accession | +|------|-----------|------|--------|-----------| +| GM12878.mcool | GM12878 (lymphoblastoid) | Hi-C contact matrix | 4DN Data Portal | 4DNFIRUMEC32 | +| IMR90.mcool | IMR-90 (lung fibroblast) | Hi-C contact matrix | 4DN Data Portal | 4DNFIABB3FHQ | +| GM12878_CTCF.bw | GM12878 | CTCF ChIP-seq (FC/control) | ENCODE | ENCFF741BAQ (exp. ENCSR000AKB) | +| GM12878_H3K27me3.bw | GM12878 | H3K27me3 ChIP-seq (FC/control) | ENCODE | ENCFF736CNQ (exp. ENCSR000AKD) | +| IMR90_CTCF.bw | IMR-90 | CTCF ChIP-seq (FC/control) | ENCODE | ENCFF770DUD (exp. ENCSR000EFI) | +| IMR90_H3K27me3.bw | IMR-90 | H3K27me3 ChIP-seq (FC/control) | ENCODE | ENCFF158HZL (exp. ENCSR431UUY) | + +**Graph statistics:** + +| Cell line | Bins (chr21, 25 kb) | Edges (contacts) | Node features | +|-----------|---------------------|------------------|---------------| +| GM12878 | 1,869 | 87,557 | 2 (CTCF, H3K27me3) | +| IMR90 | 1,869 | 136,121 | 2 (CTCF, H3K27me3) | + +IMR90 has ~55% more intra-chromosomal contacts than GM12878 at chr21, suggesting a more compact or contact-rich chromatin organisation in this fibroblast cell line. + +--- + +## Installation ```bash -# Build graph (contact matrix & bigwig needed) -python scripts/build_graph.py --mcool x.mcool --chrom chrx --res x - --bigwigs xCTCFx.bw xH3K27me3x.bw --out data/chrx_xconditionx.pt +conda create -n chromatin_gnn python=3.10 -y +conda activate chromatin_gnn -# Train model -python scripts/train_vgae.py --graph data/chrx_xconditionx.pt --epochs 100 --outdir results +# CPU-only PyTorch (replace URL for GPU builds) +pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cpu -# Encode treatment graph -python scripts/encode_graph.py --model results/model.pt --graph data/chrx_xtreatmentx.pt --out results/emb_xtreatmentx.npy +# All other dependencies +pip install torch-geometric==2.5.3 cooler==0.9.3 pyBigWig pandas \ + "numpy>=1.24,<2.0" scikit-learn matplotlib umap-learn scipy seaborn tqdm +``` -# Compare embeddings -python scripts/compare_embeddings_general.py --emb1 results/emb.npy --emb2 results/emb_xtreatmentx.npy \ - --label1 xControlx --label2 xTreatmentx --prefix results/chrx -``` \ No newline at end of file +> **Note:** `cooler==0.9.3` requires `numpy<2.0`. The env.yml captures the exact versions used for this release. + +--- + +## Workflow + +```bash +# Full end-to-end run (downloads bigwigs automatically; .mcool files must be present in data/raw/) +bash run_pipeline.sh + +# Or run individual steps: + +# 1. Build contact graph +python scripts/build_graph.py \ + --mcool data/raw/GM12878.mcool \ + --chrom chr21 --res 25000 \ + --bigwigs data/raw/GM12878_CTCF.bw data/raw/GM12878_H3K27me3.bw \ + --out data/processed/GM12878_chr21.pt + +# 2. Compute A/B compartments +python scripts/compute_compartments.py \ + --mcool data/raw/GM12878.mcool --chrom chr21 --res 25000 \ + --bigwig_orient data/raw/GM12878_CTCF.bw \ + --out results/GM12878/compartments_chr21.csv + +# 3. Train VGAE +python scripts/train_vgae.py \ + --graph data/processed/GM12878_chr21.pt \ + --epochs 300 --patience 20 --hidden 64 --latent 32 \ + --outdir results/GM12878 + +# 4. Encode a second cell line with the trained model +python scripts/encode_graph.py \ + --model results/GM12878/model.pt \ + --graph data/processed/IMR90_chr21.pt \ + --out results/IMR90/emb.npy + +# 5. UMAP visualisation +python scripts/visualize_embeddings.py \ + --emb results/GM12878/emb.npy results/IMR90/emb.npy \ + --labels GM12878 IMR90 \ + --compartments results/GM12878/compartments_chr21.csv \ + results/IMR90/compartments_chr21.csv \ + --prefix results/figures/umap + +# 6. Per-bin embedding comparison +python scripts/compare_embeddings.py \ + --emb1 results/GM12878/emb.npy --emb2 results/IMR90/emb.npy \ + --label1 GM12878 --label2 IMR90 \ + --prefix results/figures/chr21 +``` + +--- + +## Results + +### Training (GM12878, chr21, 25 kb) + +| Metric | Value | +|--------|-------| +| Epochs to convergence | 31 / 300 (early stopping, patience=20) | +| Validation AUC (link prediction) | 0.774 | +| Test AUC | 0.777 | +| Test AP | 0.759 | +| Latent dimensionality | 32 | + +The model converged rapidly, suggesting that the graph structure of chr21 at 25 kb is learnable with a shallow two-layer GCN. + +--- + +### A/B compartment separation in the latent space + +The UMAP of GM12878 node embeddings coloured by A/B compartment shows strong, clean separation of the two compartment types without the model ever receiving compartment labels during training. + +| Cell line | Silhouette score (A/B, cosine) | A bins | B bins | Masked (N) | +|-----------|-------------------------------|--------|--------|------------| +| GM12878 (training) | **0.775** | 602 | 683 | 584 | +| IMR90 (zero-shot) | 0.443 | 614 | 709 | 546 | + +The GM12878 silhouette of **0.775** indicates that the VGAE has learned a latent space in which A and B compartments are nearly linearly separable — a strong signal given that compartment identity was never provided as a training label. + +For IMR90, encoded zero-shot with the GM12878-trained model, the silhouette drops to **0.443**. This is expected: the model's BatchNorm statistics were fit to GM12878, and IMR90's chromatin organisation partially diverges. + +**Figures:** + +| Figure | Description | +|--------|-------------| +| `results/figures/umap_GM12878_compartment.png` | GM12878 UMAP coloured by A/B compartment | +| `results/figures/umap_GM12878_position.png` | GM12878 UMAP coloured by genomic position | +| `results/figures/umap_IMR90_compartment.png` | IMR90 UMAP coloured by A/B compartment | +| `results/figures/umap_joint.png` | Joint UMAP of both cell lines | +| `results/figures/chr21_delta.png` | Per-bin cosine distance track (GM12878 vs IMR90) | + +--- + +### Cell-type comparison: GM12878 vs IMR90 + +The IMR90 graph was encoded with the GM12878-trained model (zero-shot transfer). Per-bin cosine distances between the two embedding matrices reveal which genomic loci undergo the largest chromatin reorganisation between cell types. + +**Per-bin cosine distance summary:** + +| Statistic | Value | +|-----------|-------| +| Mean | 0.245 | +| Median | 0.028 | +| Bins with distance < 0.1 (stable) | 968 / 1,869 (52%) | +| Bins with distance > 0.5 (high shift) | 293 / 1,869 (16%) | + +**Mean cosine distance by GM12878 compartment:** + +| Compartment | Mean distance | Median distance | +|-------------|--------------|-----------------| +| A (active) | 0.042 | 0.001 | +| B (repressive) | 0.451 | 0.352 | +| N (masked) | 0.213 | 0.000 | + +**Key finding:** A-compartment bins are nearly invariant between the two cell types (mean Δ = 0.042), while B-compartment bins shift substantially (mean Δ = 0.451). This is consistent with the known biology: constitutively active chromatin domains tend to be conserved across cell types, while heterochromatic B-compartment organisation is more cell-type-specific. + +**Compartment switches (GM12878 → IMR90):** + +| GM12878 | IMR90 | Bins | Interpretation | +|---------|-------|------|----------------| +| A | A | 493 | Stable active | +| B | B | 581 | Stable repressive | +| B → A | A | 101 | Loci that open in IMR90 | +| A → B | B | 70 | Loci that close in IMR90 | + +101 loci switch from B (repressive in GM12878) to A (active in IMR90), versus 70 in the reverse direction. This asymmetry suggests that IMR90 fibroblasts activate more lineage-specific loci on chr21 than GM12878 lymphoblastoid cells. + +--- + +## Biological interpretation + +The results demonstrate that a VGAE trained on Hi-C data **recovers A/B compartment structure without supervision** (silhouette = 0.775). The latent space organises chr21 bins according to their chromatin state, not just their linear genomic position — the UMAP coloured by genomic position shows a broadly continuous gradient, while the compartment-coloured UMAP shows discrete clusters. + +The zero-shot application to IMR90 captures the partial conservation of compartment organisation across cell types. The B-compartment instability revealed by the cosine distance analysis is consistent with the literature: heterochromatin rewiring is a known driver of cell-type identity (Lieberman-Aiden et al. 2009; Dixon et al. 2015). + +Notably, the model achieves this with only two node features (CTCF and H3K27me3 signal), demonstrating that a modest set of epigenomic marks, combined with contact topology, is sufficient to encode the major axis of chromatin organisation. + +--- + +## Limitations + +1. **Single chromosome, single resolution.** Results are for chr21 at 25 kb only. Chr21 is acrocentric with a large masked pericentromeric region (584 / 1,869 bins masked in GM12878), which may reduce statistical power compared to gene-rich autosomes. + +2. **Shallow encoder.** The two-layer GCN has a local receptive field (2-hop neighbourhood). Long-range chromatin interactions spanning multiple TADs are not directly encoded. Deeper networks or attention-based architectures may capture these better. + +3. **Link-prediction objective ≠ compartment recovery.** The model is optimised to predict contacts, not compartments. The strong silhouette score is emergent, not guaranteed. The objective could be supplemented with biologically-informed losses. + +4. **Zero-shot transfer with fixed BatchNorm.** Encoding IMR90 with GM12878 BatchNorm statistics means the model sees IMR90 features in GM12878's normalisation frame. A domain-adaptation approach (e.g., re-fitting BatchNorm on IMR90 with frozen GCN weights) would give a fairer comparison. + +5. **Compartment calling is approximate.** The O/E → Pearson correlation → PC1 pipeline is sensitive to the choice of orientation signal (CTCF here). Bins with low coverage are masked and assigned no compartment label, which affects the silhouette calculation. + +6. **No TAD-level evaluation.** TAD boundary detection would require a graph-level or boundary-aware metric. The current evaluation is node-level only. + +7. **No statistical significance testing.** The compartment switch counts (101 B→A, 70 A→B) have not been tested for significance against a null model (e.g., random permutation of compartment labels). + +--- + +## Future work + +- Apply to all autosomes and compare genome-wide compartment recovery. +- Add a TAD-boundary evaluation metric (e.g., insulation score correlation with latent space gradients). +- Fine-tune on IMR90 (transfer learning) to improve the IMR90 silhouette score. +- Add cohesin depletion or auxin-inducible degron (AID) perturbation data as a controlled condition comparison. +- Replace the inner-product decoder with a distance-aware decoder that incorporates linear genomic distance. +- Benchmark against PCA/UMAP of the raw contact matrix and against other graph-based methods (GraphSAGE, GAT). +- Extend node features to include additional histone marks (H3K4me3, H3K27ac, H3K9me3) to test whether richer epigenomic context improves compartment recovery. + +--- + +## Scripts + +| Script | Purpose | +|--------|---------| +| `scripts/build_graph.py` | Convert .mcool + bigWigs → PyG Data object | +| `scripts/train_vgae.py` | Train VGAE with link-prediction objective + early stopping | +| `scripts/encode_graph.py` | Encode a new graph with a trained model | +| `scripts/compute_compartments.py` | O/E matrix → Pearson PCA → A/B compartment calls | +| `scripts/visualize_embeddings.py` | UMAP + compartment visualisation + silhouette | +| `scripts/compare_embeddings.py` | Per-bin cosine/L2/L1 distance between two embeddings | +| `scripts/model.py` | Shared Encoder class (imported by train and encode scripts) | + +--- + +## Citation + +If you use this code or data in your research, please cite: + +```bibtex +@software{okada_chromatin_gnn_2024, + author = {Okada, Toru}, + title = {{chromatin-gnn: Variational Graph Autoencoder for learning + latent representations of chromatin topology from Hi-C data}}, + year = {2024}, + doi = {10.5281/zenodo.XXXXXXX}, + url = {https://github.com/ToruOkadaOi/chromatin-gnn}, + version = {1.0.0} +} +``` + +See also `CITATION.cff` for CFF-format metadata. + +**Key references:** + +- Kipf, T. N. & Welling, M. (2016). Variational Graph Auto-Encoders. *arXiv:1611.07308* +- Kipf, T. N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. *ICLR 2017* +- Rao, S. S. P. et al. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. *Cell*, 159(7), 1665–1680. https://doi.org/10.1016/j.cell.2014.11.021 +- Lieberman-Aiden, E. et al. (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. *Science*, 326(5950), 289–293. + +--- + +## License + +MIT — see [LICENSE](LICENSE). diff --git a/data/processed/GM12878_chr21.pt b/data/processed/GM12878_chr21.pt new file mode 100644 index 0000000..1a292fe Binary files /dev/null and b/data/processed/GM12878_chr21.pt differ diff --git a/data/processed/IMR90_chr21.pt b/data/processed/IMR90_chr21.pt new file mode 100644 index 0000000..343a600 Binary files /dev/null and b/data/processed/IMR90_chr21.pt differ diff --git a/env.yml b/env.yml index 42aec6d..872d798 100644 --- a/env.yml +++ b/env.yml @@ -1,21 +1,27 @@ -name: chromatin_gnn_aman +name: chromatin_gnn +# Installation (pip-based, conda used only for Python): +# conda create -n chromatin_gnn python=3.10 -y +# conda activate chromatin_gnn +# pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cpu +# pip install -r requirements.txt +# +# For GPU support replace the torch line with: +# pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cu118 channels: - - pytorch - - conda-forge - defaults dependencies: - python=3.10 - - pytorch - - torchvision - - torchaudio - - cooler - - pybigwig - - pandas - - numpy - - scikit-learn - - matplotlib - - umap-learn - pip - pip: - - torch-geometric - + - torch==2.1.2 # CPU build; see GPU note above + - torch-geometric==2.5.3 + - cooler==0.9.3 + - pyBigWig==0.3.25 + - pandas==2.1.4 + - "numpy>=1.24,<2.0" # cooler 0.9.3 requires numpy<2.0 + - scikit-learn==1.4.2 + - matplotlib==3.8.4 + - umap-learn==0.5.12 + - scipy==1.12.0 + - seaborn==0.13.2 + - tqdm==4.66.2 diff --git a/results/GM12878/compartments_chr21.csv b/results/GM12878/compartments_chr21.csv new file mode 100644 index 0000000..43fcf6d --- /dev/null +++ b/results/GM12878/compartments_chr21.csv @@ -0,0 +1,1870 @@ +chrom,start,end,pc1,compartment +chr21,0,25000,,N +chr21,25000,50000,,N +chr21,50000,75000,,N +chr21,75000,100000,,N 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b/results/figures/umap_IMR90_position.png differ diff --git a/results/figures/umap_joint.png b/results/figures/umap_joint.png new file mode 100644 index 0000000..a80ca4b Binary files /dev/null and b/results/figures/umap_joint.png differ diff --git a/results/figures/umap_stats.csv b/results/figures/umap_stats.csv new file mode 100644 index 0000000..0db6a34 --- /dev/null +++ b/results/figures/umap_stats.csv @@ -0,0 +1,3 @@ +label,n_bins,latent_dim,mean_embedding_norm,std_embedding_values,silhouette_AB_cosine +GM12878,1869,32,0.6679670810699463,0.1371903121471405,0.7748898267745972 +IMR90,1869,32,0.7111130356788635,0.14772675931453705,0.4431541860103607 diff --git a/run_pipeline.sh b/run_pipeline.sh new file mode 100644 index 0000000..67d5c79 --- /dev/null +++ b/run_pipeline.sh @@ -0,0 +1,162 @@ +#!/usr/bin/env bash +# +# run_pipeline.sh – end-to-end pipeline for chromatin-gnn +# +# Prerequisites +# ------------- +# 1. Create and activate the environment: +# conda create -n chromatin_gnn python=3.10 -y +# conda activate chromatin_gnn +# pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cpu +# pip install torch-geometric==2.5.3 cooler==0.9.3 pyBigWig pandas \ +# "numpy>=1.24,<2.0" scikit-learn matplotlib umap-learn scipy seaborn tqdm +# +# 2. Download raw data into data/raw/ (see README.md § Dataset for URLs): +# GM12878.mcool 4DN accession 4DNFIRUMEC32 +# IMR90.mcool 4DN accession 4DNFIABB3FHQ +# GM12878_CTCF.bw ENCODE ENCFF741BAQ (experiment ENCSR000AKB) +# GM12878_H3K27me3.bw ENCODE ENCFF736CNQ (experiment ENCSR000AKD) +# IMR90_CTCF.bw ENCODE ENCFF770DUD (experiment ENCSR000EFI) +# IMR90_H3K27me3.bw ENCODE ENCFF158HZL (experiment ENCSR431UUY) +# +# Usage +# ----- +# bash run_pipeline.sh [--chrom chr21] [--res 25000] [--epochs 300] + +set -euo pipefail + +# ========== Configuration ========== +CHROM="${CHROM:-chr21}" +RES="${RES:-25000}" +EPOCHS="${EPOCHS:-300}" +PATIENCE="${PATIENCE:-20}" +HIDDEN="${HIDDEN:-64}" +LATENT="${LATENT:-32}" +SEED="${SEED:-42}" + +REPO="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +SCRIPTS="$REPO/scripts" +DATA="$REPO/data" +RESULTS="$REPO/results" + +# ========== Directories ========== +mkdir -p "$DATA/raw" "$DATA/processed" \ + "$RESULTS/GM12878" "$RESULTS/IMR90" "$RESULTS/figures" + +# ========== Download ENCODE bigWig tracks ========== +echo "=== Downloading ENCODE bigWig tracks ===" +for entry in \ + "GM12878_CTCF.bw|https://www.encodeproject.org/files/ENCFF741BAQ/@@download/ENCFF741BAQ.bigWig" \ + "GM12878_H3K27me3.bw|https://www.encodeproject.org/files/ENCFF736CNQ/@@download/ENCFF736CNQ.bigWig" \ + "IMR90_CTCF.bw|https://www.encodeproject.org/files/ENCFF770DUD/@@download/ENCFF770DUD.bigWig" \ + "IMR90_H3K27me3.bw|https://www.encodeproject.org/files/ENCFF158HZL/@@download/ENCFF158HZL.bigWig" +do + fname="${entry%%|*}" + url="${entry##*|}" + out="$DATA/raw/$fname" + if [ -f "$out" ]; then + echo " $fname already present, skipping" + else + echo " Downloading $fname ..." + wget -q --show-progress -O "$out" "$url" + fi +done + +# .mcool files must be downloaded manually from 4DN (requires free account): +# GM12878: https://data.4dnucleome.org/files-processed/4DNFIRUMEC32/@@download/4DNFIRUMEC32.mcool +# IMR90: https://data.4dnucleome.org/files-processed/4DNFIABB3FHQ/@@download/4DNFIABB3FHQ.mcool +for f in GM12878.mcool IMR90.mcool; do + if [ ! -f "$DATA/raw/$f" ]; then + echo "ERROR: $DATA/raw/$f not found. Download from 4DN (see README) and retry." >&2 + exit 1 + fi +done + +# ========== Step 1: Build contact graphs ========== +echo "" +echo "=== Step 1: Building chromatin contact graphs ===" +for CELL in GM12878 IMR90; do + OUT="$DATA/processed/${CELL}_${CHROM}.pt" + if [ -f "$OUT" ]; then + echo " ${CELL} graph already exists, skipping" + else + python "$SCRIPTS/build_graph.py" \ + --mcool "$DATA/raw/${CELL}.mcool" \ + --chrom "$CHROM" --res "$RES" \ + --bigwigs "$DATA/raw/${CELL}_CTCF.bw" "$DATA/raw/${CELL}_H3K27me3.bw" \ + --out "$OUT" + fi +done + +# ========== Step 2: Compute A/B compartments ========== +echo "" +echo "=== Step 2: Computing A/B compartments (PC1 of O/E Pearson correlation) ===" +for CELL in GM12878 IMR90; do + OUT="$RESULTS/${CELL}/compartments_${CHROM}.csv" + if [ -f "$OUT" ]; then + echo " ${CELL} compartments already exist, skipping" + else + python "$SCRIPTS/compute_compartments.py" \ + --mcool "$DATA/raw/${CELL}.mcool" \ + --chrom "$CHROM" --res "$RES" \ + --bigwig_orient "$DATA/raw/${CELL}_CTCF.bw" \ + --out "$OUT" + fi +done + +# ========== Step 3: Train VGAE on GM12878 ========== +echo "" +echo "=== Step 3: Training VGAE on GM12878 ===" +if [ -f "$RESULTS/GM12878/model.pt" ]; then + echo " Trained model already exists, skipping" +else + python "$SCRIPTS/train_vgae.py" \ + --graph "$DATA/processed/GM12878_${CHROM}.pt" \ + --epochs "$EPOCHS" --patience "$PATIENCE" \ + --hidden "$HIDDEN" --latent "$LATENT" \ + --seed "$SEED" \ + --outdir "$RESULTS/GM12878" +fi + +# ========== Step 4: Encode IMR90 with GM12878 model ========== +echo "" +echo "=== Step 4: Encoding IMR90 graph with trained GM12878 model ===" +if [ -f "$RESULTS/IMR90/emb.npy" ]; then + echo " IMR90 embeddings already exist, skipping" +else + python "$SCRIPTS/encode_graph.py" \ + --model "$RESULTS/GM12878/model.pt" \ + --graph "$DATA/processed/IMR90_${CHROM}.pt" \ + --out "$RESULTS/IMR90/emb.npy" +fi + +# ========== Step 5: Visualise embeddings ========== +echo "" +echo "=== Step 5: Generating UMAP visualisations ===" +python "$SCRIPTS/visualize_embeddings.py" \ + --emb "$RESULTS/GM12878/emb.npy" "$RESULTS/IMR90/emb.npy" \ + --labels GM12878 IMR90 \ + --compartments \ + "$RESULTS/GM12878/compartments_${CHROM}.csv" \ + "$RESULTS/IMR90/compartments_${CHROM}.csv" \ + --prefix "$RESULTS/figures/umap" \ + --seed "$SEED" + +# ========== Step 6: Compare embeddings ========== +echo "" +echo "=== Step 6: Comparing GM12878 vs IMR90 embeddings ===" +python "$SCRIPTS/compare_embeddings.py" \ + --emb1 "$RESULTS/GM12878/emb.npy" \ + --emb2 "$RESULTS/IMR90/emb.npy" \ + --label1 GM12878 --label2 IMR90 \ + --prefix "$RESULTS/figures/${CHROM}" + +# ========== Summary ========== +echo "" +echo "=== Pipeline complete ===" +echo "Outputs:" +echo " Model + embeddings : $RESULTS/GM12878/" +echo " Figures : $RESULTS/figures/" +echo " Metrics : $RESULTS/GM12878/metrics.json" +echo "" +cat "$RESULTS/GM12878/metrics.json" diff --git a/scripts/compare_embeddings.py b/scripts/compare_embeddings.py index 51a6d68..6418617 100644 --- a/scripts/compare_embeddings.py +++ b/scripts/compare_embeddings.py @@ -50,7 +50,8 @@ def main(): if emb1.shape != emb2.shape: raise ValueError(f"Shape mismatch: {emb1.shape} vs {emb2.shape}") - os.makedirs(os.path.dirname(args.prefix), exist_ok=True) + prefix_dir = os.path.dirname(os.path.abspath(args.prefix)) + os.makedirs(prefix_dir, exist_ok=True) n_bins, n_dim = emb1.shape print(f"Loaded embeddings: {n_bins} bins × {n_dim} dims") diff --git a/scripts/compute_compartments.py b/scripts/compute_compartments.py new file mode 100644 index 0000000..fe3e538 --- /dev/null +++ b/scripts/compute_compartments.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python3 +""" +Compute A/B chromatin compartments from a Hi-C .mcool file. + +Algorithm +--------- +1. Load ICE-balanced contact matrix for the target chromosome. +2. Distance-normalise to O/E (divide each diagonal by its mean contact frequency). +3. Compute Pearson correlation matrix of the O/E rows. +4. PCA of the correlation matrix; PC1 distinguishes A from B compartments. +5. Orient the PC1 sign using --bigwig_orient (e.g. CTCF): + positive PC1 → high signal in that track. + With CTCF: positive PC1 = CTCF-enriched = A compartment (active). + With H3K27me3: pass --flip_orient so positive PC1 = B compartment (repressive). + +Output +------ +CSV with columns: chrom, start, end, pc1, compartment (A / B / N for masked bins). +""" + +import argparse +import os +import sys + +import cooler +import numpy as np +import pandas as pd +from sklearn.decomposition import PCA + +sys.path.insert(0, os.path.dirname(__file__)) + + +def _bin_bigwig(bw_path: str, chrom: str, bins) -> np.ndarray: + """Average bigWig signal over a list of (start, end) genomic bins.""" + import pyBigWig + bw = pyBigWig.open(bw_path) + chrom_len = bw.chroms().get(chrom, 0) + vals = [] + for s, e in bins: + s, e = max(0, int(s)), min(chrom_len, int(e)) + if s >= e: + vals.append(0.0) + continue + v = bw.stats(chrom, s, e, type="mean")[0] + vals.append(0.0 if v is None or np.isnan(v) else float(v)) + bw.close() + return np.array(vals) + + +def _observed_over_expected(matrix: np.ndarray) -> np.ndarray: + """Distance-normalise a symmetric contact matrix (O/E transform).""" + n = matrix.shape[0] + oe = np.zeros((n, n), dtype=float) + for d in range(n): + idx = np.arange(n - d) + diag = matrix[idx, idx + d].astype(float) + positive = diag[diag > 0] + if positive.size == 0: + continue + mean_d = positive.mean() + norm_diag = np.where((np.isnan(diag)) | (diag == 0), 0.0, diag / mean_d) + oe[idx, idx + d] = norm_diag + if d > 0: + oe[idx + d, idx] = norm_diag + return oe + + +def compute_compartments( + mcool_path: str, + chrom: str, + res: int, + orient_signal=None, + flip_orient: bool = False, +) -> pd.DataFrame: + """ + Return a DataFrame (chrom, start, end, pc1, compartment). + + Parameters + ---------- + orient_signal : array-like, optional + Per-bin 1-D signal used to fix the sign of PC1. + Pass CTCF signal for positive-PC1 = A convention. + flip_orient : bool + If True, high orient_signal maps to negative PC1 (use with H3K27me3). + """ + c = cooler.Cooler(f"{mcool_path}::resolutions/{res}") + bins_df = c.bins().fetch(chrom).reset_index(drop=True) + matrix = c.matrix(balance=True).fetch(chrom).astype(float) + + bad_bins = np.isnan(matrix).all(axis=0) | (matrix.sum(axis=0) == 0) + np.nan_to_num(matrix, nan=0.0, copy=False) + + oe = _observed_over_expected(matrix) + + good = ~bad_bins + oe_good = oe[np.ix_(good, good)] + + # Zero rows produce NaN in corrcoef; add tiny noise to avoid singularity + row_norms = np.linalg.norm(oe_good, axis=1) + oe_good[row_norms == 0] += 1e-9 + + corr = np.corrcoef(oe_good) + np.nan_to_num(corr, nan=0.0, copy=False) + + pca = PCA(n_components=3, random_state=42) + pcs = pca.fit_transform(corr) + pc1_good = pcs[:, 0] + + pc1 = np.full(len(bins_df), np.nan) + pc1[good] = pc1_good + + if orient_signal is not None: + sig = np.asarray(orient_signal, dtype=float) + sig_good = sig[good] + valid = ~np.isnan(sig_good) & ~np.isnan(pc1_good) + if valid.sum() > 10: + r = np.corrcoef(pc1_good[valid], sig_good[valid])[0, 1] + # By default: positive orient_signal → positive PC1. + # flip_orient reverses this (e.g. H3K27me3 → positive PC1 = B). + if (r < 0 and not flip_orient) or (r > 0 and flip_orient): + pc1 = -pc1 + + bins_df["pc1"] = pc1 + bins_df["compartment"] = np.where( + np.isnan(bins_df["pc1"]), "N", + np.where(bins_df["pc1"] > 0, "A", "B"), + ) + return bins_df[["chrom", "start", "end", "pc1", "compartment"]] + + +def main(): + p = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + p.add_argument("--mcool", required=True, help="Path to .mcool file") + p.add_argument("--chrom", required=True, help="Chromosome (e.g. chr21)") + p.add_argument("--res", type=int, default=25000, help="Resolution in bp (default: 25000)") + p.add_argument("--bigwig_orient", + help="bigWig track for PC1 sign orientation (recommended: CTCF)") + p.add_argument("--flip_orient", action="store_true", + help="Flip orientation: high signal → negative PC1 (use with H3K27me3)") + p.add_argument("--out", required=True, help="Output CSV path") + args = p.parse_args() + + orient_signal = None + if args.bigwig_orient: + c = cooler.Cooler(f"{args.mcool}::resolutions/{args.res}") + bins_df = c.bins().fetch(args.chrom).reset_index(drop=True) + coords = list(zip(bins_df["start"].values, bins_df["end"].values)) + orient_signal = _bin_bigwig(args.bigwig_orient, args.chrom, coords) + print(f"Loaded orientation signal: {os.path.basename(args.bigwig_orient)}") + + df = compute_compartments( + args.mcool, args.chrom, args.res, + orient_signal=orient_signal, + flip_orient=args.flip_orient, + ) + + os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True) + df.to_csv(args.out, index=False) + + n_a = (df["compartment"] == "A").sum() + n_b = (df["compartment"] == "B").sum() + n_nan = (df["compartment"] == "N").sum() + print(f"Saved → {args.out}") + print(f" A: {n_a} B: {n_b} N/masked: {n_nan} bins") + + +if __name__ == "__main__": + main() diff --git a/scripts/encode_graph.py b/scripts/encode_graph.py index b588e10..a34d85a 100644 --- a/scripts/encode_graph.py +++ b/scripts/encode_graph.py @@ -1,63 +1,91 @@ #!/usr/bin/env python3 """ -Encode a new graph using a trained VGAE model. -Automatically infers hidden/latent dimensions from saved weights. +Encode a chromatin contact graph using a trained VGAE model. + +Dimensions (in_dim, hidden, latent) are inferred automatically from the saved +state_dict. The BatchNorm running statistics from training are restored, so the +same normalisation is applied to held-out cell lines without a separate scaler. + +Usage +----- + python scripts/encode_graph.py \\ + --model results/GM12878/model.pt \\ + --graph data/processed/IMR90_chr21.pt \\ + --out results/IMR90/emb.npy """ -import argparse, torch, numpy as np -from torch_geometric.nn import GCNConv, VGAE +import argparse +import os +import sys -# Reuse your Encoder definition directly here for clarity -class Encoder(torch.nn.Module): - def __init__(self, in_dim, hidden, latent, dropout=0.2): - super().__init__() - self.gc1 = GCNConv(in_dim, hidden) - self.gc_mu = GCNConv(hidden, latent) - self.gc_log = GCNConv(hidden, latent) - self.dropout = dropout +import numpy as np +import torch +from torch_geometric.nn.models import VGAE - def forward(self, x, edge_index): - import torch.nn.functional as F - h = self.gc1(x, edge_index) - h = F.relu(h) - h = F.dropout(h, p=self.dropout, training=self.training) - return self.gc_mu(h, edge_index), self.gc_log(h, edge_index) +sys.path.insert(0, os.path.dirname(__file__)) +from model import Encoder + + +def _infer_dims(state_dict: dict) -> tuple: + """Infer (in_dim, hidden, latent) from a VGAE state_dict.""" + keys = list(state_dict.keys()) + + def _first_weight(substr): + for k in keys: + if (substr in k + and "weight" in k + and "running" not in k + and "num_batches" not in k): + return state_dict[k].shape + raise KeyError(f"No weight key containing '{substr}' in state_dict. " + f"Available keys: {keys}") + + gc1_shape = _first_weight("gc1") # shape [hidden, in_dim] + gc_mu_shape = _first_weight("gc_mu") # shape [latent, hidden] + hidden = gc1_shape[0] + latent = gc_mu_shape[0] + + # in_dim from BatchNorm weight (shape [in_dim]) + for k in keys: + if "norm" in k and k.endswith("weight") and "running" not in k: + in_dim = state_dict[k].shape[0] + break + else: + in_dim = gc1_shape[1] # fallback: second dim of gc1 weight + + return in_dim, hidden, latent def main(): - p = argparse.ArgumentParser() - p.add_argument("--model", required=True) - p.add_argument("--graph", required=True) - p.add_argument("--out", required=True) + p = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + p.add_argument("--model", required=True, + help="Path to model.pt saved by train_vgae.py") + p.add_argument("--graph", required=True, + help="Path to Data .pt file from build_graph.py") + p.add_argument("--out", required=True, + help="Output .npy path for node embeddings") args = p.parse_args() - # ---- Load data and model state ---- - data = torch.load(args.graph) - model_state = torch.load(args.model, map_location="cpu") + data = torch.load(args.graph, weights_only=False) + state_dict = torch.load(args.model, map_location="cpu", weights_only=False) - # ---- Infer dimensions dynamically ---- - in_dim = data.x.size(1) - # detect hidden and latent dimensions safely - keys = list(model_state.keys()) - gc1_weight = [k for k in keys if "gc1" in k and "weight" in k][0] - gc_mu_weight = [k for k in keys if "gc_mu" in k and "weight" in k][0] + in_dim, hidden, latent = _infer_dims(state_dict) + print(f"Inferred: in_dim={in_dim} hidden={hidden} latent={latent}") - hidden = model_state[gc1_weight].shape[0] - latent = model_state[gc_mu_weight].shape[0] - - print(f"Inferred dims: in={in_dim}, hidden={hidden}, latent={latent}") - - enc = Encoder(in_dim=in_dim, hidden=hidden, latent=latent) + enc = Encoder(in_dim=in_dim, hidden=hidden, latent=latent) model = VGAE(enc) - model.load_state_dict(model_state) + model.load_state_dict(state_dict) model.eval() - # ---- Encode ---- with torch.no_grad(): z = model.encode(data.x.float(), data.edge_index) + + os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True) np.save(args.out, z.cpu().numpy()) - print(f"Saved embeddings → {args.out} shape={z.shape}") + print(f"Saved embeddings → {args.out} shape={z.shape}") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/model.py b/scripts/model.py new file mode 100644 index 0000000..0a85b46 --- /dev/null +++ b/scripts/model.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python3 +"""Shared VGAE encoder. Imported by train_vgae.py and encode_graph.py.""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch_geometric.nn import GCNConv + + +class Encoder(nn.Module): + """Two-layer GCN encoder for VGAE with input BatchNorm. + + Architecture: BatchNorm → GCN(hidden) → ReLU → Dropout → GCN_mu / GCN_logstd + + The BatchNorm layer normalises raw ChIP-seq signals and its running statistics + are saved in model.pt, so encode_graph.py applies identical normalisation to + held-out cell lines without a separate scaler file. + """ + + def __init__(self, in_dim: int, hidden: int, latent: int, dropout: float = 0.2): + super().__init__() + self.norm = nn.BatchNorm1d(in_dim) + self.gc1 = GCNConv(in_dim, hidden, add_self_loops=True, normalize=True) + self.gc_mu = GCNConv(hidden, latent, add_self_loops=True, normalize=True) + self.gc_log = GCNConv(hidden, latent, add_self_loops=True, normalize=True) + self.dropout = dropout + + def forward(self, x, edge_index): + x = self.norm(x) + h = F.relu(self.gc1(x, edge_index)) + h = F.dropout(h, p=self.dropout, training=self.training) + return self.gc_mu(h, edge_index), self.gc_log(h, edge_index) diff --git a/scripts/train_vgae.py b/scripts/train_vgae.py index 5086449..65f6a35 100644 --- a/scripts/train_vgae.py +++ b/scripts/train_vgae.py @@ -1,179 +1,185 @@ #!/usr/bin/env python3 """ Train a Variational Graph Autoencoder (VGAE) on a chromatin contact graph. ---- -Inputs: - - A PyTorch Geometric Data object saved with torch.save() - - from build_graph.py ---- -Outputs (under results/): - - model.pt : trained VGAE state_dict - - emb.npy : node embeddings (mean; shape [num_nodes, latent_dim]) - - metrics.json : train/val/test AUC/AP summary + +Inputs +------ + PyTorch Geometric Data object saved by build_graph.py. + +Outputs (under --outdir) +------------------------ + model.pt trained VGAE state_dict (includes BatchNorm running statistics) + emb.npy node embeddings — mu vector, shape [num_nodes, latent_dim] + metrics.json val/test AUC & AP plus all hyperparameters """ -import os, json, argparse, numpy as np, torch -import torch.nn.functional as F -from torch_geometric.nn import GCNConv +import argparse +import json +import os +import sys + +import numpy as np +import torch from torch_geometric.nn.models import VGAE from torch_geometric.transforms import RandomLinkSplit -from torch_geometric.utils import to_undirected, remove_self_loops -from torch_geometric.utils import negative_sampling -from sklearn.metrics import roc_auc_score, average_precision_score +from torch_geometric.utils import ( + negative_sampling, + remove_self_loops, + to_undirected, +) +from sklearn.metrics import average_precision_score, roc_auc_score - -class Encoder(torch.nn.Module): - def __init__(self, in_dim: int, hidden: int, latent: int, dropout: float = 0.2): - super().__init__() - self.gc1 = GCNConv(in_dim, hidden, add_self_loops=True, normalize=True) - self.gc_mu = GCNConv(hidden, latent, add_self_loops=True, normalize=True) - self.gc_log = GCNConv(hidden, latent, add_self_loops=True, normalize=True) - self.dropout = dropout - - def forward(self, x, edge_index): - h = self.gc1(x, edge_index) - h = F.relu(h) - h = F.dropout(h, p=self.dropout, training=self.training) - return self.gc_mu(h, edge_index), self.gc_log(h, edge_index) +sys.path.insert(0, os.path.dirname(__file__)) +from model import Encoder @torch.no_grad() -def eval_linkpred(model, data_like, z): - """Compute AUROC/AP using provided positive/negative edges.""" - pos = data_like.pos_edge_index - neg = data_like.neg_edge_index - # model.test returns (auc, ap) but relies on torchmetrics in some versions; - # compute explicitly for stability: - def sigmoid(x): return 1 / (1 + torch.exp(-x)) +def _eval_linkpred(z, pos_edges, neg_edges): + """Return (AUROC, AP) for link prediction.""" + def _sigmoid(x): + return 1.0 / (1.0 + torch.exp(-x)) - # Inner product decoder scores - def scores(edges): + def _score(edges): src, dst = edges - s = (z[src] * z[dst]).sum(dim=1) - return sigmoid(s).cpu().numpy() + return _sigmoid((z[src] * z[dst]).sum(dim=1)).cpu().numpy() - y_true = np.concatenate([np.ones(pos.size(1)), np.zeros(neg.size(1))]) - y_pred = np.concatenate([scores(pos), scores(neg)]) - - auc = roc_auc_score(y_true, y_pred) - ap = average_precision_score(y_true, y_pred) - return auc, ap + y_true = np.concatenate([np.ones(pos_edges.size(1)), + np.zeros(neg_edges.size(1))]) + y_pred = np.concatenate([_score(pos_edges), _score(neg_edges)]) + return roc_auc_score(y_true, y_pred), average_precision_score(y_true, y_pred) def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--graph", required=True, help="Path to Data .pt file") - ap.add_argument("--epochs", type=int, default=100) - ap.add_argument("--lr", type=float, default=1e-3) - ap.add_argument("--hidden", type=int, default=128) - ap.add_argument("--latent", type=int, default=64) - ap.add_argument("--dropout", type=float, default=0.2) - ap.add_argument("--seed", type=int, default=42) - ap.add_argument("--outdir", default="results") + ap = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + ap.add_argument("--graph", required=True, + help="Path to Data .pt file from build_graph.py") + ap.add_argument("--epochs", type=int, default=300) + ap.add_argument("--patience", type=int, default=20, + help="Early-stopping patience (val-AUC epochs without improvement)") + ap.add_argument("--lr", type=float, default=1e-3) + ap.add_argument("--hidden", type=int, default=64) + ap.add_argument("--latent", type=int, default=32) + ap.add_argument("--dropout", type=float, default=0.2) + ap.add_argument("--seed", type=int, default=42) + ap.add_argument("--outdir", default="results") args = ap.parse_args() torch.manual_seed(args.seed) np.random.seed(args.seed) os.makedirs(args.outdir, exist_ok=True) - # Load graph - data = torch.load(args.graph) - # Coalesce/clean edges + # ---- Load and clean graph ---- + data = torch.load(args.graph, weights_only=False) ei, _ = remove_self_loops(data.edge_index) data.edge_index = to_undirected(ei, num_nodes=data.num_nodes) x = data.x.float() + print(f"Graph: {data.num_nodes} nodes " + f"{data.edge_index.shape[1]} edges " + f"{x.shape[1]} node features") - # Split edges for link prediction + # ---- Edge splits for link-prediction evaluation ---- splitter = RandomLinkSplit( - num_val=0.1, - num_test=0.1, + num_val=0.1, num_test=0.1, is_undirected=True, add_negative_train_samples=False, split_labels=False, ) train_data, val_data, test_data = splitter(data) - - # Positive edges are just the edges in each split train_data.pos_edge_index = train_data.edge_index - val_data.pos_edge_index = val_data.edge_index - test_data.pos_edge_index = test_data.edge_index - # Generate negative edges for validation and test manually - for subset in [val_data, test_data]: - subset.neg_edge_index = negative_sampling( - edge_index=subset.edge_index, + for split in (val_data, test_data): + split.pos_edge_index = split.edge_index + split.neg_edge_index = negative_sampling( + edge_index=split.edge_index, num_nodes=data.num_nodes, - num_neg_samples=subset.edge_index.size(1), - method='sparse' + num_neg_samples=split.edge_index.size(1), + method="sparse", ) - - # Model - enc = Encoder(in_dim=x.size(1), hidden=args.hidden, latent=args.latent, dropout=args.dropout) + # ---- Model ---- + enc = Encoder(in_dim=x.size(1), hidden=args.hidden, + latent=args.latent, dropout=args.dropout) model = VGAE(enc) optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) - # Training loop + # ---- Training loop with early stopping ---- best_val_auc = -1.0 - best_state = None + best_state = None + no_improve = 0 + epochs_ran = 0 + for epoch in range(1, args.epochs + 1): model.train() optimizer.zero_grad() - # Encode using remaining training edges - z = model.encode(x, train_data.edge_index) - # Reconstruction loss on positive training edges (negatives sampled inside) - loss_recon = model.recon_loss(z, train_data.pos_edge_index) - # KL divergence regularizer - loss_kl = (1.0 / data.num_nodes) * model.kl_loss() - loss = loss_recon + loss_kl + z = model.encode(x, train_data.edge_index) + loss = (model.recon_loss(z, train_data.pos_edge_index) + + (1.0 / data.num_nodes) * model.kl_loss()) loss.backward() optimizer.step() - # Validation model.eval() with torch.no_grad(): - z_full = model.encode(x, data.edge_index) # use full graph for eval embeddings - val_auc, val_ap = eval_linkpred(model, val_data, z_full) + z_full = model.encode(x, data.edge_index) + val_auc, val_ap = _eval_linkpred( + z_full, val_data.pos_edge_index, val_data.neg_edge_index + ) if val_auc > best_val_auc: best_val_auc = val_auc - best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()} + best_state = {k: v.cpu().clone() + for k, v in model.state_dict().items()} + no_improve = 0 + else: + no_improve += 1 + epochs_ran = epoch if epoch % 10 == 0 or epoch == 1: - print(f"[{epoch:03d}/{args.epochs}] loss={loss.item():.4f} | val AUC={val_auc:.4f} AP={val_ap:.4f}") + print(f"[{epoch:03d}/{args.epochs}] " + f"loss={loss.item():.4f} " + f"val AUC={val_auc:.4f} AP={val_ap:.4f}") - # Save best model + if no_improve >= args.patience: + print(f"Early stopping at epoch {epoch} " + f"(no val-AUC improvement for {args.patience} epochs)") + break + + # ---- Restore best checkpoint and compute test metrics ---- model.load_state_dict(best_state) - model_path = os.path.join(args.outdir, "model.pt") - torch.save(model.state_dict(), model_path) - - # Final test metrics model.eval() with torch.no_grad(): z_final = model.encode(x, data.edge_index) - test_auc, test_ap = eval_linkpred(model, test_data, z_final) + test_auc, test_ap = _eval_linkpred( + z_final, test_data.pos_edge_index, test_data.neg_edge_index + ) + + # ---- Save outputs ---- + model_path = os.path.join(args.outdir, "model.pt") + torch.save(best_state, model_path) - # Save embeddings & metrics emb_path = os.path.join(args.outdir, "emb.npy") np.save(emb_path, z_final.cpu().numpy()) metrics = { - "val_auc": float(best_val_auc), - "test_auc": float(test_auc), - "test_ap": float(test_ap), - "epochs": args.epochs, - "hidden": args.hidden, - "latent": args.latent, - "dropout": args.dropout, - "lr": args.lr, - "seed": args.seed + "val_auc": float(best_val_auc), + "test_auc": float(test_auc), + "test_ap": float(test_ap), + "epochs_ran": epochs_ran, + "epochs_max": args.epochs, + "patience": args.patience, + "hidden": args.hidden, + "latent": args.latent, + "dropout": args.dropout, + "lr": args.lr, + "seed": args.seed, } with open(os.path.join(args.outdir, "metrics.json"), "w") as f: json.dump(metrics, f, indent=2) - print(f"Saved model -> {model_path}") - print(f"Saved embeddings -> {emb_path} (shape={z_final.shape})") - print(f"Metrics: AUC(test)={test_auc:.4f}, AP(test)={test_ap:.4f}") + print(f"\nSaved model → {model_path}") + print(f"Saved embeddings → {emb_path} shape={z_final.shape}") + print(f"Test AUC={test_auc:.4f} AP={test_ap:.4f}") if __name__ == "__main__": diff --git a/scripts/visualize_embeddings.py b/scripts/visualize_embeddings.py new file mode 100644 index 0000000..cfaa058 --- /dev/null +++ b/scripts/visualize_embeddings.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python3 +""" +Visualise VGAE node embeddings using UMAP. + +Produces (under --prefix): + {prefix}_{label}_position.png UMAP coloured by genomic position (bin index) + {prefix}_{label}_compartment.png UMAP coloured by A/B compartment (needs --compartments) + {prefix}_joint.png Joint UMAP of all supplied cell lines + {prefix}_stats.csv Per-embedding summary statistics + +Usage +----- + python scripts/visualize_embeddings.py \\ + --emb results/GM12878/emb.npy results/IMR90/emb.npy \\ + --labels GM12878 IMR90 \\ + --compartments results/GM12878/compartments_chr21.csv \\ + results/IMR90/compartments_chr21.csv \\ + --prefix results/figures/umap +""" + +import argparse +import os + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from sklearn.metrics import silhouette_score +import umap + + +COMPARTMENT_COLORS = {"A": "#E41A1C", "B": "#377EB8", "N": "#AAAAAA"} +CELL_LINE_PALETTE = ["#E41A1C", "#4DAF4A", "#984EA3", "#FF7F00", "#377EB8"] + +plt.rcParams.update({ + "font.family": "sans-serif", + "axes.spines.top": False, + "axes.spines.right": False, +}) + + +def _run_umap(emb: np.ndarray, seed: int = 42) -> np.ndarray: + reducer = umap.UMAP(n_components=2, random_state=seed, + min_dist=0.3, n_neighbors=15) + return reducer.fit_transform(emb) + + +def _plot_position(coords: np.ndarray, label: str, out_path: str): + fig, ax = plt.subplots(figsize=(6.5, 5.5)) + sc = ax.scatter(coords[:, 0], coords[:, 1], + c=np.arange(len(coords)), cmap="plasma", + s=4, alpha=0.75, linewidths=0, rasterized=True) + cbar = plt.colorbar(sc, ax=ax, shrink=0.8, pad=0.02) + cbar.set_label("Bin index (5′ → 3′)", fontsize=9) + ax.set_title(f"{label} — UMAP coloured by genomic position", fontsize=10) + ax.set_xlabel("UMAP 1", fontsize=9) + ax.set_ylabel("UMAP 2", fontsize=9) + ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False) + plt.tight_layout() + plt.savefig(out_path, dpi=300) + plt.close() + + +def _plot_compartment(coords: np.ndarray, compartments: np.ndarray, + label: str, out_path: str): + fig, ax = plt.subplots(figsize=(6.5, 5.5)) + for comp in ("A", "B", "N"): + mask = compartments == comp + if mask.sum() == 0: + continue + ax.scatter(coords[mask, 0], coords[mask, 1], + c=COMPARTMENT_COLORS[comp], s=4, alpha=0.75, + label=f"{comp} ({mask.sum()} bins)", linewidths=0, + rasterized=True) + ax.legend(markerscale=3, title="Compartment", fontsize=9, + title_fontsize=9, frameon=False) + ax.set_title(f"{label} — UMAP coloured by A/B compartment", fontsize=10) + ax.set_xlabel("UMAP 1", fontsize=9) + ax.set_ylabel("UMAP 2", fontsize=9) + ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False) + plt.tight_layout() + plt.savefig(out_path, dpi=300) + plt.close() + + +def _plot_joint(all_coords: np.ndarray, all_labels: list, out_path: str): + fig, ax = plt.subplots(figsize=(7, 6)) + unique = list(dict.fromkeys(all_labels)) + arr = np.array(all_labels) + for i, label in enumerate(unique): + mask = arr == label + ax.scatter(all_coords[mask, 0], all_coords[mask, 1], + c=CELL_LINE_PALETTE[i % len(CELL_LINE_PALETTE)], + s=3, alpha=0.6, label=label, linewidths=0, rasterized=True) + ax.legend(markerscale=4, title="Cell line", fontsize=9, + title_fontsize=9, frameon=False) + ax.set_title("Joint UMAP — chromatin topology embeddings", fontsize=11) + ax.set_xlabel("UMAP 1", fontsize=9) + ax.set_ylabel("UMAP 2", fontsize=9) + ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False) + plt.tight_layout() + plt.savefig(out_path, dpi=300) + plt.close() + + +def _silhouette(emb: np.ndarray, compartments: np.ndarray) -> float: + mask = compartments != "N" + if mask.sum() < 20 or len(set(compartments[mask])) < 2: + return float("nan") + try: + return float(silhouette_score(emb[mask], compartments[mask], metric="cosine")) + except Exception: + return float("nan") + + +def main(): + p = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + p.add_argument("--emb", nargs="+", required=True, + help="One or more .npy embedding files") + p.add_argument("--labels", nargs="+", required=True, + help="Label for each embedding (same order)") + p.add_argument("--compartments", nargs="+", + help="Compartment CSV files, one per embedding (optional)") + p.add_argument("--prefix", default="results/figures/umap", + help="Output file prefix") + p.add_argument("--seed", type=int, default=42) + args = p.parse_args() + + if len(args.emb) != len(args.labels): + raise ValueError("--emb and --labels must have the same length") + + os.makedirs(os.path.dirname(os.path.abspath(args.prefix + "_x")), exist_ok=True) + + embs = [np.load(f) for f in args.emb] + comp_dfs = [] + if args.compartments: + for f in args.compartments: + comp_dfs.append(pd.read_csv(f) if (f and os.path.exists(f)) else None) + + stats_rows = [] + + for i, (emb, label) in enumerate(zip(embs, args.labels)): + print(f"\n[{label}] {emb.shape[0]} nodes × {emb.shape[1]} dims") + coords = _run_umap(emb, seed=args.seed) + + tag = label.replace(" ", "_") + _plot_position(coords, label, f"{args.prefix}_{tag}_position.png") + print(f" → {args.prefix}_{tag}_position.png") + + comp_arr = None + sil = float("nan") + if comp_dfs and i < len(comp_dfs) and comp_dfs[i] is not None: + comp_arr = comp_dfs[i]["compartment"].values[: len(emb)] + _plot_compartment(coords, comp_arr, label, + f"{args.prefix}_{tag}_compartment.png") + print(f" → {args.prefix}_{tag}_compartment.png") + sil = _silhouette(emb, comp_arr) + print(f" Silhouette (A/B, cosine): {sil:.4f}") + + stats_rows.append({ + "label": label, + "n_bins": emb.shape[0], + "latent_dim": emb.shape[1], + "mean_embedding_norm": float(np.linalg.norm(emb, axis=1).mean()), + "std_embedding_values": float(emb.std()), + "silhouette_AB_cosine": sil, + }) + + # Joint UMAP when multiple embeddings are supplied + if len(embs) > 1: + print("\nComputing joint UMAP…") + all_emb = np.vstack(embs) + all_labels = sum([[lab] * len(e) for lab, e in zip(args.labels, embs)], []) + all_coords = _run_umap(all_emb, seed=args.seed) + _plot_joint(all_coords, all_labels, f"{args.prefix}_joint.png") + print(f" → {args.prefix}_joint.png") + + stats_df = pd.DataFrame(stats_rows) + stats_path = f"{args.prefix}_stats.csv" + stats_df.to_csv(stats_path, index=False) + print(f"\nStats → {stats_path}") + print(stats_df.to_string(index=False)) + + +if __name__ == "__main__": + main()