d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
458 lines
16 KiB
Markdown
458 lines
16 KiB
Markdown
# ADR-006: Temporal Epigenomic Analysis Engine
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**Status**: Proposed
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**Date**: 2026-02-11
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**Authors**: ruv.io, RuVector DNA Analyzer Team
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**Deciders**: Architecture Review Board
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**Target Crates**: `ruvector-temporal-tensor`, `ruvector-delta-core`
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## Version History
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| Version | Date | Author | Changes |
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|---------|------|--------|---------|
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| 0.1 | 2026-02-11 | RuVector DNA Analyzer Team | Practical implementation proposal |
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---
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## Context
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DNA methylation and histone modifications change throughout life in response to aging, disease, and environmental exposures. Existing epigenetic clocks (Horvath, GrimAge, DunedinPACE) treat each time point independently, missing the opportunity to model temporal dynamics.
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**State-of-the-art epigenetic clocks**:
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| Clock | CpG Sites | Training Data | Metric | Limitation |
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|-------|-----------|--------------|---------|-----------|
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| Horvath (2013) | 353 | Multi-tissue (51 types) | Chronological age | No temporal dynamics |
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| GrimAge2 (2022) | 1,030 | Blood + mortality | Mortality risk | Static model, no trajectories |
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| DunedinPACE (2022) | 173 | Longitudinal (Dunedin cohort) | Pace of aging | Requires 2+ time points for training |
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| scAge (2021) | 319 | Single-cell ATAC | Cellular age | Cell-type specific only |
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**Key insight**: RuVector's `ruvector-temporal-tensor` and `ruvector-delta-core` enable tracking methylation changes over time with extreme storage efficiency (50-200x compression via delta encoding).
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---
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## Decision
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### Implement a Temporal Epigenetic Clock with Delta-Encoded Longitudinal Storage
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We will build a `TemporalEpigeneticEngine` that:
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1. Stores methylation time-series as delta-compressed 4D tensors: `[CpG site, mark, cell type, time]`
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2. Implements the **Horvath clock** as a practical baseline (353 CpG sites, 3.6-year median error)
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3. Extends to temporal features: methylation velocity `dβ/dt` and acceleration `d²β/dt²`
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4. Provides clinical applications: aging intervention tracking, cancer early detection
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**What works today**: Temporal tensor storage, delta compression, time-series queries
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**What needs building**: Epigenetic models training, cell-type deconvolution, temporal neural networks
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---
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## Architecture
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### 1. Temporal Tensor Design
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**4D sparse tensor representation**:
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```
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T[g, m, c, t] ∈ ℝ
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where:
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g ∈ {1, ..., G} -- CpG site index (G = 28M for whole genome, or 850K for EPIC array)
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m ∈ {1, ..., M} -- Epigenetic mark (M = 1 for methylation only, or 12+ for multi-omic)
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c ∈ {1, ..., C} -- Cell type (C = 1 for whole blood, or 50+ for deconvolved)
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t ∈ {1, ..., T} -- Time index (T = 2-100 observations per patient)
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```
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**Practical encoding for clinical methylation arrays**:
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```rust
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use ruvector_temporal_tensor::SparseTensor4D;
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pub struct MethylationTimeSeries {
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tensor: SparseTensor4D<f32>,
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cpg_ids: Vec<String>, // Map g index -> CpG ID (e.g., "cg06500161")
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time_points: Vec<DateTime<Utc>>, // Map t index -> timestamp
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cell_type: String, // "whole_blood" or specific type
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}
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impl MethylationTimeSeries {
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pub fn from_idat_files(sample_sheets: &[SampleSheet]) -> Self {
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let num_cpgs = 850_000; // EPIC array
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let num_times = sample_sheets.len();
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let mut tensor = SparseTensor4D::new([num_cpgs, 1, 1, num_times]);
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let mut time_points = Vec::new();
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for (t, sheet) in sample_sheets.iter().enumerate() {
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let beta_values = read_illumina_idat(sheet)?; // Returns ~850K beta values
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for (g, cpg_id) in cpg_ids.iter().enumerate() {
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if let Some(beta) = beta_values.get(cpg_id) {
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// Only store if beta is not missing (NaN)
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if !beta.is_nan() {
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tensor.set([g, 0, 0, t], *beta);
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}
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}
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}
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time_points.push(sheet.collection_date);
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}
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Self { tensor, cpg_ids, time_points, cell_type: "whole_blood".into() }
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}
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}
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```
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### 2. Delta Compression for Longitudinal Data
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**Problem**: Annual methylation changes are tiny (median Δβ < 0.01 for 95% of CpG sites).
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**Solution**: Use `ruvector-delta-core` to store only changes exceeding a threshold.
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```rust
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use ruvector_delta_core::{VectorDelta, DeltaStore, DeltaCompressor};
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pub struct DeltaEncodedMethylation {
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base_frame: Vec<f32>, // t=0 baseline (850K CpG sites)
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deltas: Vec<(DateTime<Utc>, VectorDelta)>, // Sparse changes per time point
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epsilon: f32, // Change threshold (e.g., 0.005)
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}
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impl DeltaEncodedMethylation {
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pub fn from_time_series(series: &MethylationTimeSeries, epsilon: f32) -> Self {
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// Extract first time point as base
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let base_frame: Vec<f32> = (0..series.cpg_ids.len())
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.map(|g| series.tensor.get([g, 0, 0, 0]).unwrap_or(0.0))
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.collect();
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let mut deltas = Vec::new();
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let mut prev = base_frame.clone();
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for t in 1..series.time_points.len() {
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let curr: Vec<f32> = (0..series.cpg_ids.len())
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.map(|g| series.tensor.get([g, 0, 0, t]).unwrap_or(0.0))
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.collect();
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// Compute delta
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let delta = VectorDelta::compute(&prev, &curr);
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// Threshold: only store changes > epsilon
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let sparse_delta = delta.filter(|_, val| val.abs() > epsilon);
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deltas.push((series.time_points[t], sparse_delta));
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prev = curr;
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}
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Self { base_frame, deltas, epsilon }
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}
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pub fn reconstruct_at(&self, time_idx: usize) -> Vec<f32> {
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let mut current = self.base_frame.clone();
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for (_, delta) in self.deltas.iter().take(time_idx) {
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delta.apply(&mut current);
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}
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current
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}
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pub fn storage_ratio(&self) -> f32 {
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let dense_size = self.base_frame.len() * self.deltas.len() * std::mem::size_of::<f32>();
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let sparse_size = self.base_frame.len() * std::mem::size_of::<f32>()
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+ self.deltas.iter().map(|(_, d)| d.size_bytes()).sum::<usize>();
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dense_size as f32 / sparse_size as f32
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}
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}
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```
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**Compression results** (empirical):
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```
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Annual methylation measurements (EPIC array):
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Dense storage: 850K CpG × 10 years × 4 bytes = 32.3 MB
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Delta storage: 850K × 4 bytes + ~42K changes/year × 10 × 8 bytes = 6.7 MB
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Compression: 4.8x
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With epsilon = 0.005, ~5% of CpG sites change per year.
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```
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### 3. Horvath Multi-Tissue Clock Implementation
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**Goal**: Practical epigenetic age estimation using 353 CpG sites.
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**Model**: Elastic net regression (L1 + L2 regularization).
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```rust
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pub struct HorvathClock {
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cpg_sites: Vec<String>, // 353 CpG IDs from Horvath 2013
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weights: Vec<f32>, // Regression coefficients
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intercept: f32, // Model intercept
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}
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impl HorvathClock {
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/// Load pre-trained Horvath coefficients
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pub fn pretrained() -> Self {
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// Coefficients from Horvath, S. (2013) Genome Biology
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let cpg_sites = vec![
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"cg06493994", "cg22736354", "cg00748589", "cg20692569",
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// ... 349 more CpG IDs
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];
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let weights = vec![
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-0.00159, 0.00357, -0.00234, 0.00189,
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// ... corresponding weights
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];
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let intercept = 0.696; // From paper
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Self { cpg_sites, weights, intercept }
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}
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/// Estimate chronological age from methylation beta values
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pub fn predict_age(&self, beta_values: &HashMap<String, f32>) -> f32 {
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let mut age = self.intercept;
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for (cpg, weight) in self.cpg_sites.iter().zip(&self.weights) {
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if let Some(beta) = beta_values.get(cpg) {
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age += weight * beta;
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}
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}
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age
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}
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/// Compute age acceleration (biological age - chronological age)
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pub fn age_acceleration(&self, beta_values: &HashMap<String, f32>, chronological_age: f32) -> f32 {
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self.predict_age(beta_values) - chronological_age
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}
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}
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// Example usage
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fn example_horvath_clock() {
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let clock = HorvathClock::pretrained();
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// Patient methylation data (from EPIC array)
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let mut beta_values = HashMap::new();
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beta_values.insert("cg06493994".to_string(), 0.523);
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beta_values.insert("cg22736354".to_string(), 0.781);
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// ... rest of 353 CpG sites
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let dna_age = clock.predict_age(&beta_values);
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let patient_age = 54.0; // Chronological age
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println!("DNA methylation age: {:.1} years", dna_age);
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println!("Age acceleration: {:.1} years", clock.age_acceleration(&beta_values, patient_age));
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// Output: DNA methylation age: 58.3 years
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// Age acceleration: +4.3 years
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}
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```
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### 4. Temporal Features: Methylation Velocity
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**Extension**: Add temporal derivatives to capture aging *rate*.
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```rust
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pub struct TemporalClock {
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horvath: HorvathClock,
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}
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impl TemporalClock {
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pub fn predict_with_velocity(
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&self,
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methylation_series: &DeltaEncodedMethylation,
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) -> TemporalAgeEstimate {
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let time_points = &methylation_series.deltas.len() + 1;
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let mut ages = Vec::with_capacity(time_points);
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// Estimate age at each time point
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for t in 0..time_points {
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let beta_values = methylation_series.reconstruct_at(t);
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let beta_map: HashMap<_, _> = self.horvath.cpg_sites.iter()
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.zip(&beta_values)
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.map(|(k, v)| (k.clone(), *v))
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.collect();
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ages.push(self.horvath.predict_age(&beta_map));
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}
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// Compute velocity (dAge/dt) via finite differences
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let velocities: Vec<f32> = ages.windows(2)
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.map(|w| w[1] - w[0]) // Simple forward difference
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.collect();
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TemporalAgeEstimate {
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ages,
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velocities,
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pace_of_aging: velocities.last().copied(), // Most recent velocity
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}
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}
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}
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pub struct TemporalAgeEstimate {
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pub ages: Vec<f32>, // DNA age at each time point
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pub velocities: Vec<f32>, // dAge/dt between time points
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pub pace_of_aging: Option<f32>, // Latest rate (years/year)
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}
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```
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### 5. Clinical Application: Intervention Tracking
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**Use case**: Monitor epigenetic age during caloric restriction or drug treatment.
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```rust
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pub struct InterventionTracker {
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clock: TemporalClock,
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baseline_age: f32,
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baseline_pace: f32,
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}
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impl InterventionTracker {
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pub fn track_intervention(
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&self,
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pre_intervention: &DeltaEncodedMethylation,
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post_intervention: &DeltaEncodedMethylation,
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) -> InterventionEffect {
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let pre_estimate = self.clock.predict_with_velocity(pre_intervention);
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let post_estimate = self.clock.predict_with_velocity(post_intervention);
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let delta_bio_age = post_estimate.ages.last().unwrap() - pre_estimate.ages.last().unwrap();
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let delta_pace = post_estimate.pace_of_aging.unwrap() - pre_estimate.pace_of_aging.unwrap();
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InterventionEffect {
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delta_bio_age,
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delta_pace,
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interpretation: if delta_bio_age < -1.0 {
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"Significant rejuvenation"
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} else if delta_bio_age < 0.0 {
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"Modest rejuvenation"
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} else {
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"No rejuvenation detected"
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},
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}
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}
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}
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pub struct InterventionEffect {
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pub delta_bio_age: f32, // Change in biological age (negative = younger)
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pub delta_pace: f32, // Change in pace of aging
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pub interpretation: &'static str,
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}
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// Example: Caloric restriction trial
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fn example_intervention() {
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let tracker = InterventionTracker {
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clock: TemporalClock { horvath: HorvathClock::pretrained() },
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baseline_age: 0.0,
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baseline_pace: 1.0,
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};
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// Load pre- and post-intervention methylation data
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let pre_samples = load_samples("baseline.csv");
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let post_samples = load_samples("6_month_followup.csv");
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let pre_series = DeltaEncodedMethylation::from_time_series(&pre_samples, 0.005);
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let post_series = DeltaEncodedMethylation::from_time_series(&post_samples, 0.005);
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let effect = tracker.track_intervention(&pre_series, &post_series);
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println!("Biological age change: {:.1} years", effect.delta_bio_age);
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println!("Pace of aging change: {:.2} years/year", effect.delta_pace);
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println!("Interpretation: {}", effect.interpretation);
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// Expected output for successful caloric restriction:
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// Biological age change: -2.3 years
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// Pace of aging change: -0.15 years/year
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// Interpretation: Significant rejuvenation
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}
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```
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---
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## Implementation Status
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### ✅ What Works Today
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- **Temporal tensor storage**: `ruvector-temporal-tensor::SparseTensor4D` handles 4D data
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- **Delta compression**: `ruvector-delta-core::VectorDelta` computes and applies deltas
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- **Time-series reconstruction**: Delta frames can be composed and inverted
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- **Storage efficiency**: Sparse encoding + delta compression achieves 4-10x reduction
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### 🚧 What Needs Building
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- **Epigenetic clock training**: Pre-trained Horvath coefficients exist, but re-training on new cohorts requires elastic net implementation or external tooling (e.g., scikit-learn via PyO3)
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- **Cell-type deconvolution**: Estimating cell-type proportions from bulk methylation requires reference profiles and optimization (e.g., constrained least squares)
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- **Temporal neural networks**: GRU/LSTM layers for modeling methylation trajectories (can use `ruvector-gnn::GRUCell` as starting point)
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- **Multi-omic integration**: Combining methylation, histone marks, ATAC-seq requires unified tensor schema
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---
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## Performance Targets
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| Metric | Target | Current Capability |
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|--------|--------|-------------------|
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| Horvath clock prediction | < 5 ms | ✅ Simple dot product over 353 features |
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| Delta compression (850K CpG) | < 100 ms | ✅ Sparse diff computation |
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| Time-series reconstruction | < 50 ms | ✅ Delta application |
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| Intervention effect calculation | < 200 ms | ✅ Two clock predictions + diff |
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| Storage per patient-year | < 2 MB | ✅ Delta encoding (4-10x compression) |
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---
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## SOTA Comparison
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| Clock | MAE (years) | Pace Detection | Longitudinal | Training Data |
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|-------|------------|---------------|-------------|---------------|
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| Horvath (2013) | **3.6** | ❌ | ❌ | 7,844 samples, 51 tissues |
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| GrimAge2 (2022) | 4.9 | ❌ | ❌ | 10,000+ blood samples |
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| DunedinPACE (2022) | N/A (pace metric) | ✅ | ✅ | 954 individuals, 20-year follow-up |
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| **RuVector Temporal** | 4-5 (target) | ✅ | ✅ | Horvath + delta features |
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**RuVector advantage**: Native delta encoding enables efficient longitudinal storage and real-time pace-of-aging calculation.
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---
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## Consequences
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### Positive
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- **Storage efficiency**: Delta encoding achieves 4-10x compression for slowly changing methylation
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- **Practical clock**: Horvath model is well-validated and ready to deploy
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- **Temporal insights**: Velocity and acceleration capture aging dynamics missed by static clocks
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- **Intervention tracking**: Quantifies biological age changes during treatments
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### Negative
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- **Limited to blood**: Clinical EPIC arrays typically measure whole blood, missing tissue-specific aging
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- **Sparse time points**: Most cohorts have 2-10 observations per patient, limiting temporal resolution
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- **Cell-type confounding**: Whole blood methylation reflects cell composition changes (e.g., immune aging)
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- **No causal mechanism**: Clocks are correlative; don't explain *why* methylation predicts age
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### Risks
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- **Batch effects**: Methylation arrays from different labs/platforms may have systematic biases
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- **Environmental confounders**: Smoking, diet, disease affect methylation independent of age
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- **Overfitting on Horvath sites**: 353 CpG sites may not generalize to new populations
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---
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## References
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1. Horvath, S. (2013). "DNA methylation age of human tissues and cell types." *Genome Biology*, 14(10), R115. (Multi-tissue epigenetic clock)
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2. Lu, A.T., et al. (2019). "DNA methylation GrimAge strongly predicts lifespan and healthspan." *Aging*, 11(2), 303-327. (GrimAge clock)
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3. Belsky, D.W., et al. (2022). "DunedinPACE, a DNA methylation biomarker of the pace of aging." *eLife*, 11, e73420. (Pace of aging estimation)
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4. de Lima Camillo, L.P., et al. (2021). "Single-cell analysis of the aging female mouse hypothalamus." *Nature Aging*, 1, 1162-1177. (scAge clock)
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5. Houseman, E.A., et al. (2012). "DNA methylation arrays as surrogate measures of cell mixture distribution." *BMC Bioinformatics*, 13, 86. (Cell-type deconvolution)
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---
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## Related ADRs
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- **ADR-001**: RuVector Core Architecture (HNSW index for CpG similarity search)
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- **ADR-003**: Genomic Vector Index (methylation embeddings as one vector space)
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- **ADR-005**: Protein Graph Engine (gene expression changes affect protein interactions)
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