Genomic Data Smoothing

LOESS for methylation profiles, ChIP-seq signals, and other genomic data.

Overview

Genomic data often contains noise from sequencing depth variation, PCR artifacts, or biological heterogeneity. LOESS smoothing helps reveal underlying patterns.

Methylation Profile Smoothing

The Challenge

DNA methylation data (from bisulfite sequencing or arrays) shows position-dependent patterns that can be obscured by measurement noise.

Solution

A small fraction = 0.1 lets LOESS follow fine-scale spatial structure without smearing the transitions between methylated and unmethylated regions. confidence_intervals = 0.95 produces uncertainty bands that naturally widen at positions with sparser CpG coverage, making low-confidence segments immediately apparent in the plot.

const { Loess } = require('fastloess-wasm');

const n = 100;
const positions = Float64Array.from({ length: n }, (_, i) => i * 100.0);
const observed = Float64Array.from(positions, p => 50 + Math.sin(p / 100) * 20 + ((p * 7 % 17) / 17 - 0.5) * 5);

// positions and observed are your methylation data (Float64Array)
const model = new Loess({
    fraction: 0.1,
    iterations: 3,
    intervals: { confidence : 0.95 }
});
const result = model.fit(positions, observed);
console.log("CI lower[0]:", result.confidence_lower[0].toFixed(4));

CI lower[0]: 36.6609

ChIP-seq Signal Smoothing

Application

ChIP-seq experiments produce sparse, noisy coverage data. LOESS can help identify binding regions.

fraction = 0.05 provides high spatial resolution — important for resolving narrow binding peaks that would otherwise be smeared into the background. The larger iterations = 5 is deliberate: Poisson-distributed read counts produce tall, isolated spikes, and extra robustness iterations progressively down-weight them so the estimated background level is not inflated by a handful of extreme counts.

const { Loess } = require('fastloess-wasm');

const n = 100;
const positions = Float64Array.from({ length: n }, (_, i) => i * 100.0);
const observed = Float64Array.from(positions, p => 50 + Math.sin(p / 100) * 20 + ((p * 7 % 17) / 17 - 0.5) * 5);

const model = new Loess({
    fraction: 0.05,
    iterations: 5
});
const result = model.fit(positions, observed);

// Find peaks
const smoothed = result.y;
const peaks = positions.filter((p, i) => smoothed[i] > 25.0);
console.log("Peak count:", peaks.length);

Peak count: 100

Large Genome Coverage (Streaming)

For whole-genome data that doesn’t fit in memory:

const { StreamingLoess } = require('fastloess-wasm');

const xChunk = Float64Array.from({ length: 1001 }, (_, i) => i * 10.0);
const yChunk = Float64Array.from(xChunk, p => 50 + Math.sin(p / 100) * 20 + 5.0);

const processor = new StreamingLoess(
    { fraction: 0.05, iterations: 3 },
    { chunk_size: 100, overlap: 10 }
);

processor.process_chunk(xChunk, yChunk);
const result = processor.finalize();
console.log("y[0]:", result.y[0].toFixed(4));

y[0]: 41.5626

Best Practices for Genomic Data

Consideration	Recommendation
Fraction	0.05–0.15 (preserve local features)
Iterations	3–5 (handle sequencing outliers)
Large data	Use streaming mode
Sparse regions	Use `boundary_policy="extend"`
Multiple chromosomes	Process separately or ensure sorted