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Fragment Size Coverage (FSC)

Command: krewlyzer fsc

Purpose

Computes z-scored coverage of cfDNA fragments in different size ranges per genomic bin, with GC correction. This helps identify copy number variations (CNVs) and coverage anomalies specific to certain fragment sizes.

Biological Context

cfDNA fragment size profiles are informative for cancer detection and tissue-of-origin. FSC measures the normalized coverage depth of:

  • Short (65-149bp): Enriched for tumor-derived cfDNA (ctDNA) in cancer patients.
  • Intermediate (151-259bp): Represents mono-nucleosomal fragments.
  • Long (261-399bp): Represents di-nucleosomal fragments, often from healthy cells.
  • Total (65-399bp): Overall coverage.

Differences in coverage patterns between size classes can reveal: - Copy Number Alterations (CNAs): Detected via Total and size-specific coverage. - Chromatin Structure: Open chromatin (more short fragments) vs. closed chromatin (more long fragments).

Usage

krewlyzer fsc sample.bed.gz -o output_dir/ --sample-name SAMPLE [options]

Options

  • --bin-input, -b: Bin file (default: data/ChormosomeBins/hg19_window_100kb.bed)
  • --windows, -w: Window size (default: 100000)
  • --continue-n, -c: Consecutive window number (default: 50) - aggregates adjacent bins.
  • --gc-correct/--no-gc-correct: Apply GC bias correction using LOESS (default: True)
  • --verbose, -v: Enable verbose logging
  • --threads, -t: Number of threads.

Output Format

Output: {sample}.FSC.tsv

Column Description
region Genomic region (chr:start-end)
short-fragment-zscore Z-score of short fragment coverage
itermediate-fragment-zscore Z-score of intermediate fragment coverage
long-fragment-zscore Z-score of long fragment coverage
total-fragment-zscore Z-score of total fragment coverage

Interpretation Guide

Metric High Z-Score (>2) Low Z-Score (<-2)
Total Copy number gain / Accesssible region Copy number loss / Closed chromatin
Short Tumor-enriched / Open chromatin Depleted ctDNA / Closed chromatin
Long Healthy/Leukocyte DNA enriched Fragmentation / Open chromatin

Note: Counts are GC-corrected before Z-score calculation to remove sequencing bias.

Calculation Details

  1. Binning: Fragments are counted in 100kb genomic bins (or custom regions).
  2. GC Correction: Raw counts are adjusted for GC content bias using Loess regression (GC vs Count).
  3. Aggregation: Adjusted counts are summed over N=50 consecutive bins (5Mb effective window) to smooth noise.
  4. Z-Score Normalization: $$ Z = \frac{X - \mu}{\sigma} $$ Where $X$ is the summed count for the window, and $\mu, \sigma$ are the genome-wide mean and standard deviation.