kemal
5164124438
beta r 0.99998 vs sesame 1.28.1 (max |dbeta| 0.011), detection calls 99.1% identical, noob within 0.5% median; replicate r 0.997; all 21 control metrics pass. Spec deviations recorded (5-field control lines, pOOBAH formula, minfi sheet keys). User guide section.
54 lines
3.2 KiB
Markdown
54 lines
3.2 KiB
Markdown
# v2 module validation — methylation control QC (`simurg methyl qc`)
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Date: 2026-09-29. Development on the Raspberry Pi; oracle runs on AWS (c7g.xlarge, five short runs, ≈ $0.025).
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Spec: MET-001 … MET-018 (and QC-004/QC-006, QC-062).
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## Data
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* **minfiDataEPIC** (Bioconductor, Artistic-2.0): 3 EPIC v1 arrays of NA12878 (IDATs + minfi-style sample sheet).
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* **Infinium MethylationEPIC v1.0 B5 manifest (CSV)**: 865 918 probes (142 158 Infinium I: 92 212 red, 49 946 green),
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635 control beads. Read in 8.6 s on the Pi.
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## Delivered
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`simurg methyl qc`, `simurg methyl convert-manifest`, `simurg qc controls --array-type methylation`, methylation
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datasets in `simurg qc report`; modules `formats/methyl_manifest.py`, `signal/methyl.py`, `pipeline/methyl.py`;
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the 21 methylation metrics in `qc/controls.py`. User guide: "Methylation arrays".
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## Results on minfiDataEPIC
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| Check | Result |
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|---|---|
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| Run time, 3 samples, 3 workers (Pi) | 25 s (2.9 s signal processing per sample) |
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| 21 control metrics | all evaluable, all pass the `methylation_epic` profile (e.g. staining green 57, BS conversion I green 13, BS conversion II 5.3, specificity II 24) |
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| Probes passing detection (p < 0.05, analytical CG + SNP probes) | 90.9 %, 91.1 %, 91.3 % |
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| β of the three NA12878 replicates | Pearson r 0.997 – 0.9974 between replicates; bimodal distribution |
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| Sheet `Sample_Group` → PCA colours; outputs of MET-013 | present; folder loads in `qc report` |
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## Against the sesame oracle (sesame 1.28.1, same probe set via `probes.tsv`/`controls.tsv`)
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`scripts/v2/sesame_oracle.R` + `scripts/v2/methyl_oracle_compare.py`, bead-count masking off on both sides (sesame's
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`min_beads` masks every Infinium II probe because their M address is NA), 865 918 probes per sample:
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| Quantity | Agreement (3 samples) |
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| β values | Pearson r 0.99998; \|Δβ\| median 0.0009, 99th percentile 0.008, max 0.011 |
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| noob-corrected M/U signals | relative difference median 0.5 %, max 5 % |
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| Detection calls at p < 0.05 | 99.0 – 99.1 % identical; passing fraction 91.0 – 91.4 % (simurg) vs 92.0 – 92.3 % (sesame) |
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| Detection p-values | not identical (median \|Δp\| 0.0008) |
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The remaining difference is systematic (same size in all three samples) and is not the negative controls: sesame did
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not attach the supplied control table (`negControls` failed, so its background is out-of-band Infinium I signal only),
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and simurg without negative controls is not closer (noob difference 0.56 % instead of 0.53 %). The exact cause is
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open (K-026). For QC use — pass/fail per sample, detection rate, β for a PCA — the agreement is sufficient; simurg's
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methylation numerics are documented as its own [OURS] implementation of the published methods (MET-010), not as a
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bit-exact port.
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## Spec deviations (K-026)
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* MET-002: the vendor EPIC manifest has a 5th field (`AVG`) on some `NEGATIVE` control lines; ≥ 4 fields are
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accepted and the first four used. Fewer than 4 still gives `E_METH_CONTROLS_FORMAT`.
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* MET-010: the detection p-value follows sesame's pOOBAH (p = 1 − max(F(M), F(U)), each in its own channel) instead
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of the ambiguous "1 − ECDF(M + U)", so an open-source oracle exists.
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* MET-004: `Sentrix_ID`/`Sentrix_Position` (minfi/sesame sheets) accepted as a third key-column pair.
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