omicverse.metabol.sample_qc_plot

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omicverse.metabol.sample_qc_plot#

omicverse.metabol.sample_qc_plot(qc_df, *, ax=None, figsize=(5.0, 4.0), normal_color='#2980b9', outlier_color='#c0392b')[source]#

Scatter of Hotelling T² vs DModX with critical-value lines.

Standard SIMCA-style outlier diagnostic. Each sample is one point; the dashed grey lines are the alpha-level (default 0.95) critical values from omicverse.metabol.sample_qc(). Samples above either line are flagged — Hotelling T² captures distance from the PCA centre (within-model outliers), DModX captures distance to the PCA hyperplane (off-model outliers).

Parameters:

  • qc_df (pd.DataFrame) – Output of omicverse.metabol.sample_qc() — must contain T2, DModX, T2_crit, DModX_crit and is_outlier.

  • normal_color (str (default: '#2980b9')) – Hex / named colours for the two populations.

  • outlier_color (str (default: '#c0392b')) – Hex / named colours for the two populations.

  • ax (Optional[Axes] (default: None)) – Standard matplotlib hooks.

  • figsize (tuple[float, float] (default: (5.0, 4.0))) – Standard matplotlib hooks.

Return type:

(fig, ax) tuple.

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