Deconv updates - #32
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| A_centered = A - cytokine_medians | ||
| X.uns["Pf2_A"] = A_centered | ||
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| W, H, _ = deconvolution_cytokine_admm(A_centered, alpha_h=0.05, alpha_w=0.05, rho=2) |
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| def filter_components_by_cytokine_quantile( | ||
| threshold: float, | ||
| csv_path: str = "/home/nicoleb/Pf2-scRNAseq-1/pf2rnaseq/Data/donor_vs_cytokine_variability_sd.csv", | ||
| ) -> np.ndarray: | ||
| """Return 0-indexed components whose cytokine_quantile is at/above a threshold. | ||
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| Parameters | ||
| ---------- | ||
| threshold : float | ||
| Minimum cytokine_quantile value a component must have to be kept. | ||
| csv_path : str | ||
| Path to the CSV with columns component, donor_sd, cytokine_sd, | ||
| donor_quantile, cytokine_quantile. | ||
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| Returns | ||
| ------- | ||
| np.ndarray | ||
| 0-indexed component numbers with cytokine_quantile >= threshold. | ||
| """ |
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| print(f" Min value: {W.min():.4f}") | ||
| print(f" Max value: {W.max():.4f}") | ||
| print(f" Diagonal: all 1.0 (constrained)") |
| cond="cytokine", | ||
| log_scale=False, | ||
| ) | ||
| ax[1].set_title("Original Effects (A)", fontsize=12, fontweight="bold") |
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| print(f" Min value: {H.min():.4f}") | ||
| print(f" Max value: {H.max():.4f}") | ||
| print(f" Negative values: {np.sum(H < 0)} ({100 * np.sum(H < 0) / H.size:.1f}%)") |
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