mioXpektron.analysis.cnmf

Consensus non-negative matrix factorisation for sample clustering.

Functions

choose_k_by_pac(results)	Select the rank with the lowest PAC score.
plot_pac_vs_k(results, savepath)	Plot PAC stability scores across candidate rank values.
run_cnmf(X_pos, k_list, *[, R, max_iter, ...])	Run consensus NMF across multiple rank values.
save_consensus_heatmap(consensus, labels, ...)	Plot a consensus matrix ordered by group labels.
save_factor_bars(H, feature_names, outdir, *)	Save per-factor top m/z contributors as CSV and bar plots.

mioXpektron.analysis.cnmf.run_cnmf(X_pos, k_list, *, R=30, max_iter=1000, beta='frobenius', random_seeds=None, outdir=None)

Run consensus NMF across multiple rank values.

Parameters:

• X_pos (ndarray)

• k_list (List[int])

• R (int)

• max_iter (int)

• beta (str)

• random_seeds (List[int] | None)

• outdir (str | None)

Return type:

Dict[int, Dict[str, object]]

mioXpektron.analysis.cnmf.plot_pac_vs_k(results, savepath)

Plot PAC stability scores across candidate rank values.

Parameters:

• results (Dict[int, Dict[str, object]])

• savepath (str)

Return type:

None

mioXpektron.analysis.cnmf.choose_k_by_pac(results)

Select the rank with the lowest PAC score.

Parameters:

results (Dict[int, Dict[str, object]])

Return type:

int

mioXpektron.analysis.cnmf.save_consensus_heatmap(consensus, labels, savepath, *, label_col='Group')

Plot a consensus matrix ordered by group labels.

Parameters:

• consensus (ndarray)

• labels (Series)

• savepath (str)

• label_col (str)

Return type:

None

mioXpektron.analysis.cnmf.save_factor_bars(H, feature_names, outdir, *, topm=15)

Save per-factor top m/z contributors as CSV and bar plots.

Parameters:

• H (ndarray)

• feature_names (List[str])

• outdir (str)

• topm (int)

Return type:

None