omicverse.single.pyMOFAART

class omicverse.single.pyMOFAART(model_path)

Load pretrained MOFA models for downstream factor interpretation.

Parameters:

model_path (str) – Path to a trained MOFA model file.

Returns:

Initializes MOFA model reader and analysis utilities.

Return type:

None

Examples

>>> pymofa_obj = ov.single.pyMOFAART(model_path="data/sample/MOFA_POS.hdf5")

__init__(model_path)

Load a pretrained MOFA model for downstream interpretation.

Parameters:

model_path (str) – Path to a trained MOFA .hdf5 model file.

Methods

__init__(model_path)	Load a pretrained MOFA model for downstream interpretation.
get_cor(adata, cluster[, factor_list])	Compute factor-group association scores from MOFA factors.
get_factors(adata)	Attach MOFA latent factors to adata.obsm['X_mofa'].
get_r2()	Return factor-wise explained variance table.
get_top_feature(view[, log2fc_min, pval_cutoff])	Identify top features for each factor using differential marker ranking.
plot_cor(adata, cluster[, factor_list, ...])	Visualize factor-group association scores as a heatmap.
plot_factor(adata, cluster, title[, ...])	Plot cells in MOFA factor space colored by group annotation.
plot_r2([figsize, cmap, ticks_fontsize, ...])	Plot a heatmap of variance explained by each MOFA factor.
plot_top_feature_dotplot(view[, cmap, ...])	Plot top marker features per factor as a Scanpy dotplot.
plot_top_feature_heatmap(view[, cmap, ...])	Plot top marker features per factor as a Scanpy matrix heatmap.
plot_weight_gene_d1(view, factor1, factor2)	Plot gene weights for two factors with 1D threshold-based highlighting.
plot_weight_gene_d2(view, factor1, factor2)	Plot gene weights for two factors with quadrant-based labeling.
plot_weights_gene_factor(view, factor[, ...])	Plot ranked gene weights for a single MOFA factor.