omicverse.single.Fate

class omicverse.single.Fate(adata, pseudotime)

Adaptive ridge-regression framework for pseudotime-associated gene discovery.

Parameters:

• adata (anndata.AnnData) – AnnData object containing pseudotime in obs and gene expression features.

• pseudotime (str) – adata.obs key storing pseudotime values.

Returns:

Initializes Fate model state.

Return type:

None

Examples

>>> # Initialize Fate analysis

__init__(adata, pseudotime)

Initialize TimeFate model.

Parameters:

• adata (anndata.AnnData) – Input AnnData containing expression matrix and pseudotime metadata.

• pseudotime (str) – Column name in adata.obs storing pseudotime values.

Methods

ATR([test_size, random_state, alpha, stop, ...])	Adaptive Threshold Regression
__init__(adata, pseudotime)	Initialize TimeFate model.
atac_init(columns[, gene_name])	Initialize the atac model
get_coef([type])	Get the coef of model
get_mae([type])	Get the mae of model
get_mse([type])	Get the mse of model
get_r2([type])	Get the r2 of model
get_related_peak(peak)	Get the related peak of gene
get_rmse([type])	Get the rmse of model
kendalltau_filter()	Compute Kendall's tau between filtered gene trends and pseudotime.
lineage_score(cluster_key[, lineage, ...])	Compute lineage-specific change scores using low-density variability.
low_density([n_components, knn, alpha, ...])	Estimate manifold density on diffusion-map coordinates.
model_fit([test_size, random_state, alpha, ...])	Fit the model
model_init([test_size, random_state, alpha, ...])	Initialize the model
plot_color_fitting([type, cluster_key, ...])	Plot the colorful of clusters fitting result
plot_filtering([figsize, color, fontsize, alpha])	Plot the filtering result
plot_fitting([type, figsize, color, fontsize])	Plot the fitting result