scpanel.select_gene

Functions

`split_n_folds`(→ Tuple[List[List[int]], ...)
`gene_score`(→ Tuple[anndata._core.anndata.AnnData, ...)
`plot_gene_score`(→ matplotlib.axes._axes.Axes)
`decide_k`(→ int)
`select_gene`(→ anndata._core.anndata.AnnData)
`select_gene_stable`(adata_train[, n_iter, nfold, ...])

scpanel.select_gene.split_n_folds(adata_train: anndata._core.anndata.AnnData, nfold: int, out_dir: str | None = None, random_state: int = 2349) → Tuple[List[List[int]], List[List[int]], List[List[float]]]

scpanel.select_gene.gene_score(adata_train: anndata._core.anndata.AnnData, train_index_list: List[List[int]], val_index_list: List[List[int]], sample_weight_list: List[List[float]], out_dir: str, ncpus: int, step: float = 0.03, metric: str = 'average_precision', verbose: bool = False) → Tuple[anndata._core.anndata.AnnData, scpanel.SVMRFECV.RFECV]

scpanel.select_gene.plot_gene_score(adata_train: anndata._core.anndata.AnnData, n_genes_plot: int = 200, width: int = 5, height: int = 4, k: int | None = None) → matplotlib.axes._axes.Axes

scpanel.select_gene.decide_k(adata_train: anndata._core.anndata.AnnData, n_genes_plot: int = 100) → int

scpanel.select_gene.select_gene(adata_train: anndata._core.anndata.AnnData, out_dir: str | None = None, step: float = 0.03, top_n_feat: int = 5, n_genes_plot: int = 100, verbose: int = 0) → anndata._core.anndata.AnnData

scpanel.select_gene.select_gene_stable(adata_train, n_iter=20, nfold=2, downsample_prop_list=[0.6, 0.8], num_cores=1, out_dir=None)