sccloud.highly_variable_features

sccloud.highly_variable_features(data, consider_batch, flavor='sccloud', n_top=2000, span=0.02, min_disp=0.5, max_disp=inf, min_mean=0.0125, max_mean=7, n_jobs=-1)[source]

Highly variable features (HVF) selection. The input data should be logarithmized.

Parameters

  • data (anndata.AnnData) – Annotated data matrix with rows for cells and columns for genes.

  • consider_batch (bool.) – Whether consider batch effects or not.

  • flavor (str, optional, default: "sccloud") – The HVF selection method to use. Available choices are "sccloud" or "Seurat".

  • n_top (int, optional, default: 2000) – Number of genes to be selected as HVF. if None, no gene will be selected.

  • span (float, optional, default: 0.02) – Only applicable when flavor is "sccloud". The smoothing factor used by scikit-learn loess model in sccloud HVF selection method.

  • min_disp (float, optional, default: 0.5) – Minimum normalized dispersion.

  • max_disp (float, optional, default: np.inf) – Maximum normalized dispersion. Set it to np.inf for infinity bound.

  • min_mean (float, optional, default: 0.0125) – Minimum mean.

  • max_mean (float, optional, default: 7) – Maximum mean.

  • n_jobs (int, optional, default: -1) – Number of threads to be used during calculation. If -1, all available threads will be used.

Examples

>>> scc.highly_variable_features(adata, consider_batch = False)
Return type

None