grassp.tl.tagm_map_train#
- tagm_map_train(adata, gt_col='markers', method='MAP', numIter=100, mu0=None, lambda0=0.01, nu0=None, S0=None, beta0=None, u=2, v=10, seed=None, inplace=True)[source]#
Train the TAGM MAP model on an AnnData object.
The function splits the data into labelled (marker) and unlabelled subsets based on adata.obs[gt_col]. It then computes MAP estimates for a T-augmented Gaussian mixture model using an EM algorithm.
- Parameters:
- adata AnnData
AnnData object containing the proteomics data.
- gt_col str, optional
Column in adata.obs with marker definitions (default “markers”).
- numIter int, optional
Number of EM iterations (default 100).
- mu0 np.ndarray, optional
Prior mean vector (default: column means of marker data).
- lambda0 float, optional
Prior shrinkage (default 0.01).
- nu0 int, optional
Prior degrees of freedom (default: D+2).
- S0 np.ndarray, optional
Prior inverse-Wishart scale matrix (default: empirical).
- beta0 np.ndarray, optional
Prior Dirichlet concentration (default: ones for each class).
- u int, optional
Beta prior parameter for outlier probability (default u=2).
- v int, optional
Beta prior parameter for outlier probability (default v=10).
- seed int, optional
Random seed.
- inplace bool, optional
If True, modifies the input AnnData object in place.
- Returns:
dict | None Dictionary of MAP parameters. If inplace is True, the input AnnData object is modified in place.