deconvatac.tl ============= .. py:module:: deconvatac.tl Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/deconvatac/tl/cell2location/index /autoapi/deconvatac/tl/destvi/index /autoapi/deconvatac/tl/metrics/index /autoapi/deconvatac/tl/rctd/index /autoapi/deconvatac/tl/simulate/index /autoapi/deconvatac/tl/spatialdwls/index /autoapi/deconvatac/tl/tangram/index Classes ------- .. autoapisummary:: deconvatac.tl.Sampler Functions --------- .. autoapisummary:: deconvatac.tl.cell2location deconvatac.tl.tangram deconvatac.tl.destvi deconvatac.tl.jsd deconvatac.tl.rmse deconvatac.tl.rctd deconvatac.tl.conway_maxwell_poisson deconvatac.tl.generate_spatial_data deconvatac.tl.spatialdwls Package Contents ---------------- .. py:function:: cell2location(adata_spatial, adata_ref, N_cells_per_location, detection_alpha, labels_key=None, layer_spatial=None, layer_ref=None, use_gpu=True, max_epochs_spatial=30000, max_epochs_ref=None, return_adatas=False, plots=True, results_path='./cell2location_results', setup_ref_kwargs={}, train_ref_kwargs={}, setup_spatial_kwargs={}, train_spatial_kwargs={}) Run Cell2Location Parameters ----------- adata_spatial : AnnData AnnData of the spatial data, filtered by highly variable features. Feature space needs to be the same as the one of adata_ref. adata_ref : AnnData AnnData of the reference data, filtered by highly variable features. Feature space needs to be the same as the one of adata_spatial. N_cells_per_location : float Expected cell number per location. detection_alpha : float Regularisation of per-location normalisation. labels_key : str Cell type key in adata_ref.obs for label information layer_spatial : str Layer of adata_spatial to use for deconvolution. If None, uses adata_spatial.X. layer_ref : str Layer of adata_ref to use for deconvolution. If None, uses adata_ref.X. use_gpu : bool Whether to use the GPU. max_epochs_spatial: int Number of epochs for the spatial mapping model. If None, defaults to np.min([round((20000 / n_cells) * 400), 400]). max_epochs_ref: int Number of epochs for the reference model. If None, defaults to np.min([round((20000 / n_cells) * 400), 400]). return_adatas: bool Whether to return AnnDatas with deconvolution results. Returns tupel: (adata_spatial, adata_ref). plots: bool Whether to plot QC and ELBO plots. results_path: str Path to save estimated cell type abundances to. setup_ref_kwargs: dict Parameters for cell2location.models.RegressionModel.setup_anndata() train_ref_kwargs: dict Parameters for cell2location.models.RegressionModel.train() setup_spatial_kwargs: dict Parameters for cell2location.models.Cell2location.setup_anndata() train_spatial_kwargs: dict Parameters for cell2location.models.Cell2location.train() Returns -------- - Saves 'q05_cell_abundance_w_sf' and 'means_cell_abundance_w_sf' as csv-files to results_path. - If return_adatas=True, returns tupel (adata_spatial, adata_ref) with saved deconvolution results. .. py:function:: tangram(adata_spatial, adata_ref, labels_key, run_rank_genes=False, layer_rank_genes=None, num_epochs=1000, device='cpu', return_adatas=False, result_path='./tangram_results', **kwargs) Run Tangram Parameters ----------- adata_spatial : AnnData AnnData of the spatial data. adata_ref : AnnData AnnData of the reference data. labels_key : str Cell type key in adata_ref.obs for label information run_rank_genes: bool If true, will run sc.tl.rank_genes_groups on reference anndata followed by tg.pp_adatas. If false, will only run tg.pp_adatas with all peaks of the input anndatas. Thus, expects anndatas to be already filtered by HVFs. layer_rank_genes: str Only if run_rank_genes is true. Layer to use for sc.tl.rank_genes_groups. num_epochs: int Number of epochs. device : string or torch.device Which device to use. return_adatas: bool Whether to return AnnDatas with deconvolution results. Returns tupel: (adata_spatial, adata_ref). results_path: str Path to save estimated cell type abundances to. **kwargs: Parameters for tangram.mapping_utils.map_cells_to_space() Returns -------- - Saves estimated proportions as csv-file to results_path. - If return_adatas=True, returns tupel (adata_spatial, adata_ref) with saved deconvolution results. .. py:function:: destvi(adata_spatial, adata_ref, labels_key=None, layer_spatial=None, layer_ref=None, use_gpu=True, max_epochs_spatial=2000, max_epochs_ref=300, return_adatas=False, plots=True, results_path='./destvi_results', model_ref_kwargs={}, train_ref_kwargs={}, model_spatial_kwargs={}, train_spatial_kwargs={}) Run DestVI Parameters ----------- adata_spatial : AnnData AnnData of the spatial data, filtered by highly variable features. Feature space needs to be the same as the one of adata_ref. adata_ref : AnnData AnnData of the reference data, filtered by highly variable features. Feature space needs to be the same as the one of adata_spatial. labels_key : str Cell type key in adata_ref.obs for label information layer_spatial : str Layer of adata_spatial to use for deconvolution. If None, uses adata_spatial.X. layer_ref : str Layer of adata_ref to use for deconvolution. If None, uses adata_ref.X. use_gpu : bool Whether to use the GPU. max_epochs_spatial: int Number of epochs for the stLVM. max_epochs_ref: int Number of epochs for the scLVM. return_adatas: bool Whether to return AnnDatas with deconvolution results. Returns tupel: (adata_spatial, adata_ref). plots: bool Whether to plot ELBO plots and UMAP of scLVM latent space. results_path: str Path to save estimated cell type abundances to. model_ref_kwargs: dict Parameters for scvi.model.CondSCVI() train_ref_kwargs: dict Parameters for scvi.model.CondSCVI.train() model_spatial_kwargs: dict Parameters for scvi.model.DestVI.from_rna_model() train_spatial_kwargs: dict Parameters for scvi.model.DestVI.train() Returns -------- - Saves estimated proportions as csv-file to results_path. - If return_adatas=True, returns tupel (adata_spatial, adata_ref) with saved deconvolution results. .. py:function:: jsd(true: Union[pandas.DataFrame, numpy.ndarray], predicted: Union[pandas.DataFrame, numpy.ndarray]) -> float Compute Jensen-Shannon divergence on true and predicted cell type proportions. Parameters ---------- true True cell type proportions. predicted Predicted cell type proportions. Returns ------- Jensen-Shannon divergence. .. py:function:: rmse(true: Union[pandas.DataFrame, numpy.ndarray], predicted: Union[pandas.DataFrame, numpy.ndarray]) -> float Compute RMSE on true and predicted cell type proportions. Parameters ---------- true True cell type proportions. predicted Predicted cell type proportions. Returns ------- Root mean squared error. .. py:function:: rctd(adata_spatial, adata_ref, labels_key, doublet_mode='full', r_lib_path=None, results_path='./rctd_results', create_rctd_kwargs={}) Run RCTD Parameters ----------- adata_spatial : AnnData AnnData of the spatial data, filtered by highly variable features. Feature space needs to be the same as the one of adata_ref. Raw counts are expected in .X. adata_ref : AnnData AnnData of the reference data, filtered by highly variable features. Feature space needs to be the same as the one of adata_spatial. Raw counts are expected in .X. labels_key : str Cell type key in adata_ref.obs for label information doublet_mode: str ["doublet", "full"] On which mode to run RCTD: 'doublet' (at most 1-2 cell types per pixel), 'full' (no restrictions on number of cell types) r_lib_path : str Path to R library in which RCTD is installed. results_path : str Path to save estimated cell type abundances to. create_rctd_kwargs : dict Parameters for create.RCTD(). Returns -------- - Saves estimated proportions as csv-file to results_path. .. py:function:: conway_maxwell_poisson(lambda_, nu) Sample from the Conway-Maxwell-Poisson distribution. .. py:class:: Sampler(reference: [muon.MuData, anndata.AnnData], cell_type_key: str, num_spots: int, n_regions: int, region_type: str = 'stripes', cell_number_mean: [int, list] = 6, cell_number_nu: [float, list] = 20.0, cell_type_number: [int, list] = 4, balance: Optional[str] = 'balanced') Class to sample cells and clusters from a given dataset. .. py:attribute:: reference .. py:attribute:: cell_type_key .. py:attribute:: num_spots .. py:attribute:: obs .. py:attribute:: n_regions .. py:attribute:: region_type :value: 'stripes' .. py:method:: init_sample_prob(balance='unbalanced') Initialize the sample probabilities based on cell type counts. Returns ------- None .. py:method:: define_regions(used_clusters) Define the regions to sample from. Parameters ---------- used_clusters : dict A dictionary containing the clusters to be used in each region. Returns ------- None Raises ------ ValueError If the region_type parameter is not one of ['stripes', 'circles', 'gradient_number', 'gradient_celltype'] .. py:method:: stripe_regions() Define regions as stripes. Returns ------- None .. py:method:: circle_regions() Define regions in circles. Returns ------- None .. py:method:: gradient_number_regions() Define regions as a gradient. Returns ------- None .. py:method:: gradient_celltype_regions(used_clusters) Define regions as a gradient. Parameters ---------- used_clusters : list The clusters to be used in the gradient cell type regions. Returns ------- None .. py:method:: sample_data() Sample data from the given dataset. Returns ------- tuple A tuple of expression and density arrays. .. py:method:: sample_spots(params) Sample cells based on given parameters. Parameters ---------- params : np.ndarray Array of cell and cluster counts. Returns ------- tuple A tuple of expression and density arrays. Raises ------ None .. py:method:: get_coords() Get the coordinates for the spots. Returns ------- tuple A tuple of X and Y coordinates. .. py:function:: generate_spatial_data(reference: [muon.MuData, anndata.AnnData], cell_type_key: str, num_spots: int = 1024, n_regions: int = 5, balance: Optional[str] = None, cell_number_mean: [int, list] = 6, cell_number_nu: [float, list] = 20.0, cell_type_number: [int, list] = 4, **kwargs) -> [anndata.AnnData, muon.MuData] Generate spatial data. Parameters ---------- reference : Union[mu.MuData, ad.AnnData] The reference dataset used for generating spatial data. cell_type_key : str The key in the reference dataset that specifies the cell type information. num_spots : int, optional The number of spots (locations) to generate, by default 1024. n_regions : int, optional The number of spatial regions to generate, by default 5. balance : str, optional The balancing method to use for generating spatial data, by default None. cell_number_mean : Union[int, list], optional The mean number of cells per spot, by default 6. cell_number_nu : Union[float, list], optional The dispersion parameter for the negative binomial distribution used to model cell numbers, by default 20.0. cell_type_number : Union[int, list], optional The number of cell types to generate, by default 4. **kwargs Additional keyword arguments. reference : Union[mu.MuData, ad.AnnData] The reference dataset used for generating spatial data. cell_type_key : str The key in the reference dataset that specifies the cell type information. num_spots : int, optional The number of spots (locations) to generate, by default 1024. n_regions : int, optional The number of spatial regions to generate, by default 5. balance : str, optional The balancing method to use for generating spatial data, by default None. cell_number_mean : Union[int, list], optional The mean number of cells per spot, by default 6. cell_number_nu : Union[float, list], optional The dispersion parameter for the negative binomial distribution used to model cell numbers, by default 20.0. cell_type_number : Union[int, list], optional The number of cell types to generate, by default 4. **kwargs Additional keyword arguments. Returns ------- Union[ad.AnnData, mu.MuData] The generated spatial data. Notes ----- This function generates spatial data based on a reference dataset. It uses a sampling approach to generate synthetic spatial data with specified characteristics such as cell type composition, cell numbers, and spatial organization. The generated spatial data is returned as an `AnnData` object if the reference dataset is an `AnnData` object, or as a `MuData` object if the reference dataset is a `MuData` object. Union[ad.AnnData, mu.MuData] The generated spatial data. Notes ----- This function generates spatial data based on a reference dataset. It uses a sampling approach to generate synthetic spatial data with specified characteristics such as cell type composition, cell numbers, and spatial organization. The generated spatial data is returned as an `AnnData` object if the reference dataset is an `AnnData` object, or as a `MuData` object if the reference dataset is a `MuData` object. .. py:function:: spatialdwls(adata_spatial, adata_ref, labels_key, cluster_key='leiden', n_cell=50, tfidf=True, r_lib_path=None, results_path='./spatialdwls_results') Run SpatialDWLS Parameters ----------- adata_spatial : AnnData AnnData of the spatial data , filtered by highly variable features. Feature space needs to be the same as the one of adata_ref. Normalized counts are expected to be saved in .X. Names of observations and features are expected as row names of adata_spatial.obs and adata_spatial.var. adata_ref : AnnData AnnData of the reference data, filtered by highly variable features. Feature space needs to be the same as the one of adata_spatial. Normalized counts are expected to be saved in .X. Names of observations and features are expected as row names of adata_ref.obs and adata_ref.var. labels_key : str Cell type key in adata_ref.obs for label information. cluster_key : str Cluster key in adata_spatial.obs for cluster information. n_cell : int Number of cells per spot. tfidf : bool Whether the normalized counts are TFIDF-normalized. r_lib_path : str Path to R library. results_path : str Path to save estimated cell type abundances to. Returns -------- - Saves estimated proportions as csv-file to results_path.