spateo.tools.CCI_effects_modeling.MuSIC_downstream
#
Additional functionalities to characterize signaling patterns from spatial transcriptomics
 These include:
prediction of the effects of spatial perturbation on gene expression this can include the effect of perturbing
known regulators of ligand/receptor expression or the effect of perturbing the ligand/receptor itself.  following spatiallyaware regression (or a sequence of spatiallyaware regressions), combine regression results with data such that each cell can be associated with regionspecific coefficient(s).  following spatiallyaware regression (or a sequence of spatiallyaware regressions), overlay the directionality of the predicted influence of the ligand on downstream expression.
Module Contents#
Classes#
Interpretation and downstream analysis of spatially weighted regression models. 
Functions#



 class spateo.tools.CCI_effects_modeling.MuSIC_downstream.MuSIC_Interpreter(parser: argparse.ArgumentParser, args_list: List[str]  None = None, keep_column_threshold_proportion_cells: float  None = None)[source]#
Bases:
spateo.tools.CCI_effects_modeling.MuSIC.MuSIC
Interpretation and downstream analysis of spatially weighted regression models.
 Parameters:
 parser
ArgumentParser object initialized with argparse, to parse command line arguments for arguments pertinent to modeling.
 args_list
If parser is provided by function call, the arguments to parse must be provided as a separate list. It is recommended to use the return from :func define_spateo_argparse() for this.
 keep_coeff_threshold_proportion_cells
If provided, will threshold columns to only keep those that are nonzero in a proportion of cells greater than this threshold. For example, if this is set to 0.5, more than half of the cells must have a nonzero value for a given column for it to be retained for further inspection. Intended to be used to filter out likely false positives.
 compute_coeff_significance(method: str = 'fdr_bh', significance_threshold: float = 0.05)[source]#
Computes local statistical significance for fitted coefficients.
 Parameters:
 method
 Method to use for correction. Available methods can be found in the documentation for
statsmodels.stats.multitest.multipletests(), and are also listed below (in correct case) for convenience:  Named methods:
bonferroni
sidak
holmsidak
holm
simeshochberg
hommel
 Abbreviated methods:
fdr_bh: BenjaminiHochberg correction
fdr_by: BenjaminiYekutieli correction
fdr_tsbh: Twostage BenjaminiHochberg
fdr_tsbky: Twostage BenjaminiKriegerYekutieli method
significance_threshold: pvalue (or qvalue) needed to call a parameter significant.
 Returns:
Dataframe of identical shape to coeffs, where each element is True or False if it meets the threshold for significance pvalues: Dataframe of identical shape to coeffs, where each element is a pvalue for that instance of that
feature
 qvalues: Dataframe of identical shape to coeffs, where each element is a qvalue for that instance of that
feature
 Return type:
is_significant
 filter_adata_spatial(instructions: List[str])[source]#
Based on spatial coordinates, filter the adata object to only include cells that meet the criteria. Criteria provided in the form of a list of instructions of the form “x less than 0.5 and y greater than 0.5”, etc., where each instruction is executed sequentially.
 Parameters:
 instructions
List of instructions to filter adata object by. Each instruction is a string of the form “x less than 0.5 and y greater than 0.5”, etc., where each instruction is executed sequentially.
 filter_adata_custom(cell_ids: List[str])[source]#
Filter AnnData object to only the cells specified by the custom list.
 Parameters:
 cell_ids
List of cell IDs to keep. Each ID must be found in adata.obs_names
 add_interaction_effect_to_adata(targets: str  List[str], interactions: str  List[str], visualize: bool = False) anndata.AnnData [source]#
For each specified interaction/list of interactions, add the predicted interaction effect to the adata object.
 Parameters:
 targets
Target(s) to add interaction effect for. Can be a single target or a list of targets.
 interactions
Interaction(s) to add interaction effect for. Can be a single interaction or a list of interactions. Should be the name of a gene for ligand models, or an L:R pair for L:R models (for example, “Igf1:Igf1r”).
 visualize
Whether to visualize the interaction effect for each target/interaction pair. If True, will generate spatial scatter plot and save to HTML file.
 Returns:
AnnData object with interaction effects added to .obs.
 Return type:
adata
 compute_and_visualize_diagnostics(type: Literal[correlations, confusion, rmse], n_genes_per_plot: int = 20)[source]#
For true and predicted gene expression, compute and generate either: confusion matrices, or correlations, including the Pearson correlation, Spearman correlation, or root meansquarederror (RMSE).
 Parameters:
 type
Type of diagnostic to compute and visualize. Options: “correlations” for Pearson & Spearman correlation, “confusion” for confusion matrix, “rmse” for root meansquarederror.
 n_genes_per_plot
Only used if “type” is “confusion”. Number of genes to plot per figure. If there are more than this number of genes, multiple figures will be generated.
 plot_interaction_effect_3D(target: str, interaction: str, save_path: str, pcutoff: float  None = 99.7, min_value: float  None = 0, zero_opacity: float = 1.0, size: float = 2.0, n_neighbors_smooth: int  None = 0)[source]#
Quickvisualize the magnitude of the predicted effect on target for a given interaction.
 Parameters:
 target
Target gene to visualize
 interaction
Interaction to visualize (e.g. “Igf1:Igf1r” for L:R model, “Igf1” for ligand model)
 save_path
Path to save the figure to (will save as HTML file)
 pcutoff
Percentile cutoff for the colorbar. Will set all values above this percentile to this value.
 min_value
Minimum value to set the colorbar to. Will set all values below this value to this value. Defaults to 0.
 zero_opacity
Opacity of points with zero expression. Between 0.0 and 1.0. Default is 1.0.
 size
Size of the points in the scatter plot. Default is 2.
 n_neighbors_smooth
Number of neighbors to use for smoothing (to make effect patterns more apparent). If 0, no smoothing is applied. Default is 0.
 plot_multiple_interaction_effects_3D(effects: List[str], save_path: str, include_combos_of_two: bool = False)[source]#
Quickvisualize the magnitude of the predicted effect on target for a given interaction.
 Parameters:
 effects
List of effects to visualize (e.g. [“Igf1:Igf1r”, “Igf1:InsR”] for L:R model, [“Igf1”] for ligand model)
 save_path
Path to save the figure to (will save as HTML file)
 include_combos_of_two
Whether to include paired combinations of effects (e.g. “Igf1:Igf1r and Igf1:InsR”) as separate categories. If False, will include these in the generic “Multiple interactions” category.
 plot_tf_effect_3D(target: str, tf: str, save_path: str, ligand_targets: bool = True, receptor_targets: bool = False, target_gene_targets: bool = False, pcutoff: float = 99.7, min_value: float = 0, zero_opacity: float = 1.0, size: float = 2.0)[source]#
Quickvisualize the magnitude of the predicted effect on target for a given TF. Can only find the files necessary for this if :func CCI_deg_detection() has been run.
 Parameters:
 target
Target gene of interest
 tf
TF of interest (e.g. “Foxo1”)
 save_path
Path to save the figure to (will save as HTML file)
 ligand_targets
Set True if ligands were used as the target genes for the :func CCI_deg_detection() model.
 receptor_targets
Set True if receptors were used as the target genes for the :func CCI_deg_detection() model.
 target_gene_targets
Set True if target genes were used as the target genes for the :func CCI_deg_detection() model.
 pcutoff
Percentile cutoff for the colorbar. Will set all values above this percentile to this value.
 min_value
Minimum value to set the colorbar to. Will set all values below this value to this value.
 zero_opacity
Opacity of points with zero expression. Between 0.0 and 1.0. Default is 1.0.
 size
Size of the points in the scatter plot. Default is 2.
 visualize_overlap_between_interacting_components_3D(target: str, interaction: str, save_path: str, size: float = 2.0)[source]#
Visualize the spatial distribution of signaling features (ligand, receptor, or L:R field) and target gene, as well as the overlapping region. Intended for use with 3D spatial coordinates.
 Parameters:
 target
Target gene to visualize
 interaction
Interaction to visualize (e.g. “Igf1:Igf1r” for L:R model, “Igf1” for ligand model)
 save_path
Path to save the figure to (will save as HTML file)
 size
Size of the points in the plot. Defaults to 2.
 gene_expression_heatmap(use_ligands: bool = False, use_receptors: bool = False, use_target_genes: bool = False, genes: Optional[List[str]] = None, position_key: str = 'spatial', coord_column: Optional[Union[int, str]] = None, reprocess: bool = False, neatly_arrange_y: bool = True, window_size: int = 3, recompute: bool = False, title: Optional[str] = None, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, cmap: str = 'magma', save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {})[source]#
Visualize the distribution of gene expression across cells in the spatial coordinates of cells; provides an idea of the simultaneous relative positions/patternings of different genes.
 Parameters:
 use_ligands
Set True to use ligands as the genes to visualize. If True, will ignore “genes” argument. “ligands_expr” file must be present in the model’s directory.
 use_receptors
Set True to use receptors as the genes to visualize. If True, will ignore “genes” argument. “receptors_expr” file must be present in the model’s directory.
 use_target_genes
Set True to use target genes as the genes to visualize. If True, will ignore “genes” argument. “targets” file must be present in the model’s directory.
 genes
Optional list of genes to visualize. If “use_ligands”, “use_receptors”, and “use_target_genes” are all False, this must be given. This can also be used to visualize only a subset of the genes once processing & saving has already completed using e.g. “use_ligands”, “use_receptors”, etc.
 position_key
Key in adata.obs or adata.obsm that provides a relative indication of the position of cells. i.e. spatial coordinates. Defaults to “spatial”. For each value in the position array (each coordinate, each category), multiple cells must have the same value.
 coord_column
Optional, only used if “position_key” points to an entry in .obsm. In this case, this is the index or name of the column to be used to provide the positional context. Can also provide “xy”, “yz”, “xz”, “xy”, “yz”, “xz” to draw a line between the two coordinate axes. “xy” will extend the new axis in the direction of increasing x and increasing y starting from x=0 and y=0 (or min. x/min. y), “xy” will extend the new axis in the direction of decreasing x and increasing y starting from x=minimum x and y=maximum y, and so on.
 reprocess
Set to True to reprocess the data and overwrite the existing files. Use if the genes to visualize have changed compared to the saved file (if existing), e.g. if “use_ligands” is True when the initial analysis used “use_target_genes”.
 neatly_arrange_y
Set True to order the yaxis in terms of how early along the position axis the max zscores for each row occur in. Used for a more uniform plot where similarly patterned interactiontarget pairs are grouped together. If False, will sort this axis by the identity of the interaction (i.e. all “Fgf1” rows will be grouped together).
 window_size
Size of window to use for smoothing. Must be an odd integer. If 1, no smoothing is applied.
 recompute
Set to True to recompute the data and overwrite the existing files
 title
Optional, can be used to provide title for plot
 fontsize
Size of font for x and y labels.
 figsize
Size of figure.
 cmap
Colormap to use. Options: Any divergent matplotlib colormap.
 save_show_or_return
Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 effect_distribution_heatmap(target_subset: Optional[List[str]] = None, interaction_subset: Optional[List[str]] = None, position_key: str = 'spatial', coord_column: Optional[Union[int, str]] = None, effect_threshold: Optional[float] = None, check_downstream_ligand_effects: bool = False, check_downstream_receptor_effects: bool = False, check_downstream_target_effects: bool = False, use_significant: bool = False, sort_by_target: bool = False, neatly_arrange_y: bool = True, window_size: int = 3, recompute: bool = False, title: Optional[str] = None, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, cmap: str = 'magma', save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {})[source]#
Visualize the distribution of interaction effects across cells in the spatial coordinates of cells; provides an idea of the simultaneous relative positions of different interaction effects.
 Parameters:
 target_subset
List of targets to consider. If None, will use all targets used in model fitting.
 interaction_subset
List of interactions to consider. If None, will use all interactions used in model.
 position_key
Key in adata.obs or adata.obsm that provides a relative indication of the position of cells. i.e. spatial coordinates. Defaults to “spatial”. For each value in the position array (each coordinate, each category), multiple cells must have the same value.
 coord_column
Optional, only used if “position_key” points to an entry in .obsm. In this case, this is the index or name of the column to be used to provide the positional context. Can also provide “xy”, “yz”, “xz”, “xy”, “yz”, “xz” to draw a line between the two coordinate axes. “xy” will extend the new axis in the direction of increasing x and increasing y starting from x=0 and y=0 (or min. x/min. y), “xy” will extend the new axis in the direction of decreasing x and increasing y starting from x=minimum x and y=maximum y, and so on.
 effect_threshold
Optional threshold minimum effect size to consider an effect for further analysis, as an absolute value. Use this to choose only the cells for which an interaction is predicted to have a strong effect. If None, use the median interaction effect.
 check_downstream_ligand_effects
Set True to check the coefficients of downstream ligand models instead of coefficients of the upstream CCI model. Note that this may not necessarily look nice because TFtarget relationships are not spatially dependent like L:R effects are.
 check_downstream_receptor_effects
Set True to check the coefficients of downstream receptor models instead of coefficients of the upstream CCI model. Note that this may not necessarily look nice because TFtarget relationships are not spatially dependent like L:R effects are.
 check_downstream_target_effects
Set True to check the coefficients of downstream target models instead of coefficients of the upstream CCI model. Note that this may not necessarily look nice because TFtarget relationships are not spatially dependent like L:R effects are.
 use_significant
Whether to use only significant effects in computing the specificity. If True, will filter to cells + interactions where the interaction is significant for the target. Only valid if :func compute_coeff_significance() has been run.
 sort_by_target
Set True to order the yaxis in terms of the identity of the target gene. Incompatible with “neatly_arrange_y”. If both this and “neatly_arrange_y” are False, will sort this axis by the identity of the interaction (i.e. all “Fgf1” rows will be grouped together).
 neatly_arrange_y
Set True to order the yaxis in terms of how early along the position axis the max zscores for each row occur in. Used for a more uniform plot where similarly patterned interactiontarget pairs are grouped together. If False, will sort this axis by the identity of the interaction (i.e. all “Fgf1” rows will be grouped together).
 window_size
Size of window to use for smoothing. Must be an odd integer. If 1, no smoothing is applied.
 recompute
Set to True to recompute the data and overwrite the existing files
 title
Optional, can be used to provide title for plot
 fontsize
Size of font for x and y labels.
 figsize
Size of figure.
 cmap
Colormap to use. Options: Any divergent matplotlib colormap.
 save_show_or_return
Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 effect_distribution_density(effect_names: List[str], position_key: str = 'spatial', coord_column: Optional[Union[int, str]] = None, max_coord_val: float = 1.0, title: Optional[str] = None, x_label: Optional[str] = None, region_lower_bound: Optional[float] = None, region_upper_bound: Optional[float] = None, region_label: Optional[str] = None, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {})[source]#
Visualize the spatial enrichment of cellcell interaction effects using density plots over spatial coordinates. Uses existing dataframe saved by
effect_distribution_heatmap()
, which must be run first. Parameters:
 effect_names
List of interaction effects to include in plot, in format “TargetLigand:Receptor” (for L:R models) or “TargetLigand” (for ligand models).
 position_key
Key in adata.obs or adata.obsm that provides a relative indication of the position of cells. i.e. spatial coordinates. Defaults to “spatial”. For each value in the position array (each coordinate, each category), multiple cells must have the same value.
 coord_column
Optional, only used if “position_key” points to an entry in .obsm. In this case, this is the index or name of the column to be used to provide the positional context. Can also provide “xy”, “yz”, “xz”, “xy”, “yz”, “xz” to draw a line between the two coordinate axes. “xy” will extend the new axis in the direction of increasing x and increasing y starting from x=0 and y=0 (or min. x/min. y), “xy” will extend the new axis in the direction of decreasing x and increasing y starting from x=minimum x and y=maximum y, and so on.
 max_coord_val
Optional, can be used to adjust the numbers displayed along the xaxis for the relative position along the coordinate axis. Defaults to 1.0.
 title
Optional, can be used to provide title for plot
 x_label
Optional, can be used to provide xaxis label for plot
 region_lower_bound
Optional, can be used to provide a lower bound for the region of interest to label on the plot this can correspond to a spatial domain, etc.
 region_upper_bound
Optional, can be used to provide an upper bound for the region of interest to label on the plot this can correspond to a spatial domain, etc.
 region_label
Optional, can be used to provide a label for the region of interest to label on the plot
 fontsize
Size of font for x and y labels.
 figsize
Size of figure.
 cmap
Colormap to use. Options: Any divergent matplotlib colormap.
 save_show_or_return
Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 visualize_effect_specificity(agg_method: Literal[mean, percentage] = 'mean', plot_type: Literal[heatmap, volcano] = 'heatmap', target_subset: Optional[List[str]] = None, interaction_subset: Optional[List[str]] = None, ct_subset: Optional[List[str]] = None, group_key: Optional[str] = None, n_anchors: Optional[int] = None, effect_threshold: Optional[float] = None, use_significant: bool = False, target_cooccurrence_threshold: float = 0.1, significance_cutoff: float = 1.3, fold_change_cutoff: float = 1.5, fold_change_cutoff_for_labels: float = 3.0, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, cmap: str = 'seismic', save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {}, save_df: bool = False)[source]#
Computes and visualizes the specificity of each interaction on each target. This is done by first separating the targetexpressing cells (and their neighbors) from the rest of the cells (conditioned on predicted effect and also conditioned on receptor expression if L:R model is used). Then, computing the fold change of the average expression of the ligand in the neighborhood of the first subset vs. the neighborhoods of the second subset.
 Parameters:
 agg_method
Method to use for aggregating the specificity of each interaction on each target. Options: “mean” for mean ligand expression, “percentage” for the percentage of cells expressing the ligand.
 plot_type
Type of plot to use for visualization. Options: “heatmap” for heatmap, “volcano” for volcano plot.
 target_subset
List of targets to consider. If None, will use all targets used in model fitting.
 interaction_subset
List of interactions to consider. If None, will use all interactions used in model.
 ct_subset
Can be used to constrain the first group of cells (the query group) to the targetexpressing cells of a particular type (conditioned on any other relevant variables). If given, will search for cell types in “group_key” attribute from model initialization. If not given, will use all cell types.
 group_key
Can be used to specify entry in adata.obs that contains cell type groupings. If None, will use :attr group_key from model initialization.
 n_anchors
Optional, number of target geneexpressing cells to use as anchors for analysis. Will be selected randomly from the set of target geneexpressing cells (conditioned on any other relevant values).
 effect_threshold
Optional threshold minimum effect size to consider an effect for further analysis, as an absolute value. Use this to choose only the cells for which an interaction is predicted to have a strong effect. If None, use the median interaction effect.
 use_significant
Whether to use only significant effects in computing the specificity. If True, will filter to cells + interactions where the interaction is significant for the target. Only valid if :func compute_coeff_significance() has been run.
 significance_cutoff
Cutoff for negative log10 qvalue to consider an interaction/effect significant. Only used if “plot_type” is “volcano”. Defaults to 1.3 (corresponding to an approximate qvalue of 0.05).
 fold_change_cutoff
Cutoff for fold change to consider an interaction/effect significant. Only used if “plot_type” is “volcano”. Defaults to 1.5.
 fold_change_cutoff_for_labels
Cutoff for fold change to include the label for an interaction/effect. Only used if “plot_type” is “volcano”. Defaults to 3.0.
 fontsize
Size of font for x and y labels.
 figsize
Size of figure.
 cmap
Colormap to use. Options: Any divergent matplotlib colormap.
 save_show_or_return
Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 save_df
Set True to save the metric dataframe in the end
 visualize_neighborhood(target: str, interaction: str, interaction_type: Literal[secreted, membrane  bound], select_examples_criterion: Literal[positive, negative] = 'positive', effect_threshold: float  None = None, cell_type: str  None = None, group_key: str  None = None, use_significant: bool = False, n_anchors: int = 100, n_neighbors_expressing: int = 20, display_plot: bool = True) anndata.AnnData [source]#
Sets up AnnData object for visualization of interaction effects cells will be colored by expression of the target gene, potentially conditioned on receptor expression, and neighboring cells will be colored by ligand expression.
 Parameters:
 target
Target gene of interest
 interaction
Interaction feature to visualize, given in the same form as in the design matrix (if model is a ligandbased model or receptorbased model, this will be of form “Col4a1”. If model is a ligandreceptor based model, this will be of form “Col4a1:Itgb1”, for example).
 interaction_type
Specifies whether the chosen interaction is secreted or membranebound. Options: “secreted” or “membranebound”.
 select_examples_criterion
Whether to select cells with positive or negative interaction effects for visualization. Defaults to “positive”, which searches for cells for which the predicted interaction effect is above the given threshold. “Negative” will select cells for which the predicted interaction has no effect on the target expression.
 effect_threshold
Optional threshold for the effect size of an interaction/effect to be considered for analysis; only used if “to_plot” is “percentage”. If not given, will use the upper quartile value among all interaction effect values to determine the threshold.
 cell_type
Optional, can be used to select anchor cells from only a particular cell type. If None, will select from all cells.
 group_key
Can be used to specify entry in adata.obs that contains cell type groupings. If None, will use :attr group_key from model initialization. Only used if “cell_type” is not None.
 use_significant
Whether to use only significant effects in computing the specificity. If True, will filter to cells + interactions where the interaction is significant for the target. Only valid if :func compute_coeff_significance() has been run.
 n_anchors
Number of target geneexpressing cells to use as anchors for visualization. Will be selected randomly from the set of target geneexpressing cells.
 n_neighbors_expressing
Filters the set of cells that can be selected as anchors based on the number of their neighbors that express the chosen ligand. Only used for models that incorporate ligand expression.
 display_plot
Whether to save a plot. If False, will return the AnnData object without doing anything else this can then be visualized e.g. using spateoviewer.
 Returns:
 Modified AnnData object containing the expression information for the target gene and neighboring
ligand expression.
 Return type:
adata
 cell_type_specific_interactions(to_plot: Literal[mean, percentage] = 'mean', plot_type: Literal[heatmap, barplot] = 'heatmap', group_key: Optional[str] = None, ct_subset: Optional[List[str]] = None, target_subset: Optional[List[str]] = None, interaction_subset: Optional[List[str]] = None, lower_threshold: float = 0.3, upper_threshold: float = 1.0, effect_threshold: Optional[float] = None, use_significant: bool = False, row_normalize: bool = False, col_normalize: bool = False, normalize_targets: bool = False, hierarchical_cluster_ct: bool = False, group_y_cell_type: bool = False, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, center: Optional[float] = None, cmap: str = 'Reds', save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {}, save_df: bool = False)[source]#
Map interactions and interaction effects that are specific to particular cell type groupings. Returns a heatmap representing the enrichment of the interaction/effect within cells of that grouping (if “to_plot” is effect, this will be enrichment of the effect on cell typespecific expression). Enrichment determined by mean effect size or expression.
 Parameters:
 to_plot
Whether to plot the mean effect size or the proportion of cells in a cell type w/ effect on target. Options are “mean” or “percentage”.
 plot_type
Whether to plot the results as a heatmap or barplot. Options are “heatmap” or “barplot”. If “barplot”, must provide a subset of up to four interactions to visualize.
 group_key
Can be used to specify entry in adata.obs that contains cell type groupings. If None, will use :attr group_key from model initialization.
 ct_subset
Can be used to restrict the enrichment analysis to only cells of a particular type. If given, will search for cell types in “group_key” attribute from model initialization. Recommended to use to subset to cell types with sufficient numbers.
 target_subset
List of targets to consider. If None, will use all targets used in model fitting.
 interaction_subset
List of interactions to consider. If None, will use all interactions used in model. Is necessary if “plot_type” is “barplot”, since the barplot is only designed to accomodate up to three interactions at once.
 lower_threshold
Lower threshold for the proportion of cells in a cell type group that must express a particular interaction/effect for it to be colored on the plot, as a proportion of the max value. Threshold will be applied to the nonnormalized values (if normalization is applicable). Defaults to 0.3.
 upper_threshold
Upper threshold for the proportion of cells in a cell type group that must express a particular interaction/effect for it to be colored on the plot, as a proportion of the max value. Threshold will be applied to the nonnormalized values (if normalization is applicable). Defaults to 1.0 (the max value).
 effect_threshold
Optional threshold for the effect size of an interaction/effect to be considered for analysis; only used if “to_plot” is “percentage”. If not given, will use the upper quartile value among all interaction effect values to determine the threshold.
 use_significant
Whether to use only significant effects in computing the specificity. If True, will filter to cells + interactions where the interaction is significant for the target. Only valid if :func compute_coeff_significance() has been run.
 row_normalize
Whether to minmax scale the metric values by row (i.e. for each interaction/effect). Helps to alleviate visual differences that result from scale rather than differences in mean value across cell types.
 col_normalize
Whether to minmax scale the metric values by column (i.e. for each interaction/effect). Helps to alleviate visual differences that result from scale rather than differences in mean value across cell types.
 normalize_targets
Whether to minmax scale the metric values by column for each target (i.e. for each interaction/effect), to remove differences that occur as a result of scale of expression. Provides a clearer picture of enrichment for each target.
 hierarchical_cluster_ct
Whether to cluster the xaxis (target gene in cell type) using hierarchical clustering. If False, will order the xaxis by the order of the target genes for organization purposes.
 group_y_cell_type
Whether to group the yaxis (target gene in cell type) by cell type. If False, will group by target gene instead. Defaults to False.
 fontsize
Size of font for x and y labels.
 figsize
Size of figure.
 center
Optional, determines position of the colormap center. Between 0 and 1.
 cmap
Colormap to use for heatmap. If metric is “number”, “proportion”, “specificity”, the bottom end of the range is 0. It is recommended to use a sequential colormap (e.g. “Reds”, “Blues”, “Viridis”, etc.). For metric = “fc”, if a divergent colormap is not provided, “seismic” will automatically be used.
 save_show_or_return
Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 save_df
Set True to save the metric dataframe in the end
 cell_type_interaction_fold_change(ref_ct: str, query_ct: str, group_key: Optional[str] = None, target_subset: Optional[List[str]] = None, interaction_subset: Optional[List[str]] = None, to_plot: Literal[mean, percentage] = 'mean', plot_type: Literal[volcano, MuSIC_Interpreter.cell_type_interaction_fold_change.barplot] = 'barplot', source_data: Literal[interaction, effect, MuSIC_Interpreter.cell_type_interaction_fold_change.target] = 'effect', top_n_to_plot: Optional[int] = None, significance_cutoff: float = 1.3, fold_change_cutoff: float = 1.5, fold_change_cutoff_for_labels: float = 3.0, plot_query_over_ref: bool = False, plot_ref_over_query: bool = False, plot_only_significant: bool = False, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, cmap: str = 'seismic', save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {}, save_df: bool = False)[source]#
Computes fold change in predicted interaction effects between two cell types, and visualizes result.
 Parameters:
 ref_ct
Label of the first cell type to consider. Fold change will be computed with respect to the level in this cell type.
 query_ct
Label of the second cell type to consider
 group_key
Name of the key in .obs containing cell type information. If not given, will use :attr group_key from model initialization.
 target_subset
List of targets to consider. If None, will use all targets used in model fitting.
 interaction_subset
List of interactions to consider. If None, will use all interactions used in model.
 to_plot
Whether to plot the mean effect size or the proportion of cells in a cell type w/ effect on target. Options are “mean” or “percentage”.
 plot_type
Whether to plot the results as a volcano plot or barplot. Options are “volcano” or “barplot”.
 source_data
Selects what to use in computing fold changes. Options:  “interaction”: will use the design matrix (e.g. neighboring ligand expression or L:R mapping)  “effect”: will use the coefficient arrays for each target  “target”: will use the target gene expression
 top_n_to_plot
If given, will only include the top n features in the visualization. Recommended if “source_data” is “effect”, as all combinations of interaction and target will be considered in this case.
 significance_cutoff
Cutoff for negative log10 qvalue to consider an interaction/effect significant. Only used if “plot_type” is “volcano”. Defaults to 1.3 (corresponding to an approximate qvalue of 0.05).
 fold_change_cutoff
Cutoff for fold change to consider an interaction/effect significant. Only used if “plot_type” is “volcano”. Defaults to 1.5.
 fold_change_cutoff_for_labels
Cutoff for fold change to include the label for an interaction/effect. Only used if “plot_type” is “volcano”. Defaults to 3.0.
 plot_query_over_ref
Whether to plot/visualize only the portion that corresponds to the fold change of the query cell type over the reference cell type (and the portion that is significant). If False (and “plot_ref_over_query” is False), will plot the entire volcano plot. Only used if “plot_type” is “volcano”.
 plot_ref_over_query
Whether to plot/visualize only the portion that corresponds to the fold change of the reference cell type over the query cell type (and the portion that is significant). If False (and “plot_query_over_ref” is False), will plot the entire volcano plot. Only used if “plot_type” is “volcano”.
 plot_only_significant
Whether to plot/visualize only the portion that passes the “significance_cutoff” pvalue threshold. Only used if “plot_type” is “volcano”.
 fontsize
Size of font for x and y labels.
 figsize
Size of figure.
 cmap
Colormap to use for heatmap. If metric is “number”, “proportion”, “specificity”, the bottom end of the range is 0. It is recommended to use a sequential colormap (e.g. “Reds”, “Blues”, “Viridis”, etc.). For metric = “fc”, if a divergent colormap is not provided, “seismic” will automatically be used.
 save_show_or_return
Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 save_df
Set True to save the metric dataframe in the end
 enriched_interactions_barplot(interactions: Optional[Union[str, List[str]]] = None, targets: Optional[Union[str, List[str]]] = None, plot_type: Literal[average, proportion] = 'average', effect_size_threshold: float = 0.0, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, cmap: str = 'Reds', top_n: Optional[int] = None, save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {})[source]#
Visualize the top predicted effect sizes for each interaction on particular target gene(s).
 Parameters:
 interactions
Optional subset of interactions to focus on, given in the form ligand(s):receptor(s), following the formatting in the design matrix. If not given, will consider all interactions that were specified in model fitting.
 targets
Can optionally specify a subset of the targets to compute this on. If not given, will use all targets that were specified in model fitting. If multiple targets are given, “save_show_or_return” should be “save” (and provide appropriate keyword arguments for saving using “save_kwargs”), otherwise only the last target will be shown.
 plot_type
Options: “average” or “proportion”. Whether to plot the average effect size or the proportion of cells expressing the target predicted to be affected by the interaction.
 effect_size_threshold
Lower bound for average effect size to include a particular interaction in the barplot
 fontsize
Size of font for x and y labels
 figsize
Size of figure
 cmap
Colormap to use for barplot. It is recommended to use a sequential colormap (e.g. “Reds”, “Blues”, “Viridis”, etc.).
 top_n
If given, will only include the top n features in the visualization. If not given, will include all features that pass the “effect_size_threshold”.
 save_show_or_return
Whether to save, show or return the figure If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 enriched_tfs_barplot(tfs: Optional[Union[str, List[str]]] = None, targets: Optional[Union[str, List[str]]] = None, target_type: Literal[ligand, receptor, target_gene] = 'target_gene', plot_type: Literal[average, proportion] = 'average', effect_size_threshold: float = 0.0, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, cmap: str = 'Reds', top_n: Optional[int] = None, save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {})[source]#
Visualize the top predicted effect sizes for each transcription factor on particular target gene(s).
 Parameters:
 tfs
Optional subset of transcription factors to focus on. If not given, will consider all transcription factors that were specified in model fitting.
 targets
Can optionally specify a subset of the targets to compute this on. If not given, will use all targets that were specified in model fitting. If multiple targets are given, “save_show_or_return” should be “save” (and provide appropriate keyword arguments for saving using “save_kwargs”), otherwise only the last target will be shown.
 target_type
Set whether the given targets are ligands, receptors or target genes. Used to determine which folder to check for outputs.
 plot_type
Options: “average” or “proportion”. Whether to plot the average effect size or the proportion of cells expressing the target predicted to be affected by the interaction.
 effect_size_threshold
Lower bound for average effect size to include a particular interaction in the barplot
 fontsize
Size of font for x and y labels
 figsize
Size of figure
 cmap
Colormap to use for barplot. It is recommended to use a sequential colormap (e.g. “Reds”, “Blues”, “Viridis”, etc.).
 top_n
If given, will only include the top n features in the visualization. If not given, will include all features that pass the “effect_size_threshold”.
 save_show_or_return
Whether to save, show or return the figure If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 partial_correlation_interactions(interactions: Optional[Union[str, List[str]]] = None, targets: Optional[Union[str, List[str]]] = None, method: Literal[pearson, spearman] = 'pearson', filter_interactions_proportion_threshold: Optional[float] = None, plot_zero_threshold: Optional[float] = None, ignore_outliers: bool = True, alternative: Literal[twosided, less, greater] = 'twosided', fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, center: Optional[float] = None, cmap: str = 'Reds', save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {}, save_df: bool = False)[source]#
Repression is more difficult to infer from singlecell data this function computes semipartial correlations to shed light on interactions that may be overall repressive. In this case, for a given interactiontarget pair, all other interactions are used as covariates in a semipartial correlation (to account for their effects on the target, but not the other interactions which should be more independent of each other compared to the target).
 Parameters:
 interactions
Optional, given in the form ligand(s):receptor(s), following the formatting in the design matrix. If not given, will use all interactions that were specified in model fitting.
 targets
Can optionally specify a subset of the targets to compute this on. If not given, will use all targets that were specified in model fitting.
 method
Correlation type, options:  Pearson \(r\) productmoment correlation  Spearman \(\rho\) rankorder correlation
 filter_interactions_proportion_threshold
Optional, if given, will filter out interactions that are predicted to occur in below this proportion of cells beforehand (to reduce the number of computations)
 plot_zero_threshold
Optional, if given, will mask out values below this threshold in the heatmap (will keep the interactions in the dataframe, just will not color the elements in the plot). Can also be used together with filter_interactions_proportion_threshold.
 ignore_outliers
Whether to ignore extremely high values for target gene expression when computing partial correlations
 alternative
Defines the alternative hypothesis, or tail of the partial correlation. Must be one of “twosided” (default), “greater” or “less”. Both “greater” and “less” return a onesided pvalue. “greater” tests against the alternative hypothesis that the partial correlation is positive (greater than zero), “less” tests against the hypothesis that the partial correlation is negative.
 fontsize
Size of font for x and y labels
 figsize
Size of figure
 center
Optional, determines position of the colormap center. Between 0 and 1.
 cmap
Colormap to use for heatmap. If metric is “number”, “proportion”, “specificity”, the bottom end of the range is 0. It is recommended to use a sequential colormap (e.g. “Reds”, “Blues”, “Viridis”, etc.). For metric = “fc”, if a divergent colormap is not provided, “seismic” will automatically be used.
 save_show_or_return
Whether to save, show or return the figure If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 save_df
Set True to save the metric dataframe in the end
 get_effect_potential(target: str  None = None, ligand: str  None = None, receptor: str  None = None, sender_cell_type: str  None = None, receiver_cell_type: str  None = None, spatial_weights_membrane_bound: numpy.ndarray  scipy.sparse.spmatrix  None = None, spatial_weights_secreted: numpy.ndarray  scipy.sparse.spmatrix  None = None, spatial_weights_niche: numpy.ndarray  scipy.sparse.spmatrix  None = None, store_summed_potential: bool = True) Tuple[scipy.sparse.spmatrix, numpy.ndarray, numpy.ndarray] [source]#
For each cell, computes the ‘signaling effect potential’, interpreted as a quantification of the strength of effect of intercellular communication on downstream expression in a given cell mediated by any given other cell with any combination of ligands and/or cognate receptors, as inferred from the model results. Computations are similar to those of :func ~`.inferred_effect_direction`, but stops short of computing vector fields.
 Parameters:
 target
Optional string to select target from among the genes used to fit the model to compute signaling effects for. Note that this function takes only one target at a time. If not given, will take the first name from among all targets.
 ligand
Needed if :attr mod_type is ‘ligand’; select ligand from among the ligands used to fit the model to compute signaling potential.
 receptor
Needed if :attr mod_type is ‘lr’; together with ‘ligand’, used to select ligandreceptor pair from among the ligandreceptor pairs used to fit the model to compute signaling potential.
 sender_cell_type
Can optionally be used to select cell type from among the cell types used to fit the model to compute sent potential. Must be given if :attr mod_type is ‘niche’.
 receiver_cell_type
Can optionally be used to condition sent potential on receiver cell type.
 store_summed_potential
If True, will store both sent and received signaling potential as entries in .obs of the AnnData object.
 Returns:
 Sparse array of shape [n_samples, n_samples]; proxy for the “signaling effect potential”
with respect to a particular target gene between each senderreceiver pair of cells.
 normalized_effect_potential_sum_sender: Array of shape [n_samples,]; for each sending cell, the sum of the
signaling potential to all receiver cells for a given target gene, normalized between 0 and 1.
 normalized_effect_potential_sum_receiver: Array of shape [n_samples,]; for each receiving cell, the sum of
the signaling potential from all sender cells for a given target gene, normalized between 0 and 1.
 Return type:
effect_potential
 get_pathway_potential(pathway: str  None = None, target: str  None = None, spatial_weights_secreted: numpy.ndarray  scipy.sparse.spmatrix  None = None, spatial_weights_membrane_bound: numpy.ndarray  scipy.sparse.spmatrix  None = None, store_summed_potential: bool = True)[source]#
For each cell, computes the ‘pathway effect potential’, which is an aggregation of the effect potentials of all pathway member ligandreceptor pairs (or all pathway member ligands, for ligandonly models).
 Parameters:
 pathway
Name of pathway to compute pathway effect potential for.
 target
Optional string to select target from among the genes used to fit the model to compute signaling effects for. Note that this function takes only one target at a time. If not given, will take the first name from among all targets.
 spatial_weights_secreted
Optional pairwise spatial weights matrix for secreted factors
 spatial_weights_membrane_bound
Optional pairwise spatial weights matrix for membranebound factors
 store_summed_potential
If True, will store both sent and received signaling potential as entries in .obs of the AnnData object.
 Returns:
 Array of shape [n_samples, n_samples]; proxy for the combined “signaling effect
potential” with respect to a particular target gene for ligandreceptor pairs in a pathway.
 normalized_pathway_effect_potential_sum_sender: Array of shape [n_samples,]; for each sending cell,
the sum of the pathway sum potential to all receiver cells for a given target gene, normalized between 0 and 1.
 normalized_pathway_effect_potential_sum_receiver: Array of shape [n_samples,]; for each receiving cell,
the sum of the pathway sum potential from all sender cells for a given target gene, normalized between 0 and 1.
 Return type:
pathway_sum_potential
 inferred_effect_direction(targets: str  List[str]  None = None, compute_pathway_effect: bool = False)[source]#
For visualization purposes, used for models that consider ligand expression (:attr mod_type is ‘ligand’ or ‘lr’ (for receptor models, assigning directionality is impossible and for niche models, it makes much less sense to draw/compute a vector field). Construct spatial vector fields to infer the directionality of observed effects (the “sources” of the downstream expression).
Parts of this function are inspired by ‘communication_direction’ from COMMOT: https://github.com/zcang/COMMOT
 Parameters:
 targets
Optional string or list of strings to select targets from among the genes used to fit the model to compute signaling effects for. If not given, will use all targets.
 compute_pathway_effect
Whether to compute the effect potential for each pathway in the model. If True, will collectively take the effect potential of all pathway components. If False, will compute effect potential for each for each individual signal.
 define_effect_vf(effect_potential: scipy.sparse.spmatrix, normalized_effect_potential_sum_sender: numpy.ndarray, normalized_effect_potential_sum_receiver: numpy.ndarray, sig: str, target: str, max_val: float = 0.05)[source]#
Given the pairwise effect potential array, computes the effect vector field.
 Parameters:
 effect_potential
Sparse array containing computed effect potentials output from
get_effect_potential()
 normalized_effect_potential_sum_sender
Array containing the sum of the effect potentials sent by each cell. Output from
get_effect_potential()
. normalized_effect_potential_sum_receiver
Array containing the sum of the effect potentials received by each cell. Output from
get_effect_potential()
. max_val
Constrains the size of the vector field vectors. Recommended to set within the order of magnitude of 1/100 of the desired plot dimensions.
 sig
Label for the mediating interaction (e.g. name of a ligand, name of a ligandreceptor pair, etc.)
 target
Name of the target that the vector field describes the effect for
 visualize_effect_vf_3D(interaction: str, target: str, vf_key: str  None = None, vector_magnitude_lower_bound: float = 0.0, manual_vector_scale_factor: float  None = None, bin_size: float  Tuple[float]  None = None, plot_cells: bool = True, cell_size: float = 1.0, alpha: float = 0.3, no_color_coding: bool = False, only_view_effect_region: bool = False, add_group_label: str  None = None, group_label_obs_key: str  None = None, title_position: Tuple[float, float] = (0.5, 0.9), save_path: str  None = None, **kwargs)[source]#
Visualize the directionality of the effect on target for a given interaction, overlaid onto the 3D spatial plot. Can only be used for models that use ligand expression (:attr mod_type is ‘ligand’ or ‘lr’).
 Parameters:
 interaction
Interaction to incorporate into the visualization (e.g. “Igf1:Igf1r” for L:R model, “Igf1” for ligand model)
 target
Name of the target gene of interest. Will search key “spatial_effect_sender_vf_{interaction}_{ target}” to create vector field plot.
 vf_key
Optional key in .obsm to specify which vector field to use. If not given, will use the provided “interaction” and “target” to find the key specifying the vector field.
 vector_magnitude_lower_bound
Lower bound for the magnitude of the vector field vectors to be plotted, as a fraction of the maximum vector magnitude. Defaults to 0.0.
 manual_vector_scale_factor
If not None, will manually scale the vector field by this factor ( multiplicatively). Used for visualization purposes, not recommended to set above 2.0 (otherwise likely to get misleading results with vectors that are too long).
 bin_size
Optional, can be used to declutter plotting space by splitting the space into 3D bins and displaying one vector per bin. Can be given as a floating point number to create cubic bins, or as a tuple of floats to specify different bin sizes for each dimension. If not given, will plot one vector per cell. Defaults to None.
 plot_cells
If False, will not plot any of the cells (unless a group label is given), so will only visualize vector field. Defaults to True.
 cell_size
Size of the cells in the 3D plot. Defaults to 1.0.
 alpha
If visualizing cells not affected by the interaction, this argument specifies the transparency of those cells.
 no_color_coding
If True, will color all cells the same color (except cells of given category, if given).
 only_view_effect_region
If True, will only plot the region where the effect is predicted to be found, rather than the entire 3D object
 add_group_label
This optional argument represents a cell type category. Will color the cells belonging to this particular category orange. If given, it is recommended to also provide group_label_obs_key (which will be :attr group_key if not given).
 group_label_obs_key
If add_group_label is given, this argument represents the observation key in the AnnData object that contains the group label. If not given, will default to :attr group_key.
 title_position
Position of the title in the plot, given as a tuple of floats (i.e. (x, y)). Defaults to (0.5, 0.9).
 save_path
Path to save the figure to (will save as HTML file)
 kwargs
Additional arguments that can be passed to :func plotly.graph_objects.Cone. Common arguments:  “colorscale”: Sets the colorscale. The colorscale must be an array containing arrays mapping a
normalized value to an rgb, rgba, hex, hsl, hsv, or named color string.
 ”sizemode”: Determines whether sizeref is set as a “scaled” (i.e unitless) scalar (normalized by the
max u/v/w norm in the vector field) or as “absolute” value (in the same units as the vector field). Defaults to “scaled”.
 ”sizeref”: The scalar reference for the cone size. The cone size is determined by its u/v/w norm
multiplied by sizeref. Defaults to 2.0.
”showscale”: Determines whether or not a colorbar is displayed for this trace.
 CCI_deg_detection_setup(group_key: str  None = None, custom_tfs: List[str]  None = None, sender_receiver_or_target_degs: Literal[sender, receiver, target] = 'sender', use_ligands: bool = True, use_receptors: bool = False, use_pathways: bool = False, use_targets: bool = False, use_cell_types: bool = False, compute_dim_reduction: bool = False)[source]#
Computes differential expression signatures of cells with various levels of ligand expression.
 Parameters:
 group_key
Key to add to .obs of the AnnData object created by this function, containing cell type labels for each cell. If not given, will use :attr group_key.
 custom_tfs
Optional list of transcription factors to make sure to be included in analysis. If given, these TFs will be included among the regulators regardless of the expressionbased thresholding done in preprocessing.
 sender_receiver_or_target_degs
Only makes a difference if ‘use_pathways’ or ‘use_cell_types’ is specified. Determines whether to compute DEGs for ligands, receptors or target genes. If ‘use_pathways’ is True, the value of this argument will determine whether ligands or receptors are used to define the model. Note that in either case, differential expression of TFs, binding factors, etc. will be computed in association w/ ligands/receptors/target genes (only valid if ‘use_cell_types’ and not ‘use_pathways’ is specified.
 use_ligands
Use ligand array for differential expression analysis. Will take precedent over sender/receiver cell type if also provided.
 use_receptors
Use receptor array for differential expression analysis. Will take precedent over sender/receiver cell type if also provided.
 use_pathways
Use pathway array for differential expression analysis. Will use ligands in these pathways to collectively compute signaling potential score. Will take precedent over sender cell types if also provided.
 use_targets
Use target array for differential expression analysis.
 use_cell_types
Use cell types to use for differential expression analysis. If given, will preprocess/construct the necessary components to initialize cell typespecific models. Note should be used alongside ‘use_ligands’, ‘use_receptors’, ‘use_pathways’ or ‘use_targets’ to select which molecules to investigate in each cell type.
 compute_dim_reduction
Whether to compute PCA representation of the data subsetted to targets.
 CCI_deg_detection(group_key: str, cci_dir_path: str, sender_receiver_or_target_degs: Literal[sender, receiver, target] = 'sender', use_ligands: bool = True, use_receptors: bool = False, use_pathways: bool = False, use_targets: bool = False, ligand_subset: List[str]  None = None, receptor_subset: List[str]  None = None, target_subset: List[str]  None = None, cell_type: str  None = None, use_dim_reduction: bool = False, **kwargs)[source]#
Downstream method that when called, creates a separate instance of :class MuSIC specifically designed for the downstream task of detecting differentially expressed genes associated w/ ligand expression.
 Parameters:
 group_key
Key in adata.obs that corresponds to the cell type (or other grouping) labels
 cci_dir_path
Path to directory containing all Spateo databases
 sender_receiver_or_target_degs
Only makes a difference if ‘use_pathways’ or ‘use_cell_types’ is specified. Determines whether to compute DEGs for ligands, receptors or target genes. If ‘use_pathways’ is True, the value of this argument will determine whether ligands or receptors are used to define the model. Note that in either case, differential expression of TFs, binding factors, etc. will be computed in association w/ ligands/receptors/target genes (only valid if ‘use_cell_types’ and not ‘use_pathways’ is specified.
 use_ligands
Use ligand array for differential expression analysis. Will take precedent over receptors and sender/receiver cell types if also provided. Should match the input to :func CCI_sender_deg_detection_setup.
 use_receptors
Use receptor array for differential expression analysis.
 use_pathways
Use pathway array for differential expression analysis. Will use ligands in these pathways to collectively compute signaling potential score. Will take precedent over sender cell types if also provided. Should match the input to :func CCI_sender_deg_detection_setup.
 use_targets
Use target genes array for differential expression analysis.
 ligand_subset
Subset of ligands to use for differential expression analysis. If not given, will use all ligands from the upstream model.
 receptor_subset
Subset of receptors to use for differential expression analysis. If not given, will use all receptors from the upstream model.
 target_subset
Subset of target genes to use for differential expression analysis. If not given, will use all target genes from the upstream model.
 cell_type
Cell type to use to use for differential expression analysis. If given, will use the ligand/receptor subset obtained from :func ~`CCI_deg_detection_setup` and cells of the chosen cell type in the model.
 use_dim_reduction
Whether to use PCA representation of the data to find nearest neighbors. If False, will instead use the Jaccard distance. Defaults to False. Note that this will ultimately fail if dimensionality reduction was not performed in :func ~`CCI_deg_detection_setup`.
 kwargs
Keyword arguments for any of the Spateo argparse arguments. Should not include ‘adata_path’, ‘custom_lig_path’ & ‘ligand’ or ‘custom_pathways_path’ & ‘pathway’ (depending on whether ligands or pathways are being used for the analysis), and should not include ‘output_path’ (which will be determined by the output path used for the main model). Should also not include any of the other arguments for this function
 Returns:
Fitted model instance that can be used for further downstream applications
 Return type:
downstream_model
 deg_effect_barplot(target: str, interaction_subset: Optional[List[str]] = None, top_n_interactions: Optional[int] = None, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, cmap: str = 'Blues', save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {})[source]#
Visualize the proportion of cells expressing a particular target (ligand, receptor, or target gene involved in an upstream CCI model) that are predicted to be affected by each transcription factor, or that are predicted to be affected by each L:R pair/ligand.
 Parameters:
 target
Target gene
 interaction_subset
Optional, can be used to specify subset of interactions (transcription factors, L:R pairs, etc.) to visualize, e.g. [“Sox2”, “Irx3”]. If not given, will default to all TFs, L:R pairs, etc.
 top_n_interactions
Optional, can be used to specify the top n interactions (transcription factors, L:R pair, ligand, etc.) to visualize. If not given, will default to all TFs, L:R pairs, etc.
 fontsize
Font size to determine size of the axis labels, ticks, title, etc.
 figsize
Width and height of plotting window
 cmap
Name of matplotlib colormap specifying colormap to use. Must be a sequential colormap.
 save_show_or_return
Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 deg_effect_heatmap(target_subset: Optional[List[str]] = None, target_type: Literal[ligand, receptor, target_gene, tf_target] = 'target_gene', to_plot: Literal[proportion, MuSIC_Interpreter.deg_effect_heatmap.specificity] = 'proportion', interaction_subset: Optional[List[str]] = None, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, cmap: str = 'magma', lower_proportion_threshold: float = 0.1, order_interactions: bool = False, order_targets: bool = False, remove_rows_and_cols_threshold: Optional[int] = None, save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {}, save_df: bool = False)[source]#
Visualize the proportion of cells expressing any target (ligand, receptor, or target gene involved in an upstream CCI model) that are predicted to be affected by each transcription factor, or that are predicted to be affected by each L:R pair/ligand, using a heatmap for visualization.
 Parameters:
 target_subset
Optional, can be used to specify subset of targets (ligands, receptors, target genes, or “TF_target” for target genes where the interaction to plot is TF effect) to visualize, e.g. [“Tubb1a”, “Tubb1b”]. If not given, will default to all targets.
 target_type
Type of target gene to visualize. Must be one of “ligand”, “receptor”, or “target_gene”. Defaults to “target_gene”. Used to specify where to search for the target genes to process.
 to_plot
Two options, “proportion” or “specificity”: for proportion, plot the proportion of cells expressing the target that are affected by each interaction. For specificity, take the proportion of cells affected by each interaction for which the interaction is predicted to affect a specific target.
 interaction_subset
Optional, can be used to specify subset of interactions (transcription factors, L:R pairs, etc.) to visualize, e.g. [“Sox2”, “Irx3”]. If not given, will default to all TFs, L:R pairs, etc.
 fontsize
Font size to determine size of the axis labels, ticks, title, etc.
 figsize
Width and height of plotting window
 cmap
Name of matplotlib colormap specifying colormap to use. Must be a sequential colormap.
 lower_proportion_threshold
Proportion threshold below which to set the proportion to 0 in the display. Defaults to 0.1.
 order_interactions
Whether to hierarchically sort the yaxis/interactions (transcription factors, L:R pairs, etc.).
 order_targets
Whether to hierarchically sort the xaxis/targets (ligands, receptors, target genes)
 remove_rows_and_cols_threshold
Optional, can be used to specify the threshold for the number of nonzero interactions/TFs a row/column needs to be displayed. If not given, all rows and columns will be displayed.
 save_show_or_return
Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 save_df
Set True to save the metric dataframe in the end
 top_target_barplot(interaction: str, target_subset: Optional[List[str]] = None, use_ligand_targets: bool = False, use_receptor_targets: bool = False, use_target_gene_targets: bool = True, top_n_targets: Optional[int] = None, fontsize: Union[None, int] = None, figsize: Union[None, Tuple[float, float]] = None, cmap: str = 'Blues', save_show_or_return: Literal[save, show, return, both, all] = 'show', save_kwargs: Optional[dict] = {})[source]#
Visualize the proportion of cells expressing each target (ligand, receptor, or target gene involved in an upstream CCI model) that are predicted to be affected by a given interaction, i.e. transcription factor, L:R pair/ligand.
 Parameters:
 interaction
The interaction to investigate, in the form specified in the design matrix, e.g. “Sox9” or “Igf1:Igf1r”.
 target_subset
Optional, specify subset of target genes to visualize. If not given, defaults to all targets.
 use_ligand_targets
Whether ligands should be used as targets, i.e. if “interaction” is a TF and the target genes being influenced by the TF are ligands. If True, will ignore “use_receptor_targets” and “use_target_gene_targets”.
 use_receptor_targets
Whether receptors should be used as targets, i.e. if “interaction” is a TF and the target genes being influenced by the TF are receptors. If True, will ignore “use_target_gene_targets”.
 use_target_gene_targets
Whether target genes should be used as targets, i.e. if “interaction” is a TF and the target genes being influenced by the TF are target genes (that are not ligands or receptors).
 top_n_targets
Number of top targets to visualize. Defaults to 10.
 fontsize
Font size to determine size of the axis labels, ticks, title, etc.
 figsize
Width and height of plotting window
 cmap
Name of matplotlib colormap specifying colormap to use. Must be a sequential colormap.
 save_show_or_return
Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.
 save_kwargs
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modifies those keys according to your needs.
 visualize_intercellular_network(lr_model_output_dir: str, target_subset: List[str]  str  None = None, top_n_targets: int  None = 3, ligand_subset: List[str]  str  None = None, receptor_subset: List[str]  str  None = None, regulator_subset: List[str]  str  None = None, include_tf_ligand: bool = False, include_tf_target: bool = True, cell_subset: List[str]  str  None = None, select_n_lr: int = 5, select_n_tf: int = 3, effect_size_threshold: float = 0.2, coexpression_threshold: float = 0.2, aggregate_method: Literal[mean, median, sum] = 'mean', cmap_neighbors: str = 'autumn', cmap_default: str = 'winter', scale_factor: float = 3, layout: Literal[random, circular, kamada, planar, spring, spectral, spiral] = 'planar', node_fontsize: int = 8, edge_fontsize: int = 8, arrow_size: int = 1, node_label_position: str = 'middle center', edge_label_position: str = 'middle center', upper_margin: float = 40, lower_margin: float = 20, left_margin: float = 50, right_margin: float = 50, title: str  None = None, save_path: str  None = None, save_id: str  None = None, save_ext: str = 'png', dpi: int = 300)[source]#
After fitting model, construct and visualize the inferred intercellular regulatory network. Effect sizes ( edge values) will be averaged over cells specified by “cell_subset”, otherwise all cells will be used.
 Parameters:
 lr_model_output_dir
Path to directory containing the outputs of the L:R model. This function will assume :attr output_path is the output path for the downstream model, i.e. connecting regulatory factors/TFs to ligands/receptors/targets.
 target_subset
Optional, can be used to specify target genes downstream of signaling interactions of interest. If not given, will use all targets used for the model.
 top_n_targets
Optional, can be used to specify the number of top targets to include in the network instead of providing full list of custom targets (“top” judged by fraction of the chosen subset of cells each target is expressed in).
 ligand_subset
Optional, can be used to specify subset of ligands. If not given, will use all ligands present in any of the interactions for the model.
 receptor_subset
Optional, can be used to specify subset of receptors. If not given, will use all receptors present in any of the interactions for the model.
 regulator_subset
Optional, can be used to specify subset of regulators (transcription factors, etc.). If not given, will use all regulatory molecules used in fitting the downstream model(s).
 include_tf_ligand
Whether to include TFligand interactions in the network. While providing more information, this can make it more difficult to interpret the plot. Defaults to False.
 include_tf_target
Whether to include TFtarget interactions in the network. While providing more information, this can make it more difficult to interpret the plot. Defaults to True.
 cell_subset
Optional, can be used to specify subset of cells to use for averaging effect sizes. If not given, will use all cells. Can be either:
A list of cell IDs (must be in the same format as the cell IDs in the adata object)
Cell type label(s)
 select_n_lr
Threshold for filtering out edges with low effect sizes, by selecting up to the top n L:R interactions per target (fewer can be selected if the top n are all zero). Default is 5.
 select_n_tf
Threshold for filtering out edges with low effect sizes, by selecting up to the top n TFs. For TFligand edges, will select the top n for each receptor (with a theoretical maximum of n * number of receptors in the graph).
 coexpression_threshold
For receptortarget, TFligand, TFreceptor links, only draw edges if the molecule pairs in question are coexpressed in > threshold number of cells.
 aggregate_method
Only used when “include_tf_ligand” is True. For the TFligand array, each row will be replaced by the mean, median or sum of the neighboring rows. Defaults to “mean”.
 cmap_neighbors
Colormap to use for nodes belonging to “source”/receiver cells. Defaults to yelloworangered.
 cmap_default
Colormap to use for nodes belonging to “neighbor”/sender cells. Defaults to purplebluegreen.
 scale_factor
Adjust to modify the size of the nodes
 layout
Used for positioning nodes on the plot. Options:  “random”: Randomly positions nodes ini the unit square.  “circular”: Positions nodes on a circle.  “kamada”: Positions nodes using KamadaKawai pathlength costfunction.  “planar”: Positions nodes without edge intersections, if possible.  “spring”: Positions nodes using FruchtermanReingold forcedirected algorithm.  “spectral”: Positions nodes using eigenvectors of the graph Laplacian.  “spiral”: Positions nodes in a spiral layout.
 node_fontsize
Font size for node labels
 edge_fontsize
Font size for edge labels
 arrow_size
Size of the arrow for directed graphs, by default 1
 node_label_position
Position of node labels. Options: ‘top left’, ‘top center’, ‘top right’, ‘middle left’, ‘middle center’, ‘middle right’, ‘bottom left’, ‘bottom center’, ‘bottom right’
 edge_label_position
Position of edge labels. Options: ‘top left’, ‘top center’, ‘top right’, ‘middle left’, ‘middle center’, ‘middle right’, ‘bottom left’, ‘bottom center’, ‘bottom right’
 title
Optional, title for the plot. If not given, will use the AnnData object path to derive this.
 upper_margin
Margin between top of the plot and top of the figure
 lower_margin
Margin between bottom of the plot and bottom of the figure
 left_margin
Margin between left of the plot and left of the figure
 right_margin
Margin between right of the plot and right of the figure
 save_path
Optional, directory to save figure to. If not given, will save to the parent folder of the path provided for :attr output_path in the argument specification.
 save_id
Optional unique identifier that can be used in saving. If not given, will use the AnnData object path to derive this.
 save_ext
File extension to save figure as. Default is “png”.
 dpi
Resolution to save figure at. Default is 300.
 Returns:
Graph object, such that it can be separately plotted in interactive window. sizing_list: List of node sizes, for use in interactive window. color_list: List of node colors, for use in interactive window.
 Return type:
G
 permutation_test(gene: str, n_permutations: int = 100, permute_nonzeros_only: bool = False, **kwargs)[source]#
Sets up permutation test for determination of statistical significance of model diagnostics. Can be used to identify true/the strongest signalresponsive expression patterns.
 Parameters:
 gene
Target gene to perform permutation test on.
 n_permutations
Number of permutations of the gene expression to perform. Default is 100.
 permute_nonzeros_only
Whether to only perform the permutation over the geneexpressing cells
 kwargs
Keyword arguments for any of the Spateo argparse arguments. Should not include ‘adata_path’, ‘target_path’, or ‘output_path’ (which will be determined by the output path used for the main model). Also should not include ‘custom_lig_path’, ‘custom_rec_path’, ‘mod_type’, ‘bw_fixed’ or ‘kernel’ (which will be determined by the initial model instantiation).
 eval_permutation_test(gene: str)[source]#
Evaluation function for permutation tests. Will compute multiple metrics (correlation coefficients, F1 scores, AUROC in the case that all cells were permuted, etc.) to compare true and modelpredicted gene expression vectors.
 Parameters:
 gene
Target gene for which to evaluate permutation test