FACSPy.pl.cluster_heatmap

FACSPy.pl.cluster_heatmap#

FACSPy.pl.cluster_heatmap(adata, gate, layer, cluster_key, include_technical_channels=False, exclude=None, data_metric='mfi', scaling='MinMaxScaler', corr_method='pearson', cluster_method='distance', annotate=None, annotation_kwargs=None, cmap='RdYlBu_r', y_label_fontsize=10, figsize=(5, 3.8), return_dataframe=False, return_fig=False, show=True, save=None)#

Plots a heatmap where every column corresponds to one cluster and the rows display the marker expression.

Parameters:

  • adata (AnnData) – The anndata object of shape n_obs x n_vars where Rows correspond to cells and columns to the channels

  • gate (str) – The gate to be analyzed, called by the population name. This parameter has a default stored in fp.settings, but can be superseded by the user.

  • layer (str) – The layer corresponding to the data matrix. Similar to the gate parameter, it has a default stored in fp.settings which can be overwritten by user input.

  • cluster_key (str) – The .obs column where the cluster information is stored.

  • include_technical_channels (bool) – Whether to include technical channels. If set to False, will exclude all channels that are not labeled with type==”fluo” in adata.var.

  • exclude (Optional[Union`[:py:class:`list[str], str]]) – Channels to be excluded from plotting.

  • data_metric (Literal['mfi', 'fop']) – One of mfi or fop. Using a different metric will calculate the asinh fold change on mfi and fop values, respectively

  • scaling (Optional[Literal['MinMaxScaler', 'RobustScaler', 'StandardScaler']]) – Whether to apply scaling to the data for display. One of MinMaxScaler, RobustScaler or StandardScaler (Z-score).

  • corr_method (Literal['pearson', 'spearman', 'kendall']) – correlation method that is used for hierarchical clustering by cluster correlation. if cluster_method==distance, this parameter is ignored. One of pearson, spearman or kendall.

  • cluster_method (Literal['correlation', 'distance']) – Method for hierarchical clustering of displayed clusters. If correlation, the correlation specified by corr_method is computed (default: pearson). If distance, the euclidean distance is computed.

  • annotate (Optional[Literal['frequency'], str]]) – Parameter to control the annotation plot. Default: frequency. Adds a plot on top of the heatmap to display cluster-specific data. Other valid values are marker names as contained in adata.var_names

  • annotation_kwargs (Optional[dict]) – Used to specify and customize the annotation plot.

  • cmap (Optional[str]) – Sets the colormap for plotting the markers

  • y_label_fontsize (Optional[Union`[:py:class:`float, int]]) – controls the fontsize of the marker labels

  • figsize (Optional[tuple[float, float]]) – Contains the dimensions of the final figure as a tuple of two ints or floats.

  • return_dataframe (bool) – If set to True, returns the raw data that are used for plotting as a dataframe.

  • return_fig (bool) – If set to True, the figure is returned.

  • show (bool) – Whether to show the figure. Defaults to True.

  • save (Optional[str]) – Expects a file path including the file name. Saves the figure to the indicated path. Defaults to None.

Return type:

Optional[ClusterGrid, DataFrame]]

Returns:

  • If show==False a ClusterGrid

  • If return_fig==True a ClusterGrid

  • If return_dataframe==True a DataFrame containing the data used for plotting

Examples

import FACSPy as fp

dataset = fp.mouse_lineages()

fp.settings.default_gate = "CD45+"
fp.settings.default_layer = "transformed"

fp.tl.pca(dataset)
fp.tl.neighbors(dataset)
fp.tl.leiden(dataset)

fp.tl.mfi(dataset,
          groupby = "CD45+_transformed_leiden",
          aggregate = True)

fp.pl.cluster_heatmap(
    dataset,
    gate = "CD45+",
    layer = "transformed",
    cluster_key = "CD45+_transformed_leiden",
    annotate = "frequency",
    annotation_kwargs = {"groupby": "organ"},
    figsize = (4,6)
)
../_images/FACSPy-pl-cluster_heatmap-1.png
Contents