stereo.algorithm.regulatory_network_inference.PlotRegulatoryNetwork.auc_heatmap#

PlotRegulatoryNetwork.auc_heatmap(network_res_key='regulatory_network_inference', width=8, height=8, pivot_kws=None, method='average', metric='euclidean', z_score=None, standard_scale=None, cbar_kws=None, row_cluster=True, col_cluster=True, row_linkage=None, col_linkage=None, row_colors=None, col_colors=None, mask=None, dendrogram_ratio=(0.1, 0.2), colors_ratio=0.03, cbar_pos=(-0.05, 0.2, 0.03, 0.4), tree_kws=None)[source]#

Plot heatmap for auc value for regulons

Parameters:

  • network_res_key (str, optional) – the key which specifies inference regulatory network result in data.tl.result, defaults to ‘regulatory_network_inference’ # noqa

  • height (int, optional) – height of drawing

  • width (int, optional) – width of drawing

  • pivot_kws (dict, optional) – If data is a tidy dataframe, can provide keyword arguments for pivot to create a rectangular dataframe.

  • method (str, optional) – Linkage method to use for calculating clusters. See scipy.cluster.hierarchy.linkage() documentation for more information.

  • metric (str, optional) – Distance metric to use for the data. See scipy.spatial.distance.pdist() documentation for more options. To use different metrics (or methods) for rows and columns, you may construct each linkage matrix yourself and provide them as {row,col}_linkage.

  • z_score (int or None, optional) – Either 0 (rows) or 1 (columns). Whether or not to calculate z-scores for the rows or the columns. Z scores are: z = (x - mean)/std, so values in each row (column) will get the mean of the row (column) subtracted, then divided by the standard deviation of the row (column). This ensures that each row (column) has mean of 0 and variance of 1.

  • standard_scale (int or None, optional) – Either 0 (rows) or 1 (columns). Whether or not to standardize that dimension, meaning for each row or column, subtract the minimum and divide each by its maximum.

  • cbar_kws (dict, optional) – Keyword arguments to pass to cbar_kws in heatmap(), e.g. to add a label to the colorbar.

  • {row (list-like or pandas DataFrame/Series, optional) – If True, cluster the {rows, columns}.

  • col}_cluster (bool, optional) – If True, cluster the {rows, columns}.

  • {row – Precomputed linkage matrix for the rows or columns. See scipy.cluster.hierarchy.linkage() for specific formats.

  • col}_linkage (numpy.ndarray, optional) – Precomputed linkage matrix for the rows or columns. See scipy.cluster.hierarchy.linkage() for specific formats.

  • {row – List of colors to label for either the rows or columns. Useful to evaluate whether samples within a group are clustered together. Can use nested lists or DataFrame for multiple color levels of labeling. If given as a pandas.DataFrame or pandas.Series, labels for the colors are extracted from the DataFrames column names or from the name of the Series. DataFrame/Series colors are also matched to the data by their index, ensuring colors are drawn in the correct order.

  • col}_colors (list-like or pandas DataFrame/Series, optional) – List of colors to label for either the rows or columns. Useful to evaluate whether samples within a group are clustered together. Can use nested lists or DataFrame for multiple color levels of labeling. If given as a pandas.DataFrame or pandas.Series, labels for the colors are extracted from the DataFrames column names or from the name of the Series. DataFrame/Series colors are also matched to the data by their index, ensuring colors are drawn in the correct order.

  • mask (bool array or DataFrame, optional) – If passed, data will not be shown in cells where mask is True. Cells with missing values are automatically masked. Only used for visualizing, not for calculating.

  • {dendrogram (float, or pair of floats, optional) – Proportion of the figure size devoted to the two marginal elements. If a pair is given, they correspond to (row, col) ratios.

  • colors}_ratio (float, or pair of floats, optional) – Proportion of the figure size devoted to the two marginal elements. If a pair is given, they correspond to (row, col) ratios.

  • cbar_pos (tuple of (left, bottom, width, height), optional) – Position of the colorbar axes in the figure. Setting to None will disable the colorbar.

  • tree_kws (dict, optional) – Parameters for the matplotlib.collections.LineCollection that is used to plot the lines of the dendrogram tree.

:param : :type : return: matplotlib.figure