Heatmaps

Functions for plotting heat- and clustermaps with seaborn. They are rather specific, and there might be better options in R or with PyComplexHeatmap.

*Heatmaps.base_code*
Heatmaps.heatmap_cols(plot_df, cmap_cols, plot_out, row_label_col=None, column_labels=None, class_col=None, x_size=20, y_size=40, title='', annot_cols=None, width_ratios=None, wspace=0.4, rasterized=True, annot_s=10, ticksize=14, heat_ticksize=14, square=False, x_rotation=70, y_rotation=0, ax_fontweight='normal', row_label_first=False, x_label_pos='top', formats=['pdf'])

Multiple heatmaps side-by-side but the same rows. Allows to show several metrics for the same rows with different colourmaps etc. E.g. for a list of top differential genes first a heatmap of baseline expression coloured by TPM, followed by a separate heatmap-block with the log2FC for the same genes.

Parameters:

  • cmap_cols –

    Dictionary with one entry for each block. The keys don’t matter as long as they are unique. E.g. {0: {‘cols’: [‘Mean_Control_FM’, ‘Mean_FM_Mock_Ctrl’, ‘Mean_Tcf15_FM’, ‘Mean_FM_Tcf15_OE’],

    ’centre’: 0, (optional) ‘cmap’: ‘mako’, ‘cbar_label’: ‘TPM’, ‘vmax’: 200, (optional) ‘vmin’: 0, (optional) }

  • row_label_col – Column where to fetch the row-strings from. Set to None to use the index.

  • column_labels – Alternative to using the column names as indicated in cmap_cols.

  • class_col – Column that should be added as separate first heatmap-block, should be categorical.

  • annot_cols – Dictionary of {“column”: “column with annotation string”} to write the strings in the value into the cells of columns.

  • width_ratios – Ratios of the widths of each heatmap-block.

  • wspace – Additional horizontal space between blocks.

  • rasterized – Whether to draw thin white lines around cells.

  • square – Whether cells should be squares.

  • row_label_first – Only write the row names for the first entry and skip for the others.

*Heatmaps.heatmap_cols*
_images/SubPenguins_MultiColHeatmap.png
Heatmaps.clustermap(plot_df, columns, row_column, cbar_label, class_col='', class_row='', title='', plot_out='', vmin=None, vmax=None, annot_cols=None, cmap='viridis', x_size=12, y_size=10, y_dendro=False, x_dendro=True, column_labels=None, row_cluster=True, col_cluster=True, centre=None, tick_size=12, mask=None, metric='euclidean', z_score=None, class_col_colour=None, formats=['pdf'], hlines=[], vlines=[], main_space=0.82, col_colours=None, x_rotation=0, y_rotation=0)

Create a heatmap that can be additionally clustered with seaborn. CARE: the class_col_order and class_row parameters are not properly tested.

Parameters:

  • columns – List of the columns, should be present in the plot_df.

  • row_column – Column with which rows should be taken, set to ‘index’ to take the df.index.

  • class_col – Add a column into the plot with a categorical value that will be coloured and gets a separate colourbar.

  • class_col_colour – Allows a list that will be taken iteratively, or a dict with {label: colour}.

  • class_row – Same as class_col but add a row instead.

  • annot_cols – Dictionary {col: other-col} to add text into the entries from col taken from other_col.

  • y_dendro – Whether to plot the dendrogram on y.

  • x_dendro – Whether to plot the dendrogram on x.

  • column_labels – List that will replace the names from columns if given.

  • row_cluster – Whether to cluster the rows.

  • col_cluster – Whether to cluster the columns.

  • centre – Centre for the colormap, e.g. 0 for bwr.

  • mask – Must match the dimensions of the plot_df. If given will not show data where entries are True.

  • metric – Metric for clustering for the scipy function, see https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html#scipy.spatial.distance.pdist.

  • z_score – Whether to do z-score normalization before clustering. If None won’t do z-scoring, otherwise takes 0 or 1 for the axis along which the normalization should be done.

  • hlines – List of indices where white horizontal lines will be added, e.g. for grouping subsets.

  • vlines – Same but vertical.

  • main_space – How much space the main heatmap will be given. Reduce if the colourbar overlaps the heatmap.

*Heatmaps.clustermap*

clustermap1 clustermap2