standard_scale to plots

scanpy/plotting/_anndata.py

@@ -761,7 +761,7 @@ def clustermap(
 @doc_params(show_save_ax=doc_show_save_ax, common_plot_args=doc_common_plot_args)
 def stacked_violin(adata, var_names, groupby=None, log=False, use_raw=None, num_categories=7,
                    figsize=None,  dendrogram=False, gene_symbols=None,
-                   var_group_positions=None, var_group_labels=None,
+                   var_group_positions=None, var_group_labels=None, standard_scale=None,
                    var_group_rotation=None, layer=None, stripplot=False, jitter=False, size=1,
                    scale='width', order=None, swap_axes=False, show=None, save=None,
                    row_palette='muted', **kwds):
@@ -796,6 +796,9 @@ def stacked_violin(adata, var_names, groupby=None, log=False, use_raw=None, num_
         should be a valid seaborn palette name or a valic matplotlib colormap
         (see https://seaborn.pydata.org/generated/seaborn.color_palette.html). Alternatively,
         a single color name or hex value can be passed. E.g. 'red' or '#cc33ff'
+    standard_scale : {{'var', 'obs'}}, optional (default: None)
+        Whether or not to standardize that dimension between 0 and 1, meaning for each variable or observation,
+        subtract the minimum and divide each by its maximum.
     swap_axes: `bool`, optional (default: `False`)
          By default, the x axis contains `var_names` (e.g. genes) and the y axis the `groupby` categories.
          By setting `swap_axes` then x are the `groupby` categories and y the `var_names`. When swapping
@@ -822,6 +825,18 @@ def stacked_violin(adata, var_names, groupby=None, log=False, use_raw=None, num_
     categories, obs_tidy = _prepare_dataframe(adata, var_names, groupby, use_raw, log, num_categories,
                                               gene_symbols=gene_symbols, layer=layer)
 
+    if standard_scale == 'obs':
+        obs_tidy = obs_tidy.sub(obs_tidy.min(1), axis=0)
+        obs_tidy = obs_tidy.div(obs_tidy.max(1), axis=0).fillna(0)
+    elif standard_scale == 'var':
+        obs_tidy -= obs_tidy.min(0)
+        obs_tidy /= obs_tidy.max(0).fillna(0)
+    elif standard_scale is None:
+        pass
+    else:
+        logg.warn('Unknown type for standard_scale, ignored')
+
+
     if 'color' in kwds:
         row_palette = kwds['color']
         # remove color from kwds in case is set to avoid an error caused by
@@ -1045,7 +1060,8 @@ def rename_cols_to_int(value):
 @doc_params(show_save_ax=doc_show_save_ax, common_plot_args=doc_common_plot_args)
 def heatmap(adata, var_names, groupby=None, use_raw=None, log=False, num_categories=7,
             dendrogram=False, gene_symbols=None, var_group_positions=None, var_group_labels=None,
-            var_group_rotation=None, layer=None, swap_axes=False, show_gene_labels=None, show=None, save=None, figsize=None, **kwds):
+            var_group_rotation=None, layer=None, standard_scale=None, swap_axes=False,
+            show_gene_labels=None, show=None, save=None, figsize=None, **kwds):
     """\
     Heatmap of the expression values of genes.
 
@@ -1058,6 +1074,9 @@ def heatmap(adata, var_names, groupby=None, use_raw=None, log=False, num_categor
     Parameters
     ----------
     {common_plot_args}
+    standard_scale : {{'var', 'obs'}}, optional (default: None)
+        Whether or not to standardize that dimension between 0 and 1, meaning for each variable or observation,
+        subtract the minimum and divide each by its maximum.
     swap_axes: `bool`, optional (default: `False`)
          By default, the x axis contains `var_names` (e.g. genes) and the y axis the `groupby`
          categories (if any). By setting `swap_axes` then x are the `groupby` categories and y the `var_names`.
@@ -1080,26 +1099,21 @@ def heatmap(adata, var_names, groupby=None, use_raw=None, log=False, num_categor
     if use_raw is None and adata.raw is not None: use_raw = True
     if isinstance(var_names, str):
         var_names = [var_names]
-    if not use_raw and layer is None:
-        # this most likely will used a scaled version of the data
-        # and thus is better to use a diverging scale
-        param_set = False
-        if 'vmin' not in kwds:
-            kwds['vmin'] = -3
-            param_set = True
-        if 'vmax' not in kwds:
-            kwds['vmax'] = 3
-            param_set = True
-        if 'cmap' not in kwds:
-            kwds['cmap'] = 'bwr'
-            param_set = True
-        if param_set:
-            logg.info('Divergent color map has been automatically set to plot non-raw data. Use '
-                      '`vmin`, `vmax` and `cmap` to adjust the plot.')
 
     categories, obs_tidy = _prepare_dataframe(adata, var_names, groupby, use_raw, log, num_categories,
                                               gene_symbols=gene_symbols, layer=layer)
 
+    if standard_scale == 'obs':
+        obs_tidy = obs_tidy.sub(obs_tidy.min(1), axis=0)
+        obs_tidy = obs_tidy.div(obs_tidy.max(1), axis=0).fillna(0)
+    elif standard_scale == 'var':
+        obs_tidy -= obs_tidy.min(0)
+        obs_tidy /= obs_tidy.max(0).fillna(0)
+    elif standard_scale is None:
+        pass
+    else:
+        logg.warn('Unknown type for standard_scale, ignored')
+
     if groupby is None or len(categories) <= 1:
         categorical = False
         # dendrogram can only be computed  between groupby categories
@@ -1305,7 +1319,7 @@ def heatmap(adata, var_names, groupby=None, use_raw=None, log=False, num_categor
 @doc_params(show_save_ax=doc_show_save_ax, common_plot_args=doc_common_plot_args)
 def dotplot(adata, var_names, groupby=None, use_raw=None, log=False, num_categories=7,
             color_map='Reds', dot_max=None, dot_min=None, figsize=None, dendrogram=False,
-            gene_symbols=None, var_group_positions=None,
+            gene_symbols=None, var_group_positions=None, standard_scale=None, smallest_dot=0.,
             var_group_labels=None, var_group_rotation=None, layer=None, show=None, save=None, **kwds):
     """\
     Makes a *dot plot* of the expression values of `var_names`.
@@ -1334,6 +1348,12 @@ def dotplot(adata, var_names, groupby=None, use_raw=None, log=False, num_categor
         If none, the minimum dot size is set to 0. If given,
         the value should be a number between 0 and 1. All fractions smaller than dot_min are clipped to
         this value.
+    standard_scale : {{'var', 'group'}}, optional (default: None)
+        Whether or not to standardize that dimension between 0 and 1, meaning for each variable or group,
+        subtract the minimum and divide each by its maximum.
+    smallest_dot : `float` optional (default: 0.)
+        If none, the smallest dot has size 0. All expression levels with `dot_min` are potted with
+        `smallest_dot` dot size.
 
     {show_save_ax}
     **kwds : keyword arguments
@@ -1362,6 +1382,17 @@ def dotplot(adata, var_names, groupby=None, use_raw=None, log=False, num_categor
     # 1. compute mean value
     mean_obs = obs_tidy.groupby(level=0).mean()
 
+    if standard_scale == 'group':
+        mean_obs = mean_obs.sub(mean_obs.min(1), axis=0)
+        mean_obs = mean_obs.div(mean_obs.max(1), axis=0).fillna(0)
+    elif standard_scale == 'var':
+        mean_obs -= mean_obs.min(0)
+        mean_obs /= mean_obs.max(0).fillna(0)
+    elif standard_scale is None:
+        pass
+    else:
+        logg.warn('Unknown type for standard_scale, ignored')
+
     # 2. compute fraction of cells having value >0
     # transform obs_tidy into boolean matrix
     obs_bool = obs_tidy.astype(bool)
@@ -1489,6 +1520,7 @@ def dotplot(adata, var_names, groupby=None, use_raw=None, log=False, num_categor
         frac = ((frac - dot_min) / old_range)
 
     size = (frac * 10) ** 2
+    size += smallest_dot
     import matplotlib.colors
 
     normalize = matplotlib.colors.Normalize(vmin=kwds.get('vmin'), vmax=kwds.get('vmax'))
@@ -1542,13 +1574,18 @@ def dotplot(adata, var_names, groupby=None, use_raw=None, log=False, num_categor
     else:
         fracs_values = fracs_legends
     size = (fracs_values * 10) ** 2
+    size += smallest_dot
     color = [cmap(normalize(value)) for value in np.repeat(max(mean_flat) * 0.7, len(size))]
 
     # plot size bar
     size_legend = fig.add_subplot(axs3[0])
 
     size_legend.scatter(np.repeat(0, len(size)), range(len(size)), s=size, color=color)
     size_legend.set_yticks(range(len(size)))
+    labels = ["{:.0%}".format(x) for x in fracs_legends]
+    if dot_max < 1:
+        labels[-1] = ">" + labels[-1]
+    size_legend.set_yticklabels(labels)
     size_legend.set_yticklabels(["{:.0%}".format(x) for x in fracs_legends])
 
     size_legend.tick_params(axis='y', left=False, labelleft=False, labelright=True)
@@ -1573,7 +1610,9 @@ def dotplot(adata, var_names, groupby=None, use_raw=None, log=False, num_categor
 @doc_params(show_save_ax=doc_show_save_ax, common_plot_args=doc_common_plot_args)
 def matrixplot(adata, var_names, groupby=None, use_raw=None, log=False, num_categories=7,
                figsize=None, dendrogram=False, gene_symbols=None, var_group_positions=None, var_group_labels=None,
-               var_group_rotation=None, layer=None, swap_axes=False, show=None, save=None, **kwds):
+               var_group_rotation=None, layer=None, standard_scale=None, swap_axes=False, show=None,
+               save=None, **kwds):
+
     """\
     Creates a heatmap of the mean expression values per cluster of each var_names
     If groupby is not given, the matrixplot assumes that all data belongs to a single
@@ -1582,6 +1621,9 @@ def matrixplot(adata, var_names, groupby=None, use_raw=None, log=False, num_cate
     Parameters
     ----------
     {common_plot_args}
+    standard_scale : {{'var', 'group'}}, optional (default: None)
+        Whether or not to standardize that dimension between 0 and 1, meaning for each variable or group,
+        subtract the minimum and divide each by its maximum.
     {show_save_ax}
     **kwds : keyword arguments
         Are passed to `matplotlib.pyplot.pcolor`.
@@ -1601,22 +1643,6 @@ def matrixplot(adata, var_names, groupby=None, use_raw=None, log=False, num_cate
     if use_raw is None and adata.raw is not None: use_raw = True
     if isinstance(var_names, str):
         var_names = [var_names]
-    if use_raw is False:
-        # this most likely will used a scaled version of the data
-        # and thus is better to use a diverging scale
-        param_set = False
-        if 'vmin' not in kwds:
-            kwds['vmin'] = -3
-            param_set = True
-        if 'vmax' not in kwds:
-            kwds['vmax'] = 3
-            param_set = True
-        if 'cmap' not in kwds:
-            kwds['cmap'] = 'bwr'
-            param_set = True
-        if param_set:
-            logg.info('Divergent color map has been automatically set to plot non-raw data. Use '
-                      '`vmin`, `vmax` and `cmap` to adjust the plot.')
 
     categories, obs_tidy = _prepare_dataframe(adata, var_names, groupby, use_raw, log, num_categories,
                                               gene_symbols=gene_symbols, layer=layer)
@@ -1626,6 +1652,17 @@ def matrixplot(adata, var_names, groupby=None, use_raw=None, log=False, num_cate
 
     mean_obs = obs_tidy.groupby(level=0).mean()
 
+    if standard_scale == 'group':
+        mean_obs = mean_obs.sub(mean_obs.min(1), axis=0)
+        mean_obs = mean_obs.div(mean_obs.max(1), axis=0).fillna(0)
+    elif standard_scale == 'var':
+        mean_obs -= mean_obs.min(0)
+        mean_obs /= mean_obs.max(0).fillna(0)
+    elif standard_scale is None:
+        pass
+    else:
+        logg.warn('Unknown type for standard_scale, ignored')
+
     if dendrogram:
         dendro_data = _reorder_categories_after_dendrogram(adata, groupby, dendrogram,
                                                            var_names=var_names,
@@ -1850,7 +1887,7 @@ def tracksplot(adata, var_names, groupby, use_raw=None, log=False,
     groupby_height = 0.24
     num_rows = len(var_names) + 2  # +1 because of dendrogram on top and categories at bottom
     if figsize is None:
-        width = 10
+        width = 12
         track_height = 0.25
     else:
         width, height = figsize
@@ -1862,7 +1899,7 @@ def tracksplot(adata, var_names, groupby, use_raw=None, log=False,
     obs_tidy = obs_tidy.T
 
     fig = pl.figure(figsize=(width, height))
-    axs = gridspec.GridSpec(ncols=2, nrows=num_rows, wspace=0.3 / width,
+    axs = gridspec.GridSpec(ncols=2, nrows=num_rows, wspace=1.0 / width,
                             hspace=0, height_ratios=height_ratios,
                             width_ratios=[width, 0.14])
     axs_list = []
@@ -1877,7 +1914,8 @@ def tracksplot(adata, var_names, groupby, use_raw=None, log=False,
         axs_list.append(ax)
         for cat_idx, category in enumerate(categories):
             x_start, x_end = x_values[cat_idx]
-            ax.fill_between(range(x_start, x_end), 0, obs_tidy.iloc[idx, x_start:x_end], lw=0.1, color=groupby_colors[cat_idx])
+            ax.fill_between(range(x_start, x_end), 0, obs_tidy.iloc[idx, x_start:x_end], lw=0.1,
+                            color=groupby_colors[cat_idx])
 
         # remove the xticks labels except for the last processed plot.
         # Because the plots share the x axis it is redundant and less compact to plot the
@@ -1894,9 +1932,9 @@ def tracksplot(adata, var_names, groupby, use_raw=None, log=False,
         ymin, ymax = ax.get_ylim()
         ymax = int(ymax)
         ax.set_yticks([ymax])
-        tt = ax.set_yticklabels([str(ymax)], ha='right', va='top')
-
-        ax.tick_params(axis='y', labelsize='x-small', right=True, left=False, pad=-5,
+        tt = ax.set_yticklabels([str(ymax)], ha='left', va='top')
+        ax.spines['right'].set_position(('axes', 1.01))
+        ax.tick_params(axis='y', labelsize='x-small', right=True, left=False, length=2,
                        which='both', labelright=True, labelleft=False, direction='in')
         ax.set_ylabel(var, rotation=0, fontsize='small', ha='right', va='bottom')
         ax.yaxis.set_label_coords(-0.005, 0.1)

scanpy/tests/test_plotting.py

@@ -48,6 +48,16 @@ def test_heatmap():
                   num_categories=4, figsize=(4.5, 5), show=False)
     save_and_compare_images('master_heatmap2')
 
+    # test var/obs standardization and layer
+    adata.layers['test'] = -1 * adata.X.copy()
+    sc.pl.heatmap(adata, adata.var_names, 'cell_type', use_raw=False, dendrogram=True, show=False,
+                  standard_scale='var', layer='test')
+    save_and_compare_images('master_heatmap_std_scale_var', tolerance=15)
+
+    sc.pl.heatmap(adata, adata.var_names, 'cell_type', use_raw=False, dendrogram=True, show=False,
+                  standard_scale='obs')
+    save_and_compare_images('master_heatmap_std_scale_obs', tolerance=15)
+
 
 def test_dotplot():
     adata = sc.datasets.krumsiek11()
@@ -71,6 +81,15 @@ def test_dotplot():
                   figsize=(7, 2.5), dendrogram=True, show=False)
     save_and_compare_images('master_dotplot3', tolerance=15)
 
+    # test var/group standardization smallest_dot
+    sc.pl.dotplot(adata, adata.var_names, 'cell_type', use_raw=False, dendrogram=True, show=False,
+                  standard_scale='var', smallest_dot=40)
+    save_and_compare_images('master_dotplot_std_scale_var', tolerance=15)
+
+    sc.pl.dotplot(adata, adata.var_names, 'cell_type', use_raw=False, dendrogram=True, show=False,
+                  standard_scale='group', smallest_dot=10)
+    save_and_compare_images('master_dotplot_std_scale_group', tolerance=15)
+
 
 def test_matrixplot():
     adata = sc.datasets.krumsiek11()
@@ -81,6 +100,16 @@ def test_matrixplot():
     sc.pl.matrixplot(adata, adata.var_names, 'cell_type', use_raw=False, dendrogram=True, show=False, swap_axes=True)
     save_and_compare_images('master_matrixplot_swap_axes', tolerance=15)
 
+    # test var/group standardization and layer
+    adata.layers['test'] = -1 * adata.X.copy()
+    sc.pl.matrixplot(adata, adata.var_names, 'cell_type', use_raw=False, dendrogram=True,
+                     show=False, standard_scale='var', layer='test', cmap='Blues_r')
+    save_and_compare_images('master_matrixplot_std_scale_var', tolerance=15)
+
+    sc.pl.matrixplot(adata, adata.var_names, 'cell_type', use_raw=False, dendrogram=True,
+                     show=False, standard_scale='group', swap_axes=True)
+    save_and_compare_images('master_matrixplot_std_scale_group', tolerance=15)
+
     # test matrixplot numeric column and alternative cmap
     adata.obs['Gata2'] = adata.X[:, 0]
     sc.pl.matrixplot(adata, adata.var_names, 'Gata2', use_raw=False,
@@ -150,7 +179,7 @@ def test_rank_genes_groups():
 
     # test ranked genes using heatmap (swap_axes=True show_gene_labels=False)
     sc.pl.rank_genes_groups_heatmap(pbmc, n_genes=20, swap_axes=True, use_raw=False,
-                                    show_gene_labels=False, show=False)
+                                    show_gene_labels=False, show=False, vmin=-3, vmax=3, cmap='bwr')
     save_and_compare_images('master_ranked_genes_heatmap_swap_axes', tolerance=tolerance)
 
     # test ranked genes using stacked violin plots