aaanalysis.CPPPlot.update_seq_size

CPPPlot.update_seq_size(ax=None, fig=None, max_x_dist=0.1, fontsize_tmd_jmd=None, weight_tmd_jmd='normal', tmd_color='mediumspringgreen', jmd_color='blue', tmd_seq_color='black', jmd_seq_color='white')

Update the font size of the sequence characters to prevent overlap.

This method adjusts the font size of TMD-JMD sequence characters based on their provided sequences to ensure that the labels are clearly legible and do not overlap in the plot.

Parameters:

• ax (plt.Axes) – CPP plot axes object.

• fig (plt.Figure, optional) – CPP plot figure object. If given, fontsize_tmd_jmd will be automatically adjusted.

• max_x_dist (float, default=0.1) – Maximum allowed horizontal distance between sequence characters during font size optimization. A greater value reduces potential overlaps of sequence characters.

• fontsize_tmd_jmd (int or float, optional) – Font size (>=0) for the part labels: ‘JMD-N’, ‘TMD’, ‘JMD-C’. If None, optimized automatically.

• weight_tmd_jmd ({'normal', 'bold'}, default='normal') – Font weight for the part labels: ‘JMD-N’, ‘TMD’, ‘JMD-C’.

• tmd_color (str, default='mediumspringgreen') – Color for TMD.

• jmd_color (str, default='blue') – Color for JMDs.

• tmd_seq_color (str, default='black') – Color for TMD sequence.

• jmd_seq_color (str, default='white') – Color for JMD sequences.

Returns:

ax – CPP plot axes object.

Return type:

plt.Axes

Notes

• Use CPPPlot.update_seq_size() AFTER matplotlib.pyplot.tight_layout().

See also

• CPPPlot.profile() and CPPPlot.heatmap() methods, which also use the tmd_seq, jmd_n_seq, and jmd_c_seq parameters. CPPPlot.update_seq_size() should be called after further plot modifications that alter the size of figure or x-axis.

Examples

To demonstrate the CPPPlot().update_seq_size() method, we first load the example DOM_GSEC dataset and its respective features (see [Breimann25a]):

import matplotlib.pyplot as plt
import aaanalysis as aa
aa.options["verbose"] = False

df_seq = aa.load_dataset(name="DOM_GSEC")
df_feat = aa.load_features(name="DOM_GSEC")
df_feat = df_feat.sort_values(by="feat_importance", ascending=False).reset_index(drop=True)
aa.display_df(df_feat, show_shape=True, n_rows=7)

DataFrame shape: (150, 15)

	feature	category	subcategory	scale_name	scale_description	abs_auc	abs_mean_dif	mean_dif	std_test	std_ref	p_val_mann_whitney	p_val_fdr_bh	positions	feat_importance	feat_importance_std
1	TMD_C_JMD_C-Seg...,11)-LIFS790102	Conformation	β-strand	β-strand	Conformational ...n-Sander, 1979)	0.189000	0.125674	0.125674	0.183876	0.218813	0.000001	0.000039	28,29	4.729200	4.776785
2	TMD_C_JMD_C-Seg...2,3)-CHOP780212	Conformation	β-sheet (C-term)	β-turn (1st residue)	Frequency of th...-Fasman, 1978b)	0.199000	0.065983	-0.065983	0.087814	0.105835	0.000000	0.000016	27,28,29,30,31,32,33	4.106000	5.236574
3	TMD_C_JMD_C-Seg...3,4)-HUTJ700102	Energy	Entropy	Entropy	Absolute entrop...Hutchens, 1970)	0.229000	0.098224	0.098224	0.106865	0.124608	0.000000	0.000001	31,32,33,34,35	3.111200	3.109955
4	TMD_C_JMD_C-Seg...2,3)-AURR980110	Conformation	α-helix	α-helix (middle)	Normalized posi...ora-Rose, 1998)	0.211000	0.077355	0.077355	0.102965	0.107453	0.000000	0.000005	27,28,29,30,31,32,33	3.048800	3.623912
5	TMD_C_JMD_C-Pat...4,8)-JANJ790102	Energy	Free energy (unfolding)	Transfer free e...(TFE) to inside	Transfer free e...y (Janin, 1979)	0.187000	0.144354	-0.144354	0.181777	0.233103	0.000001	0.000049	33,37	2.833600	3.640617
6	TMD_C_JMD_C-Pat...4,8)-KANM800103	Conformation	α-helix	α-helix	Average relativ...sa-Tsong, 1980)	0.176000	0.087846	0.087846	0.140464	0.157561	0.000004	0.000113	24,28	2.704000	4.076269
7	TMD_C_JMD_C-Pat...,10)-LEVM760105	Shape	Side chain length	Side chain length	Radius of gyrat... (Levitt, 1976)	0.149000	0.073526	0.073526	0.133612	0.157088	0.000090	0.000714	31,34,38	2.050800	2.338278

We can now use the CPPPlot().profile() method to create CPP profile showing the feature importance at single-residue resolution. We first retrieve from df_seq the sequences parts of a selected sample and provide them as parameters (jmd_n_seq, tmd_seq, jmd_c_seq) together with df_feat:

# Get sequence parts of first sample
jmd_n_seq, tmd_seq, jmd_c_seq = df_seq.loc[0, ["jmd_n", "tmd", "jmd_c"]]
args_seq = dict(jmd_n_seq=jmd_n_seq, tmd_seq=tmd_seq, jmd_c_seq=jmd_c_seq)
print(jmd_n_seq, tmd_seq, jmd_c_seq)

# Plot CPP profile for first sample
cpp_plot = aa.CPPPlot()
aa.plot_settings()
cpp_plot.profile(df_feat=df_feat, **args_seq)
plt.show()

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Adding a new element, such as a legend, to the plot could cause overlapping of sequence characters:

# Plot CPP profile for first sample with color-coded scale categories
cpp_plot = aa.CPPPlot()
aa.plot_settings()
cpp_plot.profile(df_feat=df_feat, **args_seq, add_legend_cat=True)

# Add legend right next to plot
list_cats = list(sorted(set(df_feat["category"])))
dict_color = aa.plot_get_cdict(name="DICT_CAT")
dict_color = {cat: dict_color[cat] for cat in list_cats}
fs = aa.plot_gcfs()
aa.plot_legend(dict_color=dict_color, y=1, x=0.9, n_cols=1, fontsize=fs-3)
plt.tight_layout()
plt.show()

Avoid sequence character overlap

The overlap of the sequence characters can be resolved by decreasing the sequence fontsize (seq_size) of the CPPPlot().profile() manually or by simply using the CPPPlot().udpdate_seq_size() method with the ax object of the respective plot:

# Adjust size of sequence characters
fig, ax = cpp_plot.profile(df_feat=df_feat, **args_seq, add_legend_cat=True)
aa.plot_legend(dict_color=dict_color, y=1, x=0.9, n_cols=1, fontsize=fs-3)
plt.tight_layout()

cpp_plot.update_seq_size(ax=ax) # Should be called AFTER plt.tight_layout()
plt.show()

To automatically adjust the fontsize of the part labels (‘JMD-N’, ‘TMD’, and ‘JMD-C’), provide the fig parameter:

# Adjust size of sequence characters and part labels
fig, ax = cpp_plot.profile(df_feat=df_feat, **args_seq, add_legend_cat=True)
aa.plot_legend(dict_color=dict_color, y=1, x=0.9, n_cols=1, fontsize=fs-3)
plt.tight_layout()

cpp_plot.update_seq_size(ax=ax, fig=fig)
plt.show()

You can adjust the sequence fontsize optimization process using the max_x_dist (default=0.1) parameter, where smaller values will yield a bigger fontsize:

fig, ax = cpp_plot.profile(df_feat=df_feat, **args_seq, add_legend_cat=True)
aa.plot_legend(dict_color=dict_color, y=1, x=0.9, n_cols=1, fontsize=fs-3)
plt.tight_layout()

cpp_plot.update_seq_size(ax=ax, fig=fig, max_x_dist=0.0)
plt.show()

The fontsize and weight of the part labels (‘JMD-N’, ‘TMD’, and ‘JMD-C’) can be manually adjusted using the fontsize_tmd_jmd and weight_tmd_jmd (default=‘bold’) parameters:

fig, ax = cpp_plot.profile(df_feat=df_feat, **args_seq, add_legend_cat=True)
aa.plot_legend(dict_color=dict_color, y=1, x=0.9, n_cols=1, fontsize=fs-3)
plt.tight_layout()

cpp_plot.update_seq_size(ax=ax, fig=fig, fontsize_tmd_jmd=16, weight_tmd_jmd="normal")
plt.show()

You can change the color of the sequence parts by the tmd_color (default=‘mediumspringgreen’) and jmd_color (default=‘blue’) parameters; tmd_color in the CPPPlot().profile() method should match. Adjust the sequence color using the tmd_seq_color (default=‘black’) and jmd_seq_color (default=‘white’) parameters:

fig, ax = cpp_plot.profile(df_feat=df_feat, **args_seq, add_legend_cat=True, tmd_color="orange")
aa.plot_legend(dict_color=dict_color, y=1, x=0.9, n_cols=1, fontsize=fs-3)
plt.tight_layout()

cpp_plot.update_seq_size(ax=ax, fig=fig, tmd_color="orange", jmd_color="white", tmd_seq_color="blue", jmd_seq_color="blue")
plt.show()