PyComplexHeatmap进阶教程：用python画热图的【行】/【列】注释信息

生信技能树

发布于 2023-02-27 21:37:45

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发布于 2023-02-27 21:37:45

文章被收录于专栏：生信技能树

今天，给大家详细地介绍一下PyComplexheatmap(https://github.com/DingWB/PyComplexHeatmap) 中annotation的使用方法，也就是如何用python在热图中添加【行】/【列】注释信息。比如样本的疾病状态（肿瘤或者正常样本、年龄、性别、分型等）。

如何用python画热图上, 下, 左、右不同方向的【行】/【列】注释信息

# 导入示例数据
with open(os.path.join(os.path.dirname(PyComplexHeatmap.__file__),"../data/mammal_array.pkl"), 'rb') as f:
    data = pickle.load(f)
df, df_rows, df_cols, col_colors_dict = data

df

	GSM4412025	GSM4412026	GSM4412027	GSM4412028	GSM4412029	GSM4412030	GSM4412031	GSM4412032	GSM4412033	GSM4412034	...	GSM4997945	GSM4997946	GSM4997947	GSM4997948	GSM4997949	GSM4997950	GSM4997951	GSM4997952	GSM4997953	GSM4997954
sheep	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	0.435033	0.432900	0.446626	0.449123	0.497180	0.515918	0.483706	0.504681	0.529076	0.446443
beluga whale	0.687488	0.694207	0.706525	0.702734	0.687014	0.704003	0.705887	0.693806	0.719417	0.712677	...	0.560381	0.571552	0.610392	0.613619	0.675832	0.668502	0.624820	0.658377	0.702334	0.575034
house mouse	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
vaquita	0.693523	0.702525	0.716792	0.725095	0.711261	0.708651	0.717952	0.705486	0.720915	0.724171	...	0.581862	0.594443	0.628908	0.639457	0.680801	0.707493	0.662338	0.665142	0.751859	0.584952
large flying fox	0.286822	0.269406	0.296796	0.314719	0.305074	0.308419	0.268421	0.297236	0.295019	0.297973	...	0.281684	0.363926	0.367216	0.359392	0.289821	0.257351	0.234301	0.295840	0.249336	0.344848
greater horseshoe bat	0.530606	0.517989	0.520719	0.525855	0.507583	0.511993	0.528107	0.521563	0.542159	0.509526	...	0.735151	0.714805	0.708868	0.738207	0.823604	0.828040	0.823382	0.796882	0.878783	0.693625
little brown bat	0.202525	0.215990	0.238977	0.241872	0.267750	0.236729	0.202384	0.240519	0.251148	0.267257	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

7 rows × 883 columns

df_rows

	PredictedTaxid	PredictedSpecies	common_names	Family
sheep	9940.0	ovis_aries_rambouillet	sheep	Bovidae
beluga whale	9749.0	delphinapterus_leucas	beluga whale	Monodontidae
house mouse	10090.0	mus_musculus	house mouse	Muridae
vaquita	42100.0	phocoena_sinus	vaquita	Phocoenidae
large flying fox	132908.0	pteropus_vampyrus	large flying fox	Pteropodidae
greater horseshoe	NaN	NaN	NaN	NaN
bat	59479.0	rhinolophus_ferrumequinum	greater horseshoe bat	Rhinolophidae
little brown bat	59463.0	myotis_lucifugus	little brown bat	Vespertilionidae

df_cols

	GSE	Basename	NCBI_scientific_name	taxid	Tissue	Sex	Family	Order	Species	SuccessRate	common_names
GSM4412025	GSE147003	GSM4412025	Ovis aries	9940	Blood	Female	Bovidae	Artiodactyla	Ovis aries	0.765818	sheep
GSM4412026	GSE147003	GSM4412026	Ovis aries	9940	Blood	Female	Bovidae	Artiodactyla	Ovis aries	0.797669	sheep
GSM4412027	GSE147003	GSM4412027	Ovis aries	9940	Blood	Female	Bovidae	Artiodactyla	Ovis aries	0.759256	sheep
GSM4412028	GSE147003	GSM4412028	Ovis aries	9940	Blood	Male	Bovidae	Artiodactyla	Ovis aries	0.749813	sheep
GSM4412029	GSE147003	GSM4412029	Ovis aries	9940	Blood	Female	Bovidae	Artiodactyla	Ovis aries	0.770433	sheep
...	...	...	...	...	...	...	...	...	...	...	...
GSM4997950	GSE164127	GSM4997950	Eptesicus fuscus	29078	Skin	Male	Vespertilionidae	Chiroptera	Eptesicus fuscus	0.660425	big brown bat
GSM4997951	GSE164127	GSM4997951	Eptesicus fuscus	29078	Skin	Male	Vespertilionidae	Chiroptera	Eptesicus fuscus	0.652822	big brown bat
GSM4997952	GSE164127	GSM4997952	Eptesicus fuscus	29078	Skin	Female	Vespertilionidae	Chiroptera	Eptesicus fuscus	0.664746	big brown bat
GSM4997953	GSE164127	GSM4997953	Eptesicus fuscus	29078	Skin	Male	Vespertilionidae	Chiroptera	Eptesicus fuscus	0.650848	big brown bat
GSM4997954	GSE164127	GSM4997954	Eptesicus fuscus	29078	Skin	Male	Vespertilionidae	Chiroptera	Eptesicus fuscus	0.657170	big brown bat

883 rows × 11 columns

注释信息放在热图「上方」

#Put annotations on the top
col_ha = HeatmapAnnotation(label=anno_label(df_cols.Family, merge=True, rotation=45),
                               Family=anno_simple(df_cols.Family, legend=True),
                               Tissue=df_cols.Tissue,label_side='right', axis=1)
plt.figure(figsize=(7, 4))
cm = ClusterMapPlotter(data=df, top_annotation=col_ha,
                       show_rownames=True, show_colnames=False,row_names_side='left',
                       col_split=df_cols.Family, cmap='exp1', label='AUC',
                       rasterized=True, legend=True)
#plt.savefig("clustermap.pdf", bbox_inches='tight')
plt.show()

Starting calculating row orders..
Starting calculating col orders..
Starting plotting HeatmapAnnotations
Collecting annotation legends..
Incresing ncol
Incresing ncol
More than 3 cols is not supported
Legend too long, generating a new column..

如果有很多图例，PyComplexHeatmap会自动将所有的图例按顺序排列，如果一列放不下，就「自动增加一列」，用两列来画图例（figure legends）。如上图所示，就有2列图例。此外，在注释文字（比如Bovidae）与热图之间曲线的形状和颜色都会随着文字的旋转角度和颜色一起变化，会自动调整角度，使之与注释文字的角度相匹配。

注释信息放在热图「下方」

#Put annotations on the bottom
col_ha = HeatmapAnnotation(Tissue=anno_simple(df_cols.Tissue,height=5),
                           Family=anno_simple(df_cols.Family, legend=False,height=6),
                           label=anno_label(df_cols.Family, merge=True,rotation=-45),
                           label_side='right',axis=1)
plt.figure(figsize=(7, 4))
cm = ClusterMapPlotter(data=df, bottom_annotation=col_ha,
                       show_rownames=True, show_colnames=False,row_names_side='right',
                       col_split=df_cols.Family, cmap='jet', label='AUC',
                       rasterized=True, legend=True)
plt.show()

Starting calculating row orders..
Starting calculating col orders..
Starting plotting HeatmapAnnotations
Collecting annotation legends..

如果想要把列注释信息放在热图下方，那就需要「改变HeatmapAnnotation的顺序」, anno_label 应该放在「最后」：anno_label(df_cols.Family) 应该是倒数第二个。anno_label中的rotation参数应该从45度变为-45度，因为旋转方向发生了改变。此外，可以通过设置row_names_side='right'来将【行】名字row labels挪到热图右边。值得注意的是，「热图与图例之间的间隙是自动调节」的，比如，当row_names_side='right'时，热图右边有了文字，图例就自动往右边挪了，不用我们额外设置。【行】注释（annotation bar）的高度，可以通过height (mm)参数来设置，比如，在上图的anno_simple中，我们将Family这个bar图的高度设置成了5mm，而Tissue的高度仍然是默认（4mm）。

注释信息放在热图「左方」

#Put annotations on the left
row_ha = HeatmapAnnotation(label=anno_label(df_cols.Family, merge=True,rotation=45),
                           Family=anno_simple(df_cols.Family, legend=False,height=5),
                           Tissue=anno_simple(df_cols.Tissue,height=5),
                           label_side='top',
                           label_kws={'rotation':45,'rotation_mode':'anchor','color':'red'},
                           axis=0)
plt.figure(figsize=(4, 7))
cm = ClusterMapPlotter(data=df.T,left_annotation=row_ha,
                       show_rownames=False, show_colnames=True,col_names_side='top',
                       row_split=df_cols.Family, cmap='exp1', label='AUC',
                       rasterized=True, legend=True,
                       xticklabels_kws={'labelrotation':45,'labelcolor':'blue'})
plt.show()

Starting calculating row orders..
Starting calculating col orders..
Starting plotting HeatmapAnnotations
Collecting annotation legends..

我们首先把df进行转制（df.T），col_ha改成row_ha（现在变成了【行】注释）。我们可以通过改变参数col_names_side='top'来把【行】注释的标签（xlabel）放到热图上方（或者bottom，放到热图下方），另外，改变xticklabels_kws参数可以改变【行】注释标签的旋转角度和颜色（比如上图中的Tissue和Family，旋转45度，颜色为红色）：label_kws={'rotation':45,'rotation_mode':'anchor','color':'red'}。

注释信息放在热图「右方」

#Put annotation on the right
row_ha = HeatmapAnnotation(Tissue=df_cols.Tissue,
                           Family=anno_simple(df_cols.Family, legend=False,height=5),
                           label=anno_label(df_cols.Family, merge=True,rotation=45),
                           label_side='bottom',
                           label_kws={'rotation':-45,'rotation_mode':'anchor','color':'red'},
                           axis=0)
plt.figure(figsize=(4, 7))
cm = ClusterMapPlotter(data=df.T,right_annotation=row_ha,
                       show_rownames=False, show_colnames=True,col_names_side='bottom',
                       row_split=df_cols.Family, cmap='jet', label='AUC',
                       rasterized=True, legend=True,
                       xticklabels_kws={'labelrotation':-45,'labelcolor':'blue'})
#plt.savefig("annotation.pdf", bbox_inches='tight')
plt.show()

Starting calculating row orders..
Starting calculating col orders..
Starting plotting HeatmapAnnotations
Collecting annotation legends..

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原始发表：2023-01-13，如有侵权请联系 cloudcommunity@tencent.com 删除

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