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今天给大家介绍一个非常好用的Python数据可视化包：PyComplexHeatmap，对于用Python做机器学习、统计、科学研究和数据分析的朋友来说，这是一个非常有帮助的可视化工具：

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1. 安装

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pip install PyComplexHeatmap
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或者

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pip install git+https://github.com/DingWB/PyComplexHeatmap
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Github项目链接： https://github.com/DingWB/PyComplexHeatmap

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2. 各种各样的行/列注释

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[10]:
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import os,sys
-%matplotlib inline
-import matplotlib.pylab as plt
-import numpy as np
-plt.rcParams['figure.dpi'] = 100
-plt.rcParams['savefig.dpi']=300
-plt.rcParams['font.family']='sans serif' #please remove this line if font is not installed
-plt.rcParams['font.sans-serif']='Arial' # please remove this line if Arial font is not installed
-plt.rcParams['pdf.fonttype']=42
-from PyComplexHeatmap import *
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[3]:
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#Generate example dataset (random)
-df = pd.DataFrame(['GroupA'] * 5 + ['GroupB'] * 5, columns=['AB'])
-df['CD'] = ['C'] * 3 + ['D'] * 3 + ['G'] * 4
-df['EF'] = ['E'] * 6 + ['F'] * 2 + ['H'] * 2
-df['F'] = np.random.normal(0, 1, 10)
-df.index = ['sample' + str(i) for i in range(1, df.shape[0] + 1)]
-df.head()
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ABCDEFF
sample1GroupACE0.417550
sample2GroupACE0.052523
sample3GroupACE1.253483
sample4GroupADE-0.407985
sample5GroupADE-0.882742
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给一个上述Python dataframe，就可以直接画出热图的注视，比如：

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[4]:
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plt.figure(figsize=(3, 4))
-col_ha = pch.HeatmapAnnotation(
-    df=df,plot=True,legend=True,legend_gap=5,hgap=0.5,axis=1
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-plt.show()
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-Starting plotting HeatmapAnnotations
-Collecting annotation legends..
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-../_images/notebooks_pycomplexheatmap_intro_7_1.png -
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再举一个稍微复杂一点的例子：

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[13]:
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df = pd.DataFrame(['AAAA1'] * 5 + ['BBBBB2'] * 5, columns=['AB'])
-df['CD'] = ['C'] * 3 + ['D'] * 3 + ['G'] * 4
-df['F'] = np.random.normal(0, 1, 10)
-df.index = ['sample' + str(i) for i in range(1, df.shape[0] + 1)]
-df_box = pd.DataFrame(np.random.randn(10, 4), columns=['Gene' + str(i) for i in range(1, 5)])
-df_box.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
-df_bar = pd.DataFrame(np.random.uniform(0, 10, (10, 2)), columns=['TMB1', 'TMB2'])
-df_bar.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
-df_scatter = pd.DataFrame(np.random.uniform(0, 10, 10), columns=['Scatter'])
-df_scatter.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
-df_bar1 = pd.DataFrame(np.random.uniform(0, 10, (10, 2)), columns=['T1-A', 'T1-B'])
-df_bar1.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
-df_bar2 = pd.DataFrame(np.random.uniform(0, 10, (10, 2)), columns=['T2-A', 'T2-B'])
-df_bar2.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
-df_bar3 = pd.DataFrame(np.random.uniform(0, 10, (10, 2)), columns=['T3-A', 'T3-B'])
-df_bar3.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
-df_bar3.iloc[5,0]=np.nan
-df_bar4 = pd.DataFrame(np.random.uniform(0, 10, (10, 1)), columns=['T4'])
-df_bar4.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
-df_bar4.iloc[7,0]=np.nan
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[14]:
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plt.figure(figsize=(4, 8))
-col_ha = HeatmapAnnotation(
-            label=anno_label(df.AB, merge=True,rotation=15),
-            AB=anno_simple(df.AB,add_text=True), axis=1,
-            CD=anno_simple(df.CD, add_text=True,text_kws={'color':'black'}),
-            Exp=anno_boxplot(df_box, cmap='turbo',grid=True),
-            Scatter=anno_scatterplot(df_scatter,legend=True),
-            Line=anno_lineplot(df_bar2,linewidth=4,colors={'T2-B':'orangered','T2-A':'yellowgreen'},
-                              marker='D',legend=True),  #colors=['orangered','yellowgreen']
-            TMB_bar=anno_barplot(df_bar,legend=True,cmap='Set1'),
-            Bar1=anno_barplot(df_bar1,legend=True,colors=['red','black']), #colors can be str, list, tuple or dict
-            Bar4=anno_barplot(df_bar4,legend=True,cmap='turbo'),
-            Bar2=anno_barplot(df_bar2,legend=True,colors={'T2-B':'orangered','T2-A':'yellowgreen'}),
-            Bar3=anno_barplot(df_bar3,legend=True,cmap='Paired'),
-            plot=True,legend=True,legend_gap=5,hgap=4)
-col_ha.show_ticklabels(df.index.tolist(),fontdict={'color':'blue'},rotation=-30)
-plt.show()
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-Starting plotting HeatmapAnnotations
-Collecting annotation legends..
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-../_images/notebooks_pycomplexheatmap_intro_10_1.png -
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除了boxplot,barplot, scatter plot和sinmple annotation， 还有图片注释和标签注释，比如：

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680a90f85aeb41f2904533692fd11d63 f5474f74445845fab3103cf75652268d

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3. 单独画热图

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[5]:
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df_heatmap = pd.DataFrame(np.random.randn(30, 10), columns=['sample' + str(i) for i in range(1, 11)])
-df_heatmap.index = ["Fea" + str(i) for i in range(1, df_heatmap.shape[0] + 1)]
-df_heatmap.iloc[1, 2] = np.nan
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-plt.figure(figsize=(3.5, 6))
-cm = pch.ClusterMapPlotter(
-    data=df_heatmap,
-    col_cluster=True,row_cluster=True,
-    col_split=df.AB,row_split=2,
-    col_split_gap=0.5,row_split_gap=0.8,
-    label='values',row_dendrogram=True,
-    show_rownames=True,show_colnames=True,
-    row_names_side='right',
-    tree_kws={'row_cmap': 'Set1','colors':'blue'},verbose=0,legend_gap=5,
-    cmap='RdYlBu_r',xticklabels_kws={'labelrotation':-90,'labelcolor':'blue'})
-plt.savefig("example0.pdf", bbox_inches='tight')
-plt.show()
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-../_images/notebooks_pycomplexheatmap_intro_14_0.png -
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4. 热图与行/列注释一起画

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df_rows = df_heatmap.apply(lambda x:x.name if x.sample4 > 0.5 else None,axis=1)
-df_rows=df_rows.to_frame(name='Selected')
-df_rows['XY']=df_rows.index.to_series().apply(lambda x:'A' if int(x.replace('Fea',''))>=15 else 'B')
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-col_ha = pch.HeatmapAnnotation(
-    label=pch.anno_label(df.AB, merge=True,rotation=15),
-    AB=pch.anno_simple(df.AB,add_text=True,legend=True), axis=1,
-    CD=pch.anno_simple(df.CD, add_text=True,legend=True,
-                       text_kws={'color':'black'}),
-    Exp=pch.anno_boxplot(df_box, cmap='turbo',legend=True),
-    Scatter=pch.anno_scatterplot(df_scatter),
-    Bar1=pch.anno_barplot(df_bar1,legend=True,cmap='Dark2'),
-    Bar4=pch.anno_barplot(df_bar4,legend=True,cmap='turbo'),
-    legend=True,legend_gap=5,hgap=0.5)
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-row_ha = pch.HeatmapAnnotation(
-    Scatter=pch.anno_scatterplot(df_heatmap.sample4.apply(lambda x:round(x,2)),
-                    height=15,cmap='jet',legend=False),
-    Bar=pch.anno_barplot(df_heatmap.sample4.apply(lambda x:round(x,2)),
-                    height=15,cmap='rainbow',legend=False),
-    selected=pch.anno_label(df_rows,colors='red',relpos=(-0.05,0.4)),
-    label_kws={'rotation':15,'horizontalalignment':'left','verticalalignment':'bottom'},
-    axis=0,verbose=0)
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-plt.figure(figsize=(5.5, 8))
-cm = pch.ClusterMapPlotter(
-    data=df_heatmap, top_annotation=col_ha,right_annotation=row_ha,
-    col_cluster=True,row_cluster=True,
-    col_split=df.AB,row_split=2,
-    col_split_gap=0.5,row_split_gap=0.8,
-    label='values',row_dendrogram=True,
-    show_rownames=False,show_colnames=True,
-    tree_kws={'row_cmap': 'Set1'},verbose=0,legend_gap=5,
-    cmap='RdYlBu_r',xticklabels_kws={'labelrotation':-90,'labelcolor':'blue'})
-plt.savefig("example0.pdf", bbox_inches='tight')
-plt.show()
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-Starting plotting HeatmapAnnotations
-Collecting annotation legends..
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-../_images/notebooks_pycomplexheatmap_intro_16_1.png -
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热图中几乎所有的元素都可以通过代码和参数来控制，比如，如下图所示，可以控制各种距离：

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f2b583c48df74295a5f54167d5b3da25

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来自官网https://dingwb.github.io/PyComplexHeatmap的更多好看的热图示例：

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f5b9595d43764c28a7548bdc84e25777 9c670895f67840dc86caf167f38869e8 e9e8b0cdce794150a412c3afa9c1b891

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5. 点状热图：

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点状热图(dotHeatmap)可以用来展示多大5个维度的数据：行、列、点的颜色、点的大小、点的形状。如下图所示：

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76b6d2d8aa2e4ccfb3d3d9192c1d8c8d

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更多关于PyComplexHeatmap的教程和示例代码，请访问：https://github.com/DingWB/PyComplexHeatmap 和 https://dingwb.github.io/PyComplexHeatmap

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