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2022/12/31阅读：136主题：凝夜紫
Python也能画漂亮的complex heatmap？
微信公众号：「Computational Epigenetics」
关注生物信息学和计算表观遗传学。问题或建议，请公众号留言。
对于经常用R语言来画图的科研工作者来说，应该对「ComplexHeatmap」很熟悉了吧。这个包画的热图，既专业又漂亮。
可惜的是，在python中，一直没能出现一个可以画出好看complex heatmap的包，由于我们在用python做机器学习或者处理大数据的时候，也需要画热图，而在python和R中来回切换，也比较麻烦而且没有效率。
今天，给大家介绍一款可以在python中画出类似于R中ComplexHeatmap效果的包：PyComplexHeatmap。直接看下面的代码和图吧：
1. Import packages
import os,sys
import PyComplexHeatmap
from PyComplexHeatmap import *
%matplotlib inline
import matplotlib.pylab as plt
plt.rcParams['figure.dpi'] = 120
plt.rcParams['savefig.dpi']=300
2. A quick example
#Generate example dataset
df = pd.DataFrame(['AAAA1'] * 5 + ['BBBBB2'] * 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_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_heatmap = pd.DataFrame(np.random.randn(50, 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
plt.figure(figsize=(6, 12))
row_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True),
AB=anno_simple(df.AB,add_text=True),axis=1,
CD=anno_simple(df.CD, colors={'C': 'red', 'D': 'yellow', 'G': 'green'},add_text=True),
Exp=anno_boxplot(df_box, cmap='turbo'),
Scatter=anno_scatterplot(df_scatter), TMB_bar=anno_barplot(df_bar),
)
cm = ClusterMapPlotter(data=df_heatmap, top_annotation=row_ha, col_split=2, row_split=3, col_split_gap=0.5,
row_split_gap=1,col_dendrogram=False,plot=True,
tree_kws={'col_cmap': 'Set1', 'row_cmap': 'Dark2'})
plt.savefig("example1_heatmap.pdf", bbox_inches='tight')
plt.show()
3. Plotting annotations
3.1 Only plot the row/column annotation
plt.figure(figsize=(6, 4))
row_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True),
AB=anno_simple(df.AB,add_text=True,legend=True), axis=1,
CD=anno_simple(df.CD, colors={'C': 'red', 'D': 'gray', 'G': 'yellow'},
add_text=True,legend=True),
Exp=anno_boxplot(df_box, cmap='turbo',legend=True),
Scatter=anno_scatterplot(df_scatter), TMB_bar=anno_barplot(df_bar,legend=True),
plot=True,legend=True,legend_gap=5
)
plt.savefig("col_annotation.pdf", bbox_inches='tight')
plt.show()
anno_label:
anno_label is used to add a text label to the annotatin, parameter merge control whether to merge the adjacent labels with the same text, if merge != True, then, texts would be draw for each columns.
anno_simple:
anno_simple is to draw simple annotatin, cmap for anno_simple can be either categorical (Set1, Dark2, tab10 et.al) or continnuous (jet, turbo, parula). Parameter add_text control whether to add text on the annotation, if the color and fontsize in text_kws was not specified, the color and fontsize would be determined automatically, for example, if the background color is deep, then the text color would be white, otherwise the text color would be black. The text color can be changed with parameter text_kws={'color':your_color},for example:
plt.figure(figsize=(5, 4))
row_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True),
AB=anno_simple(df.AB,add_text=True,legend=True,text_kws={'color':'gold'}), axis=1,
CD=anno_simple(df.CD,add_text=True,legend=True,text_kws={'color':'purple'}),
Exp=anno_boxplot(df_box, cmap='turbo',legend=True),
Scatter=anno_scatterplot(df_scatter), TMB_bar=anno_barplot(df_bar,legend=True),
plot=True,legend=True,legend_gap=5)
plt.show()
To add a annotation quickly, you just need a dataframe
if df was given, all columns in dataframe df would be treated as a separately anno_simple annotation.
plt.figure(figsize=(3, 3))
row_ha = HeatmapAnnotation(df=df,plot=True,legend=True)
plt.show()
3.2 Plot the figure and legend separately
Sometimes, one only want to plot the figure without legend, or plot the legend in a separated pdf, you can do that by giving the parameter plot_legend=False, and plot the legend in another pdf with row_ha.plot_legends
plt.figure(figsize=(6, 4))
row_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True),
AB=anno_simple(df.AB,add_text=True,legend=True), axis=1,
CD=anno_simple(df.CD,add_text=True,legend=True),
Exp=anno_boxplot(df_box, cmap='turbo',legend=True),
Scatter=anno_scatterplot(df_scatter), TMB_bar=anno_barplot(df_bar,legend=True),
plot=True,legend=True,plot_legend=False,
legend_gap=5
)
plt.savefig("col_annotation.pdf", bbox_inches='tight')
plt.show()
plt.figure()
row_ha.plot_legends()
plt.savefig("legend.pdf",bbox_inches='tight')
plt.show()
No ax was provided, using plt.gca()
4. Plotting clustermap and annotation
Here we provided a example dataset in PyComplexHeatmap, let's visualaize it.
!wget https://github.com/DingWB/PyComplexHeatmap/raw/main/data/influence_of_snp_on_beta.pickle
--2022-05-05 22:37:43-- https://github.com/DingWB/pyclustermap/raw/main/data/influence_of_snp_on_beta.pickle
Resolving github.com (github.com)... 140.82.112.4
Connecting to github.com (github.com)|140.82.112.4|:443... connected.
HTTP request sent, awaiting response... 404 Not Found
2022-05-05 22:37:43 ERROR 404: Not Found.
import pickle
import urllib
f=open("influence_of_snp_on_beta.pickle",'rb')
data=pickle.load(f)
f.close()
beta,snp,df_row,df_col,col_colors_dict,row_colors_dict=data
# beta is DNA methylation beta values matrix, df_row and df_col are row and columns annotation respectively, col_colors_dict and row_colors_dict are color for annotation
print(beta.iloc[:,list(range(5))].head(5))
print(df_row.head(5))
print(df_col.head(5))
beta=beta.sample(2000)
snp=snp.loc[beta.index.tolist()]
df_row=df_row.loc[beta.index.tolist()]
204875570030_R01C02 204875570030_R04C01 \
cg30848532_TC21 0.525089 0.419515
cg30147375_BC21 0.803776 0.585928
cg46239718_BC21 0.443958 0.517514
cg36100119_BC21 0.351977 0.528846
cg42738582_BC21 0.783958 0.724901
204875570030_R05C01 204875570030_R06C01 204875570035_R05C02
cg30848532_TC21 0.483276 0.460750 0.390317
cg30147375_BC21 0.510269 0.831463 0.550146
cg46239718_BC21 0.535909 0.450167 0.564107
cg36100119_BC21 0.524896 0.374422 0.551200
cg42738582_BC21 0.802178 0.848621 0.850481
chr Target CpG ExtensionBase ProbeDesign CON mapFlag \
cg30848532_TC21 chr12 1 1 0 II C 16
cg30147375_BC21 chr11 0 0 0 II C 0
cg46239718_BC21 chr8 1 1 0 II C 0
cg36100119_BC21 chr19 1 1 0 II C 16
cg42738582_BC21 chr5 0 0 0 II C 16
Group \
cg30848532_TC21 Suboptimal hybridization
cg30147375_BC21 No Effect
cg46239718_BC21 Artificial low meth. reading
cg36100119_BC21 Suboptimal hybridization
cg42738582_BC21 Suboptimal hybridization
Type
cg30848532_TC21 1-1-0-CG-GG-II-C-16-GA-chr12-79760438
cg30147375_BC21 0-0-0-ca-ac-II-C-0-AG-chr11-109557651
cg46239718_BC21 1-1-0-cg-gt-II-C-0-GA-chr8-117860829
cg36100119_BC21 1-1-0-CG-GG-II-C-16-GA-chr19-5877949
cg42738582_BC21 0-0-0-AA-AA-II-C-16-AG-chr5-122031379
Strain Tissue Sex
204875570030_R01C02 MOLF_EiJ Frontal Lobe Brain Female
204875570030_R04C01 CAST_EiJ Frontal Lobe Brain Male
204875570030_R05C01 CAST_EiJ Frontal Lobe Brain Female
204875570030_R06C01 MOLF_EiJ Frontal Lobe Brain Male
204875570035_R05C02 CAST_EiJ Liver Male
row_ha = HeatmapAnnotation(Target=anno_simple(df_row.Target,colors=row_colors_dict['Target'],rasterized=True),
Group=anno_simple(df_row.Group,colors=row_colors_dict['Group'],rasterized=True),
axis=0)
col_ha= HeatmapAnnotation(label=anno_label(df_col.Strain,merge=True,rotation=15),
Strain=anno_simple(df_col.Strain,add_text=True),
Tissue=df_col.Tissue,Sex=df_col.Sex,axis=1) #df=df_col.loc[:,['Strain','Tissue','Sex']]
plt.figure(figsize=(6, 10))
cm = ClusterMapPlotter(data=beta, top_annotation=col_ha, left_annotation=row_ha,
show_rownames=False,show_colnames=False,
row_dendrogram=False,col_dendrogram=False,
row_split=df_row.loc[:, ['Target', 'Group']],
col_split=df_col['Strain'],cmap='parula',
rasterized=True,row_split_gap=1,legend=True,
tree_kws={'col_cmap':'Set1'})
plt.savefig("clustermap.pdf", bbox_inches='tight')
plt.show()
Key features:
Users can split the columns and rows into multiple subgroups by giving row_split and col_split, row_split and col_split can be number of pandas dataframe or Series.
5. Composite multiple heatmap horizontally or vertically
row_ha = HeatmapAnnotation(Target=anno_simple(df_row.Target, colors=row_colors_dict['Target'], rasterized=True),
Group=anno_simple(df_row.Group, colors=row_colors_dict['Group'], rasterized=True),
axis=0)
col_ha = HeatmapAnnotation(label=anno_label(df_col.Strain, merge=True, rotation=15),
Strain=anno_simple(df_col.Strain, add_text=True),
Tissue=df_col.Tissue, Sex=df_col.Sex,
axis=1) # df=df_col.loc[:,['Strain','Tissue','Sex']]
cm1 = ClusterMapPlotter(data=beta, top_annotation=col_ha, left_annotation=row_ha,
show_rownames=False, show_colnames=False,
row_dendrogram=False, col_dendrogram=False,
row_split=df_row.loc[:, ['Target', 'Group']],
col_split=df_col['Strain'], cmap='parula',
rasterized=True, row_split_gap=1, legend=True,
plot=False,label='beta',
tree_kws={'col_cmap': 'Set1'}) #
cm2 = ClusterMapPlotter(data=snp, top_annotation=col_ha, left_annotation=row_ha,
show_rownames=False, show_colnames=False,
row_dendrogram=False, col_dendrogram=False,
col_cluster_method='ward',row_cluster_method='ward',
col_cluster_metric='jaccard',row_cluster_metric='jaccard',
row_split=df_row.loc[:, ['Target', 'Group']],
col_split=df_col['Strain'],
rasterized=True, row_split_gap=1, legend=True,
plot=False,cmap='Greys',label='SNP',
tree_kws={'col_cmap': 'Set1'}) #
cmlist=[cm1,cm2]
plt.figure(figsize=(10,12))
composite(cmlist=cmlist, main=1,legendpad=0,legend_y=0.8)
plt.savefig("beta_snp.pdf", bbox_inches='tight')
plt.show()
希望这篇文章能对大家有帮助！扫描文末二维码或者搜索关注 Computational Epigenetics 公众号。 
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