跟着Science学画图:python的seaborn模块画下三

论文

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

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部分数据和代码是公开的，我们今天试着重复一下论文补充材料里的 Figure S29

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这个热图是用python中的seaborn模块画的，下面介绍画图代码

导入需要用到的模块

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

读入数据集

部分数据截图如下

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file = "matrix-b73-ref.csv"
b73Ref = pd.read_csv(file, index_col=0).reindex(["B97", "Ky21", "M162W", 
                                                 "Ms71", "Oh43", "Oh7B", "M37W", "Mo18W", "Tx303", "HP301", "P39",
                                                 "Il14H", "CML52", "CML69", "CML103", "CML228", "CML247", "CML277",
                                                 "CML322", "CML333", "Ki3", "Ki11", "NC350", "NC358", "Tzi8"])
b73Ref = b73Ref[["B97", "Ky21", "M162W", 
                 "Ms71", "Oh43", "Oh7B", "M37W", "Mo18W", "Tx303", "HP301", "P39", 
                 "Il14H", "CML52", "CML69", "CML103", "CML228", "CML247", "CML277", 
                 "CML322", "CML333", "Ki3", "Ki11", "NC350", "NC358", "Tzi8"]]

这里 index_col=0是用数据集中的第一列来做行名

reindx()函数是将行按照自己制定的内容排序

[[]]是把列按照指定的内容排序

查看数据集的前5行

b73Ref.head(5)

最基本的热图

sns.heatmap(b73Ref)

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只保留下三角

这里直接读取的数据集的数据类型是整数型，我们需要把数据转换为浮点型。论文中提供的代码是没有转换数据类型的，如果完全按照他的代码运行可能会遇到报错，这里可能是因为python的版本不同吧，我现在用的python是3.8.3

colnames = ["B97", "Ky21", "M162W", 
                 "Ms71", "Oh43", "Oh7B", "M37W", "Mo18W", "Tx303", "HP301", "P39", 
                 "Il14H", "CML52", "CML69", "CML103", "CML228", "CML247", "CML277", 
                 "CML322", "CML333", "Ki3", "Ki11", "NC350", "NC358", "Tzi8"]

dtype = {}

for colname in colnames:
    dtype[colname] = np.float64
    
df = b73Ref.astype(dtype)

mask = np.triu(np.ones_like(df,dtype=bool))

sns.heatmap(df,mask=mask)

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这里 np.ones_like()函数是生成一个和指定数据集维度一样的矩阵 数值全是1

np.triu()函数是将下三角变成0或者false

更改配色

cmap = sns.diverging_palette(370, 120, n=80, as_cmap=True)
sns.heatmap(df, mask=mask, cmap=cmap, robust=True,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})

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添加辅助线,去掉y轴标题

f, ax = plt.subplots(figsize=(14, 14))
cmap = sns.diverging_palette(370, 120, n=80, as_cmap=True)
sns.heatmap(df, mask=mask, cmap=cmap, robust=True,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})
plt.ylabel('')
ax.axvline(x=6, color ='blue', lw = 1.5, alpha = 0.75, ymax = 0.76)
ax.axvline(x=9, color ='blue', lw = 1.5, alpha = 0.75, ymax = 0.64)
ax.axvline(x=10, color ='blue', lw = 1.5, alpha = 0.75, ymax = 0.6)
ax.axvline(x=12, color ='blue', lw = 1.5, alpha = 0.75, ymax = 0.52)
ax.axhline(y=6, color ='black', lw = 1.5, alpha = 0.75, xmax = 0.24)
ax.axhline(y=9, color ='black', lw = 1.5, alpha = 0.75, xmax = 0.36)
ax.axhline(y=10, color ='black', lw = 1.5, alpha = 0.75, xmax = 0.4)
ax.axhline(y=12, color ='black', lw = 1.5, alpha = 0.75, xmax = 0.48)

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给坐标轴的标签赋予颜色

f, ax = plt.subplots(figsize=(14, 14))
cmap = sns.diverging_palette(370, 120, n=80, as_cmap=True)
sns.heatmap(df, mask=mask, cmap=cmap, robust=True,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})
plt.ylabel('')
ax.axvline(x=6, color ='blue', lw = 1.5, alpha = 0.75, ymax = 0.76)
ax.axvline(x=9, color ='blue', lw = 1.5, alpha = 0.75, ymax = 0.64)
ax.axvline(x=10, color ='blue', lw = 1.5, alpha = 0.75, ymax = 0.6)
ax.axvline(x=12, color ='blue', lw = 1.5, alpha = 0.75, ymax = 0.52)
ax.axhline(y=6, color ='black', lw = 1.5, alpha = 0.75, xmax = 0.24)
ax.axhline(y=9, color ='black', lw = 1.5, alpha = 0.75, xmax = 0.36)
ax.axhline(y=10, color ='black', lw = 1.5, alpha = 0.75, xmax = 0.4)
ax.axhline(y=12, color ='black', lw = 1.5, alpha = 0.75, xmax = 0.48)
mycol = ["#4169E1", "#4169E1", "#4169E1", "#4169E1", "#4169E1", "#4169E1", "#787878", "#787878", "#787878", "#DA70D6", "#FF4500", "#FF4500", "#32CD32", "#32CD32", "#32CD32", "#32CD32", "#32CD32", "#32CD32", "#32CD32", "#32CD32", "#32CD32", "#32CD32", "#32CD32", "#32CD32", "#32CD32"]
for tick, color in zip(ax.get_xticklabels(), mycol): tick.set_color(color)
for tick, color in zip(ax.get_yticklabels(), mycol): tick.set_color(color)

plt.savefig("1.pdf")

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这个是最终的结果

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