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import numpy as np

import pandas as pd

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data=pd.read_csv(r'E:\ZYH\R.project\rna-seq\lianxi1\exon_level\df.csv')

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data.head()

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# 3.尝试写循环筛选上下调基因分类赋值给 "up" 和 "down" 和 "nosig" 加入pvalue条件

###loc函数：通过行索引 "Index" 中的具体值来取行数据（如取"Index"为"A"的行）

data.loc[(data.log2FoldChange>1)&(data.padj<0.05),'type']='up'

data.loc[(data.log2FoldChange<-1)&(data.padj<0.05),'type']='down'

data.loc[(abs(data.log2FoldChange)<=1)|(data.padj>=0.05),'type']='nosig'

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data.head()

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data.type.value_counts()

up      123

down    103

Name: type, dtype: int64

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import seaborn as sns

import math

import matplotlib.pyplot as plt

import matplotlib as mpl

%matplotlib inline

# 对padj取个-log10对数

data['-logpadj']=-data.padj.apply(math.log10)

# 查看

data[['log2FoldChange','padj','type','-logpadj']].head()

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# 先设置一下自己的颜色

colors = ["#01c5c4","#ff414d", "#686d76"]

sns.set_palette(sns.color_palette(colors))

# 绘图

ax=sns.scatterplot(x='log2FoldChange', y='-logpadj',data=data,

                hue='type',#颜色映射

                edgecolor = None,#点边界颜色

                s=8,#点大小

                )

# 标签

ax.set_title("vocalno")

ax.set_xlabel("log2FC")

ax.set_ylabel("-log10(padj)")

#移动图例位置

ax.legend(loc='center right', bbox_to_anchor=(0.95,0.76), ncol=1)

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fig = ax.get_figure()

fig.savefig('./python_vocalno.pdf')