Python数据分析绘图过程详细讲解(附代码)
前言
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作者:小汤豆
来源:汤豆道课
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一. 数据准备
数据说明
示例数据,其中数据均为虚拟数据,与实际生物学过程无关
文件名:dataset_volcano.txt
列分别为基因 (gene),差异倍数(logFC),t-test的P值(P.Value)
二. 绘制火山图
先上效果图:
![](http://n4.ikafan.com/assetsj/blank.gif)
Step 1: 导入数据:
import pandas as pd # Data analysisimport numpy as np # Scientific computingimport seaborn as sns # Statistical visualization# 读取数据df = pd.read_csv('./dataset_volcano.txt', sep='\t')result = pd.DataFrame()result['x'] = df['logFC']result['y'] = df['P.Value']result['-log10(pvalue)']=-df['P.Value'].apply(np.log10)
Step2: 设置阈值
# 设置pvalue和logFC的阈值cut_off_pvalue = 0.0000001cut_off_logFC = 1
Step3: 设置分组
#分组为up, normal, downresult.loc[(result.x> cut_off_logFC )&(result.y < cut_off_pvalue),'group'] = 'up'result.loc[(result.x< -cut_off_logFC )&(result.y < cut_off_pvalue),'group'] = 'down'result.loc[(result.x>=-cut_off_logFC )&(result.x<=cut_off_logFC )|(result.y >= cut_off_pvalue),'group'] = 'normal'
Step4: 绘制散点图
#绘制散点图ax = sns.scatterplot(x="x", y="-log10(pvalue)", hue='group', hue_order = ('down','normal','up'), palette=("#377EB8","grey","#E41A1C"), alpha=0.5, s=15, data=result)
Step5: 设置散点图
#确定坐标轴显示范围xmin=-6xmax=10ymin=7ymax=13ax.spines['right'].set_visible(False) #去掉右边框ax.spines['top'].set_visible(False) #去掉上边框ax.vlines(-cut_off_logFC, ymin, ymax, color='dimgrey',linestyle='dashed', linewidth=1) #画竖直线ax.vlines(cut_off_logFC, ymin, ymax, color='dimgrey',linestyle='dashed', linewidth=1) #画竖直线ax.hlines(-np.log10(cut_off_pvalue), xmin, xmax, color='dimgrey',linestyle='dashed', linewidth=1) #画竖水平线ax.set_xticks(range(xmin, xmax, 4))# 设置x轴刻度ax.set_yticks(range(ymin, ymax, 2))# 设置y轴刻度ax.set_ylabel('-log10(pvalue)',fontweight='bold') # 设置y轴标签ax.set_xlabel('log2(fold change)',fontweight='bold') # 设置x轴标签
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