The import module
1. Read test data
2. View the data
3. Screening differential genes
4. View the data , Found more type This column
5. Number of Statistics
6. Map the volcano
7. Save the picture
The import module
import numpy as npimport pandas as pd1. Read test data data=pd.read_csv(r'E:\ZYH\R.project\rna-seq\lianxi1\exon_level\df.csv')2. View the data data.head()
# 3. Try to write down - and up-regulated gene classification assignment to "up" and "down" and "nosig" Join in pvalue Conditions ###loc function ： By row index "Index" To get the row data based on the specific value in （ If you take "Index" by "A" The line of ）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'4. View the data , Found more type This column data.head()
data.type.value_counts()up 123down 103Name: type, dtype: int646. Map the volcano import seaborn as snsimport mathimport matplotlib.pyplot as pltimport matplotlib as mpl%matplotlib inline# Yes padj Take one -log10 logarithm data['-logpadj']=-data.padj.apply(math.log10)# see data[['log2FoldChange','padj','type','-logpadj']].head()
# First set your own color colors = ["#01c5c4","#ff414d", "#686d76"]sns.set_palette(sns.color_palette(colors))# mapping ax=sns.scatterplot(x='log2FoldChange', y='-logpadj',data=data, hue='type',# Color mapping edgecolor = None,# Point boundary color s=8,# Point size )# label ax.set_title("vocalno")ax.set_xlabel("log2FC")ax.set_ylabel("-log10(padj)")# Move the legend position ax.legend(loc='center right', bbox_to_anchor=(0.95,0.76), ncol=1)
fig = ax.get_figure()fig.savefig('./python_vocalno.pdf')That's all python Details of the example of data visualization drawing volcano map , More about python For information about data visualization volcano map, please pay attention to other relevant articles on software development network ！
