Volcano plot

Volcano plot is a scatter plot specifically for showing significant levels (e.g., p-value) and fold-changes

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

df = pd.read_csv("/home/yli11/tmp/results.KO_vs_WT.csv",sep="\t",index_col=0)
df.head()

	logFC	AveExpr	t	P.Value	adj.P.Val	B	WT_1_log2CPM	WT_2_log2CPM	WT_3_log2CPM	KO_1_log2CPM	KO_2_log2CPM	KO_3_log2CPM
gene
D17H6S56E-5	-3.0830	9.3418	-97.669	2.276800e-15	3.597600e-11	25.102	10.8880	10.9120	10.8500	7.7671	7.8119	7.8218
Scd1	-2.2133	6.1060	-50.068	1.151200e-12	9.095200e-09	19.799	7.2574	7.1911	7.1920	5.0828	4.9264	4.9864
Coro2a	-1.4558	7.9154	-46.998	2.073900e-12	1.092300e-08	19.285	8.6433	8.6614	8.6256	7.1924	7.2202	7.1495
Plxnb2	-2.9373	3.6346	-42.033	5.854300e-12	1.598600e-08	17.639	5.0743	5.1443	5.1107	2.2122	2.2622	2.0040
Gzmb	-1.8469	4.9198	-41.606	6.436800e-12	1.598600e-08	18.097	5.7934	5.8635	5.8686	3.9655	3.9610	4.0665

plt.scatter(x=df['logFC'],y=df['adj.P.Val'].apply(lambda x:-np.log10(x)),s=1)

<matplotlib.collections.PathCollection at 0x2aad5daf8df0>

plt.scatter(x=df['logFC'],y=df['adj.P.Val'].apply(lambda x:-np.log10(x)),s=1)
plt.xlabel("logFC")
plt.ylabel("-logFDR")
plt.axvline(-2,color="grey",linestyle="--")
plt.axvline(2,color="grey",linestyle="--")
plt.axhline(2,color="grey",linestyle="--")

<matplotlib.lines.Line2D at 0x2aad5dde57c0>

plt.scatter(x=df['logFC'],y=df['adj.P.Val'].apply(lambda x:-np.log10(x)),s=1,label="Not significant")

# highlight down- or up- regulated genes
down = df[(df['logFC']<=-2)&(df['adj.P.Val']<=0.01)]
up = df[(df['logFC']>=2)&(df['adj.P.Val']<=0.01)]

plt.scatter(x=down['logFC'],y=down['adj.P.Val'].apply(lambda x:-np.log10(x)),s=3,label="Down-regulated",color="blue")
plt.scatter(x=up['logFC'],y=up['adj.P.Val'].apply(lambda x:-np.log10(x)),s=3,label="Up-regulated",color="red")

plt.xlabel("logFC")
plt.ylabel("-logFDR")
plt.axvline(-2,color="grey",linestyle="--")
plt.axvline(2,color="grey",linestyle="--")
plt.axhline(2,color="grey",linestyle="--")
plt.legend()

<matplotlib.legend.Legend at 0x2aad5e36fbe0>

plt.scatter(x=df['logFC'],y=df['adj.P.Val'].apply(lambda x:-np.log10(x)),s=1,label="Not significant")

# highlight down- or up- regulated genes
down = df[(df['logFC']<=-2)&(df['adj.P.Val']<=0.01)]
up = df[(df['logFC']>=2)&(df['adj.P.Val']<=0.01)]

plt.scatter(x=down['logFC'],y=down['adj.P.Val'].apply(lambda x:-np.log10(x)),s=3,label="Down-regulated",color="blue")
plt.scatter(x=up['logFC'],y=up['adj.P.Val'].apply(lambda x:-np.log10(x)),s=3,label="Up-regulated",color="red")
for i,r in up.iterrows():
    plt.text(x=r['logFC'],y=-np.log10(r['adj.P.Val']),s=i)

plt.xlabel("logFC")
plt.ylabel("-logFDR")
plt.axvline(-2,color="grey",linestyle="--")
plt.axvline(2,color="grey",linestyle="--")
plt.axhline(2,color="grey",linestyle="--")
plt.legend()

<matplotlib.legend.Legend at 0x2aad619b2700>

from adjustText import adjust_text

plt.scatter(x=df['logFC'],y=df['adj.P.Val'].apply(lambda x:-np.log10(x)),s=1,label="Not significant")

# highlight down- or up- regulated genes
down = df[(df['logFC']<=-2)&(df['adj.P.Val']<=0.01)]
up = df[(df['logFC']>=2)&(df['adj.P.Val']<=0.01)]

plt.scatter(x=down['logFC'],y=down['adj.P.Val'].apply(lambda x:-np.log10(x)),s=3,label="Down-regulated",color="blue")
plt.scatter(x=up['logFC'],y=up['adj.P.Val'].apply(lambda x:-np.log10(x)),s=3,label="Up-regulated",color="red")
texts=[]
for i,r in up.iterrows():
    texts.append(plt.text(x=r['logFC'],y=-np.log10(r['adj.P.Val']),s=i))
adjust_text(texts,arrowprops=dict(arrowstyle="-", color='black', lw=0.5))
plt.xlabel("logFC")
plt.ylabel("-logFDR")
plt.axvline(-2,color="grey",linestyle="--")
plt.axvline(2,color="grey",linestyle="--")
plt.axhline(2,color="grey",linestyle="--")
plt.legend()

<matplotlib.legend.Legend at 0x2aad61abe760>