How to add statistical annotations to matplotlib plots

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It’s actually no big deal to add some statistical annotations to matplotlib plots. Let’s recap the example from the previous post,

import numpy as np
import matplotlib.pyplot as plt

# Generate some random dummy data:
np.random.seed(1)
Group_A = np.random.randn(10)*10+15
Group_B = np.random.randn(10)*10+2

fig=plt.figure(1, figsize=(4,6))
fig.clf()

# Group A data:
plt.plot(xVals, Group_A, 'o', markeredgecolor="blue",
         markerfacecolor="blue", markersize=20, alpha=0.5)
plt.plot(1, Group_A.mean(), 'o', markeredgecolor="k",
         markerfacecolor="white", markersize=20)

# Group B data:
plt.plot(xVals+1, Group_B, 'o', markeredgecolor="orange",
         markerfacecolor="orange", markersize=20, alpha=0.5)
plt.plot(2, Group_B.mean(), 'o', markeredgecolor="k",
         markerfacecolor="white", markersize=20)

plt.xticks([1,2], labels=["A", "B"], fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel("Groups", fontsize=16)
plt.ylabel("measurements", fontsize=16)
plt.title("A dot-plot", fontsize=22, fontweight="normal")

# control the black bound box and tick sizes:
ax = plt.gca() # get current axis
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_linewidth(2)
ax.spines["left"].set_linewidth(2)
ax.tick_params(width=2, length=10)

plt.xlim([0.5, 2.5])

plt.tight_layout
plt.show()


png

and perform a simple statistical test:

stats_results = pg.ttest(Group_A, Group_B, paired=False)
p_val = stats_results["p-val"].values[0].round(4)
print(f"p-value: {p_val}")

p-value: 0.0163

We can annotate our plot just by adding a horizontal line between the two data sets and add test result:

def asteriskscheck(pval):
    if stats_results["p-val"].values<=0.0001:
        asterisks="****"
    elif stats_results["p-val"].values<=0.001: 
        asterisks="***"
    elif stats_results["p-val"].values<=0.01: 
        asterisks="**"
    elif stats_results["p-val"].values<=0.05: 
        asterisks="*"
    else: 
        asterisks="n.s."
    return asterisks

fig=plt.figure(1, figsize=(4,6))
fig.clf()

# Group A data:
plt.plot(xVals, Group_A, 'o', markeredgecolor="blue",
         markerfacecolor="blue", markersize=20, alpha=0.5)
plt.plot(1, Group_A.mean(), 'o', markeredgecolor="k",
         markerfacecolor="white", markersize=20)

# Group B data:
plt.plot(xVals+1, Group_B, 'o', markeredgecolor="orange",
         markerfacecolor="orange", markersize=20, alpha=0.5)
plt.plot(2, Group_B.mean(), 'o', markeredgecolor="k",
         markerfacecolor="white", markersize=20)

# statistical annotations:
h = 36 # height of the horizontal bar
annotation_offset = 0.5 # offset of the stats-annotation
plt.plot([1, 2], [h, h], '-k', lw=3)
plt.text(1.5, h+annotation_offset, 
         asteriskscheck(p_val), 
         ha='center', va='bottom', fontsize=16)

plt.xticks([1,2], labels=["A", "B"], fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel("Groups", fontsize=16)
plt.ylabel("measurements", fontsize=16)
plt.title("A dot-plot", fontsize=22, fontweight="normal")

# control the black bound box and tick sizes:
ax = plt.gca() # get current axis
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_linewidth(2)
ax.spines["left"].set_linewidth(2)
ax.tick_params(width=2, length=10)

plt.xlim([0.5, 2.5])
plt.ylim([-22, 40])

plt.tight_layout
plt.show()



png

That’s everything! Of course, for problems with more than two samples the commands become a bit more complex. But the principle is always the same.

Asterisks conventions: The function asteriskscheck(pval) follows the asterisks conventions from GraphPad:

Symbol Meaning
n.s. $p\gt0.05$
$\mbox{*}$ $p\le0.05$
$\mbox{**}$ $p\le0.01$
$\mbox{***}$ $p\le0.001$
$\mbox{****}$ $p\le0.0001$

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