trevismd
commented
2 years ago Hello Erica,
Thanks for giving a shot and going the extra mile to make this work ;)
Also thank you because I think there is an improvement to make to statannotations, revealed by your intervention.
What you want is almost entirely possible now. Here are the steps involved.
- (I first created some fake data.)
- Then I converted the
"Assay"strto an ordered"category"data type, so that the plotter knows that even though there are only 2 categories it has datapoints for, it should leave space for the third one. - This allows to split the dataset in two parts for your two different y scales of interest.
- Finally, it is a good practice if possible to first adjust the plot,and then annotate it, so I pushed these instructions to the end.
Especially to make it work, then you can still try to move things and see what works and what doesn't.
So you'd get
import matplotlib.pyplot as plt
import pandas as pd
from statannotations.Annotator import Annotator
import seaborn as sns
df = pd.DataFrame([
{'PTK2B_genotype': 'WT', 'Assay': 'CCL8', 'Conc': 75_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL8', 'Conc': 59_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL2', 'Conc': 1_450_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL2', 'Conc': 900_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL13', 'Conc': 10_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL13', 'Conc': 11_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL8', 'Conc': 81_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL8', 'Conc': 41_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL2', 'Conc': 1_560_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL2', 'Conc': 800_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL13', 'Conc': 12_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL13', 'Conc': 11_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL8', 'Conc': 66_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL8', 'Conc': 63_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL2', 'Conc': 1_300_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL2', 'Conc': 920_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL13', 'Conc': 12_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL13', 'Conc': 12_500},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL8', 'Conc': 64_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL8', 'Conc': 57_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL2', 'Conc': 1_400_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL2', 'Conc': 750_000},
{'PTK2B_genotype': 'WT', 'Assay': 'CCL13', 'Conc': 13_000},
{'PTK2B_genotype': 'HOM', 'Assay': 'CCL13', 'Conc': 13_000},
])
assays = pd.CategoricalDtype(categories=['CCL8', 'CCL2', 'CCL13'],
ordered=True)
df.Assay = df.Assay.astype(assays)
df1 = df.loc[df.Assay == 'CCL2', :]
df2 = df.loc[df.Assay.isin(['CCL8', 'CCL13']), :]
pairs1 = [[("CCL2", "WT"), ("CCL2", "HOM")]]
pairs2 = [[("CCL8", "WT"), ("CCL8", "HOM")], [("CCL13", "WT"), ("CCL13", "HOM")]]
hue_parameters = {
'x': 'Assay',
'y': 'Conc',
'hue': 'PTK2B_genotype',
}
hue_parameters1 = {**hue_parameters, 'data': df1}
hue_parameters2 = {**hue_parameters, 'data': df2}
fig, (ax1, ax2) = plt.subplots(ncols=1, nrows=2, sharex=True)
ax1 = sns.boxplot(ax=ax1, **hue_parameters1)
ax2 = sns.boxplot(ax=ax2, **hue_parameters2)
ax1.set_ylim(500000, 2000000) # outliers only
ax2.set_ylim(0, 90000)
ax2.get_legend().remove()
ax1.spines['bottom'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax1.xaxis.tick_top()
ax1.tick_params(labeltop=False) # don't put tick labels at the top
ax2.xaxis.tick_bottom()
ax1.set_ylabel("")
ax1.set_xlabel("")
ax2.set_ylabel("")
# adjust distance between subplots
fig.subplots_adjust(hspace=0.5)
fig.text(0.01, 0.5, 'Concentration (pg/ml)', va='center', rotation='vertical')
annotator = Annotator(ax=ax1, pairs=pairs1, **hue_parameters1)
annotator.configure(test='t-test_ind', text_format='star', loc="inside")
annotator.apply_and_annotate()
annotator2 = Annotator(ax=ax2, pairs=pairs2, **hue_parameters2)
annotator2.configure(test='t-test_ind', text_format='star', loc="inside")
annotator2.apply_and_annotate()
fig.show()for a graph like this
There are parameters to change the annotations y offset, but I don't think it was the first thing to try here. However, you can look into them if you'd like more/less spacing now.
Hope that helps!
ericabello
Hello, thanks for the package it's great! I am trying to annotate a boxplot where I want to break the y axis into two but I cannot see the annotation of the data in the bottom part (ax2 subplot) Here is the code:
from statannotations.Annotator import Annotator df_samples_media = df_samples.loc[(df_samples["Sample"].str.contains("LPS"))] assays_list = (df_samples_media["Assay"].unique()) pairs2=[[("CCL8", "WT"),("CCL8", "HOM")] , [("CCL13", "WT"), ("CCL13", "HOM")]] pairs=[[("CCL2", "WT"),("CCL2", "HOM")]] hue_parameters= {'data': df_samples_media, 'x': 'Assay', 'y': 'Conc', "hue": "PTK2B_genotype", } fig, (ax, ax2) = plt.subplots(ncols=1, nrows=2, sharex=True) ax=sns.boxplot(hue_parameters, ax=ax) annotator = Annotator(ax, pairs, hue_parameters) annotator.configure(test='t-test_ind', text_format='star', loc="inside") annotator.apply_and_annotate() ax2=sns.boxplot(hue_parameters, ax=ax2) annotator2 = Annotator(ax2, pairs2, hue_parameters) annotator2.configure(test='t-test_ind', text_format='star',loc="inside") annotator2.apply_and_annotate() ax.set_ylim(500000, 2000000) # outliers only ax2.set_ylim(0, 90000) ax2.get_legend().remove() ax.spines['bottom'].set_visible(False) ax2.spines['top'].set_visible(False) ax.xaxis.tick_top() ax.tick_params(labeltop=False) # don't put tick labels at the top ax2.xaxis.tick_bottom() ax.set_ylabel("") ax.set_xlabel("") ax2.set_ylabel("")
adjust distance between subplots
fig.subplots_adjust(hspace=0.5) fig.text(0.07, 0.5, 'Concentration (pg/ml)', va='center', rotation='vertical')
do you know how I could do that? Is there a way to control the distance of the annotations to the boxplots? Thanks a lot, Erica
://user-images.githubusercontent.com/26386285/152211212-ba5350fe-c839-43af-8b43-fb929d5b4c6d.jpeg)
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