scEnrichment¶

In this tutorial, we will provide a quick guideline for applying the scEnrichment feature of discotoolkit on DEGs (Differentially Expressed Genes). Follow these steps:

1. First, we load the DEGs from Example 1, available on the DISCO website.
2. Finally, we convert the retrieved data into a Pandas DataFrame, which will serve as the input for the dt.CELLiD_enrichment function. By running this function, we can obtain the desired enrichment analysis.

In [1]:

# for google colab
# pip install discotoolkit
# import package

import discotoolkit as dt
import scanpy as sc
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# adding ignore warning to clean the code
import warnings

# Ignore all warnings
warnings.filterwarnings('ignore')

%load_ext autoreload
%autoreload 2

sns.set_theme(rc={'figure.dpi': 300})

# for google colab # pip install discotoolkit # import package import discotoolkit as dt import scanpy as sc import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # adding ignore warning to clean the code import warnings # Ignore all warnings warnings.filterwarnings('ignore') %load_ext autoreload %autoreload 2 sns.set_theme(rc={'figure.dpi': 300})

The user can input either a gene list or a gene list with log fold change as the input to the dt.CELLiD_enrichment function.

Example of the DEGs:

In [2]:

# testing DEG from DEGs of acinar cell(Peng, Junya et al.)
# Reference is in DISCO website

test_genes = {"gene":["PRSS1", "CTRB2", "CELA3A", "CTRB1", "REG1B", "CLPS", "CPB1", "CPA1", "PLA2G1B", "REG3A", "CTRC"],
            #   "fc":[5.236605052, 5.179753462, 4.724315075, 4.702706704, 4.65145949, 4.513887613, 4.351968886, 4.311988687, 4.272185339, 4.253882194, 4.208933992]
              }

deg_df = pd.DataFrame(test_genes)

deg_df.head()

# testing DEG from DEGs of acinar cell(Peng, Junya et al.) # Reference is in DISCO website test_genes = {"gene":["PRSS1", "CTRB2", "CELA3A", "CTRB1", "REG1B", "CLPS", "CPB1", "CPA1", "PLA2G1B", "REG3A", "CTRC"], # "fc":[5.236605052, 5.179753462, 4.724315075, 4.702706704, 4.65145949, 4.513887613, 4.351968886, 4.311988687, 4.272185339, 4.253882194, 4.208933992] } deg_df = pd.DataFrame(test_genes) deg_df.head()

Out[2]:

	gene
0	PRSS1
1	CTRB2
2	CELA3A
3	CTRB1
4	REG1B

Now that we have our data ready, we can proceed to run the function.

In [3]:

# get enrichment analysis result using dt.CELLiD_enrichment
results = dt.CELLiD_enrichment(deg_df)

# get enrichment analysis result using dt.CELLiD_enrichment results = dt.CELLiD_enrichment(deg_df)

INFO:root:Comparing the ranked gene list to reference gene sets...
[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.8s
[Parallel(n_jobs=1)]: Done 161 tasks      | elapsed:    2.7s
[Parallel(n_jobs=1)]: Done 364 tasks      | elapsed:    3.5s
[Parallel(n_jobs=1)]: Done 647 tasks      | elapsed:    5.2s
[Parallel(n_jobs=1)]: Done 1012 tasks      | elapsed:    8.6s
[Parallel(n_jobs=1)]: Done 1457 tasks      | elapsed:   14.5s
[Parallel(n_jobs=1)]: Done 1984 tasks      | elapsed:   22.2s
[Parallel(n_jobs=1)]: Done 2591 tasks      | elapsed:   31.2s
[Parallel(n_jobs=1)]: Done 3280 tasks      | elapsed:   40.5s

In [4]:

# get the top 10 results
results.head(10)

# get the top 10 results results.head(10)

Out[4]:

	pval	or	name	gene	background	overlap	geneset
32	0.0	842.815	Acinar cell vs All others in pancreas	CTRC,CELA3A,REG1B,REG3A,CPB1,CLPS,CPA1,PRSS1,P...	5783	11	91
42	0.0	743.688	Acinar cell vs Ductal/EC doublet like cell in ...	CTRB2,PRSS1,CPB1,CPA1,CTRC,CTRB1,CELA3A,PLA2G1...	9711	11	164
22	0.0	569.849	Acinar cell vs TUBA1A+ ductal cell in pancreas	CTRB2,CTRB1,CELA3A,PRSS1,PLA2G1B,CTRC,CPB1,CPA...	5783	11	129
82	0.0	531.813	Acinar cell vs HSP+ pancreatic ductal cell in ...	CTRC,CTRB2,CTRB1,CELA3A,CPB1,PRSS1,CPA1,CLPS,P...	9711	11	224
40	0.0	519.397	Acinar cell vs Pancreatic ductal cell in PDAC	PRSS1,CTRB1,CTRC,CELA3A,CTRB2,CPB1,CPA1,CLPS,P...	9711	11	229
41	0.0	507.536	Acinar cell vs CCL2+ pancreatic ductal cell in...	PRSS1,CELA3A,CTRC,CTRB1,CPB1,CPA1,CTRB2,CLPS,P...	9711	11	234
54	0.0	505.227	Acinar cell vs All others in PDAC	CTRC,CELA3A,CPB1,CLPS,CPA1,PRSS1,PLA2G1B,CTRB2...	9711	11	235
81	0.0	293.242	KLRB1 CD8 T cell vs XCL1 NK cell in PDAC	CLPS,PRSS1,CTRB1,CPA1,CELA3A,CTRC,CPB1,CTRB2,P...	9711	9	117
8	0.0	227.122	Paneth cell vs Goblet cell in intestine	REG3A,REG1B	11184	2	50
77	0.0	189.087	KLRB1 CD8 T cell vs CXCL13 exhausted CD8 T cel...	CPA1,CLPS,CTRB1,CPB1,CTRC,CTRB2,PRSS1,CELA3A,P...	9711	9	176

We can visualize the results using a horizontal barplot with the help of the seaborn library.

In [5]:

# setting the theme to whitegrid
sns.set_theme(style="whitegrid")

# Initialize the matplotlib figure
f, ax = plt.subplots(figsize=(6, 15))

# plot only the top 20 results
sns.barplot(x="or", y="name", data=results.head(20), color="b")

# adding axis title
ax.set(ylabel="Gene sets",
       xlabel="Odds Ratio")

# make the plot look cleaner and nicer
sns.despine(left=True, bottom=True)

# setting the theme to whitegrid sns.set_theme(style="whitegrid") # Initialize the matplotlib figure f, ax = plt.subplots(figsize=(6, 15)) # plot only the top 20 results sns.barplot(x="or", y="name", data=results.head(20), color="b") # adding axis title ax.set(ylabel="Gene sets", xlabel="Odds Ratio") # make the plot look cleaner and nicer sns.despine(left=True, bottom=True)

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Last update: October 24, 2024
Created: May 11, 2023