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[GSEAPY] 在Python里进行基因集富集分析

作者：互联网

前言

在生物信息学数据分析中，许多分析软件都是基于R开发的。这里介绍一个可以在Python 中进行基因富集分析的Python 软件 GSEAPY (Gene Set Enrichment Analysis in Python)

GSEApy is a python wrapper for GESA and Enrichr.
It’s used for convenient GO enrichments and produce publication-quality figures from python.

GSEAPY

安装

可以通过conda 或 pip 进行安装

# if you have conda
$ conda install -c conda-forge -c bioconda gseapy

# or use pip to install the latest release
$ pip install gseapy

pip 安装要是遇到这样的报错

    data = self.read(amt=amt, decode_content=decode_content)
  File "/opt/conda/lib/python3.9/site-packages/pip/_vendor/urllib3/response.py", line 541, in read
    raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
  File "/opt/conda/lib/python3.9/contextlib.py", line 135, in __exit__
    self.gen.throw(type, value, traceback)
  File "/opt/conda/lib/python3.9/site-packages/pip/_vendor/urllib3/response.py", line 443, in _error_catcher
    raise ReadTimeoutError(self._pool, None, "Read timed out.")
pip._vendor.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out.

可以使用清华镜像,进行安装：

$ pip install gseapy -i https://pypi.tuna.tsinghua.edu.cn/simple

富集分析

背景信息

有多种基因集富集分析策略，我们常说的GO/KEGG 富集分析 应该大多数指over represent analysis(ORA)。还有个常用富集方法叫GSEA(Gene Set Enrichment Analysis), 翻译过来也是基因集富集分析。下文GSEA,特指这种策略。

ORA

测试数据，可以从GSEApy/tests/data下载。
富集的函数是enricher.

先展示一下，富集的代码：

gene_list="./gene_list.txt"
gene_sets='KEGG_2016'
gene_sets=['KEGG_2016','KEGG_2013']

enr = gp.enrichr(gene_list=gene_list,
                 gene_sets=gene_sets,
                 organism='Human', # don't forget to set organism to the one you desired! e.g. Yeast
                 description='kegg',
                 outdir='test/enrichr',
                 # no_plot=True,
                 cutoff=0.5 # test dataset, use lower value from range(0,1)
                )

运行完后，'test/enrichr'目录下存放着会有富集的图片以及文本。

(base) jovyan@95c3096ad9ae:~$ ll test/enrichr
-rw-r--r-- 1 jovyan users  22003 Dec 26 14:59 KEGG_2013.Human.enrichr.reports.pdf
-rw-r--r-- 1 jovyan users  22130 Dec 26 14:59 KEGG_2013.Human.enrichr.reports.txt
-rw-r--r-- 1 jovyan users  25722 Dec 26 14:59 KEGG_2016.Human.enrichr.reports.pdf
-rw-r--r-- 1 jovyan users  48458 Dec 26 14:59 KEGG_2016.Human.enrichr.reports.txt

查看KEGG_2016.Human.enrichr.reports.pdf，图片只显示了前10个，这是由参数top_term=10，所决定的
image.png

同时富集也结果保存在enr.results里，如查看前五个数据

enr.results.head(5)

输出

Gene_set	Term	Overlap	P-value	Adjusted P-value	Old P-value	Old Adjusted P-value	Odds Ratio	Combined Score	Genes
0	KEGG_2016	Osteoclast differentiation Homo sapiens hsa04380	28/132	3.104504e-13	7.885440e-11	0	0	6.659625	191.802220	LILRA6;ITGB3;LILRA2;LILRA5;PPP3R1;FCGR3B;SIRPA...
1	KEGG_2016	Tuberculosis Homo sapiens hsa05152	31/178	4.288559e-12	5.446470e-10	0	0	5.224941	136.763196	RAB5B;ITGB2;PPP3R1;HLA-DMA;FCGR3B;HLA-DMB;CASP...
2	KEGG_2016	Phagosome Homo sapiens hsa04145	28/154	1.614009e-11	1.366528e-09	0	0	5.490501	136.437381	ATP6V1A;RAB5B;ITGB5;ITGB3;ITGB2;HLA-DMA;FCGR3B...
3	KEGG_2016	Rheumatoid arthritis Homo sapiens hsa05323	19/90	2.197884e-09	1.395656e-07	0	0	6.554453	130.668081	ATP6V1A;ATP6V1G1;ATP6V0B;TGFB1;ITGB2;FOS;ITGAL...
4	KEGG_2016	Leishmaniasis Homo sapiens hsa05140	17/73	3.132614e-09	1.591368e-07	0	0	7.422186	145.336773	TGFB1;IFNGR1;PRKCB;IFNGR2;ITGB2;FOS;MAPK14;HLA...

查看enricher函数帮助文档

help(gp.enrichr)

Help on function enrichr in module gseapy.enrichr:

enrichr(gene_list, gene_sets, organism='human', 
            description='', outdir='Enrichr', 
            background='hsapiens_gene_ensembl', 
            cutoff=0.05, format='pdf', figsize=(8, 6), 
            top_term=10, no_plot=False, verbose=False)
......
......

由帮助文档可知enricher函数所需参数如下：

gene_sets={'term_A':['gene1', 'gene2',...], 
           'term_B':['gene2', 'gene4',...], ...}

绘图

gseapy 也提供了绘图函数进行绘制

# simple plotting function
from gseapy.plot import barplot, dotplot

# to save your figure, make sure that ``ofname`` is not None
barplot(enr.res2d, title='KEGG_2013',)

image.png

enr.res2d 存储着最近一次查询富集的结果， 上面的例子中, enr.res2d储存的是'KEGG_2013']富集结果，因为它是list最后一个.

gene_sets=['KEGG_2016','KEGG_2013']

enr.results有着所有的富集结果，所以我么也可以挑选数据可视化

barplot(enr.results.loc[enr.results["Gene_set"] == "KEGG_2016",], title='KEGG_2016',)

image.png

气泡图也是有的；

image.png

GSEA

Prerank

Prerank 用于已经排好序的数据来做GSEA。如，根据logFC 从大到小排好序后，去做GSEA。

# gsea_data.gsea_data.rnk 是已经排好序的数据
rnk = pd.read_csv("./gsea_data.gsea_data.rnk", header=None, sep="\t")
rnk.head()
0 1
CTLA2B 2.502482
SCARA3 2.095578
LOC100044683 1.116398 
pre_res = gp.prerank(rnk=rnk, gene_sets='KEGG_2016',
                     processes=4,
                     outdir='test/prerank_report_kegg', format='png', seed=6)

pre_res.res2d.head()

image.png

绘图

from gseapy.plot import gseaplot

terms = pre_res.res2d.index
# to save your figure, make sure that ofname is not None
gseaplot(rank_metric=pre_res.ranking, term=terms[0], **pre_res.results[terms[0]])

image.png

未完待续...

参考

https://gseapy.readthedocs.io/en/latest/introduction.html

标签：富集,KEGG,gene,Python,GSEAPY,set,enrichr,2016
来源： https://www.cnblogs.com/huanping/p/15737361.html