[GSEAPY] 在Python里进行基因集富集分析

2021/12/27 20:11:35

本文主要是介绍[GSEAPY] 在Python里进行基因集富集分析,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!

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condapip 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

  • gene set, 指一组具有相同特征的基因。如一个GO term 对应的多个基因,一个kegg pathway对应的多个基因
  • gene set library,多个相关的gene set 。如所有GO term组成一个gene set library.
  • enrichment analysis, gene set library 作为注释基因集合,已知的先验知识。对于一个输入基因集合,富集分析通过计算分析哪些注释gene set 显著存在于输入基因集合中。例如:GO 富集分析中,查看哪些GO terms 显著存在于输入基因列表中。

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_list, 所需查询gene_list,可以是一个列表,也可为文件(一列,每行一个基因)
  • gene_sets, gene set library。该参数,有两种形式:
    • 可以设置enricher自带的gene set library 详细列表可见https://maayanlab.cloud/Enrichr/#libraries。可单个'KEGG_2016',或多个['KEGG_2016','KEGG_2013']
    • 一种自定义gene set library。可以是gmt文件,或者输入一个字典
gene_sets={'term_A':['gene1', 'gene2',...], 
           'term_B':['gene2', 'gene4',...], ...}
  • organism,支持(human, mouse, yeast, fly, fish, worm), 自定义gene_set 则无影响。
  • description,工作运行描述
  • outdir; 输出目录
  • background: 背景基因
    • 可以是一个背景基因列表
    • 或者一个背景基因数目
    • 又或者Biomart dataset name.
  • cutoff; pvalue阈值
  • format, 输出图片格式('pdf','png','eps'...)
  • figsize, 图片大小, (width,height). Default: (6.5,6).
  • no_plot:是否不做图

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



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