IMNAis a integrative multi-omics network-based approach to capture genetic-driven regulatory networks for human complex diseases.
- This method can combine functional data from multiple biological scales to understand molecular mechanisms of disease and identify potential key genes.
- This pipeline provide several scripts facilitating data access, integration and analysis.
2.1 Download
git clone https://github.com/xjtugenetics/IMNA.git
2.2 Configure
export IMNA_tk=/path/to/IMNA
- Workflow
3.1 Export gene set and module information
Script:
1-Export_module.py
Usage:
python3 ${IMNA_tk}/script/1-Export_module.py <inpmoduledir> <oup>
inpmoduledir Directory of all gene module (gene list) oup Prefix of output file
oup.txt: Gene set (module)
oupinfo.txt: Module information
3.2 Constract bipartite based on SNP-gene pairs
Script:
2-Constract_bipartite.py
Usage:
python3 ${IMNA_tk}/script/2-Constract_bipartite.py <GS pair> <oup>
GS pair Gene-SNP pairs oup Prefix of output file
oup_DG_snp.txt: Raw SNP degree score
oup_DG_gene.txt: Raw gene degree score
oup_DG_gene.nor.txt: Normalized gene degree score
3.3 Key driver analysis and Signature score analysis
Script:
3-Enricment_combine_SScore.py
Usage:
python3 ${IMNA_tk}/script/3-Enricment_combine_SScore.py <inter_score> <modulefile> <gene_norm_dg> <mod> <oup>
inter_score Interaction score of database (PPI, GIANT) modulefile module file, conducted by 1-Export_module.py gene_norm_dg Normalized gene degree score, conducted by 2-Constract_bipartite.py mod P-value or odd ratio oup Prefix of output file
oup-KDA-EScore.txt: Gene enrichment score
oup-SScore.txt: Combined gene signiture score
3.4 CS - Composite score analysis
Script:
4-Composite_score.py
Usage:
python3 ${IMNA_tk}/script/5-Composite_score.py <PPI_SScore> <GIANT_SScore> <oup>
PPI_SScore Gene signiture score of PPI network (step 3 output) GIANT_SScore Gene signiture score of GIANT network (step 4 output) oup Prefix of output file
oup_Composite_score.txt: Gene composite score (PPI and GIANT network)
3.5 optional - Gene ID conversion (between symbol and entrz id)
Script:
convert_geneid.r
Usage:
Rscript ${IMNA_tk}/script/convert_geneid.r <inpfile> <oup> <column> <gene id type>
inpfile File before conversion oup Output file name column The number of target column gene id SYMBOL or ENTREZID
oup: Gene id after conversion
- Calculate gene composition score of 6 gene set modlue accoding to gene interaction from PPI and GIANT netwrok
###Step1:
python3 ${IMNA_tk}/script/1-Export_module.py ${IMNA_tk}/data/geneset module
## "module.txt"
gene module moduleset
ADAMDEC1 101 1
ASRGL1 101 1
C10orf116 101 1
CD55 101 1
COL1A2 101 1
COL3A1 101 1
CTHRC1 101 1
CXCL9 101 1
CXCL10 101 1
## "moduleinfo.txt"
module name
101 39-signatures-ori.txt
102 14-signatures-ori.txt
103 300-signatures-ori.txt
104 263-signatures-ori.txt
105 126-signature-ori.txt
106 100-signatures-ori.txt
###(optional)
Rscript ${IMNA_tk}/script/convert_geneid.r module.txt module.txt.corv 1 SYMBOL
## "module.txt"
gene module moduleset
ADAMDEC1 101 1
ASRGL1 101 1
C10orf116 101 1
CD55 101 1
COL1A2 101 1
COL3A1 101 1
CTHRC1 101 1
CXCL9 101 1
CXCL10 101 1
## "module.txt.corv"
ENTREZID module moduleset
13 106 6
10157 104 4
23460 104 4
22 103 3
8714 103 3
3983 103 3
52 103 3
56 103 3
59 103 3
###Step2:
python3 ${IMNA_tk}/script/2-Constract_bipartite.py snp.gene.pairs bip
## "snp.gene.pairs"
ENTREZID SNP
79575 rs13343778
79575 rs113634768
79575 rs11540855
79575 rs34546260
79575 rs62126253
79575 rs55924783
79575 rs4808616
79575 rs28473003
79575 rs12982058
## "bip_DG_snp.txt"
DG node
0.006993006993006993 chr17:43989159:I
0.006993006993006993 rs112058513
0.006993006993006993 chr17:46685076:I
0.006993006993006993 rs1457919
0.006993006993006993 rs62070651
0.006993006993006993 rs7207826
0.006993006993006993 rs138922609
0.006993006993006993 rs16940671
0.006993006993006993 rs4748775
## "bip_DG_gene.txt"
DG node
0.00022660321776569228 580
0.00022660321776569228 752
0.00022660321776569228 148345
0.00022660321776569228 246744
0.00022660321776569228 1021
0.00022660321776569228 1267
0.1907999093587129 1394
0.00045320643553138455 1545
0.00022660321776569228 83468
## "bip_DG_gene.nor.csv"
DG node norm
0.22003172445048721 284058 2.0
0.1907999093587129 1394 1.8670103092783505
0.1470654883299343 4137 1.6680412371134021
0.10151824155903014 9884 1.4608247422680412
0.10061182868796738 474170 1.4567010309278352
0.09811919329254476 51326 1.445360824742268
0.05030591434398369 389170 1.2278350515463918
0.04146838885112169 8631 1.1876288659793814
0.024699750736460458 100506084 1.111340206185567
###Step3:
python3 ${IMNA_tk}/script/3-Enricment_combine_SScore.py PPI.filter.txt module.txt bip_DG_gene.nor.txt P PPI
python3 ${IMNA_tk}/script/3-Enricment_combine_SScore.py GIANT.filter.txt module.txt bip_DG_gene.nor.txt P GIANT
## "PPI-KDA-EScore.txt"
gene norm1 norm2 norm3 norm4 norm5 norm6
23136 0.0 0.0 0.0 0.0 0.0 0.0
55586 0.0 0.0 0.0 0.0 0.0 0.0
4958 0.0 0.0 0.0 0.0 0.0 0.0
9793 0.0 0.0 0.0 0.0 0.0 0.0
8543 0.0 0.0 0.0 0.0 0.0 0.0
117 0.0 0.0 0.0 0.0 0.0 0.0
200845 0.0 0.0 0.0 0.0 0.0 0.28086621546950413
79139 0.0 0.0 0.0 0.0 0.0 0.0
3912 0.0 0.49879745804727266 0.0 0.0 0.0 0.0
## "PPI-SScore.txt"
gene SScore norm
4137 0.47177454297544336 1.0
51226 0.3023830979296318 0.6409483140453625
3292 0.28409462722290135 0.6021830373278301
5158 0.2638202235224249 0.559208264732837
26276 0.26273712445041764 0.5569124666908819
23331 0.23212051246337354 0.49201576456289586
6155 0.2275463373024836 0.48232008422363687
55839 0.22354248460319304 0.4738332916255484
1021 0.2191097252448783 0.464437364218449
## "GIANT-KDA-EScore.txt"
gene norm1 norm2 norm3 norm4 norm5 norm6
100462799 0.0 0.0 0.0 0.0 0.0 0.05798346369741207
402360 0.06340642377742223 0.0 0.0 0.0 0.0 0.0
404266 0.0 0.0 0.0 0.0 0.0 0.0
441067 0.0 0.0 0.0 0.0 0.0 0.0
400860 0.0 0.1461858411609449 0.0 0.0 0.0 0.0
100128252 0.0 0.0 0.0 0.0 0.0 0.0
143188 0.0 0.0 0.0 0.0 0.0 0.0
340107 0.0 0.0 0.0 0.0 0.0 0.0
28982 0.0 0.0 0.0 0.0 0.0 0.0
## "GIANT-SScore.txt"
gene SScore norm
10197 0.6911514953381394 1.0
3093 0.6615120899310956 0.9571159064156506
56942 0.649453035818157 0.939668133829933
8678 0.5406093711987038 0.7821865030245152
3612 0.5280639792253968 0.764035067256921
55839 0.5148197866889375 0.7448725643530102
10951 0.5049760346912975 0.7306300255405549
9055 0.44075870302240827 0.6377164861761186
130507 0.4081344528874548 0.5905137377844765
###Step4:
python3 ${IMNA_tk}/script/4-Composite_score.py PPI-SScore.txt GIANT-SScore.txt result
## "result_Composite_score.txt"
gene norm_x norm_y mean compscore
10197 0.3688901262723868 1.0 0.6844450631361934 1.0
55839 0.4738332916255484 0.7448725643530102 0.6093529279892793 0.8902875640554194
4137 1.0 0.1532682979117107 0.5766341489558553 0.8424841963409919
3093 0.14321740008504674 0.9571159064156506 0.5501666532503486 0.8038141888692014
56942 0.05267380011244613 0.939668133829933 0.49617096697118956 0.7249244587983239
8678 0.20451842336073286 0.7821865030245152 0.49335246319262405 0.720806518688345
9055 0.3255048079610211 0.6377164861761186 0.4816106470685699 0.7036512833649254
6155 0.4823200842236369 0.4469615605789528 0.4646408224012949 0.6788577307757409
3612 0.15652586829854412 0.7640350672569209 0.4602804677777325 0.672487088545402
###(optional)
Rscript convert_geneid.r result_Composite_score.txt result_Composite_score.txt.corv 1 ENTREZID
## "result_Composite_score.txt.corv"
SYMBOL norm_x norm_y mean compscore
NAT2 0 0 0 0
ADA 0 0.0139823333689054 0.00699116668445271 0.0102143576760106
CDH2 0 0.00698273440278748 0.00349136720139374 0.00510101889755177
AKT3 0 0.00698273440278748 0.00349136720139374 0.00510101889755177
GAGE12F 0 0 0 0
RNA18S5 0 0 0 0
RNA28S5 0 0.0267595577506105 0.0133797788753052 0.0195483605565037
TRF-GAA1-4 0 0.00417259333462038 0.00208629666731019 0.00304815795989612
ANO1-AS2 0 0.0551490756412158 0.0275745378206079 0.0402874376714162
###(optional)
sort -k5 -n -r result_Composite_score.txt.corv | head
This software is distributed under the terms of GPL 2.0
Yi-Xiao Chen, Yu Rong, Feng Jiang, Jia-Bin Chen, Yuan-Yuan Duan, Shan-Shan Dong, Dong-Li Zhu, Hao Chen, Tie-Lin Yang, Zhijun Dai, Yan Guo Key Laboratory of Biomedical Information & Genetics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710049, P. R. China
📧 guoyan253@mail.xjtu.edu.cn
Yi-Xiao Chen
You can contact 📧 chenyixiao@stu.xjtu.edu.cn when you have any questions, suggestions, comments, etc. Please describe in details, and attach your command line and log messages if possible.
-
Python ( >= 3.2 )
- numpy( >=1.10.4)
- mpmath( >=0.19)
- pandas( >=0.18.0)
- scipy( >= 0.17.0)
- rpy2( >= 2.9.0)
- networkx( >= 2.2)
- sklearn( >= 0.17.1)
- sys
- os
-
R ( >= 3.3.2 )
- org.Hs.eg.db
- clusterProfiler
###################### Thank you! #############################