MODAS: Multi-Omics Data Association Study toolkit
git clone https://github.com/liusy-jz/MODAS.git
cd MODAS
conda create -n modas python=3.7 -y
conda activate modas
python setup.py build
python setup.py install
pip install pyranges
conda install -y -c conda-forge r-rcppeigen r=3.6 rpy2
Rscript -e 'install.packages(c("data.table", "ggplot2", "ggsignif", "Matrix"), repos="https://cloud.r-project.org")'
Rscript -e 'install.packages("bigsnpr", dependence=T, repos="https://cloud.r-project.org")'
echo "export PATH=`pwd`/utils:\$PATH" >> ~/.bashrc
source ~/.bashrc
#Depends: R(>=3.6), python(>=3.7)
git clone https://github.com/liusy-jz/MODAS.git
cd MODAS
python setup.py build
python setup.py install
pip3 install rpy2 pyranges
Rscript -e 'install.packages(c("data.table", "ggplot2", "ggsignif", "Matrix"), repos="https://cloud.r-project.org")'
Rscript -e 'install.packages("bigsnpr",dependence=T, repos="https://cloud.r-project.org")'
echo "export PATH=`pwd`/utils:\$PATH" >> ~/.bashrc
source ~/.bashrc
MODAS_data
containing sample data for MODAS and omics data used in the article uploaded by Git extension Git Large File Storage (LFS), first download Git LFS from https://git-lfs.github.com/, and place the git-lfs
binary on your system’s executable $PATH
or equivalent, then set up Git LFS for your user account by running:
git lfs install
next download MODAS_data
by running:
git clone https://github.com/liusy-jz/MODAS_data.git
When the download is complete, first check the integrity of the downloaded data, MODAS_data
contains five folders, namely agronomic_traits
, genotype
, metabolome
, transcriptome
and example_data
, also contains a gene annotaion file for maize. The example folder contains sample data for MODAS, while other folders contain the omics data used in the article.
Then, enter the MODAS_data
directory,
cd MODAS_data
MODAS.py genoidx -g example_data/example_geno -genome_cluster -o example_geno
Pseudo-genotype files generated by genoidx subcommand will be saved as example_geno.genome_cluster.csv
.
The prescreen subcommand uses genome-wide genotype files to calculate the kinship matrix, first extract genotype files by:
tar -xvf genotype/chr_HAMP_genotype.tar.gz
Then, the pseudo-genotype file example_geno.genome_cluster.csv
generated by genoidx
and the example_phe.csv
file under the example_data
folder are used for prescreen analysis,
MODAS.py prescreen -g ./chr_HAMP -genome_cluster example_geno.genome_cluster.csv -phe example_data/example.phe.csv -o example
Prescreen subcommand generates two files including example.sig_omics_phe.csv
containing phenotype data and example.phe_sig_qtl.csv
containing candidate genomic regions of phenotype.
The prescreen subcommand outputs are used for regional association analysis,
MODAS.py regiongwas -g ./chr_HAMP -phe example.sig_omics_phe.csv -phe_sig_qtl example.phe_sig_qtl.csv -o example
Regiongwas subcommand generates two QTL files including example.region_gwas_qtl_res.csv
containing reliable QTL results and example.region_gwas_bad_qtl_res.csv
containing unreliable QTL results.
MODAS.py mr -g ./chr_HAMP -exposure ./example_data/example.exp.csv -outcome agronomic_traits/blup_traits_final.new.csv -qtl example_data/example_qtl_res.csv -mlm -o example
The results of Mendelian randomization analysis are saved as example.MR.csv
.
MR-based network analysis is carried out by the parameter net of mr subcommand. It uses transcriptome data for subnetwork modules analysis,
MODAS.py mr -g ./chr_HAMP -exposure ./example_data/network_example.exp.csv -outcome ./example_data/network_example.exp.csv -qtl example_data/network_example_qtl.csv -mlm -net -o network_example
Network analysis generated four files, including network_example.MR.csv
containing gene pairs with MR effect, network_example.edgelist
containing gene pairs with weight, network_example.cluster_one.result.csv
containing all identified subnetwork modules, network_example.sig.cluster_one.result.csv
containing significant subnetwork modules.
Co-associated genes analysis is not a modas function. It is implemented by script co-associated.py
. The analysis command line is as follows:
python3 example_data/co-associated.py example_data/co_associated.test.pvalue.csv co-associated_test
Then, a file containing co-associated gene labels and a heatmap showing relationship between co-associated genes are saved as co-associated_test.cluster.csv
and co-associated_test.cluster.heatmap.pdf
.
detail in https://modas-bio.github.io/