knowengdev/geneset_characterization_pipeline

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knowengdev/geneset_characterization_pipeline repository overview

⁠KnowEnG's Gene Set Characterization Pipeline

This is the Knowledge Engine for Genomics (KnowEnG), an NIH BD2K Center of Excellence, Gene Set Characterization Pipeline.

This pipeline ranks a user supplied gene set against a KnowEnG's gene sets collection.

There are three gene set characterization methods that one can choose from:

Options	Method	Parameters
Fisher exact test	Fisher	fisher
Discriminative Random Walks with Restart	DRaWR	DRaWR
Net Path	Net Path	net_path

⁠How to run this pipeline's docker image with Our data

Install Docker - follow the instructions in this link. ============

Install Docker Engine⁠

Get the current Docker Image. On the command line with internet connection: ============

docker pull knowengdev/geneset_characterization_pipeline: 07_26_2017

Create or change to a directory to hold the output data. ============

mkdir local_results and / or cd local_results

Start the docker image connected to the (local_results) directory. =========== docker run -v local_results:/home/test/run_dir -it knowengdev/geneset_characterization_pipeline: 07_26_2017
At the docker image command prompt change to the test directory. =========== .../home# cd test
Set up the environment. ============= .../home/test# make env_setup

Select and run a gene set characterization option: ========

Run fisher pipeline

make run_fisher

Run DRaWR pipeline

make run_drawr

Run Net Path pipeline

make run_netpath

⁠How to run this pipeline with Your data.

*** Perform steps 1-3 as described above and use the local_results directory ***

⁠Create a custom YAML file, move that and your spreadsheet to the local_results directory

*** see the git hub directions *** GeneSet Characterization ReadMe on github⁠

*** make sure file named by YAML key "spreadsheet_name:" is in your local_results directory *** *** the path-names inside docker depend on the way you "mount" the run directory in step 4 *** *** therefore you may have to change the YAML path's to ../../ instead of ../ *** *** make sure your custom YAML file is in the local_results directory ***

Start docker with the container connected to your (local_results) directory. =========== docker run -v local_results:/home/test/run_dir -it knowengdev/geneset_characterization_pipeline: 04_07_2017
Change to the directory mounted in step 4. =========== .../home# cd ./test/run_dir .../home/test/run_dir# ls

*** the files in the local_results directory will be visible in this directory ***

Run GeneSet Characterizaion with the options in your custom YAML file. ========= python3 ../../src/geneset_characterization.py -run_directory ./ -run_file your_custom_run_file.yml

⁠Description of "run_parameters" file

Key	Value	Comments
method	DRaWR or fisher or net_path	Choose DRaWR or fisher or Net Path as the gene set characterization method
pg_network_name_full_path	directory+pg_network_name	Path and file name of the 4 col property file
gg_network_name_full_path	directory+gg_network_name	Path and file name of the 4 col network file(needed in DRaWR and Net Path)
spreadsheet_name_full_path	directory+spreadsheet_name	Path and file name of user supplied gene sets
gene_names_map	directory+gene_names_map	Map ENSEMBL names to user specified gene names
results_directory	directory	Directory to save the output files
rwr_max_iterations	500	Maximum number of iterations without convergence in random walk with restart(needed in DRaWR and Net Path)
rwr_convergence_tolerence	0.0001	Frobenius norm tolerence of spreadsheet vector in random walk(needed in DRaWR and Net Path)
rwr_restart_probability	0.5	alpha in `V_(n+1) = alpha * N * Vn + (1-alpha) * Vo` (needed in DRaWR and Net Path)
k_space	100	number of the new space dimensions in SVD(only needed in Net Path)
pg_network_name = kegg_pathway_property_gene.edge
gg_network_name = STRING_experimental_gene_gene.edge
spreadsheet_name = ProGENI_rwr20_STExp_GDSC_500.rname.gxc.tsv
gene_names_map = ProGENI_rwr20_STExp_GDSC_500_MAP.rname.gxc.tsv

⁠Description of Output files saved in results directory

Output files of all three methods save sorted properties for each gene set with name

{method}_ranked_by_property_{timestamp}.df

user gene set name1	user gene set name2	...	user gene set name n
property (most significant)	property (most significant)	...	property (most significant)
...	...	...	...
property (least significant)	property (least significant)	...	property (least significant)

Fisher method saves one output file with seven columns and it is sorted in descending order based on pval. The name of the file is

fisher_sorted_by_property_score_{timestamp}.df

user_gene_set	property_gene_set	pval	universe_count	user_count	property_count	overlap_count
user gene 1	property 1	float	int	int	int	int
...	...	...	...	...	...	...
user gene n	property m	float	int	int	int	int

DRaWR method saves two output files with five columns and they are sorted in descending order based on difference_score. The files are DRaWR_sorted_by_gene_score_{timestamp}.df and

DRaWR_sorted_by_property_score_{timestamp}.df

user_gene_set	gene_node_id	difference_score	query_score	baseline_score
user gene 1	gene node 1	float	float	float
...	...	...	...	...
user gene n	gene node m	float	float	float

user_gene_set	property_gene_set	difference_score	query_score	baseline_score
user gene 1	property 1	float	float	float
...	...	...	...	...
user gene n	property m	float	float	float

Net Path method saves one output file with three columns and it is sorted in descending order based on cosine_sum.

The name of the file is net_path_sorted_by_property_score_{timestamp}.df

user_gene_set	property_gene_set	cosine_sum
user gene 1	property 1	float
...	...	...
user gene n	property m	float

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docker pull knowengdev/geneset_characterization_pipeline:01_29_2019