1. Download Docker Desktop.

2. Open "Docker Desktop Installer.exe" and follow the instruction to setup.

Tip: Docker Desktop may need to install WSL2 and enable virtualization.

3. If the docker service has been successfully turned on, a green indicator will appear on the bottom-left in the Docker app.

4. Open "Windows PowerShell" by using search bar.

5. Test Docker by following command.
    $ docker run hello-world  Copy

6. Download Gscore Docker images from Docker hub by following command.
    $ docker pull sysmednet/docker_gscore  Copy

7. Confirm that you have downloaded the Docker image by following command.
    $ docker images  Copy

Create a folder (i.e., Input folder) to put the data to be analyzed.  Example download
Input expression data file format demo:

Input gene set list file format demo:

9. Run the Docker image of Gscore through the following command.
    $ docker run -it -v [path/to/Input folder]:/app/io sysmednet/docker_gscore bash   Copy


Tip: to get [path/to/Input folder], please directly type it or drag the folder to PowerShell (without “ ” or ‘ ’) as follows.
10. Execute the Gscore analysis in Docker container through the following command.
    $ python3 gscore_Top_genelist.py -exp io/exp.txt -gs io/geneset.txt -out io/ -Top 50  Copy
    $ exit  Copy

11. Output files of Gscore analysis will appear in the created folder.

1. Download Docker Desktop.

2. Open "Docker.dmg" and follow the instruction to setup.

3. If the docker service has been successfully turned on, a green indicator will appear on the bottom-left in the Docker app.

Go to Preference / Resources / Advanced. Adjust the resources with rolling bar (recommend at least 4G RAM). Press"Apply & Restart". Waiting for the Docker to restart.
4. Open "Terminal" by using search bar.

5. Test Docker by following command.
    $ docker run hello-world  Copy

6. Download Gscore Docker images from Docker hub by following command.
    $ docker pull sysmednet/docker_gscore  Copy

7. Confirm that you have downloaded the Docker image by following command.
    $ docker images  Copy

Create a folder (i.e., Input folder) to put the data to be analyzed.  Example download
Input expression data file format demo:

Input gene set list file format demo:

9. Run the Docker image of Gscore through the following command.
    $ docker run -it -v [path/to/Input folder]:/app/io sysmednet/docker_gscore bash   Copy


Tip: to get [path/to/Input folder], please directly type it or drag the folder to Terminal (without “ ” or ‘ ’) as follows.
10. Execute the Gscore analysis in Docker container through the following command.
    $ python3 gscore_Top_genelist.py -exp io/exp.txt -gs io/geneset.txt -out io/ -Top 50  Copy
    $ exit  Copy

11. Output files of Gscore analysis will appear in the created folder.

(1) gscore_Top_genelist.py

    Gene set correlation enrichment analysis (Gscore) of gene list ranked by |log2| fold change. Given a list of genes and a category of gene sets. This Python scirption will return the Gscore analysis.

Usage

    $ python3 gscore_Top_genelist.py -exp example/exp.txt -gs example/geneset.txt -out example/ -Top 50   Copy

Argument	Variable	Description	Default value
-h	help	Get help for any of the Gscore commands	NA
-exp	EXP_PATH	Input file of the expression data	./example/exp.txt
-gs	GENESET_PATH	Input file of gene set list	./example/geneset.txt
-out	OUTPUT_PATH	Output path of analysis results of Gscore	./example
-log2	LOG2_TRANS	Apply log2 transformation to the data [1=Yes or 0=No]	0
-FC	DEG_LOG_FC	Criterion of |Fold Change（FC）| for determining differentially expressed genes（DEGs）[ranging from 1.3 to 5]	1.5
-pval	DEG_P_VAL	Criterion of adjusted p-value for determining DEGs [ranging from 0.00001 to 0.05]	0.05
-Top	TOP_DEG	The maximum number of DEGs, ranked by |log2 FC|, are selected in enrichment analysis	100
-PCC	PCC_THRESHOLD	Criterion of Pearson correlation coefficient for determining co-expressed DEG pairs between the query gene list and gene sets [ranging from 0.1 to 1.0]	0.5
-id_filter	INDGENE_FILTER	Define the enriched gene sets for each individual DEG by using FDR q-value or raw p-value [1=FDR q-value or 2=raw p-value]	1
-p_qval	INDGENE_P_Q_VAL	Criterion of FDR q-value（or raw p-value）for determining the enriched gene sets of each individual DEG [ranging from 0.00001 to 1.0]	0.05
-gl_filter	GENELIST_FILTER	Define the enriched gene sets for the query DEG list by using adjusted p-value or raw p-value [1=adj. p-value or 2=raw p-value]	1
-adjp	GENELIST_ADJP_VAL	Criterion of adjusted p-value（or raw p-value）for determining the enriched gene sets of the DEG list [ranging from 0.00001 to 1.0]	0.05
-id_co	INDGENE_COEXP	Output of the co-expressed DEG pairs between each individual DEG and the gene sets [1=Yes or 0=No; default=0]	0
-gl_co	GENELIST_COEXP	Output of the co-expressed DEG pairs between the query DEG list and the gene sets [1=Yes or 0=No; default=0]	0

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(2) gscore_user_defined_genelist.py

    Gene set correlation enrichment analysis (Gscore) of gene list provided by the user. Given a list of genes and a category of gene sets. This Python scirption will return the Gscore analysis.

Usage

    $ python3 gscore_user_defined_genelist.py -exp example/exp.txt -gs example/geneset.txt -gl example/genelist.txt -out example/   Copy

Argument	Variable	Description	Default value
-h	help	Get help for any of the Gscore commands	NA
-exp	EXP_PATH	Input file of the expression data	./example/exp.txt
-gs	GENESET_PATH	Input file of gene set list	./example/geneset.txt
-gl	GENELIST_PATH	Input file of user-defined gene list	./example/genelist.txt
-out	OUTPUT_PATH	Output path of analysis results of Gscore	./example
-log2	LOG2_TRANS	Apply log2 transformation to the data [1=Yes or 0=No]	0
-FC	DEG_LOG_FC	Criterion of |Fold Change（FC）| for determining differentially expressed genes（DEGs）[ranging from 1.3 to 5]	1.5
-pval	DEG_P_VAL	Criterion of adjusted p-value for determining DEGs [ranging from 0.00001 to 0.05]	0.05
-PCC	PCC_THRESHOLD	Criterion of Pearson correlation coefficient for determining co-expressed DEG pairs between the query gene list and gene sets [ranging from 0.1 to 1.0]	0.5
-id_filter	INDGENE_FILTER	Define the enriched gene sets for each individual DEG by using FDR q-value or raw p-value [1=FDR q-value or 2=raw p-value]	1
-p_qval	INDGENE_P_Q_VAL	Criterion of FDR q-value（or raw p-value）for determining the enriched gene sets of each individual DEG [ranging from 0.00001 to 1.0]	0.05
-gl_filter	GENELIST_FILTER	Define the enriched gene sets for the query DEG list by using adjusted p-value or raw p-value [1=adj. p-value or 2=raw p-value]	1
-adjp	GENELIST_ADJP_VAL	Criterion of adjusted p-value（or raw p-value）for determining the enriched gene sets of the DEG list [ranging from 0.00001 to 1.0]	0.05
-id_co	INDGENE_COEXP	Output of the co-expressed DEG pairs between each individual DEG and the gene sets [1=Yes or 0=No; default=0]	0
-gl_co	GENELIST_COEXP	Output of the co-expressed DEG pairs between the query DEG list and the gene sets [1=Yes or 0=No; default=0]	0

---

(3) gscore_individual_DEG.py

    Gene set correlation enrichment analysis (Gscore) of individual DEG ranked by |log2| fold change. Given a DEG and a category of gene sets. This Python scirption will return the Gscore analysis.

Usage

    $ python3 gscore_individual_DEG.py -exp example/exp.txt -gs example/geneset.txt -out example/ -Top 50   Copy

Argument	Variable	Description	Default value
-h	help	Get help for any of the Gscore commands	NA
-exp	EXP_PATH	Input file of the expression data	./example/exp.txt
-gs	GENESET_PATH	Input file of gene set list	./example/geneset.txt
-out	OUTPUT_PATH	Output path of analysis results of Gscore	./example
-log2	LOG2_TRANS	Apply log2 transformation to the data [1=Yes or 0=No]	0
-FC	DEG_LOG_FC	Criterion of |Fold Change（FC）| for determining differentially expressed genes（DEGs）[ranging from 1.3 to 5]	1.5
-pval	DEG_P_VAL	Criterion of adjusted p-value for determining DEGs [ranging from 0.00001 to 0.05]	0.05
-Top	TOP_DEG	The maximum number of DEGs, ranked by |log2 FC|, are selected in enrichment analysis	100
-PCC	PCC_THRESHOLD	Criterion of Pearson correlation coefficient for determining co-expressed DEG pairs between the query gene list and gene sets [ranging from 0.1 to 1.0]	0.5
-id_filter	INDGENE_FILTER	Define the enriched gene sets for each individual DEG by using FDR q-value or raw p-value [1=FDR q-value or 2=raw p-value]	1
-p_qval	INDGENE_P_Q_VAL	Criterion of FDR q-value（or raw p-value）for determining the enriched gene sets of each individual DEG [ranging from 0.00001 to 1.0]	0.05
-id_co	INDGENE_COEXP	Output of the co-expressed DEG pairs between each individual DEG and the gene sets [1=Yes or 0=No; default=0]	0

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