11. Standalone application

Although we would encourage you to use the Python shell to have as much flexibility as possible, we also provide a standalone applications.

Currently, there are two standalones. The gdsctools_anova and gdsctools_regression. The first one is a pure Python implementation while the second one is snakemake-based.

11.1. gdsctools_anova application

called gdsctools_anova. This standalone application should be installed with GDSCTools automatically. It focuses on the ANOVA analysis only, and can be used to analysis one set of IC50 and genomic feature at a time.

You can obtain the help by typing:

gdsctools_anova --help

The main goal is to provide an interface to the python library and consequently, one be able to redo the analysis as shown in the quickstart:

* One drug One Feature with figure(s) and HTMLs
* One Drug All Feature with figure and HTMLs
* All Drug All Feature with figures and HTMLs

We suppose the input data file is called IC50_10drugs.tsv

11.1.1. ODOF

gdsctools_anova --input-ic50 IC50_10drugs.tsv --drug
    Drug_999_IC50 --feature TP53_mut --onweb

11.1.2. ODAF

gdsctools_anova --input-ic50 IC50_10drugs.tsv --drug
    Drug_999_IC50 --onweb

11.1.3. ADAF

gdsctools_anova --input-ic50 IC50_10drugs.tsv --onweb

11.1.4. Some other settings

Again, you can use the --help to get up-to-date information about the available arguments. However, let us give a couple of interesting ones.

If you are interesting in a specific association of drug and feature, it is convenient to get the valid drug names:


or feature names:


By default the analysis is PANCAN (includes all tissues) but you can restrict the analysis to a set of tissues (or just one):

--tissues breast, cervix

To know the names of the tissues, use:


11.2. gdsctools_regression application

Let us consider the case where you have an IC50 file and a genomic file. The first step consists in preparing the working directory:

gdsctools_regression -I IC50_v17.csv.gz -F genomic_features_v17.csv.gz
    --method lasso -O lasso_analysis
cd lasso_analysis

On a local computer:

snakemake -s regression.rules -j 4

On a distributed-computing system using e.g SLURM framework, use:

srun --qos normal snakemake -s regression.rules -j 40 --cluster "sbatch --qos normal"