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Quick Start

Data

In the following examples and most of the examples in GDSCTools, we use data sets known as version 5 (v5) and version 17 (v17). Each set is made of a drug responses file (IC50s) and a genomic features file. The 4 files are copies of original files to be found on the CancerRxGene page. Altough not required for the following examples, you may want to download them directly as follows:

wget https://tinyurl.com/y7nn6e5h -O genomic_features_v17.csv.gz
wget https://tinyurl.com/ybtlsrpz -O genomic_features_v5.csv.gz
wget https://tinyurl.com/ycavjd37 -O IC50_v17.csv.gz
wget https://tinyurl.com/yakfnqmb -O IC50_v5.csv.gz

ANOVA analysis

If you already know what you can do with GDSCTools, we assume you have a well formatted :term:`IC50` matrix and a genomic features binary matrix. Then, you can run the entire ANOVA analysis as follows:

from gdsctools import ANOVA
# For example, use these test files
# from gdsctools import ic50_test as ic50_filename
# from gdsctools import gf_v17 as genomic_feature_filename
gdsc = ANOVA(IC50_filename, genomic_feature_filename)
results = gdsc.anova_all()

And create an HTML report as follows:

from gdsctools import ANOVAReport
report = ANOVAReport(gdsc, results)
report.create_html_pages()

The results variable contains all tested associations within a single dataframe. The report will focus on significant associations and create boxplots or volcano plots accordingly.

More details about the ANOVA analysis itself can be found in the :ref:`anova_partone` and :ref:`anova_parttwo` sections. The data structure can be found in the next section (:ref:`data`).

Regression analysis

Similarly, for the regression analysis, one can write a script as above. Here, we restrict the analysis to the first 4 drugs (figures are open for each drug):

from gdsctools import GDSCLasso
lasso = GDSCLasso(IC50_filename, genomic_feature_filename)
for drugid in lasso.drugIds[0:4]:
    res = lasso.runCV(drugid, kfolds=8)
    best_model = lasso.get_model(alpha=res.alpha)
    #weights = lasso.plot_weight(drugid, best_model)
    boxplots = lasso.boxplot(drugid, model=best_model, n=10, bx_vert=False)

However, we would recommend to use a worflow designed for this analysis. If you type this command in a shell:

gdsctools_regression -I IC50_filename -F genomic_feature_filename
    --method lasso -o analysis
cd analysis

it creates a Snakemake pipeline and its configuration file. You can then edit file named config.yaml and once done, execute the pipeline:

snakemake -s regression.rules

Warning

you must install Snakemake, in which case you must use Python>=3.5 (conda install snakemake)

See :ref:`multivariate_regression` section for details.