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
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.
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
you must install Snakemake, in which case you must use Python>=3.5 (conda install snakemake)
See :ref:`multivariate_regression` section for details.