Python script for the EBDT algorithm

The following arguments allow parameterisation of the algorithm;

-a Kinase inhibition specificity threshold (default = 0.5)
-r Phosphosite/kinase fingerprint ratio threshold (default = 0.5)
-p Phosphosite being PDT of kinase probability threshold (default = 0.75)
-c Specify the list of cell line file names separated by a comma. E.g.:
-c MCF7.xlsm,HL60.xlsm
Each specified cell line must appear in the listOfKinases.csv file. I.e. if "MCF7.xlsm" is specified, then there must be a row entry for "MCF7" under the cell line column.

The algorithm is run by calling ebdt.py with at least the -c parameter detailing the list of cell line files to use, e.g.
python ebdt.py -c MCF7.xlsm,HL60.xlsm
Additionally, user-defined thresholds can be applied to parameterise certain parts of the workflow. E.g. for specifying a kinase inhibition specificity threshold of 0.6:
python ebdt.py -c MCF7.xlsm,HL60.xlsm -a 0.6

1. Kinase inhibitor selectivity dataset file. This must list the remaining kinase activity, for each kinase, after inhibitor treatment in vitro (normalised from 0 to 1, where 1 is no inhibition). This file must be included in a sub-directory requiredData and be named kinaseInhibitionSpecificity.csv. An example file is provided in the repository: requiredData/kinaseInhibitionSpecificity.csv.
2. List of kinases for each cell line This file must list the the kinases known to be expressed in all the cell lines specified when calling the script.
3. Phosphoproteomics dataset of kinase inhibitor effects. This must be an Excel file consisting of two worksheets named pvalue and fold in which the p-Values and fold-change values are provided, respectively, for each phosphorylation site/compound pair. This file must be located in the same directory from which the script is being run. Column heading formats must strictly follow that of the provided example file requiredData/MCF7.xlsm - this is to ensure that compound names can be matched to those in the kinase inhibitor selectivity dataset file.