Quick start

The package contains example data from a publication by de Graaf et al., in which the phosphoproteome of Jurkat T-cells after prostaglandin E2 stimulation was analysed. The data can be loaded with:

import kinact
data_fc, data_p_value = kinact.get_example_data()
print data_fc.head()
>              5min       10min      20min      30min      60min
>A0AVK6_S71   −0.319306  −0.484960  −0.798082  −0.856103  −0.928753 
>A0FGR8_S743  −0.856661  −0.981951  −1.500412  −1.441868  −0.861470 
>A0FGR8_S758  −1.445386  −2.397915  −2.692994  −2.794762  −1.553398
>A0FGR8_S691   0.271458   0.264596   0.501685   0.461984   0.655501 
>A0JLT2_S226  −0.080786   1.069710   0.519780   0.520883  −0.296040

Prior-knowledge information about kinase-substrate interactions can be loaded from the pypath package (see also the documentation or the github repository). Per default, interactions from PhosphoSitePlus and Signor are loaded, but other sources from pypath can be specified with the sources parameter of the function. For kinase-substrate interactions from other organisms, specify organim='yeast' or organism='mouse' in the function call.

adjacency_matrix = kinact.get_kinase_targets()

Finally, estimation of the kinase activities can be performed as follows for the example of the KSEA protocol:

scores, p_values = kinact.ksea.ksea_mean(data_fc=data_fc["5min"].dropna(),

Methods included in kinact: