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 >ID >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
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(), interactions=adjacency_matrix, mP=data_fc["5min"].values.mean(), delta=data_fc["5min"].values.std())
KSEA is a method for the inference of kinase activities from phosphoproteomics data based on kinase substrate sets, which are constructed from curated information about kinase-substrate interactions from public resources like PhosphoSitePlus. The values of the fold changes of the phospho-sites in the substrate set of a given kinase are used to compute a score for the activity of this kinase. The main scoring system for KSEA is the mean or the median of the fold changes in the substrate set. KSEA was first proposed in the publication from Casado et al..
to be extended