Kinases inference analysis#

  1. Read your input file

Note

Make sure to have the phospho-sequences on the first column and the log2 transformed Fold Change on the second column.

input_sites = pd.read_csv('path/to/your/input_sites.csv')
input_sites

              Sequence  Fold Change: a/a' KO Clone A vs WT
0     KLEEKQKs*DAEEDGV                          -88.159789
1     EEDGVTGs*QDEEDSK                          -88.159789
..                 ...                                 ...
462   AKEESEEs*DEDMGFG                           19.421218
463   RNGPRDAs*PPGSEPE                           63.187703

[464 rows x 2 columns]
pandas.DataFrame

Note

Data: CK2 catalytic sub-units knockdown

  1. Run enrichment analysis with your input phospho-sequences

Note

Supported methods are min, max, avg, all

enrich = kinex.get_enrichment(input_sites, fc_threshold=1.5, phospho_priming=False, favorability=True, method="max")

  1. Access the total number of up-regulated, down-regulated, and un-regulated phospho-sequences

print("Total upregulated Ser/Thr kinases:", enrich.ser_thr.total_upregulated)
print("Total downregulated Ser/Thr kinases:", enrich.ser_thr.total_downregulated)
print("Total unregulated Ser/Thr kinases:", enrich.ser_thr.total_unregulated)

Total upregulated Ser/Thr kinases: 63
Total downregulated Ser/Thr kinases: 86
Total unregulated Ser/Thr kinases: 309

  1. Check the sites that were marked as failed

enrich.failed_sites

  1. Show enrichment table

enrich.ser_thr.enrichment_table

        upregulated  downregulated  ... dominant_enrichment_value_log2 dominant_p_value_log10_abs
kinase
AAK1             0            1.0   ...                      -0.263034                   0.202666
ACVR2A        12.0           23.0   ...                      -1.562107                   3.346702
...            ...            ...   ...                            ...                        ...
YSK4             0            2.0   ...                      -1.869777                    0.68218
ZAK            1.0            3.0   ...                       -3.47671                     1.4713

[303 rows x 19 columns]
pandas.DataFrame

  1. Vulcano plot of Enrichment Odds Ratio (EOR) and p-value

Note

Kinases are represented with colours corresponding to their class.

fig = enrich.ser_thr.plot(use_adjusted_pval=False)
fig.show()

Note

You can update your figure (marker point, axis, legend, etc.) using Plotly’s functions: https://plotly.com/python/creating-and-updating-figures

  1. Save the figure in a desired format

  • .html

fig.write_html("path/to/file.html")

  • .svg

fig.write_image("images/fig1.svg")

  • .pdf

fig.write_image("images/fig1.pdf")

  • .png

fig.write_image("images/fig1.png", scale=10)

  • .jpeg

fig.write_image("images/fig1.jpeg", scale=10)