Supplementary Materials
Supplementary Materials for:
Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks
Mark Grimes,* Benjamin Hall, Lauren Foltz, Tyler Levy, Klarisa Rikova, Jeremiah Gaiser, William Cook, Ekaterina Smirnova, Travis Wheeler, Neil R. Clark, Alexander Lachmann, Bin Zhang, Peter Hornbeck, Avi Ma'ayan, Michael Comb
*Corresponding author. Email: mark.grimes{at}mso.umt.edu
This PDF file includes:
- Text S1. Data analysis considerations and network development.
- Fig. S1. Example t-SNE embeddings.
- Fig. S2. Initial evaluation of clusters derived from t-SNE embeddings.
- Fig. S3. Correlation between replicates.
- Fig. S4. Number of edges per gene in clusters.
- Fig. S5. Correlation between degree and betweenness.
- Fig. S6. CFN betweenness is not biased by overrepresentation in PPI databases.
- Fig. S7. The number of posttranslational modifications per protein weakly correlates with CFN betweenness.
- Fig. S8. The effect of applying PTM correlation threshold on CFNs.
- Fig. S9. Network density as a function of PTM correlation threshold defines a minimum for construction of a PTM CCCN.
- Fig. S10. Relative network density of drug-affected proteins.
- Fig. S11. EP300 interactions with PTM-modifying enzymes.
- Fig. S12. Combined EP300 CCCN and CFN.
- Fig. S13. Comparison of tyrosine phosphorylation, serine/threonine phosphorylation, and acetylation.
- Fig. S14. Western blot data from cell fractionation experiments.
- Fig. S15. Separation of tyrosine and serine/threonine phosphorylation and relative abundance compared to paxDB.
- Legends for tables S1 and S2
- Legend for data file S1
- References (89–91)
Other Supplementary Material for this manuscript includes the following:
- Table S1 (Microsoft Excel format). Proteins with PTMs that have negative correlations.
- Table S2 (Microsoft Excel format). Amount of proteins in cell fractions.
- Data file S1 (.cys format). Cytoscape file: Complete cocluster correlation network.
