Wrangling phosphoproteomic data to elucidate cancer signaling pathways

Wrangling phosphoproteomic data to elucidate cancer signaling pathways

Grimes, M.L.
Lee, W.J.
Van der Maaten, L.
Shannon, P.

Electrical Engineering, Mathematics and Computer Science

Computer Science

2013-01-03

The interpretation of biological data sets is essential for generating hypotheses that guide research, yet modern methods of global analysis challenge our ability to discern meaningful patterns and then convey results in a way that can be easily appreciated. Proteomic data is especially challenging because mass spectrometry detectors often miss peptides in complex samples, resulting in sparsely populated data sets. Using the R programming language and techniques from the field of pattern recognition, we have devised methods to resolve and evaluate clusters of proteins related by their pattern of expression in different samples in proteomic data sets. We examined tyrosine phosphoproteomic data from lung cancer samples. We calculated dissimilarities between the proteins based on Pearson or Spearman correlations and on Euclidean distances, whilst dealing with large amounts of missing data. The dissimilarities were then used as feature vectors in clustering and visualization algorithms. The quality of the clusterings and visualizations were evaluated internally based on the primary data and externally based on gene ontology and protein interaction networks. The results show that tdistributed stochastic neighbor embedding (t-SNE) followed by minimum spanning tree methods groups sparse proteomic data into meaningful clusters more effectively than other methods such as k-means and classical multidimensional scaling. Furthermore, our results show that using a combination of Spearman correlation and Euclidean distance as a dissimilarity representation increases the resolution of clusters. Our analyses show that many clusters contain one or more tyrosine kinases and include known effectors as well as proteins with no known interactions. Visualizing these clusters as networks elucidated previously unknown tyrosine kinase signal transduction pathways that drive cancer. Our approach can be applied to other data types, and can be easily adopted because open source software packages are employed.

http://resolver.tudelft.nl/uuid:d2baea76-424d-4854-a5ea-81fe4ce20ffe

https://doi.org/10.1371/journal.pone.0052884

Public Library of Science

1932-6203

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0052884

Plos One, 8 (1), 2013

Institutional Repository

journal article

(c) 2012 Grimes et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.