Abstract
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 t-distributed 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.
Highlights
Cell behavior is controlled by functional interactions among biological molecules, which have been classically studied one at a time, and communicated with pathway diagrams or cartoons
It is widely acknowledged that highthroughput characterization technologies will benefit from improved visualization and bioinformatic tools [7], and this is true for phosphoproteomic data analysis [4,8,9]
We evaluated and filtered clusters based on internal criteria, that is, based on the primary data, and external criteria from protein interaction and gene ontology (GO) databases [30,31,32]
Summary
Cell behavior is controlled by functional interactions among biological molecules, which have been classically studied one at a time, and communicated with pathway diagrams or cartoons. Signaling networks are much more complicated than these simple models, as revealed by large-scale approaches to studying the genome, transcriptome, and proteome. These studies produce a large amount of data that are difficult to comprehend prima facia. To overcome this problem, a combination of statistical analysis and visualization techniques may be helpful [1,2,3,4]. Much work has been done on exploratory data analysis and inferential statistics [5], and on the ‘‘network’’ metaphor, which describes relationships between biological molecules [6]. It is widely acknowledged that highthroughput characterization technologies will benefit from improved visualization and bioinformatic tools [7], and this is true for phosphoproteomic data analysis [4,8,9]
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