Visualization of Patient Samples by Dimensionality Reduction of Genome-Wide Measurements
As the cost of genome-wide profiling is decreasing, the possibility for using such technologies for routine diagnostics as well as for classification and stratification of patients in clinical settings is increasing. However, the high dimensionality of such data makes it challenging to interpret and visualize for comparing and contrasting patient samples. Here we propose two visualization methods that display unsupervised clustering of genome-wide profiling of mRNA from breast cancer tumors from patients as images that can quickly show clusters of patients based on their expression profiles with perspective of their clinical outcome. The first visualization method converts expression profiles into a sparse network, whereas the second method visualizes patient samples on a hexagonal grid. Both visualization methods use the first three coordinates from principle component analysis (PCA) applied to reduce the dimensionality of the data. Colors of nodes in the network or hexagons are based on clinical outcome or tumor estrogen receptor (ER) status. Such visualization methods could be useful for grouping patients in an unsupervised manner to predict outcome and tailor personalized therapeutics.
KeywordsMicroarrays Graph Theory Hexagonal Grid Principle Component Analysis Dimensionality Reduction Data Visualization
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