An intuitive graphical visualization technique for the interrogation of transcriptome data.

ABSTRACT: The complexity of gene expression data generated from microarrays and high-throughput sequencing make their analysis challenging. One goal of these analyses is to define sets of co-regulated genes and identify patterns of gene expression. To date, however, there is a lack of easily implemented methods that allow an investigator to visualize and interact with the data in an intuitive and flexible manner. Here, we show that combining a nonlinear dimensionality reduction method, t-statistic Stochastic Neighbor Embedding (t-SNE), with a novel visualization technique provides a graphical mapping that allows the intuitive investigation of transcriptome data. This approach performs better than commonly used methods, offering insight into underlying patterns of gene expression at both global and l

SUBMITTER: Bushati N 

PROVIDER: S-EPMC3177207 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature