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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 local scales and identifying clusters of similarly expressed genes. A freely available MATLAB-implemented graphical user interface to perform t-SNE and nearest neighbour plots on genomic data sets is available at www.nimr.mrc.ac.uk/research/james-briscoe/visgenex.

SUBMITTER: Bushati N

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

REPOSITORIES: biostudies-literature

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

Bushati Natascha N Smith James J Briscoe James J Watkins Christopher C

Nucleic acids research 20110619 17

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 Stochas ...[more]

PMID: 21690098

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Project description:We introduce Glaucoma Discovery Platform (GDP), an online environment for facile visualization and interrogation of complex transcription profiling datasets for glaucoma. We also report the availability of Datgan, the suite of scripts that was developed to construct GDP. This reusable software system complements existing repositories such as NCBI GEO or EBI ArrayExpress as it allows the construction of searchable databases to maximize understanding of user-selected transcription profiling datasets.Datgan scripts were used to construct both the underlying data tables and the web interface that form GDP. GDP is populated using data from a mouse model of glaucoma. The data was generated using the DBA/2J strain, a widely used mouse model of glaucoma. The DBA/2J-Gpnmb+ strain provided a genetically matched control strain that does not develop glaucoma. We separately assessed both the retina and the optic nerve head, important tissues in glaucoma. We used hierarchical clustering to identify early molecular stages of glaucoma that could not be identified using morphological assessment of disease. GDP has two components. First, an interactive search and retrieve component provides the ability to assess gene(s) of interest in all identified stages of disease in both the retina and optic nerve head. The output is returned in graphical and tabular format with statistically significant differences highlighted for easy visual analysis. Second, a bulk download component allows lists of differentially expressed genes to be retrieved as a series of files compatible with Excel. To facilitate access to additional information available for genes of interest, GDP is linked to selected external resources including Mouse Genome Informatics and Online Medelian Inheritance in Man (OMIM).Datgan-constructed databases allow user-friendly access to datasets that involve temporally ordered stages of disease or developmental stages. Datgan and GDP are available from http://glaucomadb.jax.org/glaucoma.

Dataset Information

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

Publications

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

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