SORS: Network-based analyses of transcriptomics data: new approaches to define cell signatures from tissue data

For the past decade we have been exploring the use of networks to visualise, explore and interpret the transcriptional landscape of tissues and cells. Our approach has been to turn such data into gene correlation networks, where the enormous size and complexity of the graphs generated has necessitated the development of new tools to support these analyses. Recently, we have been building a new analysis platform called Graphia. This is faster, more scalable (1,000,000s of data points), includes dynamic rendering and a very much extended range of new functionality for network analysis of many data types.

We have been using these analysis platforms to analyse large collections of transcriptomics data, such as the GTEx resource, to define expression modules associated with pathways and cell types, and through such analyses better understand the altered transcriptional landscape associated with disease.

Short bio: Prof. Tom Freeman has a long history of working in the field of functional genomics and systems biology. As head of the Gene Expression Group, Sanger Institute he developed, refined and used of a variety of contemporary expression analysis techniques for gene discovery and expression profiling, being one of the first in the UK to establish microarray technology in his lab. In 2000 he joined the HGMP-Resource Centre where he headed the newly formed UK National Microarray Programme funded by the MRC and BBSRC. As well as providing resources for the UK community, the group also had an active research programme particularly around technology development and data analysis.