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Differential Gene Expression on the Platform for Science

By 11.18.2018

Platform Interoperability Drives Science

Driving Science with Differential Gene Expression Data Analysis
Differential gene expression, the comparison of gene expression in healthy and diseased states, continues to be a growing area of focus for biomedical research.  Understanding not only what genes are present, but what triggers their expression enables scientists to better understand the disease and to develop improved treatment options for patients.  Many scientists rely on the R programming language to perform gene expression profiling.  ShinyTM from RStudio, which is now integrated into Thermo FisherTM Platform for ScienceTM software, enables scientists to perform gene expression analysis in the platform.

Driving Science with Differential Gene Expression Data Analysis

One of the challenges all scientists face is the need to integrate data across a variety of proprietary databases, programming languages, and applications.  qPCR, microarray, and Next Generation Sequencing techniques are all commonly used to determine gene expression.  Genomics data needs to be aggregated into a single platform to enable comparison between the baseline and the disease state.   The Platform for Science software is able to connect to other systems through our OData API as part of Thermo ScientificTM Core ConnectTM. The OData API is an open standards-based API that improves interoperability and simplifies integration across applications.  It effectively unlocks data that resides in Platform for Science software to be used by other OData services and software, like Shiny.

Shiny has multiple data analysis options for differential gene expression.  By incorporating the DEApp opensource code1 and count-based sequence data into our existing Next Gen Sequencing (NGS) workflow, we provide our customers with the tools they need to prepare, run and analyze genomic samples.  Samples move seamlessly from one step to the next, allowing researchers to track where samples are in their process.  This new functionality empowers labs to leverage R-based NGS applications for differential gene expression analysis without relying on R developers, data scientists or bioinformaticians.

Welcome to the R Community

R is best known for statistics and data analysis. Users now have access to the open source R community including access to a whopping 10,000+ statistical packages from the Comprehensive R Archive Network (CRAN), Bioconductor and GitHub and can communicate with developers, domain experts, and testers to help drive their science. When a new technique or algorithm is peer reviewed and published, this solution can be configured into a Shiny data analysis application that integrates directly with Platform for Science software.

Drag and drop file parsing and summary statistics

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Visualize the level of similarity between samples

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Interactive differential gene expression reporting options

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References

  1. Yan Li and Jorge Andrade; DEApp: An Interactive Web Interface for Differential Expression Analysis of Next Generation Sequence; Source Code for Biology and Medicine, 03 Feb 2017 12:2; https://doi.org/10.1186/s13029-017-0063-4

 

Marc Siladi leads the Data Science Team and oversees the strategy and development of statistical analysis software at Thermo Fisher Scientific. His scientific expertise and experience allow him to better understand the problems facing our customers. Before joining Thermo Fisher Scientific, Marc was a Research Scientist at Vertex Pharmaceuticals with hands on involvement in innovative drug discovery efforts to benefit patients with deadly diseases.

Marc Siladi

Marc Siladi is a Data Analytics Product Manager for Thermo Fisher Scientific. Previously, Marc was a Research Scientist at Vertex Pharmaceuticals with hands on involvement in innovative drug discovery efforts to benefit patients with deadly diseases. Prior to Vertx, Marc worked at Ambrx, and The Scripps Research Institute.

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