Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data

Frontiers in Genetics
Astrid Wachter, Tim Beißbarth

Abstract

Identification of dynamic signaling mechanisms on different cellular layers is now facilitated as the increased usage of various high-throughput techniques goes along with decreasing costs for individual experiments. A lot of these signaling mechanisms are known to be coordinated by their dynamics, turning time-course data sets into valuable information sources for inference of regulatory mechanisms. However, the combined analysis of parallel time-course measurements from different high-throughput platforms still constitutes a major challenge requiring sophisticated bioinformatic tools in order to ease biological interpretation. We developed a new pathway-based integration approach for the analysis of coupled omics time-series data, which we implemented in the R package pwOmics. Unlike many other approaches, our approach acknowledges the role of the different cellular layers of measurement and infers consensus profiles and time profile clusters for further biological interpretation. We investigated a time-course data set on epidermal growth factor stimulation of human mammary epithelial cells generated on the two layers of RNA and proteins. The data was analyzed using our new approach with a focus on feedback signaling and path...Continue Reading

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Datasets Mentioned

BETA
GSE15668

Methods Mentioned

BETA
nucleotide exchange
environmental stresses
flow cytometry

Software Mentioned

Reactome
R package
QIAGEN Pathway Analysis
AnnotationHub R package
Biocarta
pwOmics
Bioconductor
R package ebdbNet
ebdbNet R
R package pwOmics

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