Pathway‐Based Analysis of Microarray and RNAseq Data Using Pathway Processor 2.0
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- Abstract
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Abstract
The constant improvement of high?throughput technologies has led to a great increase in generated data per single experiment. Pathway analysis is a widespread method to understand experimental results at the system level. Pathway Processor 2.0 is an upgrade over the original Pathway Processor program developed in 2002, extended to support more species, analysis methods, and RNAseq data in addition to microarrays through a simple Web?based interface. The tool can perform two different types of analysis: the first covers the traditional Fisher's Test used by Pathway Processor and topology?aware analyses, which take into account the propagation of changes over the whole structure of a pathway, and the second is a new pathway?based method to investigate differences between phenotypes of interest. Common problems and troubleshooting are also discussed. Curr. Protoc. Bioinform. 41:7.6.1?7.6.12. © 2013 by John Wiley & Sons, Inc.
Keywords: biological pathways; gene expression analysis; Web tool
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- Introduction
- Basic Protocol 1: Functional Analysis of Differentially Expressed Genes
- Basic Protocol 2: Gene Set Variation Analysis of Microarray or RNAseq Data
- Commentary
- Literature Cited
- Figures
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Figure 7.6.1 Example of a properly formatted DEG file. View Image -
Figure 7.6.2 Example of a properly formatted platform file. View Image -
Figure 7.6.3 Example showing the structure of a PWF format file. View Image -
Figure 7.6.4 The Pathway Processor 2.0 main page. View Image -
Figure 7.6.5 The Pathway Processor 2.0 interface for the Fisher's Exact Test and impact analysis. View Image -
Figure 7.6.6 Example results from a Fisher's Test analysis run. View Image -
Figure 7.6.7 Example results from an impact analysis run. View Image -
Figure 7.6.8 Overlay of DEGs over a significant pathway. View Image -
Figure 7.6.9 The Pathway Processor 2.0 interface for GSVA. View Image -
Figure 7.6.10 The interface for selecting cases and controls during GSVA. View Image -
Figure 7.6.11 Example results from a GSVA run. View Image
Videos
Literature Cited
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