Introduction
The Ingenuity Pathway Analysis plugin provides the ability to upload Statistical comparison data generated using the RNA-Seq tools from CLC Genomics Workbench to Ingenuity Pathway Analysis (IPA). In addition it provides support for import of expression data provided as a count matrix.
IPA provides valuable biological insight into the results of gene expression experiments by uncovering enriched signaling and metabolic pathways, activated and inhibited upstream regulators and effects on downstream diseases, functions, and phenotypes. IPA can visualize at the isoform level for human genes.
The plugin comes with two tools and two template workflows (figure 1.1):
Figure 1.1: IPA plugin tools and workflow when installed in the CLC Genomics Workbench Toolbox.
- The Pathway Analysis tool uploads statistical comparison data (generated by the tool Differential Expression for RNA-Seq) to IPA. The Pathway Analysis tool has been implemented to succeed on many aspects such as usage of the new IPA API, stability, error handling, and user feedback during the upload process. The tool will output one or more Statistical Comparison tracks.
- The Import Expression Data tool which handles import of expression count data provided as a data matrix and produces an expression track per entry in the table. The tool is workflow enabled and requires metadata. It handles import of raw counts, TPM and RPKM.
- The template workflow Analyze Expression Data and Upload Comparisons to IPA, which takes expression data as input. The workflow analyzes them using the RNA-Seq Analysis tools, and submits the comparisons to IPA using the Pathway Analysis tool.
- The template workflow Analyze Count Matrix and Upload Comparisons to IPA, which takes an expression matrix as input. The workflow imports the counts and analyzes them using the RNA-Seq Analysis tools, and submits the comparisons to IPA using the Pathway Analysis tool.
It is possible to use gene and transcript based RNA-Seq experiments as basis for the analysis, but also microarrays from Illumina and Affymetrix are supported. You can also upload small RNA based experiments (Statistical Comparison Table format) where the seeds are most appropriate to upload.
Once the experiment data are ready, it is possible to annotate with any of the supported statistics:
- Transformed and normalized foldchange
- Baggerley's test
- Kal's Z test
- ANOVA
- edgeR