Extending the capabilities of deconvolution to provide cell type specific pathway analysis of bulk RNA-seq data for idiopathic pulmonary fibrosis
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Motivation
Transcriptome data are confounded by differences in cell type proportions. Differentiating between regulated changes in gene expression and changes due to differing cell type proportions remains a challenge. Therefore, we apply a deconvolution method to correct for changes in cell type proportions and provide a novel cell-type specific pathway analysis.
Results
We demonstrate the technique in the context of idiopathic pulmonary fibrosis. Inferred cell type proportions indicated a significant increase in fibroblasts, myofibroblasts, and a decrease in vascular endothelial capillary cells. Pathway analysis after adjustment for proportions indicated IPF-related changes in extracellular matrix organization and TGF-β regulation. Cell-type specific pathway analysis suggested the role of interferon signaling in ATII cells. These results demonstrate that deconvolution is not only useful for assessing cell type proportions, but also can provide cell type-specific pathway analysis, allowing for a much more nuanced interpretation of bulk RNA-seq data.
