Porto-sinusoidal vascular disease (PSVD) or previously known as INCPH is a complex rare disease that causes liver splenomegaly and obliterative portal venopathy, resulting in portal hypertension. The disease is thought to have a genetic predisposition, and the prevalence rates vary between the eastern and western world, being 23% of the patients with portal hypertension in India in the 1980s and 0.75/10⁶ incidence rate in Japan, versus 3-5% in western countries^. The prevalence rates in the Western world might not be reflective of the present rates due to limited data available, as only two small cohort studies were conducted over the last decade. The limited data availability has severely affected the diagnosis and the prognosis of patients suffering from PSVD. It is, therefore, imperative to identify the biological processes that are affected in PSVD patients in order to understand the underlying disease mechanism . For this purpose, bioinformatics tools provide an effective platform to combine prior knowledge and omics data to elucidate the disease-specific pathways.

 The transcriptomics dataset used for the analysis consists of 14 histologically healthy liver (HNL) biopsy samples and 18 PSVD biopsy samples. Principal component analysis (PCA) was performed on this gene expression data to separate the healthy from the diseased patients/clusters. Furthermore, components that best separate the healthy from the diseased condition will be analyzed and the genes involved within these components will be studied using gene ontology and pathway enrichment tools like gProfiler and PathVisio respectively. Using this approach, we expect to find pathways and/or processes that were not only previously known to be associated with PSVD but also ones that are not yet associated with this disease. Identification of PSVD-specific pathways and/or processes would have a significant impact on the diagnosis,  prognosis and treatment options for these patients.

Acknowledgements
This initiative has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N 825575.