R Markdown analysis on omics data
We describe the developed pipelines that implement state-of-the-art algorithms which rely on transcriptomics data to predict upstream targets and/or derive custom pathways. All procedures produce graph visualisations that we use for algorithm selection and result interpretation.
This work examines lung omics analyses from a 6-month inhalation exposure study with ApoE-/- mice and enrich them with known curated data to increase biological insight. Transcriptomics data has been obtained in the labs while benchmark data is retrieved from online sources. After benchmarking and in-depth analysis, algorithms which predict upstream regulators based on transcriptomics outperform the de novo pathway building ones, both on time efficiency and result insight.