Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs. Issue 4 (1st July 2022)

• Record Type: Journal Article
• Authors: Sharma, Madhu; Jha, Indra Prakash; Chawla, Smriti; Pandey, Neetesh; Chandra, Omkar; Mishra, Shreya; Kumar, Vibhor
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 4(2022)
• Publication Date: 2022-07-01
• Subjects: single-cell expression -- pathway -- disease association -- cell-type inference -- diabetes -- Alzheimer's disease; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac241
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways–disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.