Arsan, TanerRinch,W.A.Sogunmez,N.Altaf,A.Alsan,H.F.Arsan,T.2024-06-232024-06-2320230979-835030659-0https://doi.org/10.1109/ASYU58738.2023.10296679https://hdl.handle.net/20.500.12469/5844Experimental data from brain tissues are critical for tackling the problems in brain development and revealing the underlying mechanisms of disease states. However, obtaining the brain tissue is a major challenge. Human brain organoids hold remarkable promise for this goal, but they suffer from substantial organoid-to-organoid variability. We performed a data-driven analysis on single-cell RNA-sequencing data using 17775 cells isolated from 2 individual organoids. The main goal was to accurately integrate the data coming from unmatched datasets, cluster the cells based on their similarity levels and predict the differentially expressed genes per cell types to reveal novel brain cell types and markers. This research opens a way to map human brain cells and develop novel and precise machine learning algorithms for accurate scRNA-Seq data analysis. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessclustering analysisHuman brain organoidsMachine LearningMarker GenesSC-RNA seq analysisConference Object10.1109/ASYU58738.2023.102966792-s2.0-85178258527N/AN/A