Integration and analysis of scRNAseq data using genome-scale metabolic modelsNastran, Jurij (Avtor) Moškon, Miha (Mentor) Mraz, Miha (Komentor) genome-scale metabolic modelsmodel extraction methodscontext-specific metabolic modelsomics data integrationmetabolismcancersingle-cell RNA sequencing dataRecent advancements in high-throughput biological screening have led to the emergence of large datasets of single-cell RNA sequencing data. These new datasets enable us to better understand the underlying biological systems behind diseases such as prostate cancer. We introduce a linear discriminant analysis-based method to effectively filter out the least informative genes from the obtained single-cell RNA dataset containing prostate cancer cells, thereby enhancing the dataset's discriminatory power. We also develop a new non-parametric genome-scale metabolic model extraction method. We then use this method as well as a publicly available genome-scale metabolic model to generate reaction flux data of each cell. We compare the reaction flux data with the single-cell RNA data by training and analyzing separate random forest models to classify cell types from samples with prostate cancer. The improved classification results as well as the analysis of attribute importances show the single-cell RNA sequencing data to be more informative and thus better suited for the task of effectively differentiating the cell types from samples containing prostate cancer.20242024-04-24 13:15:00Magistrsko delo/naloga155937VisID: 37129COBISS_ID: 195566595sl