A graph theoretical approach for aligning cell lines and tumors

Cell line have now been shown in lots of research to be the most important primary tool for preclinical cancer research, but their representative quality in patient tumor samples remains unclear. The comparison of RNA-seq of tumor and cell line without any step is complex with various factors, consisting of different presence of regular cells in the tumor samples. Therefore, we developed an alignment method without any outside information using graph-based method using the networks and applied it to integrate multiple extensive tumor and cell lines RNA-seq dataset. Our approach also showed that there are diversity in tumor correlation between cell lines, even though the vast bulk of cell lines are aligned with tumors of the identical cancer type. Furthermore, the challenge in integrating tumor and cell line RNA sequencing datasets is essentially combining the datasets while keeping the cell populations separate while preserving the originality of the data. In this regard, it has been overcome by using the iterative correction part of SciTuna (Marouf, et al., 2021), which is one of the batch effects correction methods. In this thesis, we introduce CellTuNeter, which we have developed as a result of graph theoretic approaches for the alignment of cell lines and tumor RNA sequencing datasets.