A new marker selection method from single cell RNA sequencing data for spatial transcriptomics analyses

Recent studies showed that cell-cell communication regulated by biochemical signals is an important aspect of tissue structure and function and regulates individual cellular processes. Therefore, knowing the spatial positions of cells in the tissue is critical to understanding normal development and disease pathology. With the help of the recent technological developments, it has now become possible to spatially analyze the transcriptome of a cell at any time. However, the experimental costs of emerging spatial transcriptomics technologies are high, and some of these technologies require predetermination of the limited number of marker genes that can distinguish cell types from each other prior to experimentation. Within the scope of this thesis, the methods that perform the gene selection process for in situ spatial transcriptomics experiments in the literature were examined, the deficiencies were determined, and a method was developed to eliminate the deficiencies. The developed method was compared with other methods suggested in the literature in many respects, and its advantages and disadvantages were discussed. The results clearly show that the proposed method is superior to existing methods for many parameters evaluated. In addition, the proposed method has been turned into an open access software package and made available to researchers.