Single-cell RNA sequencing (scRNA-seq) revolutionized the study of gene expression and RNA analysis. The study of spatial transcriptomics (ST) developed alongside it, allowing researchers to measure all gene activity in a sample and map where it occurred relative to all other activity. By using software developed to process raw scRNA-seq and ST data, researchers can determine the type of cell, how it is expressing genes, and its location inside a sample. This project aims to create a data processing pipeline for scRNA-seq using the Seurat v5 software, and test the pipeline on tissue samples from the entorhinal and occipital regions of the brain. These regions have well-documented cell types and gene expression, allowing testing of the effectiveness of the pipeline by comparing its results to known controls. Raw scRNA-seq and ST data was gathered from tissue samples using a 10x Genomics Xenium Analyzer, and was passed through multiple iterations of a data processing pipeline created using Seurat v5 toolkits. The pipeline’s accuracy was measured by comparing cell types assigned by the pipeline to control cells found in the tissue samples, and by comparing gene expression results in astrocytes to control genes that express specifically in the astrocytes of the entorhinal and occipital regions. High expression of the PLP1 and MBP genes was found in the entorhinal astrocytes, which encode proteins that make up the myelin sheaths of oligodendrocytes. High expression of the ATP1A2 gene was found in occipital astrocytes, which encodes for a protein vital to maintaining Na+/K+ ion balances across cell membranes. This pipeline can serve as a control comparison to the development of future scRNA-seq pipelines that aim to process raw scRNA-seq data, and can be modified to fit specific needs such as looking for specific gene expression or cell types in samples.