Comparative Analysis Of Lung And Breast Cancer Complexity Using Single-Cell RNA Sequencing Data

Abstract

We performed a thorough analysis of six single-cell RNA sequencing (scRNA-seq) datasets from the 10X Genomics Database in this work. We used Principal Component Analysis (PCA) to reduce dimensionality, clustering, quality control, normalization, identification of high variable features, data preprocessing, and Uniform Manifold Approximation and Projection (UMAP) for visualization. To better comprehend cellular heterogeneity, we also identified marker genes for each cluster and looked at gene correlation networks. In comparison to breast cancer datasets, our results showed that lung cancer datasets had more edges and marker genes in their gene correlation networks. This implies that the lung cancer samples have higher levels of molecular complexity and heterogeneity. Furthermore, a detailed depiction of the cellular environment that highlighted the complex interactions between cell groups was made possible by the UMAP visualization. The underlying biology of lung and breast cancers is better understood because to the discovery of marker genes and the examination of gene correlation networks. The found intricacy in datasets related to lung cancer could have consequences for comprehending disease subgroups, signaling pathways, and overall heterogeneity. This work lays the groundwork for future investigations into the molecular details of cancer and the development of tailored treatment plans.